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Mariani M, Viale G, Galbardi B, Licata L, Bosi C, Dugo M, Notini G, Naldini MM, Callari M, Criscitiello C, Pusztai L, Bianchini G. Completion Rate and Positive Results Reporting Among Immunotherapy Trials in Breast Cancer, 2004-2023. JAMA Netw Open 2024; 7:e2423390. [PMID: 39028669 PMCID: PMC11259908 DOI: 10.1001/jamanetworkopen.2024.23390] [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: 01/17/2024] [Accepted: 05/22/2024] [Indexed: 07/21/2024] Open
Abstract
Importance Clinical trials are the path to test and introduce new therapies in the clinic. Trials that are unable to produce results represent inefficiency in the system and may also undermine patient confidence in the new drug development process. Objectives To survey the immunotherapy clinical trial landscape of breast cancer between January 2004 and April 2023 and examine what fraction of trials with primary completion date up to November 30, 2022, failed to report outcome, assessing the proportion of trials that yielded positive results and describing trial features associated with these 2 outcomes. Design, Setting, and Participants This cross-sectional study included breast cancer immunotherapy trials identified in ClinicalTrials.gov. Trial details and results were retrieved in December 2023. Google Scholar, PubMed, and LARVOL CLIN websites were also searched for reports. Main Outcomes and Measures Trial outcome reported as abstract or manuscript. Reported trials were categorized as positive (ie, met its end point) or negative. Association between reporting and trial features were tested using Fisher exact test. Results A total of 331 immuno-oncology trials were initiated in breast cancer by April 2023; 242 trials were phase II, 47 were phase I, and 42 phase III. By setting, 212 studies (64.0%) were conducted in metastatic, 94 (28.4%) in neoadjuvant, and 25 (7.6%) in adjuvant settings. Among phase II and III trials, 168 (59.2%) were nonrandomized. One hundred twenty trials had primary completion dates up to November 30, 2022, of which 30 (25.0%; enrolling a combined 2428 patients) failed to report their outcomes; 7 phase I trials (31.8%), 21 phase II trials (23.6%), and 2 phase III trials (22.2%) were unreported. Single-center studies were significantly more likely to be unreported than multicenter studies (19 of 54 [35.2%] vs 9 of 60 [15.0%]; P = .02). Of the 90 reported trials, 47 (52.2%) and 43 (47.8%) were positive and negative, respectively. Seventeen of 19 (89.5%) of the reported randomized trials (accruing a total of 4189 patients) were negative. Conclusions and Relevance In this cross-sectional study of immunotherapy breast cancer trials, the large number of trials yielded modest clinical impact. Single-center trials commonly failed to report their outcomes and many phase II studies have not translated into corresponding successful phase III trials.
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Affiliation(s)
- Marco Mariani
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giulia Viale
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Barbara Galbardi
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Luca Licata
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Carlo Bosi
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Matteo Dugo
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giulia Notini
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Matteo M. Naldini
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Carmen Criscitiello
- Division of Early Drug Development, European Institute of Oncology, IRCCS, Milano, Italy
- Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy
| | - Lajos Pusztai
- Department of Internal Medicine, Section of Medical Oncology, Yale School of Medicine, New Haven, Connecticut
| | - Giampaolo Bianchini
- Università Vita-Salute San Raffaele, Milan, Italy
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
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2
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Mouysset B, Le Grand M, Camoin L, Pasquier E. Poly-pharmacology of existing drugs: How to crack the code? Cancer Lett 2024; 588:216800. [PMID: 38492768 DOI: 10.1016/j.canlet.2024.216800] [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: 11/03/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.
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Affiliation(s)
- Baptiste Mouysset
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Marion Le Grand
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Luc Camoin
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
| | - Eddy Pasquier
- Centre de Recherche en Cancérologie de Marseille Inserm U1068, CNRS UMR7258, Aix-Marseille University U105, Marseille, France.
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3
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Gagg H, Williams ST, Conroy S, Myers KN, McGarrity-Cottrell C, Jones C, Helleday T, Rantala J, Rominiyi O, Danson SJ, Collis SJ, Wells G. Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients. F1000Res 2024; 12:954. [PMID: 37799492 PMCID: PMC10548111 DOI: 10.12688/f1000research.135809.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 10/07/2023] Open
Abstract
With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.
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Affiliation(s)
- Hannah Gagg
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
| | - Sophie T. Williams
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Neurosurgery, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK
| | - Samantha Conroy
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Urology, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK
| | - Katie N. Myers
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
| | | | - Callum Jones
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
| | - Thomas Helleday
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Karolinska Institut, Solnavägen, Solna, 171 77, Sweden
| | - Juha Rantala
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Misvik Biology Ltd, Karjakatu, Turku, FI-20520, Finland
| | - Ola Rominiyi
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Neurosurgery, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK
| | - Sarah J. Danson
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
- Weston Park Hospital, Sheffield, S10 2SJ, UK
| | - Spencer J. Collis
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
| | - Greg Wells
- Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK
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Jamaladdin N, Sigaud R, Kocher D, Kolodziejczak AS, Nonnenbroich LF, Ecker J, Usta D, Benzel J, Peterziel H, Pajtler KW, van Tilburg CM, Oehme I, Witt O, Milde T. Key Pharmacokinetic Parameters of 74 Pediatric Anticancer Drugs Providing Assistance in Preclinical Studies. Clin Pharmacol Ther 2023; 114:904-913. [PMID: 37441736 DOI: 10.1002/cpt.3002] [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: 03/03/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
Novel drug treatments for pediatric patients with cancer are urgently needed. Success of drug development in pediatric oncology has been promising, but many drugs still fail in translation from preclinical to clinical phases. To increase the translational potential, several improvements have been implemented, including the use of clinically achievable concentrations in the drug testing phase. Although pharmacokinetic (PK) parameters of numerous investigated drugs are published, a comprehensive PK overview of the most common drugs in pediatric oncology could guide preclinical trial design and improve the translatability into clinical trials. A review of the literature was conducted for PK parameters of 74 anticancer drugs, from the drug sensitivity profiling library of the INdividualized Therapy FOr Relapsed Malignancies in Childhood (INFORM) registry. PK data in the pediatric population were reported and complemented by adult parameters when no pediatric data were available. In addition, blood-brain barrier (BBB)-penetration assessment of drugs was provided by using the BBB score. Maximum plasma concentration was available for 73 (97%), area under the plasma concentration-time curve for 69 (92%), plasma protein binding for 66 (88%), plasma half-life for 57 (76%), time to maximum concentration for 54 (72%), clearance for 52 (69%), volume of distribution for 37 (49%), lowest plasma concentration reached by the drug before the next dose administration for 21 (28%), and steady-state concentration for 4 (5%) of drugs. Pediatric PK data were available for 48 (65%) drugs. We provide a comprehensive review of PK data for 74 drugs studied in pediatric oncology. This data set can serve as a reference to design experiments more closely mimicking drug PK conditions in patients, and may thereby increase the probability of successful clinical translation.
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Affiliation(s)
- Nora Jamaladdin
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Romain Sigaud
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
| | - Daniela Kocher
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Anna S Kolodziejczak
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
| | - Leo F Nonnenbroich
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Jonas Ecker
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Diren Usta
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Julia Benzel
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
| | - Heike Peterziel
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Kristian W Pajtler
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Cornelis M van Tilburg
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Ina Oehme
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
| | - Olaf Witt
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Till Milde
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany
- KiTZ Clinical Trial Unit (ZIPO), Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
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5
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Diniz MA, Gallardo DI, Magalhães TM. Improved inference for MCP-Mod approach using time-to-event endpoints with small sample sizes. Pharm Stat 2023; 22:760-772. [PMID: 37119000 DOI: 10.1002/pst.2303] [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: 07/02/2022] [Revised: 03/06/2023] [Accepted: 03/30/2023] [Indexed: 04/30/2023]
Abstract
The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food, Administration, and European Medicines Agency as fit-for-purpose for phase II studies. Nonetheless, this approach relies on the asymptotic properties of Maximum Likelihood (ML) estimators, which might not be reasonable for small sample sizes. In this paper, we derived improved ML estimators and correction for their covariance matrices in the censored Weibull regression model based on the corrective and preventive approaches. We performed two simulation studies to evaluate ML and improved ML estimators with their covariance matrices in (i) a regression framework (ii) the Multiple Comparison Procedures with Modeling Techniques framework. We have shown that improved ML estimators are less biased than ML estimators yielding Wald-type statistics that controls type I error without loss of power in both frameworks. Therefore, we recommend the use of improved ML estimators in the MCP-Mod approach to control type I error at nominal value for sample sizes ranging from 5 to 25 subjects per dose.
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Affiliation(s)
- Márcio A Diniz
- Biostatistics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, California, Los Angeles, USA
| | - Diego I Gallardo
- Department of Mathematics, Engineering School, University of Atacama, Copiapó, Chile
| | - Tiago M Magalhães
- Department of Statistics, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
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Sampayo-Cordero M, Miguel-Huguet B, Malfettone A, López-Miranda E, Gion M, Abad E, Alcalá-López D, Pérez-Escuredo J, Pérez-García JM, Llombart-Cussac A, Cortés J. A single-arm study design with non-inferiority and superiority time-to-event endpoints: a tool for proof-of-concept and de-intensification strategies in breast cancer. Front Oncol 2023; 13:1048242. [PMID: 37496662 PMCID: PMC10368397 DOI: 10.3389/fonc.2023.1048242] [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: 09/19/2022] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
De-escalation trials in oncology evaluate therapies that aim to improve the quality of life of patients with low-risk cancer by avoiding overtreatment. Non-inferiority randomized trials are commonly used to investigate de-intensified regimens with similar efficacy to that of standard regimens but with fewer adverse effects (ESMO evidence tier A). In cases where it is not feasible to recruit the number of patients needed for a randomized trial, single-arm prospective studies with a hypothesis of non-inferiority can be conducted as an alternative. Single-arm studies are also commonly used to evaluate novel treatment strategies (ESMO evidence tier B). A single-arm design that includes both non-inferiority and superiority primary objectives will enable the ranking of clinical activity and other parameters such as safety, pharmacokinetics, and pharmacodynamics data. Here, we describe the statistical principles and procedures to support such a strategy. The non-inferiority margin is calculated using the fixed margin method. Sample size and statistical analyses are based on the maximum likelihood method for exponential distributions. We present example analyses in metastatic and adjuvant settings to illustrate the usefulness of our methodology. We also explain its implementation with nonparametric methods. Single-arm designs with non-inferiority and superiority analyses are optimal for proof-of-concept and de-escalation studies in oncology.
