The ClinicalTrials.gov results database--update and key issues

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

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.

Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction

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.

Compliance with requirements for registration and reporting of results in trials of mesenchymal stromal cells for musculoskeletal disorders: a systematic review

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.

National Estimates of the Participation of Patients With Cancer in Clinical Research Studies Based on Commission on Cancer Accreditation Data

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.

Supporting study registration to reduce research waste

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.

Analysis of Clinical Trials Using Anti-Tumor Traditional Chinese Medicine Monomers

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.

Selective outcome reporting in randomized clinical trials using the third molar surgery model

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.

Transgender people in clinical trials of drugs and biologics: An analysis of ClinicalTrials.gov from 2007 to 2023

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.

Advances in machine intelligence-driven virtual screening approaches for big-data

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.

Heterogeneity of outcome measures in depression trials and the relevance of the content of outcome measures to patients: a systematic review

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.

An overview and comprehensive analysis of interdisciplinary clinical research in endometriosis based on trial registry

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.

Underrepresentation of racial and ethnic minority groups in gynecologic oncology: An analysis of over 250 trials

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.

Changing trends in phase 1 oncology clinical trials

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).

Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support

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.

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research

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.

Vaccine development: Current trends and technologies

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.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

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.

Real-World Experience on Why Research Flatlines: A Review of Trials From the Coordinator's Perspective

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.

MPTN: A message-passing transformer network for drug repurposing from knowledge graph

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.

Discontinuation and nonpublication of nasopharyngeal carcinoma clinical trials

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.

The clinical trials puzzle: How network effects limit drug discovery

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.

Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence

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.

Chinese Clinical Trial Registry 13-year data collection and analysis: geographic distribution, financial support, research phase, duration, and disease categories

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.

Definition and rationale for placebo composition: Cross-sectional analysis of randomized trials and protocols published in high-impact medical journals

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.

PheroxyPyrabenz and Carbopyrropyridin against major proteins of SARS CoV-2: a comprehensive in-silico molecular docking and dynamics simulation studies

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.

Clinical trial registration has become more prevalent in physical therapy but it is still inadequate: A meta-research study

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.

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

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).

Characteristics of clinical trials of influenza and respiratory syncytial virus registered in ClinicalTrials.gov between 2014 and 2021

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.

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases

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.

A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning

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.

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

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.

Credibility at stake: only two-thirds of randomized trials of nutrition interventions are registered and lack transparency in outcome and treatment effect definitions

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.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

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.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events

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.

myCMIE: My cancer molecular information exchange

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.

Characteristics of immunotherapy trials for nasopharyngeal carcinoma over a 15-year period

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.

Food and Drug Administration novel drug decisions in 2017: transparency and disclosure prior to and 5 years following approval

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.

Reporting characteristics of nonsurgical periodontal therapy trials registered in ClinicalTrials.gov: an observational study

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.

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

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.

An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets

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.

CONSORT Harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomized trials

Randomized controlled trials remain the reference standard for healthcare research on effects of interventions, and the need to report both benefits and harms is essential. The Consolidated Standards of Reporting Trials (the main CONSORT) statement includes one item on reporting harms (i.e., all important harms or unintended effects in each group). In 2004, the CONSORT group developed the CONSORT Harms extension; however, it has not been consistently applied and needs to be updated. Here, we describe CONSORT Harms 2022, which replaces the CONSORT Harms 2004 checklist, and shows how CONSORT Harms 2022 items could be incorporated into the main CONSORT checklist. Thirteen items from the main CONSORT were modified to improve harms reporting. Three new items were added. In this article, we describe CONSORT Harms 2022 and how it was integrated into the main CONSORT checklist and elaborate on each item relevant to complete reporting of harms in randomized controlled trials. Until future work from the CONSORT group produces an updated checklist, authors, journal reviewers, and editors of randomized controlled trials should use the integrated checklist presented in this paper.

Characterizing Clinical Trials in Plastic and Reconstructive Surgery: A Systematic Review of ClinicalTrials.gov From 2007 to 2020

BACKGROUND

Clinical trials form the backbone of evidence-based medicine. ClinicalTrials.gov is the world's largest clinical trial registry, and the state of clinical trials in plastic and reconstructive surgery (PRS) within that database has not been comprehensively studied. To that end, we explored the distribution of therapeutic areas that are under investigation, impact of funding on study design and data reporting, and trends in research patterns of all PRS interventional clinical trials registered with ClinicalTrials.gov.

METHODS

Using the ClinicalTrials.gov database, we identified and extracted all clinical trials relevant to PRS that were submitted between 2007 and 2020. Studies were classified based on anatomic locations, therapeutic categories, and specialty topics. Cox proportional hazard was used to calculate adjusted hazard ratios (HRs) for early discontinuation and results reporting.

RESULTS

A total of 3224 trials that included 372,095 participants were identified. The PRS trials grew at an annual rate of 7.9%. The therapeutic classes most represented were wound healing (41.3%) and cosmetics (18.1%). Funding for PRS clinical trials is largely provided through academic institutions (72.7%), while industry and US government constituted a minority. Industry-funded studies were more likely to be discontinued early than those funded by academics (HR, 1.89) or government (HR, 1.92) and to be nonblinded and nonrandomized. Academic-funded studies were the least likely to report results data within 3 years of trial completion (odds ratio, 0.87).

