Literature DB >> 36241954

Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients.

Robert A Beckman1, Zoran Antonijevic2, Mercedeh Ghadessi3, Heng Xu4, Cong Chen5, Yi Liu4, Rui Tang6.   

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.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Year:  2022        PMID: 36241954     DOI: 10.1007/s40272-022-00538-7

Source DB:  PubMed          Journal:  Paediatr Drugs        ISSN: 1174-5878            Impact factor:   3.930


  32 in total

1.  Modelling and simulation in the pharmaceutical industry--some reflections.

Authors:  Carl-Fredrik Burman; Stig Johan Wiklund
Journal:  Pharm Stat       Date:  2011-12-08       Impact factor: 1.894

Review 2.  Integrating predictive biomarkers and classifiers into oncology clinical development programmes.

Authors:  Robert A Beckman; Jason Clark; Cong Chen
Journal:  Nat Rev Drug Discov       Date:  2011-09-30       Impact factor: 84.694

3.  Optimal cost-effective designs of Phase II proof of concept trials and associated go-no go decisions.

Authors:  Cong Chen; Robert A Beckman
Journal:  J Biopharm Stat       Date:  2009       Impact factor: 1.051

4.  A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk.

Authors:  Nitin R Patel; Suresh Ankolekar; Zoran Antonijevic; Natasa Rajicic
Journal:  Stat Med       Date:  2013-01-09       Impact factor: 2.373

5.  Maximizing return on socioeconomic investment in phase II proof-of-concept trials.

Authors:  Cong Chen; Robert A Beckman
Journal:  Clin Cancer Res       Date:  2014-02-13       Impact factor: 12.531

6.  Efficient two-stage sequential arrays of proof of concept studies for pharmaceutical portfolios.

Authors:  Linchen He; Linqiu Du; Zoran Antonijevic; Martin Posch; Valeriy R Korostyshevskiy; Robert A Beckman
Journal:  Stat Methods Med Res       Date:  2020-09-21       Impact factor: 3.021

Review 7.  Real-World Data as External Controls: Practical Experience from Notable Marketing Applications of New Therapies.

Authors:  Rima Izem; Joan Buenconsejo; Ruthanna Davi; Jingyu Julia Luan; LaRee Tracy; Margaret Gamalo
Journal:  Ther Innov Regul Sci       Date:  2022-06-08       Impact factor: 1.337

Review 8.  Decision-theoretic designs for small trials and pilot studies: A review.

Authors:  Siew Wan Hee; Thomas Hamborg; Simon Day; Jason Madan; Frank Miller; Martin Posch; Sarah Zohar; Nigel Stallard
Journal:  Stat Methods Med Res       Date:  2015-06-05       Impact factor: 3.021

9.  Optimized adaptive enrichment designs.

Authors:  Thomas Ondra; Sebastian Jobjörnsson; Robert A Beckman; Carl-Fredrik Burman; Franz König; Nigel Stallard; Martin Posch
Journal:  Stat Methods Med Res       Date:  2017-12-18       Impact factor: 3.021

Review 10.  A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG).

Authors:  Mercedeh Ghadessi; Rui Tang; Joey Zhou; Rong Liu; Chenkun Wang; Kiichiro Toyoizumi; Chaoqun Mei; Lixia Zhang; C Q Deng; Robert A Beckman
Journal:  Orphanet J Rare Dis       Date:  2020-03-12       Impact factor: 4.123

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