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Affiliation(s)
| | - Bernat Miguel-Huguet
- Gerència Territorial Metropolitana Sud, Institut Català De La Salud, Hospital Universitari De Bellvitge, Barcelona, Spain
| | | | - Elena López-Miranda
- Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain
- Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - María Gion
- Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Elena Abad
- Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain
| | | | | | - José Manuel Pérez-García
- Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain
- International Breast Cancer Center (IBCC), Quiron Group, Barcelona, Spain
| | - Antonio Llombart-Cussac
- Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain
- Hospital Arnau de Vilanova, FISABIO, Universidad Católica de Valencia, Valencia, Spain
| | - Javier Cortés
- Medica Scientia Innovation Research (MEDSIR), Barcelona, Spain
- International Breast Cancer Center (IBCC), Quiron Group, Barcelona, Spain
- Vall d´Hebron Institute of Oncology (VHIO), Barcelona, Spain
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7
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Henderson RH, French D, Stewart E, Smart D, Idica A, Redmond S, Eckstein M, Clark J, Sullivan R, Keeling P, Lawler M. Delivering the precision oncology paradigm: reduced R&D costs and greater return on investment through a companion diagnostic informed precision oncology medicines approach. J Pharm Policy Pract 2023; 16:84. [PMID: 37408046 DOI: 10.1186/s40545-023-00590-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/26/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Precision oncology medicines represent a paradigm shift compared to non-precision oncology medicines in cancer therapy, in some situations delivering more clinical benefit, and potentially lowering healthcare costs. We determined whether employing a companion diagnostic (CDx) approach during oncology medicines development delivers effective therapies that are within the cost constraints of current health systems. R&D costs of developing a medicine are subject to debate, with average estimates ranging from $765 million (m) to $4.6 billion (b). Our aim was to determine whether precision oncology medicines are cheaper to bring from R&D to market; a secondary goal was to determine whether precision oncology medicines have a greater return on investment (ROI). METHOD Data on oncology medicines approved between 1997 and 2020 by the US Food and Drug Administration (FDA) were analysed from the Securities and Exchange Commission (SEC) filings. Data were compiled from 10-K, 10-Q, and 20-F financial performance filings on medicines' development costs through their R&D lifetime. Clinical trial data were split into clinical trial phases 1-3 and probability of success (POS) of trials was calculated, along with preclinical costs. Cost-of-capital (CoC) approach was applied and, if appropriate, a tax rebate was subtracted from the total. RESULTS Data on 42 precision and 29 non-precision oncology medicines from 56 companies listed by the National Cancer Institute which had complete data available were analysed. Estimated mean cost to deliver a new oncology medicine was $4.4b (95% CI, $3.6-5.2b). Costs to bring a precision oncology medicine to market were $1.1b less ($3.5b; 95% CI, $2.7-4.5b) compared to non-precision oncology medicines ($4.6b; 95% CI, $3.5-6.1b). The key driver of costs was POS of clinical trials, accounting for a difference of $591.3 m. Additional data analysis illustrated that there was a 27% increase in return on investment (ROI) of precision oncology medicines over non-precision oncology medicines. CONCLUSION Our results provide an accurate estimate of the R&D spend required to bring an oncology medicine to market. Deployment of a CDx at the earliest stage substantially lowers the cost associated with oncology medicines development, potentially making them available to more patients, while staying within the cost constraints of cancer health systems.
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Affiliation(s)
- Raymond H Henderson
- Patrick G. Johnston Centre for Cancer Research, Queen's University, Belfast, UK.
- Queen's Management School, Queen's University Belfast, Belfast, UK.
- Diaceutics PLC, Dataworks at Kings Hall Health and Wellbeing Park, Co Antrim, Belfast, BT9 6GW, UK.
- Salutem Insights Ltd, Clough, Portlaoise, Garryduff, R32 V653, Ireland.
| | - Declan French
- Queen's Management School, Queen's University Belfast, Belfast, UK
| | - Elaine Stewart
- Queen's Management School, Queen's University Belfast, Belfast, UK
| | - Dave Smart
- Diaceutics PLC, Dataworks at Kings Hall Health and Wellbeing Park, Co Antrim, Belfast, BT9 6GW, UK
| | - Adam Idica
- Inovalon Inc., 4321 Collington Road, Bowie, MD, 20716, USA
| | - Sandra Redmond
- Salutem Insights Ltd, Clough, Portlaoise, Garryduff, R32 V653, Ireland
| | - Markus Eckstein
- Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Jordan Clark
- Diaceutics PLC, Dataworks at Kings Hall Health and Wellbeing Park, Co Antrim, Belfast, BT9 6GW, UK
| | - Richard Sullivan
- Institute of Cancer Policy, School of Cancer Sciences, King's College London, London, UK
| | - Peter Keeling
- Diaceutics PLC, Dataworks at Kings Hall Health and Wellbeing Park, Co Antrim, Belfast, BT9 6GW, UK
| | - Mark Lawler
- Patrick G. Johnston Centre for Cancer Research, Queen's University, Belfast, UK
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8
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Moyer H, Bittlinger M, Nelson A, Fernandez L, Sheng J, Wang Y, Del Paggio JC, Kimmelman J. Bypassing phase 2 in cancer drug development erodes the risk/benefit balance in phase 3 trials. J Clin Epidemiol 2023; 158:134-140. [PMID: 37028684 DOI: 10.1016/j.jclinepi.2023.03.028] [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: 01/14/2023] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023]
Abstract
OBJECTIVES Drug developers sometimes launch phase 3 (P3) trials without supporting evidence from phase 2 (P2) trials. We call this practice "P2 bypass." The aims of this study were to estimate the prevalence of P2 bypass and to compare the safety and efficacy results for P3 trials that bypassed with those that did not. STUDY DESIGN AND SETTING We created a sample of P3 solid tumor trials registered on ClinicalTrials.gov with primary completion dates between 2013 and 2019. We then attempted to match each with a supporting P2 trial using strict and broad criteria. P3 outcomes were meta-analyzed using a random effects model with subgroup contrast between trials that bypassed and those that did not. RESULTS 129 P3 trial arms met eligibility and nearly half involved P2 bypass. P3 trials involving P2 bypass produced significantly and nonsignificantly worse pooled efficacy estimates using broad and strict matching criteria, respectively. We did not observe significant differences in safety outcomes between P3 trials that bypassed P2 and those that did not. CONCLUSION The risk/benefit balance of P3 trials that bypassed P2 is less favourable than for trials supported by P2.
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Affiliation(s)
- Hannah Moyer
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Merlin Bittlinger
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Angela Nelson
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Luciano Fernandez
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Jacky Sheng
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Yuetong Wang
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
| | - Joseph C Del Paggio
- Department of Medical Oncology, Northern Ontario School of Medicine University, Thunder Bay, Ontario, Canada
| | - Jonathan Kimmelman
- Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada.
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9
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Entenberg D, Oktay MH, Condeelis JS. Intravital imaging to study cancer progression and metastasis. Nat Rev Cancer 2023; 23:25-42. [PMID: 36385560 PMCID: PMC9912378 DOI: 10.1038/s41568-022-00527-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/11/2022] [Indexed: 11/17/2022]
Abstract
Navigation through the bulk tumour, entry into the blood vasculature, survival in the circulation, exit at distant sites and resumption of proliferation are all steps necessary for tumour cells to successfully metastasize. The ability of tumour cells to complete these steps is highly dependent on the timing and sequence of the interactions that these cells have with the tumour microenvironment (TME), including stromal cells, the extracellular matrix and soluble factors. The TME thus plays a major role in determining the overall metastatic phenotype of tumours. The complexity and cause-and-effect dynamics of the TME cannot currently be recapitulated in vitro or inferred from studies of fixed tissue, and are best studied in vivo, in real time and at single-cell resolution. Intravital imaging (IVI) offers these capabilities, and recent years have been a time of immense growth and innovation in the field. Here we review some of the recent advances in IVI of mammalian models of cancer and describe how IVI is being used to understand cancer progression and metastasis, and to develop novel treatments and therapies. We describe new techniques that allow access to a range of tissue and cancer types, novel fluorescent reporters and biosensors that allow fate mapping and the probing of functional and phenotypic states, and the clinical applications that have arisen from applying these techniques, reporters and biosensors to study cancer. We finish by presenting some of the challenges that remain in the field, how to address them and future perspectives.
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Affiliation(s)
- David Entenberg
- Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
| | - Maja H Oktay
- Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Department of Surgery, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
| | - John S Condeelis
- Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Integrated Imaging Program, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Department of Surgery, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
- Department of Cell Biology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY, USA.
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10
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Ciray F, Doğan T. Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features. Expert Opin Drug Discov 2022; 17:1425-1441. [PMID: 36444655 DOI: 10.1080/17460441.2023.2153830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND Drug development productivity has been declining lately due to elevated costs and reduced discovery rates. Therefore, pharmaceutical companies have been seeking alternative ways to determine and evaluate drug candidates. RESEARCH DESIGN AND METHODS In this work, we proposed a new computational approach to directly predict the regulatory approval of drug candidates, and implemented it as a method called 'DrugApp.' To accomplish this task, we employed multiple types of features including molecular and physicochemical properties of drug candidates, together with clinical trial and patent-related features, which are then processed by random forest classifiers to train our disease group-specific approval prediction models. RESULTS Our evaluations indicated DrugApp has a high and robust prediction performance. Within a use-case study, we showed our method can predict phase IV trial drugs that are later withdrawn from the market due to severe side effects. Finally, we used DrugApp models to forecast the approval of drug candidates that are currently in phases I/II/III of clinical trials. CONCLUSIONS We hope that our study will aid the research community in terms of evaluating and improving the process of drug development. The datasets, source code, results, and pre-trained models of DrugApp are freely available at https://github.com/HUBioDataLab/DrugApp.
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Affiliation(s)
- Fulya Ciray
- Biological Data Science Laboratory, Department of Computer Engineering, Hacettepe University, Ankara, Turkey.,Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey
| | - Tunca Doğan
- Biological Data Science Laboratory, Department of Computer Engineering, Hacettepe University, Ankara, Turkey.,Department of Health Informatics, Institute of Informatics, Hacettepe University, Ankara, Turkey.,Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey
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11
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Do Early Phase Oncology Trials Predict Clinical Efficacy in Subsequent Biomarker-Enriched Phase III Randomized Trials? Target Oncol 2022; 17:665-674. [PMID: 36197635 DOI: 10.1007/s11523-022-00920-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2022] [Indexed: 10/10/2022]
Abstract
BACKGROUND Promising early phase trial results of biomarker-targeted therapies have occasionally led to regulatory approval. OBJECTIVE We examined if early phase trials were predictive of efficacy in randomized controlled trials (RCTs) with matching treatment settings. PATIENTS AND METHODS Cancer drug RCTs conducted between January 2006 and March 2021 were identified through Clinicaltrials.gov. Biomarker-enriched RCTs and associated matching early phase trials were included. Trial pairs were compared using objective response rate (ORR) and progression-free survival (PFS). We examined whether early phase trials results were associated with RCT results using logistic regression. RESULTS The search yielded 2157 unique RCTs and 27 RCTs pairing with early phase trials were included. Based on average difference of trial pairs, ORR was similar (1.6%; 95% confidence interval (CI) - 2.5 to 5.6, p = 0.50) and median PFS was higher in early phase trials (2.0 months; 95% CI 0.9-3.0, p < 0.05). On an individual pair basis, there was large variability in difference for ORR (range - 23.9 to 20.2%) and median PFS (range - 0.8 to 7.4 months). The probability of the RCT meeting its primary endpoint is 95% (95% prediction interval (PI) 72.8-99.3%) when the early phase trial ORR is 77.7%. CONCLUSIONS Overall, in early phase trials, ORR has minimal bias and median PFS appears to be slightly overestimated. Substantial variability between trials suggests early phase trial results may be inconsistent with subsequent RCT. Early phase trial results may be associated with RCTs meeting their primary endpoint when ORR is very high; however, caution must be exercised when using early phase trials as representative of RCTs.
