Reverse Translation in PBPK and QSP: Going Backwards in Order to Go Forward With Confidence

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Excerpt

Broader use of drug‐independent “system” information is a concept that distinguishes quantitative systems pharmacology (QSP) from classical descriptive models of observed data using purely statistical/mathematical models. However, building QSP models requires a series of drug‐dependent parameters that are usually, but not exclusively, measured in vitro or in species other than human. Translation of these values within QSP models is associated with uncertainties related not only to the gaps in system parameters, but also the accuracy and translatability (scaling) of the drug parameters. Conversely, the majority of system parameters, particularly those related to physiology and anatomy, as opposed to biology, are mostly derived directly or indirectly from human studies (e.g., transit time through various segments of the gastrointestinal tract, tissue blood flows, renal glomerular filtration rate, functional turnover rate of enzymes). In the absence of wide experience in forward translational in vitro–in vivo extrapolation (IVIVE) approaches, qualification for the overarching model can be obtained by verifying the specific use examples through reverse translation. This involves fitting the models to observed data and optimizing the drug or system parameters for which prior confidence is not high. Needless to say, optimizing the system parameters is only valid as long as observations from several independent drugs can be described simultaneously with such optimized values. Physiologically based pharmacokinetic (PBPK) models are a branch of QSP models. They share the common principles with QSP regarding the separation of the systems data from the drug data. PBPK models rely heavily, but not exclusively, on the IVIVE process and the data generated from in vitro systems. Like QSP models, whenever there are information gaps they resort to combining observed clinical data from human studies and even preclinical animal studies. The distinction between PBPK and other QSP models, which sometimes becomes vague when talking about local kinetics in tissues, is the fact that PBPK models focus on how the body handles the drugs rather than the more holistic view of QSP that defines the way drugs affects the body.
Over the last 6 years and since the publication of reviews on scientific rationale1 and regulatory benefits of using PBPK‐IVIVE linked models2 in this journal, applications of PBPK models have increased momentum. Industrialization of PBPK‐IVIVE use led to the publication of draft guidance documents by the European Medicines Agency (EMA)3 and the US Food and Drug Administration (FDA)4 last year. These guidance documents acknowledge the limitations in the use of a purely IVIVE‐driven (bottom‐up) approach while emphasizing the added benefits of PBPK‐IVIVE models in “extrapolation” to conditions that have not yet been studied. These benefits are not commonly associated with the classical data analysis of clinical studies (top‐down approach). Hence, combining the two approaches with the purpose of optimizing model parameters of PBPK‐IVIVE models using some observed clinical data is becoming more popular.
These so‐called “middle‐out” models, which are also known as hybrid multilevel models, take the advantages and strength of two other approaches. Therefore, they are not just restricted to explaining the observed data but they intend to go backwards (in explaining the clinical observations) in order to go forwards beyond the perimeters of the initial clinical study using the prior in vitro and system information. This provides the necessary “qualification” for the model to be used with confidence for “Pre”‐dictions. This reverse translation approach can begin with the clinical data (if the purely bottom‐up models are not built) but goes backwards to use that data to create models that were not previously developed, but can now be readily created, providing immense benefit in understanding those clinical study results.
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