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Salihoglu R, Balkenhol J, Dandekar G, Liang C, Dandekar T, Bencurova E. Cat-E: A comprehensive web tool for exploring cancer targeting strategies. Comput Struct Biotechnol J 2024; 23:1376-1386. [PMID: 38596315 PMCID: PMC11001601 DOI: 10.1016/j.csbj.2024.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/26/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.
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Affiliation(s)
- Rana Salihoglu
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
| | - Johannes Balkenhol
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University Hospital of Wurzburg, 97080 Wurzburg, Germany
| | - Gudrun Dandekar
- Chair of Tissue Engineering and Regenerative Medicine, University Hospital of Wurzburg, 97080 Wurzburg, Germany
| | - Chunguang Liang
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Institute of Immunology, Jena University Hospital, Friedrich-Schiller-University, 07743 Jena, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
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Schonrock Z, Brackeen S, Delarose KE, Tran TQD, Cirrincione LR. Transgender people in clinical trials of drugs and biologics: An analysis of ClinicalTrials.gov from 2007 to 2023. Br J Clin Pharmacol 2024; 90:2332-2342. [PMID: 38710989 DOI: 10.1111/bcp.16076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 05/08/2024] Open
Abstract
AIMS Transgender people have unmet health needs related to chronic conditions such as dementia, osteoporosis and hypertension. Community-driven advocacy increased transgender representation in phase III trials for pharmacological prevention of HIV, but the extent to which drug trials for other conditions have included transgender people is unknown. We investigated the extent to which trials of drugs and biologics represented transgender people across therapeutic areas on ClinicalTrials.gov. METHODS Cross-sectional analysis of trials of drugs and biologics registered on ClinicalTrials.gov from 2007-2023. We included efficacy and effectiveness trials (phase II-IV) with transgender-related terms (e.g. 'transgend*'). We labelled trials as Inclusive or Exclusive of transgender people using the trial eligibility criteria. We compared trials (therapeutic area, trial design, enrolment), summarized trials registered from 2008 onward and characterized participant enrolment for Inclusive trials with primary trial publications. We summarized continuous data using median (range), categorical data using frequencies and percentages and compared trial characteristics using Fisher's exact test. RESULTS Ninety-seven trials represented transgender people. Characteristics were similar between 85 Inclusive and 12 Exclusive trials. Among Inclusive trials, 58% focused on infectious diseases (e.g. treatment or prevention of HIV and COVID-19), 15% on mental health (e.g. post-traumatic stress disorder, substance use-related disorders), and the remainder focused on endocrine (9%), pain (5%), digestive system disorders (1%) and neoplasms (1%). Twenty (of 25) trials reported enrolment of transgender participants in primary trial publications or reported results. CONCLUSION Transgender-inclusive trials have increased since 2008. Most trials focused on infectious diseases and mental health. Investigators should increase opportunities to include of transgender people in trials of drugs and biologics for chronic diseases.
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Affiliation(s)
- Zachary Schonrock
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Sierra Brackeen
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Kikka E Delarose
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Tiffany Q-D Tran
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
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D’Andria Ursoleo J, Bugo S, Losiggio R, Bottussi A, Agosta VT, Monaco F. Characteristics of Out-of-Hospital Cardiac Arrest Trials Registered in ClinicalTrials.gov. J Clin Med 2024; 13:5421. [PMID: 39336907 PMCID: PMC11432273 DOI: 10.3390/jcm13185421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 09/07/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
Background/Objective: Out-of-hospital cardiac arrest (OHCA) poses a substantial public health concern. A collective evaluation of clinical trials is crucial for understanding systemic trends and progress within a specific research area of interest, ultimately shaping future directions. We performed a comprehensive analysis of the characteristics of trials in the adult OHCA population registered on ClinicalTrials.gov. Methods: Aided by medical subject headings (MeSH), we systematically searched the ClinicalTrials.gov database. Trends over time were assessed with the Cochran-Mantel-Haenszel test. The association between publication year and annual number was assessed with the Pearson correlation coefficient. Results: Out of 152 trials spanning the 2003-2023 period, 29.6% were observational and 70.4% were interventional. Compared with the observational trials, interventional trials were more often randomized (RCT) and achieved full publication status in 84% of cases (p = 0.03). The primary focus of interventional trials was "procedures" (43%), "devices" (23%), and "drugs" (21%). Observational studies focused on "biomarkers" (16%) and "diagnostic test" (13%) (p < 0.001). A decrement in the number of interventional trials with a sample size ≥100 patients across three temporal study points was observed. Nevertheless, published studies predominantly had a sample size ≥100 patients (76%), in contrast to unpublished trials (p ≤ 0.001). An increase in the number of interventional studies funded by the "academic/university" sector was also recorded. Conclusions: Clinical trials on OHCA primarily involved interventions aimed at treatment and were more often randomized, single-center, with small (<100) sample sizes, and funded by the "academic/university" sector.
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Affiliation(s)
| | | | | | | | | | - Fabrizio Monaco
- Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy; (J.D.U.); (S.B.); (R.L.); (A.B.); (V.T.A.)
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4
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Ekhtiari H, Zare-Bidoky M, Sangchooli A, Valyan A, Abi-Dargham A, Cannon DM, Carter CS, Garavan H, George TP, Ghobadi-Azbari P, Juchem C, Krystal JH, Nichols TE, Öngür D, Pernet CR, Poldrack RA, Thompson PM, Paulus MP. Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology 2024:10.1038/s41386-024-01973-5. [PMID: 39242922 DOI: 10.1038/s41386-024-01973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/09/2024]
Abstract
Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.
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Affiliation(s)
- Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Arshiya Sangchooli
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Alireza Valyan
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Anissa Abi-Dargham
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University Vagelos School of Medicine and New York State Psychiatric Institute, New York, NY, USA
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, Center for Neuroimaging, Cognition & Genomics, College of Medicine, Nursing & Health Sciences, University of Galway, Galway, Ireland
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California at Irvine, Irvine, CA, USA
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Tony P George
- Institute for Mental Health Policy and Research at CAMH, Toronto, ON, Canada
- Department of Psychiatry, Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Peyman Ghobadi-Azbari
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation, School of Engineering and Applied Science, New York, NY, USA
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Dost Öngür
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
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Iken AR, Poolman RW, Gademan MGJ. Data quality assessment of interventional trials in public trial databases. J Clin Epidemiol 2024; 175:111516. [PMID: 39243872 DOI: 10.1016/j.jclinepi.2024.111516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 08/26/2024] [Accepted: 09/02/2024] [Indexed: 09/09/2024]
Abstract
OBJECTIVE High-quality data entry in clinical trial databases is crucial to the usefulness, validity, and replicability of research findings, as it influences evidence-based medical practice and future research. Our aim is to assess the quality of self-reported data in trial registries and present practical and systematic methods for identifying and evaluating data quality. STUDY DESIGN AND SETTING We searched ClinicalTrials.Gov (CTG) for interventional total knee arthroplasty (TKA) trials between 2000 and 2015. We extracted required and optional trial information elements and used the CTG's variables' definitions. We performed a literature review on data quality reporting on frameworks, checklists, and overviews of irregularities in healthcare databases. We identified and assessed data quality attributes as follows: consistency, accuracy, completeness, and timeliness. RESULTS We included 816 interventional TKA trials. Data irregularities varied widely: 0%-100%. Inconsistency ranged from 0% to 36%, and most often nonrandomized labeled allocation was combined with a "single-group" assignment trial design. Inaccuracy ranged from 0% to 100%. Incompleteness ranged from 0% to 61%; 61% of finished TKA trials did not report their outcome. With regard to irregularities in timeliness, 49% of the trials were registered more than 3 months after the start date. CONCLUSION We found significant variations in the data quality of registered clinical TKA trials. Trial sponsors should be committed to ensuring that the information they provide is reliable, consistent, up-to-date, transparent, and accurate. CTG's users need to be critical when drawing conclusions based on the registered data. We believe this awareness will increase well-informed decisions about published articles and treatment protocols, including replicating and improving trial designs.
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Affiliation(s)
- Annabelle R Iken
- Leiden University Medical Center, Department of Orthopaedics, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands.
| | - Rudolf W Poolman
- Leiden University Medical Center, Department of Orthopaedics, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands; Department of Orthopaedic Surgery, Joint Research, OLVG, Amsterdam, The Netherlands
| | - Maaike G J Gademan
- Leiden University Medical Center, Department of Orthopaedics, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands; Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
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Purgar M, Glasziou P, Klanjscek T, Nakagawa S, Culina A. Supporting study registration to reduce research waste. Nat Ecol Evol 2024; 8:1391-1399. [PMID: 38839851 DOI: 10.1038/s41559-024-02433-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
An estimated 82-89% of ecological research and 85% of medical research has limited or no value to the end user because of various inefficiencies. We argue that registration and registered reports can enhance the quality and impact of ecological research. Drawing on evidence from other fields, chiefly medicine, we support our claim that registration can reduce research waste. However, increasing registration rates, quality and impact will be very slow without coordinated effort of funders, publishers and research institutions. We therefore call on them to facilitate the adoption of registration by providing adequate support. We outline several aspects to be considered when designing a registration system that would best serve the field of ecology. To further inform the development of such a system, we call for more research to identify the causes of low registration rates in ecology. We suggest short- and long-term actions to bolster registration and reduce research waste.
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Affiliation(s)
| | - Paul Glasziou
- Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Queensland, Australia
| | | | - Shinichi Nakagawa
- Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology Graduate University, Onna, Japan
| | - Antica Culina
- Ruđer Bošković Institute, Zagreb, Croatia.
- Netherlands Institute of Ecology, Royal Netherlands Academy of Arts and Sciences, Wageningen, the Netherlands.
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7
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Cho H, Froelicher D, Dokmai N, Nandi A, Sadhuka S, Hong MM, Berger B. Privacy-Enhancing Technologies in Biomedical Data Science. Annu Rev Biomed Data Sci 2024; 7:317-343. [PMID: 39178425 PMCID: PMC11346580 DOI: 10.1146/annurev-biodatasci-120423-120107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.
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Affiliation(s)
- Hyunghoon Cho
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - David Froelicher
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Natnatee Dokmai
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - Anupama Nandi
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - Shuvom Sadhuka
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Matthew M Hong
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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8
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Beck EJ, Sherry AD, Florez MA, Kouzy R, Abi Jaoude J, Lin TA, Miller AM, Passy AH, Kupferman GS, Patel RR, Chino F, Higbie VS, Parseghian CM, Overman MJ, Minsky BD, Thomas CR, Tang C, Msaouel P, Ludmir EB. Secondary Endpoint Utilization and Publication Rate among Phase III Oncology Trials. CANCER RESEARCH COMMUNICATIONS 2024; 4:2183-2188. [PMID: 39099199 PMCID: PMC11333994 DOI: 10.1158/2767-9764.crc-24-0265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/21/2024] [Accepted: 07/31/2024] [Indexed: 08/06/2024]
Abstract
Secondary endpoints (SEP) provide crucial information in the interpretation of clinical trials, but their features are not yet well understood. Thus, we sought to empirically characterize the scope and publication rate of SEPs among late-phase oncology trials. We assessed SEPs for each randomized, published phase III oncology trial across all publications and ClinicalTrials.gov, performing logistic regressions to evaluate associations between trial characteristics and SEP publication rates. After screening, a total of 280 trials enrolling 244,576 patients and containing 2,562 SEPs met the inclusion criteria. Only 22% of trials (62/280) listed all SEPs consistently between ClinicalTrials.gov and the trial protocol. The absolute number of SEPs per trial increased over time, and trials sponsored by industry had a greater number of SEPs (median 9 vs. 5 SEPs per trial; P < 0.0001). In total, 69% of SEPs (1,770/2,562) were published. The publication rate significantly varied by SEP category [X2 (5, N = 2,562) = 245.86; P < 0.001]. SEPs that place the most burden on patients, such as patient-reported outcomes and translational correlatives, were published at 63% (246/393) and 44% (39/88), respectively. Trials with more SEPs were associated with lower overall SEP publication rates. Overall, our findings are that SEP publication rates in late-phase oncology trials are highly variable based on the type of SEP. To avoid undue burden on patients and promote transparency of findings, trialists should weigh the biological and clinical relevance of each SEP together with its feasibility at the time of trial design. SIGNIFICANCE In this investigation, we characterized the utilization and publication rates of SEPs among late-phase oncology trials. Our results draw attention to the proliferation of SEPs in recent years. Although overall publication rates were high, underpublication was detected among endpoints that may increase patient burden (such as translational correlatives and patient-reported outcomes).