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12
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Atkins MB, Abu-Sbeih H, Ascierto PA, Bishop MR, Chen DS, Dhodapkar M, Emens LA, Ernstoff MS, Ferris RL, Greten TF, Gulley JL, Herbst RS, Humphrey RW, Larkin J, Margolin KA, Mazzarella L, Ramalingam SS, Regan MM, Rini BI, Sznol M. Maximizing the value of phase III trials in immuno-oncology: A checklist from the Society for Immunotherapy of Cancer (SITC). J Immunother Cancer 2022; 10:jitc-2022-005413. [PMID: 36175037 PMCID: PMC9528604 DOI: 10.1136/jitc-2022-005413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/20/2022] [Indexed: 11/03/2022] Open
Abstract
The broad activity of agents blocking the programmed cell death protein 1 and its ligand (the PD-(L)1 axis) revolutionized oncology, offering long-term benefit to patients and even curative responses for tumors that were once associated with dismal prognosis. However, only a minority of patients experience durable clinical benefit with immune checkpoint inhibitor monotherapy in most disease settings. Spurred by preclinical and correlative studies to understand mechanisms of non-response to the PD-(L)1 antagonists and by combination studies in animal tumor models, many drug development programs were designed to combine anti-PD-(L)1 with a variety of approved and investigational chemotherapies, tumor-targeted therapies, antiangiogenic therapies, and other immunotherapies. Several immunotherapy combinations improved survival outcomes in a variety of indications including melanoma, lung, kidney, and liver cancer, among others. This immunotherapy renaissance, however, has led to many combinations being advanced to late-stage development without definitive predictive biomarkers, limited phase I and phase II data, or clinical trial designs that are not optimized for demonstrating the unique attributes of immune-related antitumor activity-for example, landmark progression-free survival and overall survival. The decision to activate a study at an individual site is investigator-driven, and generalized frameworks to evaluate the potential for phase III trials in immuno-oncology to yield positive data, particularly to increase the number of curative responses or otherwise advance the field have thus far been lacking. To assist in evaluating the potential value to patients and the immunotherapy field of phase III trials, the Society for Immunotherapy of Cancer (SITC) has developed a checklist for investigators, described in this manuscript. Although the checklist focuses on anti-PD-(L)1-based combinations, it may be applied to any regimen in which immune modulation is an important component of the antitumor effect.
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Affiliation(s)
- Michael B Atkins
- Georgetown Lombardi Comprehensive Cancer Center, Washington, District of Columbia, USA
| | | | - Paolo A Ascierto
- Istituto Nazionale Tumori IRCCS Fondazione "G Pascale", Napoli, Italy
| | - Michael R Bishop
- The David and Etta Jonas Center for Cellular Therapy, University of Chicago, Chicago, Illinois, USA
| | - Daniel S Chen
- Engenuity Life Sciences, Burlingame, California, USA
| | - Madhav Dhodapkar
- Center for Cancer Immunology, Winship Cancer Institute at Emory University, Atlanta, Georgia, USA
| | - Leisha A Emens
- UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Marc S Ernstoff
- DCTD/DTP-IOB, ImmunoOncology Branch, NCI, Bethesda, Maryland, USA
| | | | - Tim F Greten
- Gastrointestinal Malignancies Section, National Cancer Institue CCR Liver Program, Bethesda, Maryland, USA
| | - James L Gulley
- Center for Immuno-Oncology, National Cancer Institute, Bethesda, Maryland, USA
| | | | | | | | - Kim A Margolin
- St. John's Cancer Institute, Santa Monica, California, USA
| | - Luca Mazzarella
- Experimental Oncology, New Drug Development, European Instititue of Oncology IRCCS, Milan, Italy
| | | | - Meredith M Regan
- Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - Mario Sznol
- Yale School of Medicine, New Haven, Connecticut, USA
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13
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Guimarães CF, Cruz-Moreira D, Caballero D, Pirraco RP, Gasperini L, Kundu SC, Reis RL. Shining a Light on Cancer - Photonics in Microfluidic Tumor Modelling and Biosensing. Adv Healthc Mater 2022:e2201442. [PMID: 35998112 DOI: 10.1002/adhm.202201442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/03/2022] [Indexed: 11/08/2022]
Abstract
Microfluidic platforms represent a powerful approach to miniaturizing important characteristics of cancers, improving in vitro testing by increasing physiological relevance. Different tools can manipulate cells and materials at the microscale, but few offer the efficiency and versatility of light and optical technologies. Moreover, light-driven technologies englobe a broad toolbox for quantifying critical biological phenomena. Herein, we review the role of photonics in microfluidic 3D cancer modeling and biosensing from three major perspectives. First, we look at optical-driven technologies that allow biomaterials and living cells to be manipulated with micro-sized precision and the opportunities to advance 3D microfluidic models by engineering cancer microenvironments' hallmarks, such as their architecture, cellular complexity, and vascularization. Second, we delve into the growing field of optofluidics, exploring how optical tools can directly interface microfluidic chips, enabling the extraction of relevant biological data, from single fluorescent signals to the complete 3D imaging of diseased cells within microchannels. Third, we review advances in optical cancer biosensing, focusing on how light-matter interactions can detect biomarkers, rare circulating tumor cells, and cell-derived structures such as exosomes. We overview photonic technologies' current challenges and caveats in microfluidic 3D cancer models, outlining future research avenues that may catapult the field. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Carlos F Guimarães
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - Daniela Cruz-Moreira
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - David Caballero
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - Rogério P Pirraco
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - Luca Gasperini
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - Subhas C Kundu
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
| | - Rui L Reis
- 3B's Research Group -Biomaterials, Biodegradables and Biomimetics, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, University of Minho, AvePark, Parque de Ciência e Tecnologia, Barco, Guimarães, 4805-017, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga and Guimarães, Portugal
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14
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Munnik C, Xaba MP, Malindisa ST, Russell BL, Sooklal SA. Drosophila melanogaster: A platform for anticancer drug discovery and personalized therapies. Front Genet 2022; 13:949241. [PMID: 36003330 PMCID: PMC9393232 DOI: 10.3389/fgene.2022.949241] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/06/2022] [Indexed: 12/12/2022] Open
Abstract
Cancer is a complex disease whereby multiple genetic aberrations, epigenetic modifications, metabolic reprogramming, and the microenvironment contribute to the development of a tumor. In the traditional anticancer drug discovery pipeline, drug candidates are usually screened in vitro using two-dimensional or three-dimensional cell culture. However, these methods fail to accurately mimic the human disease state. This has led to the poor success rate of anticancer drugs in the preclinical stages since many drugs are abandoned due to inefficacy or toxicity when transitioned to whole-organism models. The common fruit fly, Drosophila melanogaster, has emerged as a beneficial system for modeling human cancers. Decades of fundamental research have shown the evolutionary conservation of key genes and signaling pathways between flies and humans. Moreover, Drosophila has a lower genetic redundancy in comparison to mammals. These factors, in addition to the advancement of genetic toolkits for manipulating gene expression, allow for the generation of complex Drosophila genotypes and phenotypes. Numerous studies have successfully created Drosophila models for colorectal, lung, thyroid, and brain cancers. These models were utilized in the high-throughput screening of FDA-approved drugs which led to the identification of several compounds capable of reducing proliferation and rescuing phenotypes. More noteworthy, Drosophila has also unlocked the potential for personalized therapies. Drosophila ‘avatars’ presenting the same mutations as a patient are used to screen multiple therapeutic agents targeting multiple pathways to find the most appropriate combination of drugs. The outcomes of these studies have translated to significant responses in patients with adenoid cystic carcinoma and metastatic colorectal cancers. Despite not being widely utilized, the concept of in vivo screening of drugs in Drosophila is making significant contributions to the current drug discovery pipeline. In this review, we discuss the application of Drosophila as a platform in anticancer drug discovery; with special focus on the cancer models that have been generated, drug libraries that have been screened and the status of personalized therapies. In addition, we elaborate on the biological and technical limitations of this system.
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Affiliation(s)
- Chamoné Munnik
- Department of Life and Consumer Sciences, University of South Africa, Pretoria, South Africa
| | - Malungi P. Xaba
- Department of Life and Consumer Sciences, University of South Africa, Pretoria, South Africa
| | - Sibusiso T. Malindisa
- Department of Life and Consumer Sciences, University of South Africa, Pretoria, South Africa
| | - Bonnie L. Russell
- Department of Life and Consumer Sciences, University of South Africa, Pretoria, South Africa
- Buboo (Pty) Ltd, The Innovation Hub, Pretoria, South Africa
| | - Selisha A. Sooklal
- Department of Life and Consumer Sciences, University of South Africa, Pretoria, South Africa
- *Correspondence: Selisha A. Sooklal,
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15
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Marzagalli M, Pelizzoni G, Fedi A, Vitale C, Fontana F, Bruno S, Poggi A, Dondero A, Aiello M, Castriconi R, Bottino C, Scaglione S. A multi-organ-on-chip to recapitulate the infiltration and the cytotoxic activity of circulating NK cells in 3D matrix-based tumor model. Front Bioeng Biotechnol 2022; 10:945149. [PMID: 35957642 PMCID: PMC9358021 DOI: 10.3389/fbioe.2022.945149] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/29/2022] [Indexed: 11/22/2022] Open
Abstract
The success of immunotherapeutic approaches strictly depends on the immune cells interaction with cancer cells. While conventional in vitro cell cultures under-represent the complexity and dynamic crosstalk of the tumor microenvironment, animal models do not allow deciphering the anti-tumor activity of the human immune system. Therefore, the development of reliable and predictive preclinical models has become crucial for the screening of immune-therapeutic approaches. We here present an organ-on-chip organ on chips (OOC)-based approach for recapitulating the immune cell Natural Killer (NK) migration under physiological fluid flow, infiltration within a 3D tumor matrix, and activation against neuroblastoma cancer cells in a humanized, fluid-dynamic environment. Circulating NK cells actively initiate a spontaneous “extravasation” process toward the physically separated tumor niche, retaining their ability to interact with matrix-embedded tumor cells, and to display a cytotoxic effect (tumor cell apoptosis). Since NK cells infiltration and phenotype is correlated with prognosis and response to immunotherapy, their phenotype is also investigated: most importantly, a clear decrease in CD16-positive NK cells within the migrated and infiltrated population is observed. The proposed immune-tumor OOC-based model represents a promising approach for faithfully recapitulating the human pathology and efficiently employing the immunotherapies testing, eventually in a personalized perspective. An immune-organ on chip to recapitulate the tumor-mediated infiltration of circulating immune cells within 3D tumor model.
