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Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients. Paediatr Drugs 2022; 24:657-669. [PMID: 36241954 DOI: 10.1007/s40272-022-00538-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/11/2022] [Indexed: 10/17/2022]
Abstract
Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.
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2
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Dane A, Rex JH, Newell P, Stallard N. The Value of the Information That Can Be Generated: Optimizing Study Design to Enable the Study of Treatments Addressing an Unmet Need for Rare Pathogens. Open Forum Infect Dis 2022; 9:ofac266. [PMID: 35854983 PMCID: PMC9290570 DOI: 10.1093/ofid/ofac266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
In traditional phase 3 trials confirming safety and efficacy of new treatments relative to a comparator, a one-sided type I error rate of 2.5% is traditionally used, and typically leads to minimum sizes of 300-600 subjects per study. However, for rare pathogens, it may be necessary to work with data from as few as 50–100 subjects. For areas with a high unmet need, there is a balance between traditional type I error and power and enabling feasible studies. In such cases, an alternative one-sided alpha level of 5% or 10% should be considered and we review herein the implications of such approaches. Resolving this question requires engagement of patients, the medical community, regulatory agencies, and trial sponsors.
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McMillan G, Mayer C, Tang R, Liu Y, LaVange L, Antonijevic Z, Beckman RA. Planning for the Next Pandemic: Ethics and Innovation Today for Improved Clinical Trials Tomorrow. Stat Biopharm Res 2021; 14:22-27. [PMID: 37006380 PMCID: PMC10061983 DOI: 10.1080/19466315.2021.1918236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/22/2021] [Accepted: 04/12/2021] [Indexed: 01/05/2023]
Abstract
The coronavirus pandemic has brought public attention to the steps required to produce valid scientific clinical research in drug development. Traditional ethical principles that guide clinical research remain the guiding compass for physicians, patients, public health officials, investigators, drug developers and the public. Accelerating the process of delivering safe and effective treatments and vaccines against COVID-19 is a moral imperative. The apparent clash between the regulated system of phased randomized clinical trials and urgent public health need requires leveraging innovation with ethical scientific rigor. We reflect on the Belmont principles of autonomy, beneficence and justice as the pandemic unfolds, and illustrate the role of innovative clinical trial designs in alleviating pandemic challenges. Our discussion highlights selected types of innovative trial design and correlates them with ethical parameters and public health benefits. Details are provided for platform trials and other innovative designs such as basket and umbrella trials, designs leveraging external data sources, multi-stage seamless trials, preplanned control arm data sharing between larger trials, and higher order systems of linked trials coordinated more broadly between individual trials and phases of development, recently introduced conceptually as "PIPELINEs."
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Affiliation(s)
- Gianna McMillan
- Bioethics Institute, Loyola Marymount University, Los Angeles, CA
| | | | - Rui Tang
- Servier Pharmaceuticals, Boston, MA
| | - Yi Liu
- Nektar Therapeutics, Data Science and Systems, San Francisco, CA
| | - Lisa LaVange
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | | | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
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4
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Collignon O, Burman CF, Posch M, Schiel A. Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome. Clin Pharmacol Ther 2021; 110:311-320. [PMID: 33506495 PMCID: PMC8014457 DOI: 10.1002/cpt.2183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 01/19/2021] [Indexed: 12/19/2022]
Abstract
For the development of coronavirus disease 2019 (COVID‐19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID‐19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life‐saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time‐varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence‐generation initiatives when a positive return on investment is not met.
