Systems Medicine in Pharmaceutical Research and Development

Abstract

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.

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

“…Different mechanistic modeling approaches (Table 1) can help to estimate the parameters of interactions during the parameter estimation process and the validation of model performance with experimental data. 4 Although a description of modeling methods can be done in an atypical manner too, we focus more on the description of classical applications.…”

“…It can help to find new indications or patient subgroups as good as a 10-year-long drug research study, and it can contribute toward changing biology from being a qualitative and descriptive to a quantitative and explanatory approach. 4 Mathematical modeling, also seen as the translation of beliefs about the functioning of the world into the language of mathematics, has been widely used for developing complex technical systems. 5 We can define a mechanistic mathematical model as the mathematical description of the elements forming a system, their mutual interactions and the interaction with the environment.…”

“…7,8 Mathematical modeling (development of mathematical description of processes, parameter fitting, and model validation), simulation (prediction of different behaviors using the validated model), and optimization (search of the most appropriate action to reach desired behavior) are already used in systems medicine, and mechanistic modeling is currently the main focus. 4,9 In contrast to engineering fields, features of elements and mechanisms of their interactions are mostly unknown. Therefore, the complexity of human biology and the lack of detailed information about the biological system elements interaction limit the accuracy and applicability of a mathematical description of the bioprocesses.…”

“…Therefore, the complexity of human biology and the lack of detailed information about the biological system elements interaction limit the accuracy and applicability of a mathematical description of the bioprocesses. 4 However, it is useful to exploit the available, although incomplete, knowledge by different modeling approaches. ''It is better to be almost right than exactly wrong'' as John Maynard Keynes said.…”

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin?

“…Nevertheless, in chemical engineering AI is mostly dedicated to machine learning and further-on deep learning i.e., in majority to neural networks and data mining tools [75][76][77]. In pharmaceutical industry, expectations rank from big data mining analysis in drug development up to neuronal networks trained by operational data and used to predict maintenance, life cycles or operational parameter ranges [78][79][80][81]. Learning needs training data sets and the prediction quality is dominated by the variety and accuracy of those data sets.…”