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Affiliation(s)
| | - Giorgia Pelizzoni
- Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza Della Scienza, Milan, Italy
| | - Arianna Fedi
- National Research Council, CNR-IEIIT, Genoa, Italy
| | - Chiara Vitale
- National Research Council, CNR-IEIIT, Genoa, Italy
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
| | - Fabrizio Fontana
- Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, Milan, Italy
| | - Silvia Bruno
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
| | - Alessandro Poggi
- Molecular Oncology and Angiogenesis Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alessandra Dondero
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Roberta Castriconi
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Cristina Bottino
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Silvia Scaglione
- National Research Council, CNR-IEIIT, Genoa, Italy
- *Correspondence: Silvia Scaglione,
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16
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Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology. DISEASE MARKERS 2022; 2022:2461055. [PMID: 35915735 PMCID: PMC9338845 DOI: 10.1155/2022/2461055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/13/2022] [Accepted: 06/21/2022] [Indexed: 11/17/2022]
Abstract
Background. Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods. Methods. Identification of genes associated with drug-resistant melanomas was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by GO and KEGG pathway enrichment analyses. The PPI network was constructed using STRING database and Cytoscape. GEPIA was used to perform the survival analysis and conduct the Kaplan-Meier curve. Drugs targeted at these genes were selected in Pharmaprojects. The binding affinity scores of drug-target interactions were predicted by DeepPurpose. Results. A total of 433 genes were found associated with drug-resistant melanomas by text mining. The most statistically differential functional enriched pathways of GO and KEGG analyses contained 348 genes, and 27 hub genes were further screened out by MCODE in Cytoscape. Six genes were identified with statistical differences after survival analysis and literature review. 16 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 11 ERBB2-targeted drugs with top affinity scores were predicted by DeepPurpose, including 10 ERBB2 kinase inhibitors and 1 antibody-drug conjugate. Conclusion. Text mining and bioinformatics are valuable methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the candidate drugs.
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17
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Peña Q, Wang A, Zaremba O, Shi Y, Scheeren HW, Metselaar JM, Kiessling F, Pallares RM, Wuttke S, Lammers T. Metallodrugs in cancer nanomedicine. Chem Soc Rev 2022; 51:2544-2582. [PMID: 35262108 DOI: 10.1039/d1cs00468a] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metal complexes are extensively used for cancer therapy. The multiple variables available for tuning (metal, ligand, and metal-ligand interaction) offer unique opportunities for drug design, and have led to a vast portfolio of metallodrugs that can display a higher diversity of functions and mechanisms of action with respect to pure organic structures. Clinically approved metallodrugs, such as cisplatin, carboplatin and oxaliplatin, are used to treat many types of cancer and play prominent roles in combination regimens, including with immunotherapy. However, metallodrugs generally suffer from poor pharmacokinetics, low levels of target site accumulation, metal-mediated off-target reactivity and development of drug resistance, which can all limit their efficacy and clinical translation. Nanomedicine has arisen as a powerful tool to help overcome these shortcomings. Several nanoformulations have already significantly improved the efficacy and reduced the toxicity of (chemo-)therapeutic drugs, including some promising metallodrug-containing nanomedicines currently in clinical trials. In this critical review, we analyse the opportunities and clinical challenges of metallodrugs, and we assess the advantages and limitations of metallodrug delivery, both from a nanocarrier and from a metal-nano interaction perspective. We describe the latest and most relevant nanomedicine formulations developed for metal complexes, and we discuss how the rational combination of coordination chemistry with nanomedicine technology can assist in promoting the clinical translation of metallodrugs.
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Affiliation(s)
- Quim Peña
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Alec Wang
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Orysia Zaremba
- BCMaterials, Bld. Martina Casiano, 3rd. Floor, UPV/EHU Science Park, 48940, Leioa, Spain
| | - Yang Shi
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Hans W Scheeren
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Josbert M Metselaar
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Fabian Kiessling
- Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany
| | - Roger M Pallares
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
| | - Stefan Wuttke
- BCMaterials, Bld. Martina Casiano, 3rd. Floor, UPV/EHU Science Park, 48940, Leioa, Spain.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Twan Lammers
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Uniklinik RWTH Aachen and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany.
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18
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Eun K, Hwang SU, Kim M, Yoon JD, Kim E, Choi H, Kim G, Jeon HY, Kim JK, Kim JY, Hong N, Park MG, Jang J, Jeong HJ, Kim SJ, Ko BW, Lee SC, Kim H, Hyun SH. Generation of reproductive transgenic pigs of a CRISPR-Cas9-based oncogene-inducible system by somatic cell nuclear transfer. Biotechnol J 2022; 17:e2100434. [PMID: 35233982 DOI: 10.1002/biot.202100434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 01/12/2022] [Accepted: 01/26/2022] [Indexed: 11/06/2022]
Abstract
Alternative cancer models that are close to humans are required to create more valuable preclinical results during oncology studies. Here, we developed a new onco-pig model via developing a CRISPR-Cas9-based Conditional Polycistronic gene expression Cassette (CRI-CPC) system to control the tumor inducing simian virus 40 large T antigen (SV40LT) and oncogenic HRASG12V. After conducting somatic cell nuclear transfer (SCNT), transgenic embryos were transplanted into surrogate mothers and five male piglets were born. Umbilical cord analysis confirmed that all piglets were transgenic. Two of them survived, and they expressed a detectable green fluorescence. We tested whether our CRI-CPC models were naturally fertile and whether the CRI-CPC system was stably transferred to the offspring. By mating with a normal female pig, four offspring piglets were successfully produced. Among them, only three male piglets were transgenic. Finally, we tested their applicability as cancer models after transduction of Cas9 into fibroblasts from each CRI-CPC pig in vitro, resulting in cell acquisition of cancerous characteristics via the induction of oncogene expression. These results showed that our new CRISPR-Cas9-based onco-pig model was successfully developed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Kiyoung Eun
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Seon-Ung Hwang
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Mirae Kim
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Junchul David Yoon
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Eunhye Kim
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Hyerin Choi
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Gahye Kim
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Hee-Young Jeon
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jun-Kyum Kim
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Yun Kim
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Nayoung Hong
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Min-Gi Park
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Junseok Jang
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Hyeon Ju Jeong
- Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Sung Jin Kim
- Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Bong-Woo Ko
- Songbaek Pig Farm, Jeju, 63014, Republic of Korea
| | - Sang Chul Lee
- Cronex Corporation, Cheongju, 28174, Republic of Korea
| | - Hyunggee Kim
- Institute of Animal Molecular Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea.,Department of Biotechnology, School of Life Sciences and Biotechnology, Korea University, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Sang-Hwan Hyun
- Laboratory of Veterinary Embryology and Biotechnology, Korea University, Seongbuk-gu.,Institute of Stem Cell & Regenerative Medicine, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
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19
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Rupp B, Ball H, Wuchu F, Nagrath D, Nagrath S. Circulating tumor cells in precision medicine: challenges and opportunities. Trends Pharmacol Sci 2022; 43:378-391. [DOI: 10.1016/j.tips.2022.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/25/2022] [Accepted: 02/09/2022] [Indexed: 12/12/2022]
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20
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Martín AM, De la Fuente L, Hernández A, Zaldívar F, Ortega-Campos E, García-García J. Psychosocial Profile of Juvenile and Adult Offenders Who Acknowledge Having Committed Child-to-Parent Violence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19010601. [PMID: 35010868 PMCID: PMC8744974 DOI: 10.3390/ijerph19010601] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/28/2021] [Accepted: 12/30/2021] [Indexed: 02/04/2023]
Abstract
The main objective of this study was to establish the psychosocial profile of adolescents and adults who have admitted to committing child-to-parent violence (CPV) and were serving a judicial sanction or prison sentence, respectively. Two groups of participants took part in this study. The first group was made up of 89 male youths who were serving judicial sanctions, and the second group was made up of 70 men serving a prison sentence. A cross-sectional retrospective design with concurrent measurements was used in this study. Group differences in the exposure-to-violence variables were conducted. Automatic regression models were used to estimate a self-reported CPV. In relation to the variables of indirect exposure to violence, statistically significant differences between those who admitted having committed CPV and those who did not, irrespective of being adults or adolescents, were found for seeing violence in class and at home but not for seeing violence on the street or on television. Regarding the variables related to experiencing violence, the results showed statistically significant differences in experiencing violence at home but not in class or on the street. The best predictive model of CPV includes some of the dimensions of self-concept, specifically academic and family self-concept, as well as the avoidant and rational problem-solving styles and the negative orientation toward problems. The results have shown the existence of a CPV offender profile that is common to minors and adults.
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Affiliation(s)
- Ana M. Martín
- Department of Cognitive, Social and Organizational Psychology, Universidad de La Laguna, 38296 La Laguna, Spain;
| | - Leticia De la Fuente
- Health Research Center, Universidad de Almería (CEINSA/UAL), 04120 Almería, Spain; (L.D.l.F.); (F.Z.); (E.O.-C.)
- Department of Psychology, Universidad de Almería, 04120 Almería, Spain
| | - Antonia Hernández
- Fundación Canaria de Juventud Ideo, 38005 Santa Cruz de Tenerife, Spain;
| | - Flor Zaldívar
- Health Research Center, Universidad de Almería (CEINSA/UAL), 04120 Almería, Spain; (L.D.l.F.); (F.Z.); (E.O.-C.)
- Department of Psychology, Universidad de Almería, 04120 Almería, Spain
| | - Elena Ortega-Campos
- Health Research Center, Universidad de Almería (CEINSA/UAL), 04120 Almería, Spain; (L.D.l.F.); (F.Z.); (E.O.-C.)
- Department of Psychology, Universidad de Almería, 04120 Almería, Spain
| | - Juan García-García
- Health Research Center, Universidad de Almería (CEINSA/UAL), 04120 Almería, Spain; (L.D.l.F.); (F.Z.); (E.O.-C.)
- Department of Psychology, Universidad de Almería, 04120 Almería, Spain
- Correspondence:
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21
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Hargrove-Grimes P, Low LA, Tagle DA. Microphysiological Systems: Stakeholder Challenges to Adoption in Drug Development. Cells Tissues Organs 2022; 211:269-281. [PMID: 34380142 PMCID: PMC8831652 DOI: 10.1159/000517422] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 05/14/2021] [Indexed: 01/03/2023] Open
Abstract
Microphysiological systems (MPS) or tissue chips/organs-on-chips are novel in vitro models that emulate human physiology at the most basic functional level. In this review, we discuss various hurdles to widespread adoption of MPS technology focusing on issues from multiple stakeholder sectors, e.g., academic MPS developers, commercial suppliers of platforms, the pharmaceutical and biotechnology industries, and regulatory organizations. Broad adoption of MPS technology has thus far been limited by a gap in translation between platform developers, end-users, regulatory agencies, and the pharmaceutical industry. In this brief review, we offer a perspective on the existing barriers and how end-users may help surmount these obstacles to achieve broader adoption of MPS technology.
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Affiliation(s)
- Passley Hargrove-Grimes
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Lucie A. Low
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Danilo A. Tagle
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
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22
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Barbosa MAG, Xavier CPR, Pereira RF, Petrikaitė V, Vasconcelos MH. 3D Cell Culture Models as Recapitulators of the Tumor Microenvironment for the Screening of Anti-Cancer Drugs. Cancers (Basel) 2021; 14:190. [PMID: 35008353 PMCID: PMC8749977 DOI: 10.3390/cancers14010190] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/23/2021] [Accepted: 12/29/2021] [Indexed: 12/12/2022] Open
Abstract
Today, innovative three-dimensional (3D) cell culture models have been proposed as viable and biomimetic alternatives for initial drug screening, allowing the improvement of the efficiency of drug development. These models are gaining popularity, given their ability to reproduce key aspects of the tumor microenvironment, concerning the 3D tumor architecture as well as the interactions of tumor cells with the extracellular matrix and surrounding non-tumor cells. The development of accurate 3D models may become beneficial to decrease the use of laboratory animals in scientific research, in accordance with the European Union's regulation on the 3R rule (Replacement, Reduction, Refinement). This review focuses on the impact of 3D cell culture models on cancer research, discussing their advantages, limitations, and compatibility with high-throughput screenings and automated systems. An insight is also given on the adequacy of the available readouts for the interpretation of the data obtained from the 3D cell culture models. Importantly, we also emphasize the need for the incorporation of additional and complementary microenvironment elements on the design of 3D cell culture models, towards improved predictive value of drug efficacy.