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Affiliation(s)
- Esther J. Beck
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Alexander D. Sherry
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | | | - Ramez Kouzy
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Joseph Abi Jaoude
- Department of Radiation Oncology, Stanford University, Stanford, California.
| | - Timothy A. Lin
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Avital M. Miller
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Adina H. Passy
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Gabrielle S. Kupferman
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Roshal R. Patel
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Fumiko Chino
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Victoria Serpas Higbie
- Division of Cancer Medicine, Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Christine M. Parseghian
- Division of Cancer Medicine, Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Michael J. Overman
- Division of Cancer Medicine, Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Bruce D. Minsky
- Division of Radiation Oncology, Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Charles R. Thomas
- Department of Radiation Oncology and Applied Sciences, Dartmouth Cancer Center, Geisel School of Medicine, Lebanon, New Hampshire.
| | - Chad Tang
- Division of Radiation Oncology, Department of Genitourinary Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Pavlos Msaouel
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Ethan B. Ludmir
- Division of Radiation Oncology, Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Lim YMF, Asselbergs FW, Bagheri A, Denaxas S, Tay WT, Voors A, Lam CSP, Koudstaal S, Grobbee DE, Vaartjes I. Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction. ESC Heart Fail 2024. [PMID: 38984466 DOI: 10.1002/ehf2.14751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/29/2024] [Accepted: 02/19/2024] [Indexed: 07/11/2024] Open
Abstract
AIMS Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. METHODS AND RESULTS Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. CONCLUSIONS Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.
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Affiliation(s)
- Yvonne Mei Fong Lim
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia
| | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Ayoub Bagheri
- Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands
| | - Spiros Denaxas
- Institute of Health Informatics, UCL BHF Research Accelerator and Health Data Research UK, University College London, London, UK
- British Heart Foundation Data Science Center, London, UK
| | - Wan Ting Tay
- National Heart Centre Singapore, Singapore, Singapore
| | - Adriaan Voors
- Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Stefan Koudstaal
- Department of Cardiology, Groene Hart Ziekenhuis, Gouda, The Netherlands
| | - Diederick E Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Julius Clinical, Zeist, The Netherlands
| | - Ilonca Vaartjes
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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10
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Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartsson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CDCM, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS, Ciani O. Reporting of surrogate endpoints in randomised controlled trial reports (CONSORT-Surrogate): extension checklist with explanation and elaboration. BMJ 2024; 386:e078524. [PMID: 38981645 PMCID: PMC11231881 DOI: 10.1136/bmj-2023-078524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Anthony Muchai Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Global Health and Ageing Research Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Amber E Young
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J Butcher
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dalia Dawoud
- Science, Evidence, and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Martin Offringa
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | | | - Asbjørn Hróbjartsson
- Centre for Evidence-Based Medicine Odense and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient data Explorative Network, Odense University hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Centre, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- IRCCS Galeazzi-Sant'Ambrogio Hospital, Department of Minimally Invasive Cardiac Surgery, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D C M Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Marissa Lassere
- St George Hospital and School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Patient author, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John H Powers
- George Washington University School of Medicine, Washington, DC, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Centre, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University of Bordeaux, Centre d'Investigation Clinique-Epidémiologie Clinique 1401, Research in Clinical Epidemiology and in Public Health and European Clinical Trials Platform & Development/French Clinical Research Infrastructure Network, Institut National de la Santé et de la Recherche Médicale/Institut Bergonié/Centre Hospitalier Universitaire Bordeaux, Bordeaux, France
| | | | - Larisa G Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anaesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen and Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Robertson Centre for Biostatistics, School of Health and Well Being, University of Glasgow, Glasgow, UK
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, Bocconi University, Milan 20136, Italy
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11
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Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartsson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CDCM, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS, Ciani O. Reporting of surrogate endpoints in randomised controlled trial protocols (SPIRIT-Surrogate): extension checklist with explanation and elaboration. BMJ 2024; 386:e078525. [PMID: 38981624 PMCID: PMC11231880 DOI: 10.1136/bmj-2023-078525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Anthony Muchai Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Global Health and Ageing Research Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Amber E Young
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J Butcher
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dalia Dawoud
- Science, Evidence, and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Martin Offringa
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | | | - Asbjørn Hróbjartsson
- Centre for Evidence-Based Medicine Odense and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient data Explorative Network, Odense University hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Centre, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- IRCCS Galeazzi-Sant'Ambrogio Hospital, Department of Minimally Invasive Cardiac Surgery, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D C M Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Marissa Lassere
- St George Hospital and School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Patient author, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John H Powers
- George Washington University School of Medicine, Washington, DC, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Centre, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University of Bordeaux, Centre d'Investigation Clinique-Epidémiologie Clinique 1401, Research in Clinical Epidemiology and in Public Health and European Clinical Trials Platform & Development/French Clinical Research Infrastructure Network, Institut National de la Santé et de la Recherche Médicale/Institut Bergonié/Centre Hospitalier Universitaire Bordeaux, Bordeaux, France
| | | | - Larisa G Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anaesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen and Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Robertson Centre for Biostatistics, School of Health and Well Being, University of Glasgow, Glasgow, UK
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, Bocconi University, Milan 20136, Italy
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12
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Holtedahl R, Brox JI. Compliance with requirements for registration and reporting of results in trials of mesenchymal stromal cells for musculoskeletal disorders: a systematic review. BMJ Open 2024; 14:e081343. [PMID: 38925685 PMCID: PMC11202644 DOI: 10.1136/bmjopen-2023-081343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE To assess compliance with statutory requirements to register and report outcomes in interventional trials of mesenchymal stromal cells (MSCs) for musculoskeletal disorders and to describe the trials' clinical and design characteristics. DESIGN A systematic review of published trials and trials submitted to public registries. DATA SOURCES The databases Medline, Cochrane Library and McMaster; six public clinical registries. All searches were done until 31 January 2023. ELIGIBILITY CRITERIA Trials submitted to registries and completed before January 2021. Prospective interventional trials published in peer-reviewed journals. DATA EXTRACTION AND SYNTHESIS The first author searched for trials that had (1) posted trial results in a public registry, (2) presented results in a peer-reviewed publication and (3) submitted a pretrial protocol to a registry before publication. Other extracted variables included trial design, number of participants, funding source, follow-up duration and cell type. RESULTS In total 124 trials were found in registries and literature databases. Knee osteoarthritis was the most common indication. Of the 100 registry trials, 52 trials with in total 2 993 participants had neither posted results in the registry nor published results. Fifty-two of the registry trials submitted a protocol retrospectively. Forty-three of the 67 published trials (64%) had registered a pretrial protocol. Funding source was not associated with compliance with reporting requirements. A discrepancy between primary endpoints in the registry and publication was found in 16 of 25 trials. In 28% of trials, the treatment groups used adjuvant therapies. Only 39% of controlled trials were double-blinded. CONCLUSIONS A large proportion of trials failed to comply with statutory requirements for the registration and reporting of results, thereby increasing the risk of bias in outcome assessments. To improve confidence in the role of MSCs for musculoskeletal disorders, registries and medical journals should more rigorously enforce existing requirements for registration and reporting.
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Affiliation(s)
| | - Jens Ivar Brox
- Phys med & rehab, Oslo University Hospital and Medical Faculty, University in Oslo, Oslo, Norway
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13
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Unger JM, Shulman LN, Facktor MA, Nelson H, Fleury ME. National Estimates of the Participation of Patients With Cancer in Clinical Research Studies Based on Commission on Cancer Accreditation Data. J Clin Oncol 2024; 42:2139-2148. [PMID: 38564681 PMCID: PMC11191051 DOI: 10.1200/jco.23.01030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 01/15/2024] [Accepted: 02/08/2024] [Indexed: 04/04/2024] Open
Abstract
PURPOSE National estimates of cancer clinical trial participation are nearly two decades old and have focused solely on enrollment to treatment trials, which does not reflect the willingness of patients to contribute to other elements of clinical research. We determined inclusive, contemporary estimates of clinical trial participation for adults with cancer using a national sample of data from the Commission on Cancer (CoC). METHODS The data were obtained from accreditation information submitted by the 1,200 CoC programs, which represent more than 70% of all cancer cases diagnosed in the United States each year. Deidentified, institution-level aggregate counts of annual enrollment to treatment, biorepository, diagnostic, economic, genetic, prevention, quality-of-life (QOL), and registry studies were examined. Overall, study-type estimates for the period 2013-2017 were estimated. Multiple imputation by chained equations was used to account for missing data, with summary estimates calculated separately by type of program (eg, National Cancer Institute [NCI]-designated cancer centers) and pooled. RESULTS The overall estimated patient participation rate to cancer treatment trials was 7.1%. Patients with cancer participated in a wide variety of other studies, including biorepository (12.9%), registry (7.3%), genetic (3.6%), QOL (2.8%), diagnostic (2.5%), and economic (2.4%) studies. Treatment trial enrollment was 21.6% at NCI-designated comprehensive cancer centers, 5.4% at academic (non-NCI-designated) comprehensive cancer programs, 5.7% at integrated network cancer programs, and 4.1% at community programs. One in five patients (21.9%) participated in one or more cancer clinical research studies. CONCLUSION In a first-time use of national accreditation information from the CoC, enrollment to cancer treatment trials was 7.1%, higher than historical estimates of <5%. Patients participated in a diverse set of other study types. Contributions of adult patients with cancer to clinical research is more common than previously understood.
