Concepts and challenges in quantitative pharmacology and model-based drug development

Abstract

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Fig. 1

Model-based drug development. A Application of modeling and simulation during various preclinical and clinical phases in traditional drug development. Listed are potential applications for PK–PD concepts as well as the frequently applied two consecutive learn–confirm cycles. Model-based data analysis and simulation is usually performed in discrete isolated events throughout the development process. From (11). B Model-based drug development as a cornerstone of the drug development process. Model-based drug development is a new paradigm and mindset that embraces all aspects of drug development from drug discovery to post-marketing. By facilitating the rigorous development of a scientific knowledgebase though continuous integration of knowledge generated along the development path, it provides a data-driven model framework that serves as a key decision-making tool enabling rationale, scientifically based choices at critical decision points. The top half of Panel B represents a reservoir for the interdisciplinary knowledgebase required by MBDD. Sources of information in the top half of Panel B typically are not specific to the compound under development and are publicly available. Single arrows indicate the typically unidirectional flow of information. In contrast, information generated from the bottom half of Panel B is compound-specific, is integrated into the knowledgebase, and the knowledgebase guides how it is further being utilized to generate additional compound-specific knowledge. The double arrows indicate this bidirectional information flow