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Affiliation(s)
- Mélanie A. G. Barbosa
- Cancer Drug Resistance Group, IPATIMUP—Institute of Molecular Pathology and Immunology, University of Porto, 4200-135 Porto, Portugal; (M.A.G.B.); (C.P.R.X.)
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
| | - Cristina P. R. Xavier
- Cancer Drug Resistance Group, IPATIMUP—Institute of Molecular Pathology and Immunology, University of Porto, 4200-135 Porto, Portugal; (M.A.G.B.); (C.P.R.X.)
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
| | - Rúben F. Pereira
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
- Biofabrication Group, INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, 4050-313 Porto, Portugal
| | - Vilma Petrikaitė
- Laboratory of Drug Targets Histopathology, Institute of Cardiology, Lithuanian University of Health Sciences, A. Mickevičiaus g 9, LT-44307 Kaunas, Lithuania;
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania
| | - M. Helena Vasconcelos
- Cancer Drug Resistance Group, IPATIMUP—Institute of Molecular Pathology and Immunology, University of Porto, 4200-135 Porto, Portugal; (M.A.G.B.); (C.P.R.X.)
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal;
- Department of Biological Sciences, FFUP—Faculty of Pharmacy of the University of Porto, 4050-313 Porto, Portugal
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23
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Otsuka Y, Kaneko M, Narukawa M. Factors associated with successful phase III trials for solid tumors: A systematic review. Contemp Clin Trials Commun 2021; 24:100855. [PMID: 34841122 PMCID: PMC8606338 DOI: 10.1016/j.conctc.2021.100855] [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: 04/29/2021] [Revised: 09/11/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022] Open
Abstract
Background It is known that the success rates of phase III trials for solid cancers are low. The aim of this study was to investigate factors related to trial design and operation that were associated with the probability of the success of phase III trials for solid cancers based on the latest comprehensive data. Methods Relevant clinical trials, started between September 2007 and December 2017, were retrieved from ClinicalTrials.gov. Then, variables related to the selected trials such as types of primary endpoint and duration of trial enrollment were collected from the literature and ClinicalTrials.gov. Based on the collected data, a multivariate logistic regression analysis was conducted to find factors associated with the successful results. Results Four hundred phase III trials were found eligible for the study. Unsuccessful trials were 207 and successful trials were 193. As a result of multivariate logistic regression analysis, factors that presented a statistically significant relationship were primary endpoint (Odds ratio [OR]: 2.79 [95% CI: 1.59–4.89]), control arm (OR: 3.06 [95% CI: 1.39–6.73]), start year of trial (OR: 3.28 [95% CI: 1.87–5.77]), and duration of trial enrollment (OR: 0.77 [95% CI: 0.60–0.99]). Conclusion Type of primary endpoints (time-to-event endpoints other than overall survival), control arm (treatments with lower evidence level, placebo or best supportive care), and duration of trial enrollment (faster enrollment speed) were associated with phase III trial success.
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Affiliation(s)
- Yasushi Otsuka
- Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of Pharmaceutical Sciences, Kitasato University, Shirokane 5-9-1, Minato-ku, Tokyo, 108-8641, Japan.,Research & Development Division, Alexion Pharma GK, Ebisu First Square 1-18-4 Ebisu, Shibuya-ku, Tokyo, 150-0013, Japan
| | - Masayuki Kaneko
- Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of Pharmaceutical Sciences, Kitasato University, Shirokane 5-9-1, Minato-ku, Tokyo, 108-8641, Japan
| | - Mamoru Narukawa
- Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of Pharmaceutical Sciences, Kitasato University, Shirokane 5-9-1, Minato-ku, Tokyo, 108-8641, Japan
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Honkala A, Malhotra SV, Kummar S, Junttila MR. Harnessing the predictive power of preclinical models for oncology drug development. Nat Rev Drug Discov 2021; 21:99-114. [PMID: 34702990 DOI: 10.1038/s41573-021-00301-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/27/2021] [Indexed: 12/21/2022]
Abstract
Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.
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Affiliation(s)
- Alexander Honkala
- Department of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Sanjay V Malhotra
- Department of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA.,Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Shivaani Kummar
- Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. .,Division of Hematology & Medical Oncology, Oregon Health & Science University, Portland, OR, USA.
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25
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Sampayo-Cordero M, Miguel-Huguet B, Malfettone A, Pérez-García JM, Llombart-Cussac A, Cortés J, Pardo A, Pérez-López J. The Impact of Excluding Nonrandomized Studies From Systematic Reviews in Rare Diseases: "The Example of Meta-Analyses Evaluating the Efficacy and Safety of Enzyme Replacement Therapy in Patients With Mucopolysaccharidosis". Front Mol Biosci 2021; 8:690615. [PMID: 34239895 PMCID: PMC8257960 DOI: 10.3389/fmolb.2021.690615] [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: 04/03/2021] [Accepted: 05/24/2021] [Indexed: 12/01/2022] Open
Abstract
Nonrandomized studies are usually excluded from systematic reviews. This could lead to loss of a considerable amount of information on rare diseases. In this article, we explore the impact of excluding nonrandomized studies on the generalizability of meta-analyses results on mucopolysaccharidosis (MPS) disease. A comprehensive search of systematic reviews on MPS patients up to May 2020 was carried out (CRD42020191217). The primary endpoint was the rate of patients excluded from systematic reviews if only randomized studies were considered. Secondary outcomes included the differences in patient and study characteristics between randomized and nonrandomized studies, the methods used to combine data from studies with different designs, and the number of patients excluded from systematic reviews if case reports were not considered. More than 50% of the patients analyzed have been recruited in nonrandomized studies. Patient characteristics, duration of follow-up, and the clinical outcomes evaluated differ between the randomized and nonrandomized studies. There are feasible strategies to combine the data from different randomized and nonrandomized designs. The analyses suggest the relevance of including case reports in the systematic reviews, since the smaller the number of patients in the reference population, the larger the selection bias associated to excluding case reports. Our results recommend including nonrandomized studies in the systematic reviews of MPS to increase the representativeness of the results and to avoid a selection bias. The recommendations obtained from this study should be considered when conducting systematic reviews on rare diseases.
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Affiliation(s)
| | | | | | - José Manuel Pérez-García
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- IOB Institute of Oncology, Quiron Salud Group, Madrid, Spain
| | - Antonio Llombart-Cussac
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Hospital Arnau de Vilanova, Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain
| | - Javier Cortés
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- IOB Institute of Oncology, Quiron Salud Group, Madrid, Spain
- Vall d´Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Almudena Pardo
- Albiotech Consultores y Redacción Científica S.L., Madrid, Spain
| | - Jordi Pérez-López
- Department of Internal Medicine, Hospital Vall d’Hebron, Barcelona, Spain
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26
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Delorme J, Charvet V, Wartelle M, Lion F, Thuillier B, Mercier S, Soria JC, Azoulay M, Besse B, Massard C, Hollebecque A, Verlingue L. Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials. JCO Clin Cancer Inform 2021; 5:709-718. [PMID: 34197179 DOI: 10.1200/cci.21.00003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Early discontinuation affects more than one third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We aimed at predicting the successful screening and dose-limiting toxicity period completion (SSD) from automatic analysis of consultation reports. MATERIALS AND METHODS We retrieved the consultation reports of patients included in phase I and/or phase II oncology trials for any tumor type at Gustave Roussy, France. We designed a preprocessing pipeline that transformed free text into numerical vectors and gathered them into semantic clusters. These document-based semantic vectors were then fed into a machine learning model that we trained to output a binary prediction of SSD status. RESULTS Between September 2012 and July 2020, 56,924 consultation reports were used to build the dictionary and 1,858 phase I or II inclusion reports were used to train (72%), validate (14%), and test (14%) a random forest model. Preprocessing could efficiently cluster words with semantic proximity. On the unseen test cohort of 264 consultation reports, the performances of the model reached: F1 score 0.80, recall 0.81, and area under the curve 0.88. Using this model, we could have reduced the screen fail rate (including dose-limiting toxicity period) from 39.8% to 12.8% (relative risk, 0.322; 95% CI, 0.209 to 0.498; P < .0001) within the test cohort. Most important semantic clusters for predictions comprised words related to hematologic malignancies, anatomopathologic features, and laboratory and imaging interpretation. CONCLUSION Machine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve SSD in early-phase oncology clinical trials.
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Affiliation(s)
| | - Valentin Charvet
- Telecom Paris Tech, Paris, France.,Department of Computing Science, University of Glasgow, Glasgow, Scotland
| | | | - François Lion
- Informatic Team (DTNSI), Gustave Roussy, Villejuif, France
| | - Bruno Thuillier
- Drug Development Department (DITEP), Gustave Roussy, Villejuif, France
| | - Sandrine Mercier
- Drug Development Department (DITEP), Gustave Roussy, Villejuif, France
| | - Jean-Charles Soria
- Drug Development Department (DITEP), Gustave Roussy, Villejuif, France.,University Paris-Saclay, Gif-sur-Yvette, France.,Medical Oncology Department, Gustave Roussy, Villejuif, France
| | - Mikael Azoulay
- Informatic Team (DTNSI), Gustave Roussy, Villejuif, France
| | - Benjamin Besse
- University Paris-Saclay, Gif-sur-Yvette, France.,Medical Oncology Department, Gustave Roussy, Villejuif, France
| | - Christophe Massard
- Drug Development Department (DITEP), Gustave Roussy, Villejuif, France.,University Paris-Saclay, Gif-sur-Yvette, France
| | | | - Loic Verlingue
- Drug Development Department (DITEP), Gustave Roussy, Villejuif, France.,University Paris-Saclay, Gif-sur-Yvette, France.,INSERM UMR1030, Molecular Radiotherapy and Therapeutic Innovations, Gustave Roussy, Villejuif, France
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27
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Gough A, Soto-Gutierrez A, Vernetti L, Ebrahimkhani MR, Stern AM, Taylor DL. Human biomimetic liver microphysiology systems in drug development and precision medicine. Nat Rev Gastroenterol Hepatol 2021; 18:252-268. [PMID: 33335282 PMCID: PMC9106093 DOI: 10.1038/s41575-020-00386-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 02/07/2023]
Abstract
Microphysiology systems (MPS), also called organs-on-chips and tissue chips, are miniaturized functional units of organs constructed with multiple cell types under a variety of physical and biochemical environmental cues that complement animal models as part of a new paradigm of drug discovery and development. Biomimetic human liver MPS have evolved from simpler 2D cell models, spheroids and organoids to address the increasing need to understand patient-specific mechanisms of complex and rare diseases, the response to therapeutic treatments, and the absorption, distribution, metabolism, excretion and toxicity of potential therapeutics. The parallel development and application of transdisciplinary technologies, including microfluidic devices, bioprinting, engineered matrix materials, defined physiological and pathophysiological media, patient-derived primary cells, and pluripotent stem cells as well as synthetic biology to engineer cell genes and functions, have created the potential to produce patient-specific, biomimetic MPS for detailed mechanistic studies. It is projected that success in the development and maturation of patient-derived MPS with known genotypes and fully matured adult phenotypes will lead to advanced applications in precision medicine. In this Review, we examine human biomimetic liver MPS that are designed to recapitulate the liver acinus structure and functions to enhance our knowledge of the mechanisms of disease progression and of the absorption, distribution, metabolism, excretion and toxicity of therapeutic candidates and drugs as well as to evaluate their mechanisms of action and their application in precision medicine and preclinical trials.