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Affiliation(s)
| | | | | | | | - Mark E. Fleury
- American Cancer Society Cancer Action Network, Washington, DC
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14
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Lv D, Liu Y, Tang R, Fu S, Kong S, Liao Q, Li H, Lin L. Analysis of Clinical Trials Using Anti-Tumor Traditional Chinese Medicine Monomers. Drug Des Devel Ther 2024; 18:1997-2020. [PMID: 38855536 PMCID: PMC11162644 DOI: 10.2147/dddt.s454774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/25/2024] [Indexed: 06/11/2024] Open
Abstract
The potential anti-cancer effect of traditional Chinese medicine (TCM) monomers has been widely studied due to their advantages of well-defined structure, clear therapeutic effects, and easy quality control during the manufacturing process. However, clinical trial information on these monomers is scarce, resulting in a lack of knowledge regarding the research progress, efficacy, and adverse reactions at the clinical stage. Therefore, this study systematically reviewed the clinical trials on the anti-cancer effect of TCM monomers registered in the Clinicaltrials.gov website before 2023.4.30, paying special attention to the trials on tumors, aiming to explore the research results and development prospects in this field. A total of 1982 trials were started using 69 of the 131 TCM monomers. The number of clinical trials performed each year showed an overall upward trend. However, only 26 monomers entered into 519 interventional anti-tumor trials, with vinblastine (194, 37.38%) and camptothecin (146, 28.13%) being the most used. A total of 45 tumors were studied in these 519 trials, with lymphoma (112, 21.58%) being the most frequently studied. Clinical trials are also unevenly distributed across locations and sponsors/collaborators. The location and the sponsor/collaborator with the highest number of performed trials were the United States (651,32.85%) and NIH (77). Therefore, China and its institutions still have large room for progress in promoting TCM monomers in anti-tumor clinical trials. In the next step, priority should be given to the improvement of the research and development ability of domestic enterprises, universities and other institutions, using modern scientific and technological means to solve the problems of poor water solubility and strong toxic and side effects of monomers, so as to promote the clinical research of TCM monomers.
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Affiliation(s)
- Dan Lv
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Yuling Liu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Ruying Tang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Sai Fu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Shasha Kong
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Qian Liao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Hui Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
- Institute of Traditional Chinese Medicine Health Industry, China Academy of Chinese Medical Sciences, Jiangxi, 330006, People's Republic of China
| | - Longfei Lin
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
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15
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Assis Santos VPD, Sendyk DI, Barretto MDDA, Nunes JP, Pannuti CM, Deboni MCZ. Selective outcome reporting in randomized clinical trials using the third molar surgery model. J Craniomaxillofac Surg 2024; 52:755-762. [PMID: 38582673 DOI: 10.1016/j.jcms.2024.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/13/2024] [Indexed: 04/08/2024] Open
Abstract
Selective outcome reporting (SOR) can threaten the validity of results found in clinical trials. Some studies in the literature have analyzed SOR in dentistry, but there is no study that has observed SOR in clinical trials in oral and maxillofacial surgery. Impacted third molar surgery is one of the most used models in clinical trials to study mainly analgesic and anti-inflammatory drug interventions. Our study aimed to evaluate the prevalence of SOR in publications employing the third molar extraction clinical trial model, and to verify whether there was an association between the statistical significance of outcomes and other characteristics that could lead to SOR. A systematic search was performed on the ClinicialTrials.gov platform for randomized clinical trial protocols, using the condition of third molar extraction. The corresponding published articles were sourced in PubMed, Scopus, and Embase databases, and compared with the registered protocols regarding the methodological data, in terms of: sample calculation, primary outcome identification, end-point periods, insertion of new outcomes in the publication, and results of outcomes. 358 protocol records were retrieved; 87 presented their corresponding articles. SOR was identified in 28.74% of the publications, and had a significant relationship with changes in the protocol, insertions of new outcomes, and discrepancies in the types of study. General risk of bias was found to be low. There were associations between SOR and the discrepancies in terms of the type of study, the choice of new outcome, and changes in the history of protocol records. The prevalence of SOR in clinical research using the third molar extraction surgery model is moderate. The quality of the scientific reporting of the results and, consequently, the certainty of evidence relating to the intervention tested can be overstated, increasing the chances of misinterpretation by health professionals.
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Affiliation(s)
| | - Daniel Isaac Sendyk
- Implantology Department, São Leopoldo Mandic Institute and Research Center, Brazil; Stomatology Department, Faculty of Dentistry, University of São Paulo, Brazil
| | | | - Julia Puglia Nunes
- Oral Surgery Department, Faculty of Dentistry, University of São Paulo, Brazil
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16
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Kottner J, Beaton D, Clarke M, Dodd S, Kirkham J, Lange T, Nieuwlaat R, Schmitt J, Tugwell P, Williamson P. Core outcome set developers should consider and specify the level of granularity of outcome domains. J Clin Epidemiol 2024; 169:111307. [PMID: 38428539 DOI: 10.1016/j.jclinepi.2024.111307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/03/2024]
Affiliation(s)
- Jan Kottner
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Dorcas Beaton
- Institute for Work & Health, Toronto, Ontario, Canada
| | - Mike Clarke
- Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, UK
| | - Susanna Dodd
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jamie Kirkham
- Centre for Biostatistics, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Toni Lange
- Center for Evidence-Based Healthcare, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307 Dresden, Germany
| | - Robby Nieuwlaat
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Jochen Schmitt
- Center for Evidence-Based Healthcare, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307 Dresden, Germany
| | - Peter Tugwell
- Department of Medicine, University of Ottawa, Ottawa Hospital Research Institute, Bruyere Research Institute, Ottawa, Ontario, Canada
| | - Paula Williamson
- Department of Health Data Science, University of Liverpool, Liverpool, UK
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17
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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18
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Veal C, Tomlinson A, Cipriani A, Bulteau S, Henry C, Müh C, Touboul S, De Waal N, Levy-Soussan H, Furukawa TA, Fried EI, Tran VT, Chevance A. Heterogeneity of outcome measures in depression trials and the relevance of the content of outcome measures to patients: a systematic review. Lancet Psychiatry 2024; 11:285-294. [PMID: 38490761 DOI: 10.1016/s2215-0366(23)00438-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 03/17/2024]
Abstract
Research waste occurs when randomised controlled trial (RCT) outcomes are heterogeneous or overlook domains that matter to patients (eg, relating to symptoms or functions). In this systematic review, we reviewed the outcome measures used in 450 RCTs of adult unipolar and bipolar depression registered between 2018 and 2022 and identified 388 different measures. 40% of the RCTs used the same measure (Hamilton Depression Rating Scale [HAMD]). Patients and clinicians matched each item within the 25 most frequently used measures with 80 previously identified domains of depression that matter to patients. Seven (9%) domains were not covered by the 25 most frequently used outcome measures (eg, mental pain and irritability). The HAMD covered a maximum of 47 (59%) of the 80 domains that matter to patients. An interim solution to facilitate evidence synthesis before a core outcome set is developed would be to use the most common measures and choose complementary scales to optimise domain coverage. TRANSLATIONS: For the French and Dutch translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Christopher Veal
- Université Paris Cité and Université Sorbonne Paris Nord, INSERM INRAE, Centre for Research in Epidemiology and Statistics, Paris, France; Centre d'Epidémiologie Clinique, AP-HP, Hôpital Hôtel Dieu, Paris, France
| | | | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Samuel Bulteau
- UMR INSERM 1246, SPHERE, University of Nantes and University of Tours, Nantes, France; CHU Nantes, Department of Addictology, Psychiatry and Old Age Psychiatry, Nantes, France
| | - Chantal Henry
- Université Paris Cité, Paris, France; Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie and Neurosciences, Paris, France
| | - Chlöé Müh
- Perception and Memory Unit, Institut Pasteur, UMR3571, CNRS, Paris, France; Université Paris Cité, Collège Doctoral, Paris, France
| | | | | | | | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Eiko I Fried
- Clinical Psychology Unit, Psychology Department, Leiden University, Leiden, Netherlands
| | - Viet-Thi Tran
- Université Paris Cité and Université Sorbonne Paris Nord, INSERM INRAE, Centre for Research in Epidemiology and Statistics, Paris, France; Centre d'Epidémiologie Clinique, AP-HP, Hôpital Hôtel Dieu, Paris, France
| | - Astrid Chevance
- Université Paris Cité and Université Sorbonne Paris Nord, INSERM INRAE, Centre for Research in Epidemiology and Statistics, Paris, France; Centre d'Epidémiologie Clinique, AP-HP, Hôpital Hôtel Dieu, Paris, France.
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19
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Xu Y, Deng Z, Fei F, Zhou S. An overview and comprehensive analysis of interdisciplinary clinical research in endometriosis based on trial registry. iScience 2024; 27:109298. [PMID: 38455973 PMCID: PMC10918267 DOI: 10.1016/j.isci.2024.109298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/27/2023] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
Endometriosis is a chronic multisystem disease associated with immunological, genetic, hormonal, psychological, and neuroscientific factors, leading to a significant socioeconomic impact worldwide. Though multidisciplinary management is the ideal approach, there remains a scarcity of published interdisciplinary clinical trials at present. Here, we have conducted a comprehensive analysis of the characteristics and issues of interdisciplinary trials on endometriosis based on the clinical registration database ClinicalTrials.gov. Among all 387 endometriosis trials, 30% (116) were identified as interdisciplinary, mostly conducted in Europe and North America, and fully funded by non-industrial sources. We documented growth in both patient-centered multidisciplinary comprehensive management and collaboration between fundamental biomedical science and applied medicine. However, compared to traditional obstetric-gynecological trials, interdisciplinary studies exhibited negative characteristics such as less likely to be randomized and less likely to report results. Our study provides insights for future trial investigators and may contribute to fostering greater collaboration in medical research.
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Affiliation(s)
- Yicong Xu
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P.R. China
| | - Zhengrong Deng
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P.R. China
| | - Fan Fei
- Department of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital; School of Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P.R. China
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20
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Richardson MT, Barry D, Steinberg JR, Thirunavu V, Strom DE, Holder K, Zhang N, Turner BE, Magnani CJ, Weeks BT, Young AMP, Lu CF, Wolgemuth TR, Laasiri N, Squires NA, Anderson JN, Karlan BY, Chan JK, Kapp DS, Roque DR, Salani R. Underrepresentation of racial and ethnic minority groups in gynecologic oncology: An analysis of over 250 trials. Gynecol Oncol 2024; 181:1-7. [PMID: 38096673 DOI: 10.1016/j.ygyno.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 03/01/2024]
Abstract
OBJECTIVE To describe the participation of racial and ethnic minority groups (REMGs) in gynecologic oncology trials. METHODS Gynecologic oncology studies registered on ClinicalTrials.gov between 2007 and 2020 were identified. Trials with published results were analyzed based on reporting of race/ethnicity in relation to disease site and trial characteristics. Expected enrollment by race/ethnicity was calculated and compared to actual enrollment, adjusted for 2010 US Census population data. RESULTS 2146 gynecologic oncology trials were identified. Of published trials (n = 252), 99 (39.3%) reported race/ethnicity data. Recent trials were more likely to report these data (36% from 2007 to 2009; 51% 2013-2015; and 53% from 2016 to 2018, p = 0.01). Of all trials, ovarian cancer trials were least likely to report race/ethnicity data (32.1% vs 39.3%, p = 0.011). Population-adjusted under-enrollment for Blacks was 7-fold in ovarian cancer, Latinx 10-fold for ovarian and 6-fold in uterine cancer trials, Asians 2.5-fold in uterine cancer trials, and American Indian and Alaska Native individuals 6-fold in ovarian trials. Trials for most disease sites have enrolled more REMGs in recent years - REMGs made up 19.6% of trial participants in 2007-2009 compared to 38.1% in 2016-2018 (p < 0.0001). CONCLUSION Less than half of trials that published results reported race/ethnicity data. Available data reveals that enrollment of REMGs is significantly below expected rates based on national census data. These disparities persisted even after additionally adjusting for population size. Despite improvement in recent years, additional recruitment of REMGs is needed to achieve more representative and equitable participation in gynecologic cancer clinical trials.