CONCLUSIONS

A gulf exists in the representation of different PRS specialties among clinical trials. We highlight the role of funding source in trial design and data reporting to identify a potential source of financial waste and to stress the need for continued appropriate oversight.

Determining human-coronavirus protein-protein interaction using machine intelligence

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.

Reliability and Validity of the Inpatient Rehabilitation Facility Discharge Mobility and Self-Care Quality Measures

OBJECTIVE

To describe the reliability and validity of the publicly reported facility-level quality measures Inpatient Rehabilitation Facility (IRF) Discharge Mobility Score for Medical Rehabilitation Patients ("Discharge mobility score") and IRF Discharge Self-Care Score for Medical Rehabilitation Patients ("Discharge self-care score").

DESIGN

Observational study using standardized patient assessment data to examine facility-level split-half reliability and construct validity of quality measure scores.

SETTING AND PARTICIPANTS

All IRFs (n = 1117) in the United States with at least 20 Medicare stays. Facility-level quality measure scores were calculated from 2017 data on 428,192 Medicare (fee-for-service and Medicare Advantage) IRF patient stays.

METHODS

Using clinician-reported assessment data, we calculated facility-level mobility and self-care quality measure scores and examined reliability of these scores using split-half analysis and Pearson product-moment correlations, Spearman rank correlations, and intraclass correlation coefficients (ICC_2,1). We examined construct validity of these scores by comparing facility-level quality measure scores by facility stroke disease-specific certification status.

RESULTS

Reported as percentages meeting or exceeding expectations, IRF quality measure scores ranged from 8.3% to 90.1% for mobility and 9.0% to 90.3% for self-care. IRF scores, when split in half to examine reliability, showed strong, positive correlations for the mobility (Pearson = 0.898, Spearman = 0.898, ICC = 0.898) and self-care (Pearson = 0.886, Spearman = 0.874, ICC = 0.886) scores. When stratified by provider volume, ICCs remained strong. Construct validity analyses showed IRFs with stroke disease-specific certification had higher mean and median scores than IRFs without certification, and a greater proportion of IRFs that were certified had higher scores.

CONCLUSION AND IMPLICATIONS

Our results support the reliability and construct validity of the IRF quality measures Discharge mobility and Discharge self-care scores. Reported as percentages meeting or exceeding expectations, these quality measures are designed to be more consumer-friendly compared to change scores.

Design and Conduct of Randomized Clinical Trials Evaluating Surgical Innovations in Ophthalmology: A Systematic Review

PURPOSE

Surgical innovations are necessary to improve patient care. After an initial exploratory phase, novel surgical technique should be compared with alternative options or standard care in randomized controlled trials (RCTs). However, surgical RCTs have unique methodological challenges. Our study sought to investigate key aspects of the design, conduct, and reporting of RCTs of novel surgeries.

DESIGN

Systematic review.

METHODS

The protocol was prospectively registered in PROSPERO (CRD42021253297). RCTs evaluating novel surgeries for cataract, vitreoretinal, glaucoma, and corneal diseases were included. Medline, EMBASE, Cochrane Library, and Clinicaltrials.gov were searched. The search period was January 1, 2016, to June 16, 2021.

RESULTS

A total of 52 ophthalmic surgery RCTs were identified in the fields of glaucoma (n = 12), vitreoretinal surgery (n = 5), cataract (n = 19), and cornea (n = 16). A description defining the surgeon's experience or level of expertise was reported in 30 RCTs (57%) and was presented in both control and intervention groups in 11 (21%). Specification of the number of cases performed in the particular surgical innovation being assessed prior to the trial was reported in 10 RCTs (19%) and an evaluation of quality of the surgical intervention in 7 (13%). Prospective trial registration was recorded in 12 RCTs (23%) and retrospective registration in 13 (25%); and there was no registration record in the remaining 28 (53%) studies.

CONCLUSIONS

Important aspects of the study design such as the surgical learning curve, surgeon's previous experience, quality assurance, and trial registration details were often missing in novel ophthalmic surgical procedures. The Idea, Development, Exploration, Assessment, Long-term follow-up (IDEAL) framework aims to improve the quality of study design.

Identification of potential molecular mechanisms and candidate drugs for radiotherapy- and chemotherapy-induced mucositis

BACKGROUND

Radiotherapy-induced oral mucositis (RIOM) and chemotherapy-induced oral mucositis (CIOM) are common complications in cancer patients, leading to negative clinical manifestations, reduced quality of life, and unsatisfactory treatment outcomes.

OBJECTIVE

The present study aimed to identify potential molecular mechanisms and candidate drugs by data mining.

METHODS

We obtained a preliminary list of genes associated with RIOM and CIOM. In-depth information on these genes was explored by functional and enrichment analyses. Then, the drug-gene interaction database was used to determine the interaction of the final enriched gene list with known drugs and analyze the drug candidates.

RESULTS AND CONCLUSION

This study identified 21 hub genes that may play an important role in RIOM and CIOM, respectively. Through our data mining, bioinformatics survey, and candidate drug selection, TNF, IL-6, and TLR9 could play an important role in disease progression and treatment. In addition, eight candidate drugs (olokizumab, chloroquine, hydroxychloroquine, adalimumab, etanercept, golimumab, infliximab, and thalidomide) were selected by the drug-gene interaction literature search additionally, as candidates for treating RIOM and CIOM.