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Affiliation(s)
- Albert Gough
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alejandro Soto-Gutierrez
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lawrence Vernetti
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M Stern
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA.
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28
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Abstract
The deployment of molecular biomarkers that are indicative of sensitivity to tumor-targeted or immune-targeted cancer therapies improves the outcome of individual patients and increases the chances of successful drug approval. However, for many lethal malignancies, the majority of clinical trials are conducted with patients who do not have biomarkers and hence they miss the target.
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Affiliation(s)
- Jacob J Adashek
- Department of Internal Medicine, University of South Florida, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alexey Goloubev
- Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Shumei Kato
- Center for Personalized Cancer Therapy and Division of Hematology and Oncology, Department of Medicine, University of California San Diego Moores Cancer Center, La Jolla, CA, USA
| | - Razelle Kurzrock
- Center for Personalized Cancer Therapy and Division of Hematology and Oncology, Department of Medicine, University of California San Diego Moores Cancer Center, La Jolla, CA, USA.
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29
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Kumar S, Singh SK, Rana B, Rana A. The regulatory function of mixed lineage kinase 3 in tumor and host immunity. Pharmacol Ther 2021; 219:107704. [PMID: 33045253 PMCID: PMC7887016 DOI: 10.1016/j.pharmthera.2020.107704] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/02/2020] [Indexed: 12/26/2022]
Abstract
Protein kinases are the second most sought-after G-protein coupled receptors as drug targets because of their overexpression, mutations, and dysregulated catalytic activities in various pathological conditions. Till 2019, 48 protein kinase inhibitors have received FDA approval for the treatment of multiple illnesses, of which the majority of them are indicated for different malignancies. One of the attractive sub-group of protein kinases that has attracted attention for drug development is the family members of MAPKs that are recognized to play significant roles in different cancers. Several inhibitors have been developed against various MAPK members; however, none of them as monotherapy has shown sustainable efficacy. One of the MAPK members, called Mixed Lineage Kinase 3 (MLK3), has attracted considerable attention due to its role in inflammation and neurodegenerative diseases; however, its role in cancer is an emerging area that needs more investigation. Recent advances have shown that MLK3 plays a role in cancer cell survival, migration, drug resistance, cell death, and tumor immunity. This review describes how MLK3 regulates different MAPK pathways, cancer cell growth and survival, apoptosis, and host's immunity. We also discuss how MLK3 inhibitors can potentially be used along with immunotherapy for different malignancies.
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Affiliation(s)
- Sandeep Kumar
- Department of Surgery, Division of Surgical Oncology, University of Illinois at Chicago, IL 60612, USA.
| | - Sunil Kumar Singh
- Department of Surgery, Division of Surgical Oncology, University of Illinois at Chicago, IL 60612, USA
| | - Basabi Rana
- Department of Surgery, Division of Surgical Oncology, University of Illinois at Chicago, IL 60612, USA; University of Illinois Hospital & Health Sciences System Cancer Center, University of Illinois at Chicago, Chicago, IL 60612, USA; Jesse Brown VA Medical Center, Chicago, IL 60612, USA
| | - Ajay Rana
- Department of Surgery, Division of Surgical Oncology, University of Illinois at Chicago, IL 60612, USA; University of Illinois Hospital & Health Sciences System Cancer Center, University of Illinois at Chicago, Chicago, IL 60612, USA; Jesse Brown VA Medical Center, Chicago, IL 60612, USA.
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30
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Bhutani P, Joshi G, Raja N, Bachhav N, Rajanna PK, Bhutani H, Paul AT, Kumar R. U.S. FDA Approved Drugs from 2015-June 2020: A Perspective. J Med Chem 2021; 64:2339-2381. [PMID: 33617716 DOI: 10.1021/acs.jmedchem.0c01786] [Citation(s) in RCA: 252] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
In the present work, we report compilation and analysis of 245 drugs, including small and macromolecules approved by the U.S. FDA from 2015 until June 2020. Nearly 29% of the drugs were approved for the treatment of various types of cancers. Other major therapeutic areas of focus were infectious diseases (14%); neurological conditions (12%); and genetic, metabolic, and cardiovascular disorders (7-8% each). Itemization of the approved drugs according to the year of approval, sponsor, target, chemical class, major drug-metabolizing enzyme(s), route of administration/elimination, and drug-drug interaction liability (perpetrator or/and victim) is presented and discussed. An effort has been made to analyze the pharmacophores to identify the structural (e.g., aromatic, heterocycle, and aliphatic), elemental (e.g., boron, sulfur, fluorine, phosphorus, and deuterium), and functional group (e.g., nitro drugs) diversity among the approved drugs. Further, descriptor-based chemical space analysis of FDA approved drugs and several strategies utilized for optimizing metabolism leading to their discoveries have been emphasized. Finally, an analysis of drug-likeness for the approved drugs is presented.
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Affiliation(s)
- Priyadeep Bhutani
- Pharmaceutical Candidate Optimization, Biocon Bristol-Myers Squibb R&D Centre, Syngene International Limited, Bangalore 560099, India.,Department of Pharmacy, Birla Institute of Technology and Science (BITS) Pilani, Pilani Campus, Rajasthan 333031, India
| | - Gaurav Joshi
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda 151001, India
| | - Nivethitha Raja
- Pharmaceutical Candidate Optimization, Biocon Bristol-Myers Squibb R&D Centre, Syngene International Limited, Bangalore 560099, India
| | - Namrata Bachhav
- 1015 E Cozza Drive # 12, Spokane Washington 99208, United States
| | - Prabhakar K Rajanna
- Pharmaceutical Candidate Optimization, Biocon Bristol-Myers Squibb R&D Centre, Syngene International Limited, Bangalore 560099, India
| | - Hemant Bhutani
- Pharmaceutical Development, Biocon Bristol-Myers Squibb R&D Centre, Bristol-Myers Squibb India Private Limited, Bangalore 560099, India
| | - Atish T Paul
- Department of Pharmacy, Birla Institute of Technology and Science (BITS) Pilani, Pilani Campus, Rajasthan 333031, India
| | - Raj Kumar
- Laboratory for Drug Design and Synthesis, Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda 151001, India
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31
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Pan Y, Chen Z, Qi F, Liu J. Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:347. [PMID: 33708974 PMCID: PMC7944324 DOI: 10.21037/atm-21-218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Keloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods. Methods Text mining and GeneCodis were used to mine genes closely related to KL and HS. Protein-protein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained. Results Our analysis using text mining identified 69 KL- and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists. Conclusions Drug discovery using in silico text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.
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Affiliation(s)
- Yuyan Pan
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhiwei Chen
- Big Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fazhi Qi
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiaqi Liu
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.,Artificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, China
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32
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Hajari MA, Baheri Islami S, Chen X. A numerical study on tumor-on-chip performance and its optimization for nanodrug-based combination therapy. Biomech Model Mechanobiol 2021; 20:983-1002. [PMID: 33521884 DOI: 10.1007/s10237-021-01426-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 01/15/2021] [Indexed: 12/24/2022]
Abstract
Microfluidic devices, such as the tumor-on-a-chip (ToC), allow for the delivery of multiple drugs as desired for various therapies such as cancer treatment. Due to the complexity involved, visualizing, and gaining knowledge of the performance of such devices through experimentation alone is difficult if not impossible. In this paper, we performed a numerical simulation study on ToC performance, which focuses on the ability to combine multiple nanodrugs and optimized ToC performance. The numerical simulations of the chip performance were performed based on the typical chip design and operating parameters, as well as the established governing equations, boundary conditions, and fluid-structure interaction. The effect of cell injection time and position, inlet flow rate, number of inlets, medium viscosity, and cell concentration on the chip performance in terms of shear stress and cell distribution were examined. The results illustrate the profound effect of operation parameters, thus allowing for rigorously determining operational parameters to prevent spheroids ejection from microwells and to restrict the shear stresses within a physiological range. Also, the results show that triple-inlets can increase the uniformity of cell distribution in comparison with single or double inlets. Based on the simulation results, the architecture of the primary ToC was further optimized, resulting in a novel design that enables applying multiple, yet simultaneous, nanodrugs with optimal drug combination as desired for an individual patient. Furthermore, our simulations on the optimized chip showed a uniform cell distribution required for uniform-sized tumor spheroids generation, and complete medium exchange. Taken together, this study not only illustrates that numerical simulations are effective to visualize the ToCs performance, but also develops a novel ToC design optimized for nanodrug-based combination therapy.
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Affiliation(s)
| | - Sima Baheri Islami
- Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran.,Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Xiongbiao Chen
- Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
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33
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Ubels J, Schaefers T, Punt C, Guchelaar HJ, de Ridder J. RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials. Bioinformatics 2020; 36:i601-i609. [PMID: 33381829 DOI: 10.1093/bioinformatics/btaa799] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting. RESULTS We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit. AVAILABILITY AND IMPLEMENTATION The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar (h.j.guchelaar@lumc.nl). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joske Ubels
- Center for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.,Erasmus MC Cancer Institute, ErasmusMC, Rotterdam, The Netherlands.,SkylineDx, Rotterdam, The Netherlands.,Oncode Institute, Utrecht, The Netherlands
| | - Tilman Schaefers
- Center for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.,Oncode Institute, Utrecht, The Netherlands
| | - Cornelis Punt
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht,The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.,Oncode Institute, Utrecht, The Netherlands
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34
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Li J, Kataoka K. Chemo-physical Strategies to Advance the in Vivo Functionality of Targeted Nanomedicine: The Next Generation. J Am Chem Soc 2020; 143:538-559. [PMID: 33370092 DOI: 10.1021/jacs.0c09029] [Citation(s) in RCA: 133] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The past few decades have witnessed an evolution of nanomedicine from biologically inert entities to more smart systems, aimed at advancing in vivo functionality. However, we should recognize that most systems still rely on reasonable explanation-including some over-explanation-rather than definitive evidence, which is a watershed radically determining the speed and extent of advancing nanomedicine. Probing nano-bio interactions and desirable functionality at the tissue, cellular, and molecular levels is most frequently overlooked. Progress toward answering these questions will provide instructive insight guiding more effective chemo-physical strategies. Thus, in the next generation, we argue that much effort should be made to provide definitive evidence for proof-of-mechanism, in lieu of creating many new and complicated systems for similar proof-of-concept.
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Affiliation(s)
- Junjie Li
- Innovation Center of NanoMedicne, Kawasaki Institute of Industrial Promotion, 3-25-14 Tonomachi, Kawasaki-ku, Kawasaki 210-0821, Japan
| | - Kazunori Kataoka
- Innovation Center of NanoMedicne, Kawasaki Institute of Industrial Promotion, 3-25-14 Tonomachi, Kawasaki-ku, Kawasaki 210-0821, Japan.,Institute for Future Initiatives, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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35
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Vergetis V, Skaltsas D, Gorgoulis VG, Tsirigos A. Assessing Drug Development Risk Using Big Data and Machine Learning. Cancer Res 2020; 81:816-819. [PMID: 33355183 DOI: 10.1158/0008-5472.can-20-0866] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 10/14/2020] [Accepted: 12/14/2020] [Indexed: 11/16/2022]
Abstract
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.