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Affiliation(s)
| | - Carl-Fredrik Burman
- Statistical Innovation, Data Science, and Artificial Intelligence, AstraZeneca R&D, Gothenburg, Sweden
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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Hill-McManus D, Hughes DA. Combining Model-Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 10:75-83. [PMID: 33314752 PMCID: PMC7825194 DOI: 10.1002/psp4.12579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 10/20/2020] [Indexed: 11/25/2022]
Abstract
The Bayesian decision‐analytic approach to trial design uses prior distributions for treatment effects, updated with likelihoods for proposed trial data. Prior distributions for treatment effects based on previous trial results risks sample selection bias and difficulties when a proposed trial differs in terms of patient characteristics, medication adherence, or treatment doses and regimens. The aim of this study was to demonstrate the utility of using pharmacometric‐based clinical trial simulation (CTS) to generate prior distributions for use in Bayesian decision‐theoretic trial design. The methods consisted of four principal stages: a CTS to predict the distribution of treatment response for a range of trial designs; Bayesian updating for a proposed sample size; a pharmacoeconomic model to represent the perspective of a reimbursement authority in which price is contingent on trial outcome; and a model of the pharmaceutical company return on investment linking drug prices to sales revenue. We used a case study of febuxostat versus allopurinol for the treatment of hyperuricemia in patients with gout. Trial design scenarios studied included alternative treatment doses, inclusion criteria, input uncertainty, and sample size. Optimal trial sample sizes varied depending on the uncertainty of model inputs, trial inclusion criteria, and treatment doses. This interdisciplinary framework for trial design and sample size calculation may have value in supporting decisions during later phases of drug development and in identifying costly sources of uncertainty, and thus inform future research and development strategies.
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Affiliation(s)
- Daniel Hill-McManus
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
| | - Dyfrig A Hughes
- Centre for Health Economics and Medicines Evaluation, Bangor University, Bangor, UK
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Annett RD, Chervinskiy S, Chun TH, Cowan K, Foster K, Goodrich N, Hirschfeld M, Hsia DS, Jarvis JD, Kulbeth K, Madden C, Nesmith C, Raissy H, Ross J, Saul JP, Shiramizu B, Smith P, Sullivan JE, Tucker L, Atz AM. IDeA States Pediatric Clinical Trials Network for Underserved and Rural Communities. Pediatrics 2020; 146:peds.2020-0290. [PMID: 32943534 PMCID: PMC7786822 DOI: 10.1542/peds.2020-0290] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/16/2020] [Indexed: 01/19/2023] Open
Abstract
The National Institutes of Health's Environmental Influences on Child Health Outcomes (ECHO) program aims to study high-priority and high-impact pediatric conditions. This broad-based health initiative is unique in the National Institutes of Health research portfolio and involves 2 research components: (1) a large group of established centers with pediatric cohorts combining data to support longitudinal studies (ECHO cohorts) and (2) pediatric trials program for institutions within Institutional Development Awards states, known as the ECHO Institutional Development Awards States Pediatric Clinical Trials Network (ISPCTN). In the current presentation, we provide a broad overview of the ISPCTN and, particularly, its importance in enhancing clinical trials capabilities of pediatrician scientists through the support of research infrastructure, while at the same time implementing clinical trials that inform future health care for children. The ISPCTN research mission is aligned with the health priority conditions emphasized in the ECHO program, with a commitment to bringing state-of-the-science trials to children residing in underserved and rural communities. ISPCTN site infrastructure is critical to successful trial implementation and includes research training for pediatric faculty and coordinators. Network sites exist in settings that have historically had limited National Institutes of Health funding success and lacked pediatric research infrastructure, with the initial funding directed to considerable efforts in professional development, implementation of regulatory procedures, and engagement of communities and families. The Network has made considerable headway with these objectives, opening two large research studies during its initial 18 months as well as producing findings that serve as markers of success that will optimize sustainability.
