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      Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them

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      Clinical Pharmacokinetics
      Springer International Publishing

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          Abstract

          When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

          Electronic supplementary material

          The online version of this article (10.1007/s40262-019-00790-0) contains supplementary material, which is available to authorized users.

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          Most cited references35

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          Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective

          This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.
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            Physiologically-based Pharmacokinetic (PBPK) Modeling in Regulatory Science: An Update from the US Food and Drug Administration’s Office of Clinical Pharmacology

            This commentary provides an update on the status of physiologically based pharmacokinetic modeling and simulation at the U.S. Food and Drug Administration's Office of Clinical Pharmacology. Limitations and knowledge gaps in integration of physiologically based pharmacokinetic approach to inform regulatory decision making, as well as the importance of scientific engagement with drug developers who intend to use this approach, are highlighted.
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              Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data.

              Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.
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                Author and article information

                Contributors
                +49 6151 72-40216 , Sheila-annie.peters@merckgroup.com
                Journal
                Clin Pharmacokinet
                Clin Pharmacokinet
                Clinical Pharmacokinetics
                Springer International Publishing (Cham )
                0312-5963
                1179-1926
                25 June 2019
                25 June 2019
                2019
                : 58
                : 11
                : 1355-1371
                Affiliations
                Merck Healthcare KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
                Article
                790
                10.1007/s40262-019-00790-0
                6856026
                31236775
                9183379c-e935-4263-a62d-ad43c9502970
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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                © Springer Nature Switzerland AG 2019

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