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Affiliation(s)
- Michael T Richardson
- Department of Obstetrics and Gynecology, University of California Los Angeles, Los Angeles, CA, United States of America
| | - Danika Barry
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Jecca R Steinberg
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Vineeth Thirunavu
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Danielle E Strom
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Kai Holder
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Naixin Zhang
- Division of Gynecologic Oncology, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Brandon E Turner
- Harvard Radiation Oncology Program, Boston, MA, United States of America
| | - Christopher J Magnani
- Division of Urological Surgery, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Brannon T Weeks
- Brigham and Women's Hospital/Massachusetts General Hospital Integrated Residency Program in Obstetrics and Gynecology, Boston, MA, United States of America
| | - Anna Marie P Young
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Connie F Lu
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Tierney R Wolgemuth
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Nora Laasiri
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Natalie A Squires
- Department of Obstetrics and Gynecology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY, United States of America
| | - Jill N Anderson
- Department of Obstetrics and Gynecology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY, United States of America
| | - Beth Y Karlan
- Department of Obstetrics and Gynecology, University of California Los Angeles, Los Angeles, CA, United States of America
| | - John K Chan
- California Pacific / Palo Alto Medical Foundation / Sutter Research Institute, San Francisco, CA, United States of America
| | - Daniel S Kapp
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Dario R Roque
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - Ritu Salani
- Department of Obstetrics and Gynecology, University of California Los Angeles, Los Angeles, CA, United States of America.
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21
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Kelly R, Guo C, Desai J, Tran B. Changing trends in phase 1 oncology clinical trials. Contemp Clin Trials Commun 2024; 37:101239. [PMID: 38204884 PMCID: PMC10776421 DOI: 10.1016/j.conctc.2023.101239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/04/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
The Ph1 oncology trial landscape is evolving in response to advances in understanding of cancer biology, novel drug discovery platforms, and therapeutic modalities. To uncover emerging trends in oncology drug development, we identified 7,061 solid tumour Ph1 trials (2009-2021) from clinicaltrials.gov to determine the numbers of trials commenced, therapeutic classes, combinations, tumour streams, and geographical distribution. Ph1 oncology trials increased by an average of 5.2 %/year. There was a significant relative increase in the number of immunotherapy studies and a significant relative decrease in trials containing chemotherapy. Between 2009 and 2021, multi-agent combination trials outnumbered single-agent trials and single-class trials outnumbered multimodal combination trials. The proportion conducted in the Asia-Pacific significantly increased. Multiregional trials decreased during the COVID-19 pandemic, reducing projected trial numbers in Asia-Pacific and Europe whilst increasing single-region trials in North America. Further study is required to track recovery post-pandemic, and the emergence of novel modalities (e.g. ADCs and cellular therapies).
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Affiliation(s)
- Richard Kelly
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
- Alfred Health, Melbourne, Australia
- Walter and Eliza Hall Institute, Melbourne, Australia
| | | | - Jayesh Desai
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Ben Tran
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
- Walter and Eliza Hall Institute, Melbourne, Australia
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22
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Dugas M, Blumenstock M, Dittrich T, Eisenmann U, Feder SC, Fritz-Kebede F, Kessler LJ, Klass M, Knaup P, Lehmann CU, Merzweiler A, Niklas C, Pausch TM, Zental N, Ganzinger M. Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support. NPJ Digit Med 2024; 7:10. [PMID: 38216645 PMCID: PMC10786912 DOI: 10.1038/s41746-023-00994-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/11/2023] [Indexed: 01/14/2024] Open
Abstract
Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed. Key characteristics identified include FAIR access to electronic case report forms (eCRF), regulatory compliant Electronic Data Capture (EDC), an EDC with electronic health record (EHR) integration, scalable EDC for medical documentation, patient generated data, and clinical decision support. In a local case study, we then successfully implemented a next-generation study database for 19 EDC systems (n = 2217 patients) that linked to i.s.h.med (Oracle Cerner) with the local EDC system called OpenEDC. Desiderata of next-generation study databases for patient data were identified from ongoing local clinical study projects in 11 clinical departments at Heidelberg University Hospital, Germany, a major tertiary referral hospital. We compiled and analyzed feature and functionality requests submitted to the OpenEDC team between May 2021 and July 2023. Next-generation study databases are technically and clinically feasible. Further research is needed to evaluate if our approach is feasible in a multi-center setting as well.
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Affiliation(s)
- Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Max Blumenstock
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Dittrich
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
| | - Urs Eisenmann
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Stephan Christoph Feder
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Fleur Fritz-Kebede
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Lucy J Kessler
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Ophthalmology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Klass
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Petra Knaup
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Angela Merzweiler
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Niklas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas M Pausch
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Nelly Zental
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Ganzinger
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.
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23
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Hou D, Lin H, Feng Y, Zhou K, Li X, Yang Y, Wang S, Yang X, Wang J, Zhao H, Zhang X, Fan J, Lu S, Wang D, Zhu L, Ju D, Chen YZ, Zeng X. CMAUP database update 2024: extended functional and association information of useful plants for biomedical research. Nucleic Acids Res 2024; 52:D1508-D1518. [PMID: 37897343 PMCID: PMC10767869 DOI: 10.1093/nar/gkad921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/23/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023] Open
Abstract
Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.
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Affiliation(s)
- Dongyue Hou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hanbo Lin
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuhan Feng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Kaicheng Zhou
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xingxiu Li
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yuan Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Shuaiqi Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xue Yang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiayu Wang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Hui Zhao
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Xuyao Zhang
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Jiajun Fan
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - SongLin Lu
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Dan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Lyuhan Zhu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Dianwen Ju
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
| | - Yu Zong Chen
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
- Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Xian Zeng
- Department of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, Fudan University School of Pharmacy, Shanghai 201203, China
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24
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Poria R, Kala D, Nagraik R, Dhir Y, Dhir S, Singh B, Kaushik NK, Noorani MS, Kaushal A, Gupta S. Vaccine development: Current trends and technologies. Life Sci 2024; 336:122331. [PMID: 38070863 DOI: 10.1016/j.lfs.2023.122331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/24/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023]
Abstract
Despite the effectiveness of vaccination in reducing or eradicating diseases caused by pathogens, there remain certain diseases and emerging infections for which developing effective vaccines is inherently challenging. Additionally, developing vaccines for individuals with compromised immune systems or underlying medical conditions presents significant difficulties. As well as traditional vaccine different methods such as inactivated or live attenuated vaccines, viral vector vaccines, and subunit vaccines, emerging non-viral vaccine technologies, including viral-like particle and nanoparticle vaccines, DNA/RNA vaccines, and rational vaccine design, offer new strategies to address the existing challenges in vaccine development. These advancements have also greatly enhanced our understanding of vaccine immunology, which will guide future vaccine development for a broad range of diseases, including rapidly emerging infectious diseases like COVID-19 and diseases that have historically proven resistant to vaccination. This review provides a comprehensive assessment of emerging non-viral vaccine production methods and their application in addressing the fundamental and current challenges in vaccine development.
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Affiliation(s)
- Renu Poria
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India
| | - Deepak Kala
- Centera Laboratories, Institute of High Pressure Physics PAS, 01-142 Warsaw, Poland
| | - Rupak Nagraik
- School of Bioengineering and Food Technology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, India
| | - Yashika Dhir
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India
| | - Sunny Dhir
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India
| | - Bharat Singh
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India
| | - Naveen Kumar Kaushik
- Amity Institute of Virology and Immunology, Amity University Uttar Pradesh, Sector-125, Noida, Uttar Pradesh, India
| | - Md Salik Noorani
- Department of Botany, School of Chemical and Life Sciences, Jamia Hamdard, New Delhi 110062, India
| | - Ankur Kaushal
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India.
| | - Shagun Gupta
- Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be) University, Mullana, Ambala 134003, India.
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25
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Butler M, Mehra M, Chandasir A, Kaoutzani L, Vale FL. Analysis of the discontinuation and nonpublication of neurooncological randomized clinical trials. Neurooncol Adv 2024; 6:vdae136. [PMID: 39211519 PMCID: PMC11358822 DOI: 10.1093/noajnl/vdae136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
Background Premature discontinuation and nonpublication of clinical trials contribute to research waste and compromise our ability to improve patient outcomes. However, the extent to which these problems exist in neurooncological randomized clinical trials (RCTs) is not known. This study aimed to evaluate the prevalence of discontinuation and nonpublication of neurooncological RCTs, identify contributing factors, and assess trial characteristics associated with each. Methods We performed a retrospective, cross-sectional study of neurooncological RCTs registered in Clinicaltrials.gov before March 7, 2023. Data were collected from Clinicaltrials.gov and associated publications were located. We attempted to contact authors for all trials without associated publications or an identified reason for discontinuation. Results Of 139 included RCTs, 57 (41%) were discontinued. The most common reason for discontinuation identified was slow enrollment or accrual (23%), though 30 trials (53%) were discontinued for unknown reasons. Trials funded by sources other than industry or the National Institutes of Health were more likely to be discontinued (odds ratio 4.2, 95% confidence interval 1.3-13.8). In total, 67 of the 139 (48%) RCTs were unpublished, including 50 of the 57 (88%) discontinued studies and 17 of the 82 (21%) completed studies. Conclusions In our study, discontinuation of neurooncological clinical trials was common and often occurred for unknown reasons. Trials were also frequently unpublished, particularly those that were discontinued. Addressing these findings may provide an opportunity to reduce research waste and improve outcomes for patients with neurological cancers.
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Affiliation(s)
- Molly Butler
- Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | - Mehul Mehra
- Medical College of Georgia at Augusta University, Augusta, Georgia, USA
| | | | - Lydia Kaoutzani
- Wellstar-Medical College of Georgia Health, Department of Neurosurgery, Augusta, Georgia, USA
| | - Fernando L Vale
- Wellstar-Medical College of Georgia Health, Department of Neurosurgery, Augusta, Georgia, USA
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26
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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27
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Koenig M, Castro Cara A, Woods A, Vasilopoulos T, Gunnett AM. Real-World Experience on Why Research Flatlines: A Review of Trials From the Coordinator's Perspective. Cureus 2024; 16:e51703. [PMID: 38313998 PMCID: PMC10838549 DOI: 10.7759/cureus.51703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 02/06/2024] Open
Abstract
INTRODUCTION Investigator-initiated research trial failure is a national concern that hinders the dissemination of information while wasting resources, time, and funding. The goal of this analysis was to provide an objective review of points to consider increasing an investigator's chances of success. METHODS The included trials were divided into two groups based on whether they were successful or unsuccessful in meeting enrollment goals. Common issues were noted for each trial to identify prevalent issues and compare their quantity within each group. RESULTS Unsuccessful trials averaged twice as many issues as trials in the successful group. The most common problems identified in unsuccessful studies involved study planning, whereas the most common problems identified in successful studies revolved around study staff. CONCLUSIONS There is no single definitive indicator for trial failure; however, awareness of these issues in a trial's planning phase can help prevent their occurrence and aid in overall completion and publication.
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Affiliation(s)
- Megan Koenig
- Anesthesiology, University of Florida College of Medicine, Gainesville, USA
| | - Andrea Castro Cara
- Anesthesiology, University of Florida College of Medicine, Gainesville, USA
| | - Anna Woods
- Anesthesiology, University of Florida College of Medicine, Gainesville, USA
| | | | - Amy M Gunnett
- Anesthesiology, University of Florida College of Medicine, Gainesville, USA
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28
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Liu Y, Sang G, Liu Z, Pan Y, Cheng J, Zhang Y. MPTN: A message-passing transformer network for drug repurposing from knowledge graph. Comput Biol Med 2024; 168:107800. [PMID: 38043469 DOI: 10.1016/j.compbiomed.2023.107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.