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Affiliation(s)
- Robert D. Annett
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, Mississippi
| | - Sheva Chervinskiy
- Data Coordinating and Operations Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Thomas H. Chun
- Departments of Emergency Medicine and Pediatrics, Brown University, Providence, Rhode Island
| | - Kelly Cowan
- University of Vermont Medical Center, Burlington, Vermont
| | | | | | | | - Daniel S. Hsia
- Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | | | - Kurtis Kulbeth
- Data Coordinating and Operations Center, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Christi Madden
- The Children’s Hospital at University of Oklahoma Medical Center, Oklahoma City, Oklahoma
| | | | - Hengameh Raissy
- University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Judith Ross
- Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware
| | - J. Philip Saul
- Department of Pediatrics, West Virginia University, Morgantown, West Virginia
| | - Bruce Shiramizu
- Departments of Tropical Medicine, Pediatrics, and Medicine, University of Hawai’i, Honolulu, Hawaii
| | - Paul Smith
- Department of Pediatrics, University of Montana, Missoula, Montana
| | - Janice E. Sullivan
- Department of Pediatrics, University of Louisville, Louisville, Kentucky; and
| | - Lauren Tucker
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, Mississippi
| | - Andrew M. Atz
- Medical University of South Carolina, Charleston, South Carolina
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Sun LZ, Li W, Chen C, Zhao J. Advanced Utilization of Intermediate Endpoints for Making Optimized Cost-Effective Decisions in Seamless Phase II/III Oncology Trials. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1665578] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Linda Z. Sun
- Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Kenilworth, NJ
| | - Wen Li
- Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Kenilworth, NJ
| | - Cong Chen
- Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Kenilworth, NJ
| | - Jing Zhao
- Biostatistics and Research Decision Sciences, MRL, Merck & Co., Inc., Kenilworth, NJ
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Friede T, Posch M, Zohar S, Alberti C, Benda N, Comets E, Day S, Dmitrienko A, Graf A, Günhan BK, Hee SW, Lentz F, Madan J, Miller F, Ondra T, Pearce M, Röver C, Toumazi A, Unkel S, Ursino M, Wassmer G, Stallard N. Recent advances in methodology for clinical trials in small populations: the InSPiRe project. Orphanet J Rare Dis 2018; 13:186. [PMID: 30359266 PMCID: PMC6203217 DOI: 10.1186/s13023-018-0919-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 09/24/2018] [Indexed: 12/16/2022] Open
Abstract
Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.
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Affiliation(s)
| | - Martin Posch
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Sarah Zohar
- INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Corinne Alberti
- INSERM, Hôpital Robert-Debré, APHP, University Paris 7, Paris, France
| | | | - Emmanuelle Comets
- INSERM, IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France.,INSERM, CIC 1414, Univ Rennes-1, Rennes, France
| | - Simon Day
- Clinical Trials Consulting and Training Limited, Buckingham, UK
| | | | - Alexandra Graf
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | | | - Siew Wan Hee
- Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Jason Madan
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Frank Miller
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Thomas Ondra
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | | | | | - Artemis Toumazi
- INSERM, Hôpital Robert-Debré, APHP, University Paris 7, Paris, France
| | | | - Moreno Ursino
- INSERM, U1138, team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Gernot Wassmer
- Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK.
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9
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Farrugia A, Gringeri A, von Mackensen S. The multiple benefits of sport in haemophilia. Haemophilia 2018; 24:341-343. [PMID: 29732648 DOI: 10.1111/hae.13496] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2018] [Indexed: 11/29/2022]
Affiliation(s)
- A Farrugia
- Faculty of Medicine, Dentistry and Health Sciences, The University of Western Australia, Crawley, WA, Australia
| | - A Gringeri
- Global Medical Affairs, Kedrion S.p.A., Barga, Italy
| | - S von Mackensen
- Department of Medical Psychology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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10
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Miller F, Zohar S, Stallard N, Madan J, Posch M, Hee SW, Pearce M, Vågerö M, Day S. Approaches to sample size calculation for clinical trials in rare diseases. Pharm Stat 2018; 17:214-230. [PMID: 29322632 DOI: 10.1002/pst.1848] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 09/05/2017] [Accepted: 12/08/2017] [Indexed: 01/27/2023]
Abstract
We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively.
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Affiliation(s)
- Frank Miller
- Department of Statistics, Stockholm University, Stockholm, Sweden
| | - Sarah Zohar
- INSERM, U1138, Team 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France
| | - Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jason Madan
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Siew Wan Hee
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | | | | | - Simon Day
- Clinical Trials Consulting and Training Limited, Buckingham, UK
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