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Affiliation(s)
| | | | - Vassilis G Gorgoulis
- Molecular Carcinogenesis Group, Department of Histology and Embryology, Faculty of Medicine, School of Health Sciences, National Kapodistrian University of Athens, Athens, Greece.,Biomedical Research Foundation, Academy of Athens, Athens, Greece.,Molecular and Clinical Cancer Sciences, Manchester Cancer Research Centre, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, United Kingdom.,Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Aristotelis Tsirigos
- Institute for Computational Medicine, New York University School of Medicine, New York, New York. .,Department of Pathology, New York University School of Medicine, New York, New York
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36
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Sampayo-Cordero M, Miguel-Huguet B, Pérez-García J, Páez D, Guerrero-Zotano ÁL, Garde-Noguera J, Aguirre E, Holgado E, López-Miranda E, Huang X, Malfettone A, Llombart-Cussac A, Cortés J. Inclusion of non-inferiority analysis in superiority-based clinical trials with single-arm, two-stage Simon's design. Contemp Clin Trials Commun 2020; 20:100678. [PMID: 33336109 PMCID: PMC7733004 DOI: 10.1016/j.conctc.2020.100678] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/02/2020] [Accepted: 11/22/2020] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Non-inferiority (NI) analysis is not usually considered in the early phases of clinical development. In some negative phase II trials, a post-hoc NI analysis justified additional phase III trials that were successful. However, the risk of false positive achievements was not controlled in these early phase analyses. We propose to preplan NI analyses in superiority-based Simon's two-stage designs to control type I and II error rates. METHODS Simulations have been proposed to assess the control of type I and II errors rates with this method. A total of 12,768 two-stage Simon's design trials were constructed based on different assumptions of rejection response probability, desired response probability, type I and II errors, and NI margins. P-value and type II error were calculated with stochastic ordering using Uniformly Minimum Variance Unbiased Estimator. Type I and II errors were simulated using the Monte Carlo method. The agreement between calculated and simulated values was analyzed with Bland-Altman plots. RESULTS We observed the same level of agreement between calculated and simulated type I and II errors from both two-stage Simon's superiority designs and designs in which NI analysis was allowed. Different examples has been proposed to explain the utility of this method. CONCLUSION Inclusion of NI analysis in superiority-based single-arm clinical trials may be useful for weighing additional factors such as safety, pharmacokinetics, pharmacodynamic, and biomarker data while assessing early efficacy. Implementation of this strategy can be achieved through simple adaptations to existing designs for one-arm phase II clinical trials.
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Affiliation(s)
- Miguel Sampayo-Cordero
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Medica Scientia Innovation Research (MedSIR), Ridgewood, NJ, USA
| | | | - José Pérez-García
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- IOB, Institute of Oncology, QuironSalud Group, Barcelona and Madrid, Spain
| | - David Páez
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | | | - Esther Holgado
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Ramón y Cajal University Hospital, Madrid, Spain
| | - Elena López-Miranda
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Ramón y Cajal University Hospital, Madrid, Spain
| | - Xin Huang
- Pfizer Global Research and Development, La Jolla, USA
| | - Andrea Malfettone
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- Medica Scientia Innovation Research (MedSIR), Ridgewood, NJ, USA
| | - Antonio Llombart-Cussac
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- FISABIO - Hospital Arnau de Vilanova, Valencia, Spain
| | - Javier Cortés
- Medica Scientia Innovation Research (MedSIR), Barcelona, Spain
- IOB, Institute of Oncology, QuironSalud Group, Barcelona and Madrid, Spain
- Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
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Sapio L, Salzillo A, Ragone A, Illiano M, Spina A, Naviglio S. Targeting CREB in Cancer Therapy: A Key Candidate or One of Many? An Update. Cancers (Basel) 2020; 12:cancers12113166. [PMID: 33126560 PMCID: PMC7693618 DOI: 10.3390/cancers12113166] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Only 5% of all drug-related targets currently move from preclinical to clinical in cancer, and just some of them achieve patient’s bedside. Among others, intratumor heterogeneity and preclinical cancer model limitations actually represent the main reasons for this failure. Cyclic-AMP response element-binding protein (CREB) has been defined as a proto-oncogene in different tumor types, being involved in maintenance and progression. Due to its relevance in tumor pathophysiology, many CREB inhibitor compounds have been developed and tested over the years. Herein, we examine the current state-of-the-art of both CREB and CREB inhibitors in cancer, retracing some of the most significant findings of the last years. While the scientific statement confers on CREB a proactive role in cancer, its therapeutic potential is still stuck at laboratory bench. Therefore, pursuing every concrete result to achieve CREB inhibition in clinical might give chance and future to cancer patients worldwide. Abstract Intratumor heterogeneity (ITH) is considered the major disorienting factor in cancer treatment. As a result of stochastic genetic and epigenetic alterations, the appearance of a branched evolutionary shape confers tumor plasticity, causing relapse and unfavorable clinical prognosis. The growing evidence in cancer discovery presents to us “the great paradox” consisting of countless potential targets constantly discovered and a small number of candidates being effective in human patients. Among these, cyclic-AMP response element-binding protein (CREB) has been proposed as proto-oncogene supporting tumor initiation, progression and metastasis. Overexpression and hyperactivation of CREB are frequently observed in cancer, whereas genetic and pharmacological CREB downregulation affects proliferation and apoptosis. Notably, the present review is designed to investigate the feasibility of targeting CREB in cancer therapy. In particular, starting with the latest CREB evidence in cancer pathophysiology, we evaluate the advancement state of CREB inhibitor design, including the histone lysine demethylases JMJD3/UTX inhibitor GSKJ4 that we newly identified as a promising CREB modulator in leukemia cells. Moreover, an accurate analysis of strengths and weaknesses is also conducted to figure out whether CREB can actually represent a therapeutic candidate or just one of the innumerable preclinical cancer targets.
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Wu C, Ono S. Exploratory Analysis of the Factors Associated With Success Rates of Confirmatory Randomized Controlled Trials in Cancer Drug Development. Clin Transl Sci 2020; 14:260-267. [PMID: 32702190 PMCID: PMC7877835 DOI: 10.1111/cts.12852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/30/2020] [Indexed: 12/19/2022] Open
Abstract
This study examined the outcomes of recent confirmatory randomized controlled trials (RCTs) in phase III that were initiated between 2005 and 2017 for oncologic drugs in the United States and identified several factors that were associated with the success of RCTs. Our regression analysis showed that studies with progression‐free survival or response rate as primary end point were more likely to succeed than studies with overall survival (odds ratio (OR) = 2.94 and 6.23, respectively). The status of development was also linked with success rates. Studies for non‐lead indication tended to have lower success rates than studies for lead indication (OR = 0.68). Studies for first‐line therapy were observed to have low success rates compared with studies for post second‐line therapies (OR = 0.37). Studies for which strong prior evidence was not listed in their publication tended to be more successful than studies that followed rigorous RCTs or single arm studies for the indication. These results suggest that historical success rates may reflect not only the important features of trials, which can be observed directly from study design and results, but also the background status of trials in clinical development pathways.
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Affiliation(s)
- Can Wu
- Laboratory of Pharmaceutical Regulatory Science, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
| | - Shunsuke Ono
- Laboratory of Pharmaceutical Regulatory Science, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
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39
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Patel M, Bueters T. Can quantitative pharmacology improve productivity in pharmaceutical research and development? Expert Opin Drug Discov 2020; 15:1111-1114. [DOI: 10.1080/17460441.2020.1776257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Mayankbhai Patel
- Department of Pharmacokinetics, Pharmacodynamics and Drug Metabolism (PPDM), Merck & Co., Inc., Kenilworth, NJ, USA
| | - Tjerk Bueters
- Department of Pharmacokinetics, Pharmacodynamics and Drug Metabolism (PPDM), Merck & Co., Inc., Kenilworth, NJ, USA
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40
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Ketchen S, Rohwedder A, Knipp S, Esteves F, Struve N, Peckham M, Ladbury JE, Curd A, Short SC, Brüning-Richardson A. A novel workflow for three-dimensional analysis of tumour cell migration. Interface Focus 2020; 10:20190070. [PMID: 32194931 PMCID: PMC7061943 DOI: 10.1098/rsfs.2019.0070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/13/2019] [Indexed: 12/11/2022] Open
Abstract
The limitations of two-dimensional analysis in three-dimensional (3D) cellular imaging impair the accuracy of research findings in biological studies. Here, we report a novel 3D approach to acquisition, analysis and interpretation of tumour spheroid images. Our research interest in mesenchymal-amoeboid transition led to the development of a workflow incorporating the generation and analysis of 3D data with instant structured illumination microscopy and a new ImageJ plugin.
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Affiliation(s)
- Sophie Ketchen
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Arndt Rohwedder
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Sabine Knipp
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Filomena Esteves
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Nina Struve
- Laboratory of Radiobiology and Experimental Radiation Oncology, Hubertus Wald Tumorzentrum–University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michelle Peckham
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - John E. Ladbury
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Alistair Curd
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Susan C. Short
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
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Amuthavalli A, Prakash B, Thirugnanasampandan R, Gogulramnath M, Bhuvaneswari G, Velmurugan R. Synthesis, molecular docking, antibacterial, antioxidant, and cytotoxicity activities of novel pyrido-cyclopenta[b]indole analogs. SYNTHETIC COMMUN 2020. [DOI: 10.1080/00397911.2020.1733610] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- A. Amuthavalli
- Department of Chemistry, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India
| | - B. Prakash
- Department of Chemistry, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India
| | | | - M. Gogulramnath
- Department of Biotechnology, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India
| | - G. Bhuvaneswari
- Department of Biotechnology, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India
| | - R. Velmurugan
- Department of Chemistry, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu, India
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42
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Scheinberg T, Kench J, Stockler M, Mahon KL, Sebastian L, Stricker P, Joshua AM, Woo H, Thanigasalam R, Ahmadi N, Centenera MM, Butler LM, Horvath LG. Pharmacodynamics effects of CDK4/6 inhibitor LEE011 (ribociclib) in high-risk, localised prostate cancer: a study protocol for a randomised controlled phase II trial (LEEP study: LEE011 in high-risk, localised Prostate cancer). BMJ Open 2020; 10:e033667. [PMID: 31988233 PMCID: PMC7045211 DOI: 10.1136/bmjopen-2019-033667] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION Despite the development of new therapies for advanced prostate cancer, it remains the most common cause of cancer and the second leading cause of cancer death in men. It is critical to develop novel agents for the treatment of prostate cancer, particularly those that target aspects of androgen receptor (AR) signalling or prostate biology other than inhibition of androgen synthesis or AR binding. Neoadjuvant pharmacodynamic studies allow for a rational approach to the decisions regarding which targeted therapies should progress to phase II/III trials. CDK4/6 inhibitors have evidence of efficacy in breast cancer, and have been shown to have activity in preclinical models of hormone sensitive and castrate resistant prostate cancer. The LEEP trial aims to assess the pharmacodynamic effects of LEE011 (ribociclib), an orally bioavailable and highly selective CDK4/6 inhibitor, in men undergoing radical prostatectomy for high-risk, localised prostate cancer. METHODS AND ANALYSIS The multicentre randomised, controlled 4:1 two-arm, phase II, open label pharmacodynamic study will recruit 47 men with high risk, localised prostate cancer who are planned to undergo radical prostatectomy. Participants who are randomised to receive the study treatment will be treated with LEE011 400 mg daily for 21 days for one cycle. The primary endpoint is the frequency of a 50% reduction in Ki-67 proliferation index from the pretreatment prostate biopsy compared to that present in prostate cancer tissue from radical prostatectomy. Secondary and tertiary endpoints include pharmacodynamic assessment of CDK4/6 cell cycle progression via E2F levels, apoptotic cell death by cleaved caspase-3, changes in serum and tumour levels of Prostate Specific Antigen (PSA), pathological regression, safety via incidence of adverse events and exploratory biomarker analysis. ETHICS AND DISSEMINATION The protocol was approved by a central ethics review committee (St Vincent's Hospital HREC) for all participating sites (HREC/17/SVH/294). Results will be disseminated in peer-reviewed journals and at scientific conferences. DRUG SUPPLY Novartis. PROTOCOL VERSION 2.0, 30 May 2019 TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12618000354280).