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Affiliation(s)
- Yuanxin Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Guoming Sang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Zhi Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yilin Pan
- School of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Junkai Cheng
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
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Huang H, Chen Z, Zhu M, Deng X, Yu L, Weng H, Yao Y, Hong H, Fang X, Wang Z, Tian Y, Huang H, Lin T. Discontinuation and nonpublication of nasopharyngeal carcinoma clinical trials. Oral Oncol 2024; 148:106656. [PMID: 38065019 DOI: 10.1016/j.oraloncology.2023.106656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/25/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
OBJECTIVES To determine the extent of research waste in the field of nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS In this cross-sectional study, we explored the rates, causes and predictors of discontinuation and nonpublication of NPC clinical trials. The sample was derived using the ClinicalTrials.gov advanced search function. Adjusted logistic regression was used to ascertain the effect of trial characteristics on completion and publication status. If a trial discontinuation explanation or publication status could not be determined through the systematic search, the corresponding author was emailed. RESULTS Ultimately, 311 NPC clinical trials were included (255 [82.0 %] completed and 56 [18.0 %] discontinued trials). The most common reason for trial discontinuation was poor accrual (50 %, 23/46). Industry funding (adjusted OR, 3.12; P = 0.003) and recurrent/metastatic setting (adjusted OR, 11.95; P = 0.003) were significantly associated with increased likelihood of trial discontinuation. Of the 207 completed trials included in the publication query, 141 (68.1 %) were published in peer-reviewed journals, 10 (4.8 %) had results only available on ClinicalTrials.gov, and 56 (27.1 %) remained unpublished 3 or more years after trial completion. Radiation with or without pharmacologic interventions significantly increased the potential of publication (adjusted OR, 3.20; P = 0.048). Among published trials, the median time to publication was 28.47 months (interquartile range, 15.27-44.98 months). CONCLUSION We identified the difficulties inherent in NPC clinical trials from completion to publication. This represents considerable research waste in NPC, thus raising ethical concerns about the concealment of clinical data and futile patient participation and attendant risks.
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Affiliation(s)
- Huageng Huang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - Zegeng Chen
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - Manyi Zhu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong, PR China
| | - Xinyi Deng
- Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, PR China
| | - Le Yu
- Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, PR China
| | - Huawei Weng
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China; Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, PR China
| | - Yuyi Yao
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - Huangming Hong
- Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, PR China
| | - Xiaojie Fang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - Zhao Wang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - Ying Tian
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China
| | - He Huang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China.
| | - Tongyu Lin
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, PR China; Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, PR China.
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Vasan K, Gysi DM, Barabási AL. The clinical trials puzzle: How network effects limit drug discovery. iScience 2023; 26:108361. [PMID: 38146432 PMCID: PMC10749231 DOI: 10.1016/j.isci.2023.108361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/04/2023] [Accepted: 10/25/2023] [Indexed: 12/27/2023] Open
Abstract
The depth of knowledge offered by post-genomic medicine has carried the promise of new drugs, and cures for multiple diseases. To explore the degree to which this capability has materialized, we extract meta-data from 356,403 clinical trials spanning four decades, aiming to offer mechanistic insights into the innovation practices in drug discovery. We find that convention dominates over innovation, as over 96% of the recorded trials focus on previously tested drug targets, and the tested drugs target only 12% of the human interactome. If current patterns persist, it would take 170 years to target all druggable proteins. We uncover two network-based fundamental mechanisms that currently limit target discovery: preferential attachment, leading to the repeated exploration of previously targeted proteins; and local network effects, limiting exploration to proteins interacting with highly explored proteins. We build on these insights to develop a quantitative network-based model to enhance drug discovery in clinical trials.
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Affiliation(s)
- Kishore Vasan
- Network Science Institute, Northeastern University, Boston, MA, USA
| | - Deisy Morselli Gysi
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Statistics, Federal University of Parana, Curtiba, Brazil
- Department of Veteran Affairs, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Veteran Affairs, Boston, MA, USA
- Department of Data and Network Science, Central European University, Budapest, Hungary
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Aliper A, Kudrin R, Polykovskiy D, Kamya P, Tutubalina E, Chen S, Ren F, Zhavoronkov A. Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence. Clin Pharmacol Ther 2023; 114:972-980. [PMID: 37483175 DOI: 10.1002/cpt.3008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023]
Abstract
Drug discovery and development is a notoriously risky process with high failure rates at every stage, including disease modeling, target discovery, hit discovery, lead optimization, preclinical development, human safety, and efficacy studies. Accurate prediction of clinical trial outcomes may help significantly improve the efficiency of this process by prioritizing therapeutic programs that are more likely to succeed in clinical trials and ultimately benefit patients. Here, we describe inClinico, a transformer-based artificial intelligence software platform designed to predict the outcome of phase II clinical trials. The platform combines an ensemble of clinical trial outcome prediction engines that leverage generative artificial intelligence and multimodal data, including omics, text, clinical trial design, and small molecule properties. inClinico was validated in retrospective, quasi-prospective, and prospective validation studies internally and with pharmaceutical companies and financial institutions. The platform achieved 0.88 receiver operating characteristic area under the curve in predicting the phase II to phase III transition on a quasi-prospective validation dataset. The first prospective predictions were made and placed on date-stamped preprint servers in 2016. To validate our model in a real-world setting, we published forecasted outcomes for several phase II clinical trials achieving 79% accuracy for the trials that have read out. We also present an investment application of inClinico using date stamped virtual trading portfolio demonstrating 35% 9-month return on investment.
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Affiliation(s)
- Alex Aliper
- Insilico Medicine AI Ltd, Masdar City, Abu Dhabi, United Arab Emirates
| | - Roman Kudrin
- Insilico Medicine AI Ltd, Masdar City, Abu Dhabi, United Arab Emirates
| | | | - Petrina Kamya
- Insilico Medicine Canada Inc., Quebec, Montreal, Canada
| | - Elena Tutubalina
- Insilico Medicine Hong Kong Ltd, New Territories, Pak Shek Kok, Hong Kong
| | - Shan Chen
- Insilico Medicine Shanghai Ltd, Pudong New District, Shanghai, China
| | - Feng Ren
- Insilico Medicine Shanghai Ltd, Pudong New District, Shanghai, China
| | - Alex Zhavoronkov
- Insilico Medicine AI Ltd, Masdar City, Abu Dhabi, United Arab Emirates
- Insilico Medicine Hong Kong Ltd, New Territories, Pak Shek Kok, Hong Kong
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Fan R, Zheng Y, Zhou R, Beeraka NM, Sukocheva OA, Zhao R, Li S, Zhao X, Liu C, He S, Mahesh PA, Gurupadayya BM, Nikolenko VN, Zhao D, Liu J. Chinese Clinical Trial Registry 13-year data collection and analysis: geographic distribution, financial support, research phase, duration, and disease categories. Front Med (Lausanne) 2023; 10:1203346. [PMID: 37901406 PMCID: PMC10602811 DOI: 10.3389/fmed.2023.1203346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023] Open
Abstract
Objective To evaluate the current status of trial registration on the Chinese Clinical Trial Registry (ChiCTR). Design In this descriptive study, a multi-dimensional grouping analysis was conducted to estimate trends in the annual trial registration, geographical distribution, sources of funding, targeted diseases, and trial subtypes. Setting We have analyzed all clinical trial records (over 30,000) registered on the Chinese Clinical Trial Registry (ChiCTR) from 2007 to 2020 executed in China. Main outcomes and measures The main outcome was the baseline characteristics of registered trials. These trials were categorized and analyzed based on geographical distribution, year of implementation, disease type, resource and funding type, trial duration, trial phase, and the type of experimental approach. Results From 2008 to 2017, a consistent upward trend in clinical trial registrations was observed, showing an average annual growth rate of 29.2%. The most significant year-on-year (yoy%) growth in registrations occurred in 2014 (62%) and 2018 (68.5%). Public funding represented the predominant source of funding in the Chinese healthcare system. The top five ChiCTR registration sites for all disease types were highly populated urban regions of China, including Shanghai (5,658 trials, 18%), Beijing (5,127 trials, 16%), Guangdong (3,612 trials, 11%), Sichuan (2,448 trials, 8%), and Jiangsu (2,196 trials, 7%). Trials targeting neoplastic diseases accounted for the largest portion of registrations, followed by cardio/cerebrovascular disease (CCVD) and orthopedic diseases-related trials. The largest proportions of registration trial duration were 1-2 years, less than 1 year, and 2-3 years (at 27.36, 26.71, and 22.46%). In the case of the research phase, the top three types of all the registered trials are exploratory research, post-marketing drugs, and clinical trials of new therapeutic technology. Conclusion and relevance Oncological and cardiovascular diseases receive the highest share of national public funding for medical clinical trial-based research in China. Publicly funded trials represent a major segment of the ChiCTR registry, indicating the dominating role of public governance in this health research sector. Furthermore, the growing number of analyzed records reflect the escalation of clinical research activities in China. The tendency to distribute funding resources toward exceedingly populated areas with the highest incidence of oncological and cardiovascular diseases reveals an aim to reduce the dominating disease burden in the urban conglomerates in China.
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Affiliation(s)
- Ruitai Fan
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yufei Zheng
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Runze Zhou
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Narasimha M. Beeraka
- Raghavendra Institute of Pharmaceutical Education and Research (RIPER), Anantapuramu, Andhra Pradesh, India
- Department of Human Anatomy, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
- Department of Pediatrics, Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Olga A. Sukocheva
- College of Nursing and Health Sciences, Flinders University of South Australia, Bedford Park, SA, Australia
| | - Ruiwen Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shijie Li
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Xiang Zhao
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Chunying Liu
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- College of Medicine, Zhengzhou University, Zhengzhou, China
| | - Song He
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- College of Medicine, Zhengzhou University, Zhengzhou, China
| | - P. A. Mahesh
- Department of Pulmonary Medicine, JSS Medical College, JSS Academy of Higher Education and Research (JSS AHER), Mysuru, Karnataka, India
| | - B. M. Gurupadayya
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research (JSS AHER), Mysuru, Karnataka, India
| | - Vladimir N. Nikolenko
- Department of Human Anatomy, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Di Zhao
- Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junqi Liu
- Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Hong K, Rowhani-Farid A, Doshi P. Definition and rationale for placebo composition: Cross-sectional analysis of randomized trials and protocols published in high-impact medical journals. Clin Trials 2023; 20:564-570. [PMID: 37050893 DOI: 10.1177/17407745231167756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
BACKGROUND/AIMS Inadequate description of trial interventions in publications has been repeatedly reported, a problem that extends to the description of placebo controls. Without describing placebo contents, it cannot be assumed that a placebo is inert. Pharmacologically active placebos complicate accurate estimation and interpretation of efficacy and safety data. In this study, we sought to assess whether placebo contents are described in study protocols and publications of trials published in high-impact medical journals. METHODS We identified all placebo-controlled randomized clinical trials (RCTs) published in 2016 in Annals of Internal Medicine, The BMJ, the Journal of the American Medical Association (JAMA), The Lancet, and the New England Journal of Medicine (NEJM). We included all trials with publicly available study protocols. From journal publications and associated study protocols, we searched and recorded: description of placebo contents; the amount of each placebo ingredient; and investigators' stated rationale for selection of placebo ingredients. RESULTS We included 113 placebo-controlled RCTs. Of the 113 trials, placebo content was described in 22 (19.5%) journal publications and 51 (45.1%) study protocols. The amount of each placebo ingredient was described in 15 (13.3%) journal publications and 47 (41.6%) study protocols. None of the journal publications explained the rationale for the choice of placebo ingredients, whereas a rationale was provided in 4 (3.5%) study protocols. The stated rationales were to ensure the placebo was visually indistinguishable from the experimental intervention (N = 3) and ensure comparability with a previous study (N = 1). CONCLUSION There is no accessible record of the composition of placebos for approximately half of high-impact RCTs, even with access to study protocols. This impedes reproducibility and raises unanswerable questions about what effects-beneficial or harmful-the placebo may have had on trial participants, potentially confounding an accurate assessment of the experimental intervention's safety and efficacy. Considering that study protocols are unabridged, detailed documents describing the trial design and methodology, the fact that less than half of the study protocols described the placebo contents raises concerns about clinical trial transparency. To improve the reproducibility and potential of placebo-controlled RCTs to provide reliable evidence on the efficacy and safety profile of drugs and other experimental interventions, more detail regarding placebo contents must be included in trial documents.
