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Park JJH, Sharif B, Harari O, Dron L, Heath A, Meade M, Zarychanski R, Lee R, Tremblay G, Mills EJ, Jemiai Y, Mehta C, Wathen JK. Economic Evaluation of Cost and Time Required for a Platform Trial vs Conventional Trials. JAMA Netw Open 2022; 5:e2221140. [PMID: 35819785 PMCID: PMC9277502 DOI: 10.1001/jamanetworkopen.2022.21140] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE Platform trial design allows the introduction of new interventions after the trial is initiated and offers efficiencies to clinical research. However, limited guidance exists on the economic resources required to establish and maintain platform trials. OBJECTIVE To compare cost (US dollars) and time requirements of conducting a platform trial vs a series of conventional (nonplatform) trials using a real-life example. DESIGN, SETTING, AND PARTICIPANTS For this economic evaluation, an online survey was administered to a group of international experts (146 participants) with publication records of platform trials to elicit their opinions on cost and time to set up and conduct platform, multigroup, and 2-group trials. Using the reported entry dates of 10 interventions into Systemic Therapy in Advancing Metastatic Prostate Cancer: Evaluation of Drug Efficacy, the longest ongoing platform trial, 3 scenarios were designed involving a single platform trial (scenario 1), 1 multigroup followed by 5 2-group trials (scenario 2), and a series of 10 2-group trials (scenario 3). All scenarios started with 5 interventions, then 5 more interventions were either added to the platform or evaluated independently. Simulations with the survey results as inputs were used to compare the platform vs conventional trial designs. Data were analyzed from July to September 2021. EXPOSURE Platform trial design. MAIN OUTCOMES AND MEASURES Total trial setup and conduct cost and cumulative duration. RESULTS Although setup time and cost requirements of a single trial were highest for the platform trial, cumulative requirements of setting up a series of multiple trials in scenarios 2 and 3 were larger. Compared with the platform trial, there was a median (IQR) increase of 216.7% (202.2%-242.5%) in cumulative setup costs for scenario 2 and 391.1% (365.3%-437.9%) for scenario 3. In terms of total cost, there was a median (IQR) increase of 17.4% (12.1%-22.5%) for scenario 2 and 57.5% (43.1%-69.9%) for scenario 3. There was a median (IQR) increase in cumulative trial duration of 171.1% (158.3%-184.3%) for scenario 2 and 311.9% (282.0%-349.1%) for scenario 3. Cost and time reductions in the platform trial were observed in both the initial and subsequently evaluated interventions. CONCLUSIONS AND RELEVANCE Although setting up platform trials can take longer and be costly, the findings of this study suggest that having a single infrastructure can improve efficiencies with respect to costs and efforts.
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Affiliation(s)
- Jay J. H. Park
- Experimental Medicine, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
| | | | | | | | - Anna Heath
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Science, University College London, London, United Kingdom
| | - Maureen Meade
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
- Interdepartmental Division of Critical Care, Hamilton Health Sciences, Critical Care, Hamilton, Ontario, Canada
| | - Ryan Zarychanski
- Department of Internal Medicine, Section of Critical Care, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Section of Hematology/Medical Oncology, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Edward J. Mills
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
| | | | - Cyrus Mehta
- Cytel, Inc, Waltham, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts
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Rando HM, Wellhausen N, Ghosh S, Lee AJ, Dattoli AA, Hu F, Byrd JB, Rafizadeh DN, Lordan R, Qi Y, Sun Y, Brueffer C, Field JM, Ben Guebila M, Jadavji NM, Skelly AN, Ramsundar B, Wang J, Goel RR, Park Y, Boca SM, Gitter A, Greene CS. Identification and Development of Therapeutics for COVID-19. mSystems 2021; 6:e0023321. [PMID: 34726496 PMCID: PMC8562484 DOI: 10.1128/msystems.00233-21] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.
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Affiliation(s)
- Halie M. Rando
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Nils Wellhausen
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Soumita Ghosh
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alexandra J. Lee
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anna Ada Dattoli
- Department of Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James Brian Byrd
- University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Diane N. Rafizadeh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ronan Lordan
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA
| | - Yuchen Sun
- Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA
| | | | - Jeffrey M. Field
- Department of Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Nafisa M. Jadavji
- Biomedical Science, Midwestern University, Glendale, Arizona, USA
- Department of Neuroscience, Carleton University, Ottawa, Ontario, Canada
| | - Ashwin N. Skelly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Jinhui Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rishi Raj Goel
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - YoSon Park
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - COVID-19 Review Consortium
BansalVikasBartonJohn P.BocaSimina M.BoerckelJoel D.BruefferChristianByrdJames BrianCaponeStephenDasShiktaDattoliAnna AdaDziakJohn J.FieldJeffrey M.GhoshSoumitaGitterAnthonyGoelRishi RajGreeneCasey S.GuebilaMarouen BenHimmelsteinDaniel S.HuFenglingJadavjiNafisa M.KamilJeremy P.KnyazevSergeyKollaLikhithaLeeAlexandra J.LordanRonanLubianaTiagoLukanTemitayoMacLeanAdam L.MaiDavidMangulSergheiManheimDavidMcGowanLucy D’AgostinoNaikAmrutaParkYoSonPerrinDimitriQiYanjunRafizadehDiane N.RamsundarBharathRandoHalie M.RaySandipanRobsonMichael P.RubinettiVincentSellElizabethShinholsterLamonicaSkellyAshwin N.SunYuchenSunYushaSzetoGregory L.VelazquezRyanWangJinhuiWellhausenNils
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- University of Michigan School of Medicine, Ann Arbor, Michigan, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA
- Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
- Biomedical Science, Midwestern University, Glendale, Arizona, USA
- Department of Neuroscience, Carleton University, Ottawa, Ontario, Canada
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- The DeepChem Project
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R & D, AstraZeneca, Gaithersburg, Maryland, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin—Madison, Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, Pennsylvania, USA
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
- Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R & D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin—Madison, Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Casey S. Greene
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, USA
- Department of Systems Pharmacology & Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, Pennsylvania, USA
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