Consideration of a Credibility Assessment Framework in Model-Informed Drug Development: Potential Application to Physiologically-Based Pharmacokinetic Modeling and Simulation.
Colleen Kuemmel,Yuching Yang,Xinyuan Zhang,Jeffry Florian,Hao Zhu,Million A. Tegenge,Shiew-Mei Huang,Yaning Wang,Tina M. Morrison,Issam Zineh +9 more
- Vol. 9, Iss: 1, pp 21-28
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TLDR
A risk‐informed credibility assessment framework is applied to physiologically‐based pharmacokinetic modeling and simulation and it is hypothesize this evidentiary framework may also be useful for evaluating other MIDD approaches.Abstract:
The use of computational models in drug development has grown during the past decade. These model-informed drug development (MIDD) approaches can inform a variety of drug development and regulatory decisions. When used for regulatory decision making, it is important to establish that the model is credible for its intended use. Currently, there is no consensus on how to establish and assess model credibility, including the selection of appropriate verification and validation activities. In this article, we apply a risk-informed credibility assessment framework to physiologically-based pharmacokinetic modeling and simulation and hypothesize this evidentiary framework may also be useful for evaluating other MIDD approaches. We seek to stimulate a scientific discussion around this framework as a potential starting point for uniform assessment of model credibility across MIDD. Ultimately, an overarching framework may help to standardize regulatory evaluation across therapeutic products (i.e., drugs and medical devices).read more
Citations
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United States food and drug administration
TL;DR: The US Food and Drug Administration (FDA) regulates medical devices primarily through the law known as the Federal Food, Drug, and Cosmetic Act, as Amended (FD&C Act, or “the Act”) as discussed by the authors.
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Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective.
Tycho Heimbach,Yuan Chen,Jun Chen,Vaishali Dixit,Neil Parrott,Sheila Annie Peters,Italo Poggesi,Pradeep Sharma,Jan Snoeys,Mohamad Shebley,Guoying Tai,Susanna Tse,Vijay V. Upreti,Ying-Hong Wang,Alice Tsai,Binfeng Xia,Ming Zheng,Andy Z. X. Zhu,Stephen D. Hall +18 more
TL;DR: PBPK modeling may be used to support mild impairment study waivers or clinical study design, and may be suitable for predicting the impact of RI on PK of drugs predominantly cleared by metabolism with varying contribution of renal clearance.
Journal ArticleDOI
Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices.
TL;DR: To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations.
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An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design
TL;DR: An combined computational tools have been developed to in silico predict formulation composition, in vitro release and in vivo absorption behavior of SD formulations, which will significantly facilitate pharmaceutical formulation development than the traditional trial-and-error approach in the laboratory.
Journal ArticleDOI
Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility
Flora T. Musuamba,Ine Skottheim Rusten,Raphaëlle Lesage,Giulia Russo,Roberta Bursi,Luca Emili,Gaby Wangorsch,Efthymios Manolis,Kristin E. Karlsson,Alexander Kulesza,Eulalie Courcelles,Jean-Pierre Boissel,Cécile F. Rousseau,Emmanuelle M. Voisin,Rossana Alessandrello,Nuno Curado,Enrico Dall'Ara,Blanca Rodriguez,Francesco Pappalardo,Liesbet Geris,Liesbet Geris +20 more
TL;DR: In this paper, a risk-informed evaluation framework for mechanistic model credibility evaluation is proposed, which considers context of use, regulatory impact, and risk-based analysis to evaluate model credibility.
References
More filters
Book ChapterDOI
United States food and drug administration
TL;DR: The US Food and Drug Administration (FDA) regulates medical devices primarily through the law known as the Federal Food, Drug, and Cosmetic Act, as Amended (FD&C Act, or “the Act”) as discussed by the authors.
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Learning versus confirming in clinical drug development
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Establishing Best Practices and Guidance in Population Modeling: An Experience With an Internal Population Pharmacokinetic Analysis Guidance
W Byon,Mike K. Smith,P Chan,MA Tortorici,S Riley,Honghua Dai,J Dong,A Ruiz‐Garcia,Kevin Sweeney,C Cronenberger +9 more
TL;DR: This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building.
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Model-Informed Drug Development: Current US Regulatory Practice and Future Considerations
TL;DR: Model‐informed drug development (MIDD) refers to the application of a wide range of quantitative models in drug development to facilitate the decision‐making process and can be broadly classified into four categories: dose optimization, supportive evidence for efficacy, clinical trial design, and informing policy.
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Reverse Translation in PBPK and QSP: Going Backwards in Order to Go Forward With Confidence
TL;DR: This review demonstrates the advantages of combining classical data analysis with PBPK‐IVIVE‐linked models in filling knowledge gaps and discusses the perceived hurdles in widening applications.
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