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Affiliation(s)
- Tahlia Scheinberg
- Medical Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - James Kench
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Anatomical Pathology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Martin Stockler
- Medical Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
- Concord Cancer Centre, Concord Repatriation General Hospital, Concord, NSW, Australia
| | - Kate L Mahon
- Medical Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - Lucille Sebastian
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Phillip Stricker
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Urology, St Vincent's Clinic, Darlinghurst, NSW, Australia
| | - Anthony M Joshua
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Medical Oncology, St Vincent's Hospital, Sydney, New South Wales, Australia
| | - H Woo
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Urology, Chris O'Brien Lifehouse, Camperdown, NSW, Australia
| | - Ruban Thanigasalam
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Urology, Chris O'Brien Lifehouse, Camperdown, NSW, Australia
- Urology, Concord Repatriation General Hospital, Concord, NSW, Australia
| | - Nariman Ahmadi
- Urology, Chris O'Brien Lifehouse, Camperdown, NSW, Australia
| | - Margaret M Centenera
- Prostate Cancer Research Group, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Lisa M Butler
- Prostate Cancer Research Group, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Lisa G Horvath
- Medical Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
- School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Cancer Research, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
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Lara Gongora AB, Carvalho Oliveira LJ, Jardim DL. Impact of the biomarker enrichment strategy in drug development. Expert Rev Mol Diagn 2020; 20:611-618. [DOI: 10.1080/14737159.2020.1711734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Ethyl benzoate bearing pyrrolizine/indolizine moieties: Design, synthesis and biological evaluation of anti-inflammatory and cytotoxic activities. Bioorg Chem 2020; 94:103371. [DOI: 10.1016/j.bioorg.2019.103371] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/07/2019] [Accepted: 10/16/2019] [Indexed: 12/19/2022]
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Michiels S, Wason J. Overestimated treatment effects in randomised phase II trials: What's up doctor? Eur J Cancer 2019; 123:116-117. [PMID: 31678769 DOI: 10.1016/j.ejca.2019.09.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 09/26/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Stefan Michiels
- Service de Biostatistique et D'Épidémiologie, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France; CESP INSERM U1018, Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France.
| | - James Wason
- Institute of Health and Society, Newcastle University, Newcastle Upon Tyne, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology. Hum Genomics 2019; 13:55. [PMID: 31699147 PMCID: PMC6836481 DOI: 10.1186/s40246-019-0239-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 09/25/2019] [Indexed: 01/16/2023] Open
Abstract
Background Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases. Results In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs. Conclusions Drug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.
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Sampayo-Cordero M, Miguel-Huguet B, Pardo-Mateos A, Malfettone A, Pérez-García J, Llombart-Cussac A, Cortés J, Moltó-Abad M, Muñoz-Delgado C, Pérez-Quintana M, Pérez-López J. Agreement between results of meta-analyses from case reports and clinical studies, regarding efficacy and safety of idursulfase therapy in patients with mucopolysaccharidosis type II (MPS-II). A new tool for evidence-based medicine in rare diseases. Orphanet J Rare Dis 2019; 14:230. [PMID: 31639024 PMCID: PMC6805333 DOI: 10.1186/s13023-019-1202-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 09/13/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND A preliminary exploratory study shows solid agreement between the results of case reports and clinical study meta-analyses in mucopolysaccharidosis Type I (MPS-I) adult patients. The aim of the present study is to confirm previous results in another patient population, suffering from mucopolysaccharidosis Type II (MPS-II). METHODS A systematic review and meta-analysis of case reports published by April 2018 was conducted for MPS-II patients treated with enzyme replacement therapy (ERT). The study is reported in accordance with PRISMA and MOOSE guidelines (PROSPERO database code CRD42018093408). The assessed population and outcomes were the same as previously analyzed in a meta-analysis of MPS-II clinical studies. The primary endpoint was the percent of clinical cases showing improvement in efficacy outcome, or no harm in safety outcome after ERT initiation. A restrictive procedure to aggregate case reports, by selecting standardized and well-defined outcomes, was proposed. Different sensitivity analyses were able to evaluate the robustness of results. RESULTS Every outcome classified as "acceptable evidence group" in our case report meta-analysis had been graded as "moderate strength of evidence" in the aforementioned meta-analysis of clinical studies. Sensitivity, specificity, and positive-negative predictive values for results of both meta-analyses reached 100%, and were deemed equivalent. CONCLUSIONS Aggregating case reports quantitatively, rather than analyzing them qualitatively, may improve conclusions in rare diseases and personalized medicine. Additionally, we propose some methods to evaluate publication bias and heterogeneity of the included studies in a meta-analysis of case reports.
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Affiliation(s)
- Miguel Sampayo-Cordero
- Medica Scientia Innovation Research (MedSIR), Rambla de Catalunya 2, 2D, 08007, Barcelona, Spain.
| | | | | | - Andrea Malfettone
- Medica Scientia Innovation Research (MedSIR), Rambla de Catalunya 2, 2D, 08007, Barcelona, Spain
| | - José Pérez-García
- Medica Scientia Innovation Research (MedSIR), Rambla de Catalunya 2, 2D, 08007, Barcelona, Spain
- IOB, Institute of Oncology, QuironSalud Group, Madrid & Barcelona, Spain
| | - Antonio Llombart-Cussac
- Medica Scientia Innovation Research (MedSIR), Rambla de Catalunya 2, 2D, 08007, Barcelona, Spain
- FISABIO - Hospital Arnau de Vilanova, Valencia, Spain
| | - Javier Cortés
- Medica Scientia Innovation Research (MedSIR), Rambla de Catalunya 2, 2D, 08007, Barcelona, Spain
- IOB, Institute of Oncology, QuironSalud Group, Madrid & Barcelona, Spain
| | - Marc Moltó-Abad
- Unit of Rare Diseases, Hospital Vall d'Hebron, Barcelona, Spain
| | | | - Marta Pérez-Quintana
- Department of Internal Medicine, Hospital San Juan de Dios, Aljarafe, Seville, Spain
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Adashek JJ, LoRusso PM, Hong DS, Kurzrock R. Phase I trials as valid therapeutic options for patients with cancer. Nat Rev Clin Oncol 2019; 16:773-778. [PMID: 31477881 DOI: 10.1038/s41571-019-0262-9] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2019] [Indexed: 12/17/2022]
Abstract
For many years, oncology phase I trials have been referred to as 'toxicity trials' and have been believed to have low clinical utility other than that of establishing the adverse event profile of novel therapeutic agents. The traditional distinction of clinical trials into three phases has been challenged in the past few years by the introduction of targeted therapies and immunotherapies into the routine management of patients with cancer. This transformation has especially affected early phase trials, leading to the current situation in which response rates are increasingly reported from phase I trials. In this Perspectives, we highlight key elements of phase I trials and discuss how each one of them contributes to a new paradigm whereby preliminary measurements of the clinical benefit from a novel treatment can be obtained in current phase I trials, which can therefore be considered to have a therapeutic intent.
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Affiliation(s)
- Jacob J Adashek
- Department of Internal Medicine, University of South Florida, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | - David S Hong
- Department of Investigational Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Razelle Kurzrock
- Center for Personalized Cancer Therapy, Department of Medicine, University of California San Diego Moores Cancer Center, La Jolla, CA, USA.
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Paller CJ, Huang EP, Luechtefeld T, Massett HA, Williams CC, Zhao J, Gravell AE, Tamashiro T, Reeves SA, Rosner GL, Carducci MA, Rubinstein L, Ivy SP. Factors Affecting Combination Trial Success (FACTS): Investigator Survey Results on Early-Phase Combination Trials. Front Med (Lausanne) 2019; 6:122. [PMID: 31214592 PMCID: PMC6558040 DOI: 10.3389/fmed.2019.00122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 05/15/2019] [Indexed: 12/02/2022] Open
Abstract
Experimental therapeutic oncology agents are often combined to circumvent tumor resistance to individual agents. However, most combination trials fail to demonstrate sufficient safety and efficacy to advance to a later phase. This study collected survey data on phase 1 combination therapy trials identified from ClinicalTrials.gov between January 1, 2003 and November 30, 2017 to assess trial design and the progress of combinations toward regulatory approval. Online surveys (N = 289, 23 questions total) were emailed to Principal Investigators (PIs) of early-phase National Cancer Institute and/or industry trials; 263 emails (91%) were received and 113 surveys completed (43%). Among phase 1 combination trials, 24.9% (95%CI: 15.3%, 34.4%) progressed to phase 2 or further; 18.7% (95%CI: 5.90%, 31.4%) progressed to phase 3 or regulatory approval; and 12.4% (95%CI: 0.00%, 25.5%) achieved regulatory approval. Observations of “clinical promise” in phase 1 combination studies were associated with higher rates of advancement past each milestone toward regulatory approval (cumulative OR = 11.9; p = 0.0002). Phase 1 combination study designs were concordant with Clinical Trial Design Task Force (CTD-TF) Recommendations 79.6% of the time (95%CI: 72.2%, 87.1%). Most discordances occurred where no plausible pharmacokinetic or pharmacodynamic interactions were expected. Investigator-defined “clinical promise” of a combination is associated with progress toward regulatory approval. Although concordance between study designs of phase 1 combination trials and CTD-TF Recommendations was relatively high, it may be beneficial to raise awareness about the best study design to use when no plausible pharmacokinetic or pharmacodynamic interactions are expected.
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Affiliation(s)
- Channing J Paller
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Erich P Huang
- Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, United States
| | - Thomas Luechtefeld
- Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Holly A Massett
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, United States
| | - Christopher C Williams
- Clinical Trials Information Management Services, Emmes Corporation, Rockville, MD, United States
| | - Jinxiu Zhao
- Clinical Trials Information Management Services, Emmes Corporation, Rockville, MD, United States
| | - Amy E Gravell
- Clinical Trials Information Management Services, Emmes Corporation, Rockville, MD, United States
| | - Tami Tamashiro
- Clinical Trials Information Management Services, Emmes Corporation, Rockville, MD, United States
| | - Steven A Reeves
- Coordinating Center for Clinical Trials, National Cancer Institute, Rockville, MD, United States
| | - Gary L Rosner
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Michael A Carducci
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Lawrence Rubinstein
- Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, United States
| | - S Percy Ivy
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, United States
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