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Affiliation(s)
- Kyungwan Hong
- Department of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - Anisa Rowhani-Farid
- Department of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore, MD, USA
| | - Peter Doshi
- Department of Practice, Sciences, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore, MD, USA
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Silva NS, Elkins MR, Lemes ÍR, Stubbs PW, Franco MR, Pinto RZ. Clinical trial registration has become more prevalent in physical therapy but it is still inadequate: A meta-research study. Musculoskelet Sci Pract 2023; 67:102854. [PMID: 37657398 DOI: 10.1016/j.msksp.2023.102854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/16/2023] [Accepted: 08/25/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND A study using data from 2009 showed low prevalence and inadequate trial registration in physiotherapy. In 2013, a joint editorial recommended prospective registration in physiotherapy journals. Ten years later it is unclear whether the joint editorial achieved its intended benefit. OBJECTIVES To investigate the proportion of randomized trials adequately registered and the extent of selective reporting of outcomes in trials of physiotherapy interventions published in 2019 and to compare these data with equivalent published data from 2009. DESIGN Meta-research study. METHOD A random sample of 200 trials published in 2019 was used. Evidence of registration was sought on trial registers and by contacting authors. Data from the article was compared with data from the trial registration. Data from this sample of trial published in 2019 were compared with equivalent published data from 2009. RESULTS In 2019, the proportion of trials that were registered was 63% versus 34% in 2009 (absolute difference 29%). In 2019, 18% of the trials were prospectively registered compared to 6% in 2009 (absolute difference 12%). Unambiguous primary outcomes (i.e., method and timepoints of measurement clearly defined in the trial registry entry) were registered for 30% in 2019. Registration was adequate (i.e., prospective with unambiguous primary outcomes) for 8%, compared with 3% in 2009 (absolute difference 5%). Selective outcome reporting occurred in 73% of the trials in which it was assessable; in 2009 this proportion was 47% (absolute difference 26%). CONCLUSIONS Registration of randomized trials in physiotherapy increased in the past decade, but it is still inadequate. More effort is still required to implement and enforce adequate registration.
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Affiliation(s)
- Nayara Santos Silva
- Department of Physical Therapy, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil
| | - Mark R Elkins
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Ítalo R Lemes
- Department of Physical Therapy, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil; Department of Physical Therapy, School of Science and Technology, Sao Paulo State University (UNESP), Presidente Prudente, Sao Paulo, Brazil
| | - Peter W Stubbs
- Discipline of Physiotherapy, Graduate School of Health, University of Technology Sydney, Sydney, New South Wales, Australia
| | | | - Rafael Zambelli Pinto
- Department of Physical Therapy, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil.
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Rafeeq MM, Nahhas AF, Binothman N, Habib AH, Aljadani M, Sain ZM, Tuwaijri AA, Alshehri MA, Alzahrani OR. PheroxyPyrabenz and Carbopyrropyridin against major proteins of SARS CoV-2: a comprehensive in-silico molecular docking and dynamics simulation studies. J Biomol Struct Dyn 2023; 41:9121-9133. [PMID: 36318617 DOI: 10.1080/07391102.2022.2140202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The pandemic that started in 2020 left us with so much information about viruses and respiratory diseases, and the cause behind it was severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). The world is still recovering, which costs so many economic and other indirect disasters; despite that, no medications are available on the market. Although the WHO approved a few vaccines on an emergency basis, the remarks and the reinfection chances are still under investigation, and a few pharmaceutical companies are also claiming that a few medications can be effective. However, there is no situation in control. SARS CoV-2 mutates and comes in different forms, making the situation unpredictable. In this study, we have screened the complete Asinex's BioDesign library, which contains 170,269 compounds, and shorted the data against the docking score that helps in the identification of 4-[5-(3-Ethoxy-4-hydroxyphenyl)-1-(2-hydroxyethyl)-1H-pyrazol-3-yl]-1, 2-benzenediol (PheroxyPyrabenz) and 1-[(3R,4R)-1-(5-Aminopentanoyl)-4-hydroxy-3-pyrrolidinyl]-1H-pyrrolo[2,3-b]pyridine-4-carboxamide (Carbopyrropyridin) as a significant drug candidate that can work against the multiple proteins of the SARS CoV-2 resulting in seizing the complete biological process of the virus. Further, the study extended to Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and molecular dynamics (MD) simulation of both the compounds with their complexity. The complete workflow of the study has shown satisfactory results, and both drug candidates can potentially stop the hunt for drugs against this virus after its experimental validation. Further, we checked both compounds' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, showing case-proof validatory results.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Misbahuddin M Rafeeq
- Department of Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Alaa F Nahhas
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Najat Binothman
- Department of Chemistry, College of Sciences & Arts, King Abdulaziz University, Rabigh, Kingdom of Saudi Arabia
| | - Alaa Hamed Habib
- Department of Physiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Majidah Aljadani
- Department of Chemistry, College of Sciences & Arts, King Abdulaziz University, Rabigh, Kingdom of Saudi Arabia
| | - Ziaullah M Sain
- Department of Microbiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Kingdom of Saudi Arabia
| | - Abeer Al Tuwaijri
- Medical Genomics Research Department, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGH), Kingdom of Saudi Arabia
- Clinical Laboratory Sciences Department, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Kingdom of Saudi Arabia
| | - Mohammed Ali Alshehri
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, Najran, Kingdom of Saudi Arabia
| | - Othman R Alzahrani
- Department of Biology, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia
- Genome and Biotechnology Unit, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia
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Holst M, Haslberger M, Yerunkar S, Strech D, Hemkens LG, Carlisle BG. Frequency of multiple changes to prespecified primary outcomes of clinical trials completed between 2009 and 2017 in German university medical centers: A meta-research study. PLoS Med 2023; 20:e1004306. [PMID: 37906614 PMCID: PMC10645365 DOI: 10.1371/journal.pmed.1004306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/14/2023] [Accepted: 10/03/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Clinical trial registries allow assessment of deviations of published trials from their protocol, which may indicate a considerable risk of bias. However, since entries in many registries can be updated at any time, deviations may go unnoticed. We aimed to assess the frequency of changes to primary outcomes in different historical versions of registry entries, and how often they would go unnoticed if only deviations between published trial reports and the most recent registry entry are assessed. METHODS AND FINDINGS We analyzed the complete history of changes of registry entries in all 1746 randomized controlled trials completed at German university medical centers between 2009 and 2017, with published results up to 2022, that were registered in ClinicalTrials.gov or the German WHO primary registry (German Clinical Trials Register; DRKS). Data were retrieved on 24 January 2022. We assessed deviations between registry entries and publications in a random subsample of 292 trials. We determined changes of primary outcomes (1) between different versions of registry entries at key trial milestones, (2) between the latest registry entry version and the results publication, and (3) changes that occurred after trial start with no change between latest registry entry version and publication (so that assessing the full history of changes is required for detection of changes). We categorized changes as major if primary outcomes were added, dropped, changed to secondary outcomes, or secondary outcomes were turned into primary outcomes. We also assessed (4) the proportion of publications transparently reporting changes and (5) characteristics associated with changes. Of all 1746 trials, 23% (n = 393) had a primary outcome change between trial start and latest registry entry version, with 8% (n = 142) being major changes, that is, primary outcomes were added, dropped, changed to secondary outcomes, or secondary outcomes were turned into primary outcomes. Primary outcomes in publications were different from the latest registry entry version in 41% of trials (120 of the 292 sampled trials; 95% confidence interval (CI) [35%, 47%]), with major changes in 18% (54 of 292; 95% CI [14%, 23%]). Overall, 55% of trials (161 of 292; 95% CI [49%, 61%]) had primary outcome changes at any timepoint over the course of a trial, with 23% of trials (67 of 292; 95% CI [18%, 28%]) having major changes. Changes only within registry records, with no apparent discrepancy between latest registry entry version and publication, were observed in 14% of trials (41 of 292; 95% CI [10%, 19%]), with 4% (13 of 292; 95% CI [2%, 7%]) being major changes. One percent of trials with a change reported this in their publication (2 of 161 trials; 95% CI [0%, 4%]). An exploratory logistic regression analysis indicated that trials were less likely to have a discrepant registry entry if they were registered more recently (odds ratio (OR) 0.74; 95% CI [0.69, 0.80]; p<0.001), were not registered on ClinicalTrials.gov (OR 0.41; 95% CI [0.23, 0.70]; p = 0.002), or were not industry-sponsored (OR 0.29; 95% CI [0.21, 0.41]; p<0.001). Key limitations include some degree of subjectivity in the categorization of outcome changes and inclusion of a single geographic region. CONCLUSIONS In this study, we observed that changes to primary outcomes occur in 55% of trials, with 23% trials having major changes. They are rarely transparently reported in the results publication and often not visible in the latest registry entry version. More transparency is needed, supported by deeper analysis of registry entries to make these changes more easily recognizable. Protocol registration: Open Science Framework (https://osf.io/t3qva; amendment in https://osf.io/qtd2b).
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Affiliation(s)
- Martin Holst
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
- Institute for Ethics, History and Philosophy of Medicine, Medizinische Hochschule Hannover, Hannover, Germany
| | - Martin Haslberger
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Samruddhi Yerunkar
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Strech
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Lars G. Hemkens
- Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
- Meta-Research Innovation Center Berlin, QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
- Meta-Research Innovation Center at Stanford, Stanford University, Stanford, California, United States of America
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benjamin G. Carlisle
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany
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Lora D, García-Reyne A, Lalueza A, Maestro de la Calle G, Ruíz-Ruigómez M, Calderón EJ, Menéndez-Orenga M. Characteristics of clinical trials of influenza and respiratory syncytial virus registered in ClinicalTrials.gov between 2014 and 2021. Front Public Health 2023; 11:1171975. [PMID: 37841720 PMCID: PMC10569070 DOI: 10.3389/fpubh.2023.1171975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
The randomized clinical trial (RCT) is the ideal and mandatory type of study to verify the effect and safety of a drug. Our aim is to examine the fundamental characteristics of interventional clinical trials on influenza and respiratory syncytial virus (RSV). This is a cross-sectional study of RCTs on influenza and RSV in humans between 2014 and 2021 registered in ClinicalTrials.gov. A total of 516 studies were identified: 94 for RSV, 423 for influenza, and 1 for both viruses. There were 51 RCTs of RSV vaccines (54.3%) and 344 (81.3%) for influenza virus vaccines (p < 0.001). Twelve (12.8%) RCTs for RSV were conducted only with women, and 6 were conducted only with pregnant women; for RCTs for influenza, 4 (0.9%) and 3, respectively. For RSV, 29 (31%) of the RCTs were exclusive to people under 5 years of age, and 21 (5%) for influenza virus (p < 0.001). For RSV, there are no RCTs exclusively for people older than or equal to 65 years and no phase 4 trials. RCTs on influenza virus and RSV has focused on vaccines. For the influenza virus, research has been consolidated, and for RSV, research is still in the development phase and directed at children and pregnant women.
