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Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. #104

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Stergiou opened this Issue Jul 11, 2017 · 0 comments

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Stergiou commented Jul 11, 2017

https://www.ncbi.nlm.nih.gov/pubmed/28649438

Krauss M1, Hofmann U2, Schafmayer C3, Igel S2, Schlender J1, Mueller C4, Brosch M5, von Schoenfels W3, Erhart W3, Schuppert A6,7, Block M1, Schaeffeler E2, Boehmer G8, Goerlitz L4, Hoecker J3, Lippert J9, Kerb R2, Hampe J5, Kuepfer L1, Schwab M2,8,10.

Abstract

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

PMID: 28649438 PMCID: PMC5460240 DOI: 10.1038/s41540-017-0012-5

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