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NPJ Syst Biol Appl. 2017 Mar 28;3:11. doi: 10.1038/s41540-017-0012-5. eCollection 2017.

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

Author information

1
Systems Pharmacology, Bayer AG, Leverkusen, 51368 Germany.
2
Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tuebingen, Stuttgart, 70376 Germany.
3
Department of General Surgery and Thoracic Surgery, University Hospital Schleswig-Holstein, Kiel, 24105 Germany.
4
Applied Mathematics, Bayer AG, Leverkusen, 51368 Germany.
5
Department of Medicine I, University Medical Center Dresden, Technical University Dresden, Dresden, 01307 Germany.
6
Technology Development, Bayer AG, Leverkusen, 51368 Germany.
7
Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen, 52074 Germany.
8
Department of Clinical Pharmacology, University Hospital Tuebingen, Tuebingen, 72076 Germany.
9
Clinical Pharmacometrics, Bayer Pharma AG, Berlin, 13353 Germany.
10
Department of Pharmacy and Biochemistry, University of Tuebingen, Tuebingen, 72074 Germany.
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Contributed equally

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.

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