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Affiliation(s)
- David Lora
- Instituto de Investigación Sanitaria del Hospital Universitario 12 de Octubre (imas12), Madrid, Spain
- Spanish Clinical Research Network (SCReN), Madrid, Spain
- Facultad de Estudios Estadísticos, Universidad Complutense de Madrid (UCM), Madrid, Spain
| | - Ana García-Reyne
- Servicio de Medicina Interna, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Antonio Lalueza
- Instituto de Investigación Sanitaria del Hospital Universitario 12 de Octubre (imas12), Madrid, Spain
- Hospital Universitario 12 de Octubre, Madrid, Spain
- Facultad de Medicina, Universidad Complutense de Madrid (UCM), Madrid, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain
| | - Guillermo Maestro de la Calle
- Facultad de Medicina, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Servicio de Medicina Interna, Antimicrobial Stewardship Program, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - María Ruíz-Ruigómez
- Servicio de Medicina Interna, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Enrique J Calderón
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Consejo Superior de Investigaciones Científicas, Universidad de Sevilla, Sevilla, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Departamento de Medicina, Facultad de Medicina, Universidad de Sevilla, Sevilla, Spain
| | - Miguel Menéndez-Orenga
- Instituto de Investigación Sanitaria del Hospital Universitario 12 de Octubre (imas12), Madrid, Spain
- Servicio Madrileño de Salud, Centro de Salud La Ventilla, Madrid, Spain
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Sobczyk MK, Zheng J, Davey Smith G, Gaunt TR. Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases. BMJ Open 2023; 13:e072087. [PMID: 37751957 PMCID: PMC10533809 DOI: 10.1136/bmjopen-2023-072087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023] Open
Abstract
OBJECTIVE To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable. METHODS We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications. RESULTS We found that only 13% of completed RCTs identified in ClinicalTrials.Gov submitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications -36% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with Medical Subject Headings ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed. CONCLUSIONS We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.
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Affiliation(s)
- Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
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Gu J, Bang D, Yi J, Lee S, Kim DK, Kim S. A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. Brief Bioinform 2023; 24:bbad285. [PMID: 37544660 DOI: 10.1093/bib/bbad285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/05/2023] [Accepted: 07/21/2023] [Indexed: 08/08/2023] Open
Abstract
Combination therapies have brought significant advancements to the treatment of various diseases in the medical field. However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug-drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.
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Affiliation(s)
- Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Sangseon Lee
- Institute of Computer Technology Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dong Kyu Kim
- PHARMGENSCIENCE Co., Ltd., 216, Dongjak-daero, 06554 Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Institute of Computer Technology, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
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Namiot ED, Smirnovová D, Sokolov AV, Chubarev VN, Tarasov VV, Schiöth HB. The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs. Front Pharmacol 2023; 14:1228148. [PMID: 37790806 PMCID: PMC10544909 DOI: 10.3389/fphar.2023.1228148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/31/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction: Clinical trials are the gold standard for testing new therapies. Databases like ClinicalTrials.gov provide access to trial information, mainly covering the US and Europe. In 2006, WHO introduced the global ICTRP, aggregating data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform by June 2019. This study conducts a comprehensive global analysis of the ICTRP database and provides framework for large-scale data analysis, data preparation, curation, and filtering. Materials and methods: The trends in 689,793 records from the ICTRP database (covering trials registered from 1990 to 2020) were analyzed. Records were adjusted for duplicates and mapping of agents to drug classes was performed. Several databases, including DrugBank, MESH, and the NIH Drug Information Portal were used to investigate trends in agent classes. Results: Our novel approach unveiled that 0.5% of the trials we identified were hidden duplicates, primarily originating from the EUCTR database, which accounted for 82.9% of these duplicates. However, the overall number of hidden duplicates within the ICTRP seems to be decreasing. In total, 689 793 trials (478 345 interventional) were registered in the ICTRP between 1990 and 2020, surpassing the count of trials in ClinicalTrials.gov (362 500 trials by the end of 2020). We identified 4 865 unique agents in trials with DrugBank, whereas 2 633 agents were identified with NIH Drug Information Portal data. After the ClinicalTrials.gov, EUCTR had the most trials in the ICTRP, followed by CTRI, IRCT, CHiCTR, and ISRCTN. CHiCTR displayed a significant surge in trial registration around 2015, while CTRI experienced rapid growth starting in 2016. Conclusion: This study highlights both the strengths and weaknesses of using the ICTRP as a data source for analyzing trends in clinical trials, and emphasizes the value of utilizing multiple registries for a comprehensive analysis.
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Affiliation(s)
- Eugenia D. Namiot
- Department of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Diana Smirnovová
- Department of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Aleksandr V. Sokolov
- Department of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | | | - Vadim V. Tarasov
- Advanced Molecular Technology, Limited Liable Company (LLC), Moscow, Russia
| | - Helgi B. Schiöth
- Department of Surgical Sciences, Division of Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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Mello AT, Kammer PV, Nascimento GM, de Lima LP, Pessini J, Valmorbida A, Page MJ, Trindade EBSM. Credibility at stake: only two-thirds of randomized trials of nutrition interventions are registered and lack transparency in outcome and treatment effect definitions. J Clin Epidemiol 2023; 161:74-83. [PMID: 37399969 DOI: 10.1016/j.jclinepi.2023.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/02/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
OBJECTIVES This study aimed to investigate the adherence of randomized controlled trials of nutrition interventions to transparency practices informing assessments of selective reporting biases, including the availability of a trial registration entry, protocol and statistical analysis plan (SAP). STUDY DESIGN AND SETTING Retrospective observational study with cross-sectional design. We systematically searched for trials published from 1 July 2019, to 30 June 2020, and included a randomly selected sample of 400 studies. We searched for registry entries, protocols, and SAPs for all included studies. We extracted data to characterize the disclosure of sufficient information in the available materials to inform assessments of selective reporting biases, considering the definition of outcome domain, measure, metric, method of aggregation, time point, analysis population, methods to handle missing data and method of adjustment. RESULTS Most trials (69%) were registered, but these often lacked sufficient specification of outcomes and intended treatment effects. Protocols and SAPs provided more details but were less often available (14% and 3%, respectively), and even then, almost all studies presented limited information to inform the assessments of risk of bias due to the selection of the reported result. CONCLUSION Lack of full specification of outcomes and intended treatment effects hinder a full adherence of randomized controlled trials of nutrition interventions to transparency practices and may affect their credibility.
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Affiliation(s)
- Arthur T Mello
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Pedro V Kammer
- Post-Graduate Program in Dentistry, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Giovanna M Nascimento
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Luana P de Lima
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Júlia Pessini
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Aline Valmorbida
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Erasmo B S M Trindade
- Post-Graduate Program in Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil; Department of Nutrition, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil.
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Zhu C, Xia X, Li N, Zhong F, Yang Z, Liu L. RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding. Comput Biol Med 2023; 164:107262. [PMID: 37481946 DOI: 10.1016/j.compbiomed.2023.107262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/07/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.
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Affiliation(s)
- Chaoyu Zhu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Xiaoqiong Xia
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Nan Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Fan Zhong
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Lei Liu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, 200120, China.
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Krix S, DeLong LN, Madan S, Domingo-Fernández D, Ahmad A, Gul S, Zaliani A, Fröhlich H. MultiGML: Multimodal graph machine learning for prediction of adverse drug events. Heliyon 2023; 9:e19441. [PMID: 37681175 PMCID: PMC10481305 DOI: 10.1016/j.heliyon.2023.e19441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023] Open
Abstract
Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
- Fraunhofer Center for Machine Learning, Germany
| | - Lauren Nicole DeLong
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, 10 Crichton Street, EH8 9AB, UK
| | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, 53115, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
- Enveda Biosciences, Boulder, CO, 80301, USA
| | - Ashar Ahmad
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
- Grunenthal GmbH, 52099, Aachen, Germany
| | - Sheraz Gul
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases CIMD, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases CIMD, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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Xu Q, Kowalski J. myCMIE: My cancer molecular information exchange. iScience 2023; 26:107324. [PMID: 37575188 PMCID: PMC10415923 DOI: 10.1016/j.isci.2023.107324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/15/2023] [Accepted: 07/05/2023] [Indexed: 08/15/2023] Open
Abstract
Molecular profiling reports (MPRs) are critical for determining treatment options for cancer patients. They include several pages of information on genomic findings, drugs, and trial options that are challenging to synthesize for effectively and expeditiously informing on treatment. Xu and Kowalski present a web application, myCMIE, that synthesizes MPR content to define a patient-centric, information system in which molecular profiles are exchanged between a query case(s) and public resources or user-input case series for context-informed treatment and conjecture with therapeutic implication. myCMIE offers an interactive build of coordinately connected digital-twin communities to expand our understanding of treatment context with multiple visuals to stimulate discussions among diverse stakeholders in care.
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Affiliation(s)
- Qi Xu
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Jeanne Kowalski
- Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
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Huang H, Yao Y, Deng X, Weng H, Chen Z, Yu L, Wang Z, Fang X, Hong H, Huang H, Lin T. Characteristics of immunotherapy trials for nasopharyngeal carcinoma over a 15-year period. Front Immunol 2023; 14:1195659. [PMID: 37622113 PMCID: PMC10445486 DOI: 10.3389/fimmu.2023.1195659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/24/2023] [Indexed: 08/26/2023] Open
Abstract
Background Immunotherapy has been a hotspot in nasopharyngeal carcinoma (NPC) in recent years. This study aimed to provide a comprehensive landscape of the characteristics of immunotherapy clinical trials in NPC and to determine whether contemporary studies are of sufficient quality to demonstrate therapeutic value. Methods This is a cross-sectional analysis of NPC trials registered on ClinicalTrials.gov in the last 15 years (Jan 1, 2008-Nov 20, 2022). Only interventional trials with a primary purpose of treatment were included in the final analysis. Characteristics of immunotherapy trials were compared with those of other NPC trials. Chronological shifts in NPC immunotherapy trials were also analyzed. Results Of the 440 NPC studies selected, 161 (36.6%) were immunotherapy trials and 279 (63.4%) were other NPC trials. NPC immunotherapy trials were more likely than other NPC trials to be phase 1-2 (82.6% vs. 66.7%, P < 0.001), single-arm (51.3% vs. 39.6%, P = 0.020), non-randomized (64.8% vs. 44.4%, P < 0.001), and enroll fewer than 50 participants (46.3% vs. 34.4%, P = 0.015). Blinding was used in 8.8% of NPC immunotherapy trials. Also, 90.7% of NPC immunotherapy trials were recruited nationally and 82.6% were Asia-centric. Although academic institutions and governments (72.7%) were the major sponsors of NPC trials, immunotherapy trials were more likely to be industry-funded than other NPC trials (34.2% vs. 11.5%, P < 0.001). The number of NPC immunotherapy trials increased exponentially after 2017, attributed to the exploration of immune checkpoint inhibitors. Immunotherapy combined with chemotherapy was the most commonly investigated regimen. Conclusion NPC immunotherapy trials over a 15-year period were predominantly exploratory. To generate high-quality evidence and advance the clinical application of immunotherapy in NPC, more attention and concerted efforts are needed.
