Systems Medicine in Pharmaceutical Research and Development.

The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.

A systematic evaluation of the use of physiologically based pharmacokinetic modeling for cross-species extrapolation.

Transfer of knowledge along the different phases of drug development is a fundamental process in pharmaceutical research. In particular, cross-species extrapolation between different laboratory animals and further on to first-in-human trials is challenging because of the uncertain comparability of physiological processes. Physiologically based pharmacokinetic (PBPK) modeling allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical differences in between. We here evaluated different knowledge-driven approaches for cross-species extrapolation by systematically incorporating specific model parameter domains of a target species into the PBPK model of a reference species. Altogether, 15 knowledge-driven approaches were applied to murine and human PBPK models of 10 exemplary drugs resulting in 300 different extrapolations. Statistical analysis of the quality of the different extrapolations revealed not only species-specific physiology as the key determinant in cross-species extrapolation but also identified a synergistic effect when considering both kinetic rate constants and gene expression profiles of relevant enzymes and transporters. Moreover, we show that considering species-specific physiology, plasma protein binding, enzyme and transport kinetics, as well as tissue-specific gene expression profiles in PBPK modeling increases accuracy of cross-species extrapolations and thus supports first-in-human trials based on prior preclinical knowledge.