Translational systems pharmacology for acquisition of knowledge and prediction of drug pharmacokinetics across patient populations
Markus Krauss (1), Ute Hofmann (2), Clemens Schafmayer (3), Svitlana Igel (2), Jan Schlender (1), Christian Mueller (4), Mario Brosch (5,6), Witigo von Schoenfels (3), Wiebke Erhart (3), Andreas Schuppert (7,8), Michael Block (1), Elke Schaeffeler (2), Gabriele Boehmer (9), Linus Goerlitz (4), Jan Hoecker (3), Joerg Lippert (10), Reinhold Kerb (2), Jochen Hampe (5,6), Lars Kuepfer (1), Matthias Schwab (2,9,11)
(1) Systems Pharmacology and Medicine, Bayer AG, 51368 Leverkusen, Germany, (2) Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tuebingen, 70376 Stuttgart, Germany, (3) Department of General Surgery and Thoracic Surgery, University Hospital Schleswig-Holstein, 24105 Kiel, Germany, (4) Applied Mathematics, Bayer AG, 51368 Leverkusen, Germany, (5) Department of Medicine I, University Medical Center Dresden, Technical University Dresden, 01307 Dresden, Germany, (6) Department of Internal Medicine I, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany, (7) Technology Development, Bayer AG, 51368 Leverkusen, Germany, (8) Joint Research Center for Computational Biomedicine, RWTH Aachen University, 52074 Aachen, Germany, (9) Department of Clinical Pharmacology, University Hospital Tuebingen, 72076 Tuebingen, Germany, (10) Clinical Pharmacometrics, Bayer Pharma AG, 13353 Berlin, Germany, (11) Department of Pharmacy and Biochemistry, University of Tuebingen, 72074 Tuebingen, Germany
Objectives: Identify and translate (patho-) physiological and drug-specific knowledge across distinct patient populations for the subsequent prediction of drug PK in a specific population of interest [1].
Methods: A previously developed Bayesian population PBPK approach is used to extract information about physiological and drug-specific parameters, taking into account available prior information about corresponding parameters in the PBPK model [2]. Translation of generated posterior knowledge is possible due to the mechanistic consideration of the PBPK models, as the underlying structure and parameters allows transferring assessed parameter distributions as prior knowledge in the subsequent Bayesian-PBPK analyses. Data for the application of the translational approach was collected in a clinical study, conducted within the Virtual Liver program [3]. The study involved 103 healthy volunteers and 79 diseased patients. Both cohorts received the same cocktail of six approved and commonly used drugs at sub-therapeutic doses.
Results: In three learning steps, prior information about drug physicochemistry and individual physiology information was used together with experimental PK data to acquire posterior knowledge about a reference probe drug (midazolam) and a candidate probe drug (torsemide), respectively, both in a healthy population and an obese patient cohort. The population approach of our Bayesian-PBPK analysis allowed to consider both individualized PK profiles as well as population simulations which were both used for qualification of each step of the translational workflow. Every learning step demonstrated significant improvement in the agreement between simulations and observed data as well as in the information gained about physiological parameters of the model. The posterior knowledge about the candidate probe drug torsemide and the pathophysiological changes of the reference probe drug midazolam was then used for successful prediction of the population PK of torsemide in the obese population [1].
Conclusions: The presented systems pharmacology approach is a prototype for model-based translation across different stages of pharmaceutical development programs. It has the potential to systematically improve predictivity in drug development programs by incorporating results of clinical trials and translating them to subsequent studies.
References:
[1] Krauss, M., et al., Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. NPJ Systems Biology and Applications, accepted.
[2] Krauss, M., et al., Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations. PLoS One, 2015. 10(10): p. e0139423.
[3] Kuepfer, L., R. Kerb, and A.M. Henney, Clinical translation in the virtual liver network. CPT Pharmacometrics Syst Pharmacol, 2014. 3: p. e127.