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Affiliation(s)
- Huageng Huang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yuyi Yao
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Xinyi Deng
- Department of Dermatology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huawei Weng
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Zegeng Chen
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Le Yu
- Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao Wang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Xiaojie Fang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Huangming Hong
- Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - He Huang
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Tongyu Lin
- Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
- Department of Oncology, Senior Ward and Phase I Clinical Trial Ward, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Kaplan RM, Koong AJ, Irvin V. Food and Drug Administration novel drug decisions in 2017: transparency and disclosure prior to and 5 years following approval. HEALTH AFFAIRS SCHOLAR 2023; 1:qxad028. [PMID: 38756242 PMCID: PMC10986233 DOI: 10.1093/haschl/qxad028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/05/2023] [Accepted: 07/17/2023] [Indexed: 05/18/2024]
Abstract
The Food and Drug Administration (FDA) approved 46 novel drugs in 2017. We reviewed availability of results prior to and during the 5 years following each approval. Using the FDA website and ClinicalTrials.gov, we recorded trials cited as evidence for the approval, total number of studies registered in ClinicalTrials.gov, number started and completed before approval, and the frequency and timing of reporting results. The 46 drugs approved in 2017 were evaluated in 1149 studies. The number of studies used to evaluate the 46 drugs ranged from 2 to 165 (mean: 24.98; SD = 28.95). Among these, an average of 9.22 studies (SD = 9.21) were started and 5.82 studies (SD = 6.89) were completed before the approval. A single trial justified approval for 19 of 46 (41%) of the approved products. Public posting of results prior to the FDA approval was available for an average of only 1.42 studies (SD = 3.12). No results were publicly reported before approval for 9 of the 46 drugs (20%). Health care providers and consumers depend on complete and transparent reporting of information about FDA-approved medications. Only a fraction of evidence from completed studies was available before approval and a substantial portion of research evidence remained undisclosed after 5 years.
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Affiliation(s)
- Robert M Kaplan
- Clinical Excellence Research Center, Stanford University School of Medicine,Stanford, CA 94305, United States
| | - Amanda J Koong
- University of Texas Health Science Center at Houston, McGovern School of Medicine,Houston, TX 77030, United States
| | - Veronica Irvin
- College of Health, Oregon State University, Corvallis, OR 97331, United States
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Stazić P, Jurić D, Turić A, Šošić A, Marušić A, Roguljić M. Reporting characteristics of nonsurgical periodontal therapy trials registered in ClinicalTrials.gov: an observational study. J Comp Eff Res 2023; 12:e230058. [PMID: 37418255 PMCID: PMC10508296 DOI: 10.57264/cer-2023-0058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/27/2023] [Indexed: 07/08/2023] Open
Abstract
Aim: To evaluate the completeness of the description of nonsurgical periodontal therapy interventions in clinical trials registered in ClinicalTrials.gov and correspondence of registered information for trial participants and outcome measures with published articles. Materials & methods: We retrieved data from ClinicalTrials.gov and corresponding publications. The completeness of intervention reporting was assessed using the Template for Intervention Description and Replication (TIDieR) checklist for oral hygiene instructions (OHI), professional mechanical plaque removal (PMPR), and subgingival instrumentation, antiseptics and antibiotics. The completeness of registration of trial protocol information was assessed according to the WHO Trial Registration DataSet for participant information (enrollment, sample size calculation, age, gender, condition) and primary/secondary outcome measures. Results: 79 included trials involved OHI (n = 38 trials, 48.1%), PMPR (n = 19, 24.1%), antiseptics (n = 11, 12.7%), or antibiotics (n = 11, 12.7%). There was a great variety in the terms used to describe these interventions. Most of the analyzed trials (93.7%) were completed and did not provide any data on study phase (74.7%). The description of intervention in the registry in ClinicalTrials.gov was inadequate for all analyzed interventions, with description inconsistencies in matching publications. There were also discrepancies in registered and published outcomes: for 39 trials with published results, 18 had different registered and reported primary outcomes, and 29 different registered and reported secondary outcomes. Conclusion: The completeness of the description of nonsurgical therapy of periodontitis in clinical trials is unsatisfactory, reducing the quality of translation of the new evidence and procedures into clinical practice. Significant discrepancy in registered and reported trial outcomes calls into question the validity of reported results and relevance for practice.
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Affiliation(s)
- Petra Stazić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
| | - Diana Jurić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
| | - Antonela Turić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
| | - Antonio Šošić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
| | - Ana Marušić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
| | - Marija Roguljić
- University of Split School of Medicine, Šoltanska 2, Split, 21000, Croatia
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Sievänen H, Kari J, Aarnivala H, Becker S, Huurre A, Långström S, Palmu S. Success and complications in lumbar punctures of pediatric patients with leukemia: a study protocol for a randomized clinical crossover trial of a bioimpedance needle system versus conventional procedure. Trials 2023; 24:464. [PMID: 37475006 PMCID: PMC10360266 DOI: 10.1186/s13063-023-07498-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 07/06/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Acute lymphoblastic leukemia (ALL) is the most common malignancy diagnosed in children. At present, the long-term survival from pediatric ALL is well over 90%. However, the probability of event-free survival is reduced if the lumbar puncture (LP) procedures at the beginning of the patient's intrathecal therapy cause blood leakage into the spinal canal and blast cells contaminate the cerebrospinal fluid. According to the literature, such traumatic LP procedures concern one out of five pediatric patients with ALL. Recently, a novel medical device measuring the tissue bioimpedance at the tip of a spinal needle was found feasible in pediatric patients with ALL. The LP procedure was successful at the first attempt in 80% of procedures, and the incidence of traumatic LPs was then 11%. The purpose of the present study is to compare the bioimpedance spinal needle system with the standard clinical practice resting on a conventional spinal needle and investigate its efficacy in clinical practice. METHODS The study is a multicenter, randomized, two-arm crossover noninferiority trial of pediatric hemato-oncology patients that will be conducted within the usual clinical workflow. Patients' LP procedures will be performed alternately either with the IQ-Tip system (study arm A) or a conventional Quincke-type 22G spinal needle (study arm B). For each enrolled patient, the order of procedures is randomly assigned either as ABAB or BABA. The total number of LP procedures will be at least 300, and the number of procedures per patient between two and four. After each study LP procedure, the performance will be recorded immediately, and 1-week diary-based and 4-week record-based follow-ups on symptoms, complications, and adverse events will be conducted thereafter. The main outcomes are the incidence of traumatic LP, first puncture success rate, and incidence of post-dural puncture headache. DISCUSSION The present study will provide sound scientific evidence on the clinical benefit, performance, and safety of the novel bioimpedance spinal needle compared with the standard clinical practice of using conventional spinal needles in the LP procedures of pediatric patients with leukemia. TRIAL REGISTRATION ISRCTN ISRCTN16161453. Registered on 8 July 2022.
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Affiliation(s)
| | | | - Henri Aarnivala
- Department of Pediatric Hematology and Oncology, Oulu University Hospital, Oulu, Finland
| | - Stefan Becker
- Department of Pediatric Hematology and Oncology, Kuopio University Hospital, Kuopio, Finland
| | - Anu Huurre
- Department of Pediatric and Adolescent Hematology and Oncology, Turku University Hospital, Turku, Finland
| | - Satu Långström
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, New Children's Hospital, Helsinki University Hospital, Helsinki, Finland
| | - Sauli Palmu
- Tampere Center for Child, Adolescent and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University and Tampere University Hospital, Tampere, Finland.
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Griger J, Widholz SA, Jesinghaus M, de Andrade Krätzig N, Lange S, Engleitner T, Montero JJ, Zhigalova E, Öllinger R, Suresh V, Winkler W, Lier S, Baranov O, Trozzo R, Ben Khaled N, Chakraborty S, Yu J, Konukiewitz B, Steiger K, Pfarr N, Rajput A, Sailer D, Keller G, Schirmacher P, Röcken C, Fagerstedt KW, Mayerle J, Schmidt-Supprian M, Schneider G, Weichert W, Calado DP, Sommermann T, Klöppel G, Rajewsky K, Saur D, Rad R. An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets. Cancer Cell 2023:S1535-6108(23)00208-8. [PMID: 37352862 DOI: 10.1016/j.ccell.2023.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/14/2023] [Accepted: 06/01/2023] [Indexed: 06/25/2023]
Abstract
Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.
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Affiliation(s)
- Joscha Griger
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Sebastian A Widholz
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany; Institute of Pathology, Philipps University Marburg and University Hospital Marburg (UKGM), Marburg, Germany; Institute for Experimental Cancer Therapy, School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Niklas de Andrade Krätzig
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Sebastian Lange
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Thomas Engleitner
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Juan José Montero
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Ekaterina Zhigalova
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Rupert Öllinger
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Veveeyan Suresh
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Wiebke Winkler
- Immune Regulation and Cancer, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany
| | - Svenja Lier
- Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Olga Baranov
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Riccardo Trozzo
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Najib Ben Khaled
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Department of Medicine II, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Shounak Chakraborty
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Jiakun Yu
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Björn Konukiewitz
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany; Institute of Pathology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel 24105, Germany
| | - Katja Steiger
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Nicole Pfarr
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Ashish Rajput
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - David Sailer
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Gisela Keller
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Peter Schirmacher
- Institute of Pathology, Universitätsklinikum Heidelberg, Heidelberg 69120, Germany
| | - Christoph Röcken
- Institute of Pathology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Kiel 24105, Germany
| | | | - Julia Mayerle
- German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Department of Medicine II, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Marc Schmidt-Supprian
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Institute of Experimental Hematology, School of Medicine, Technical University of Munich, Munich 81675, Germany
| | - Günter Schneider
- Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technische Universität München, 81675 Munich, Germany; Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Wilko Weichert
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Dinis P Calado
- Immune Regulation and Cancer, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany; Immunity and Cancer, Francis Crick Institute, NW1 1AT London, UK
| | - Thomas Sommermann
- Immune Regulation and Cancer, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany
| | - Günter Klöppel
- Institute of Pathology, School of Medicine, Technische Universität München, Munich 81675, Germany
| | - Klaus Rajewsky
- Immune Regulation and Cancer, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin 13125, Germany
| | - Dieter Saur
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technische Universität München, 81675 Munich, Germany; Institute for Experimental Cancer Therapy, School of Medicine, Technische Universität München, 81675 Munich, Germany
| | - Roland Rad
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, 81675 Munich, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Department of Medicine II, Klinikum rechts der Isar, School of Medicine, Technische Universität München, 81675 Munich, Germany.
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Chakraborty A, Mitra S, Bhattacharjee M, De D, Pal AJ. Determining human-coronavirus protein-protein interaction using machine intelligence. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2023; 18:100228. [PMID: 37056696 PMCID: PMC10077817 DOI: 10.1016/j.medntd.2023.100228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/29/2023] [Accepted: 04/01/2023] [Indexed: 04/08/2023] Open
Abstract
The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus -19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.
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Affiliation(s)
- Arijit Chakraborty
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Sajal Mitra
- Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India
| | | | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India
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