Systems Pharmacology: Bridging Systems Biology and Pharmacokinetics-Pharmacodynamics (PKPD) in Drug Discovery and Development

Abstract

Mechanistic PKPD models are now advocated not only by academic and industrial researchers, but also by regulators. A recent development in this area is based on the growing realisation that innovation could be dramatically catalysed by creating synergy at the interface between Systems Biology and PKPD, two disciplines which until now have largely existed in 'parallel universes' with a limited track record of impactful collaboration. This has led to the emergence of systems pharmacology. Broadly speaking, this is the quantitative analysis of the dynamic interactions between drug(s) and a biological system to understand the behaviour of the system as a whole, as opposed to the behaviour of its individual constituents; thus, it has become the interface between PKPD and systems biology. It applies the concepts of Systems Engineering, Systems Biology, and PKPD to the study of complex biological systems through iteration between computational and/or mathematical modelling and experimentation. Application of systems pharmacology can now impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies, and has the potential to become an integral component of a new 'enhanced quantitative drug discovery and development' (EQD3) R&D paradigm.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

“…QSP has been characterized as a Bquantitative analysis of the dynamic interactions between drug(s) and a biological system that aims to understand the behavior of the system as a whole (1).^There are various existing QSP approaches and applications, and one common feature of QSP models is that they strive to incorporate key biological pathways from the systems of interest and the pharmacology of therapeutic interventions, aiming not only a better holistic understanding of the biology but also Boptimal and translatable pharmacological pathway interventions (2).^QSP models are often multi-scale in that they characterize processes that occur at multiple scales of space and time (e.g., ligand binding vs. disease progression) and mechanistic meaning that fundamental biological processes are represented with mechanistic fidelity. This Bsystems^approach can better inform target selection and the decision process for advancing compounds through preclinical and clinical research (3); as such, it is becoming increasingly important in pharmaceutical research and development as a potential means of reducing attrition and improving productivity (4)(5)(6)(7).…”

Mechanistic Pharmacokinetic-Pharmacodynamic Modeling of BACE1 Inhibition in Monkeys: Development of a Predictive Model for Amyloid Precursor Protein Processing

“…Mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling can play an important role in the drug discovery and development process by providing an integrated understanding of relationships between compound plasma concentrations and biomarker responses (Gabrielsson and Green, 2009;Gibbs, 2010;van der Graaf and Benson, 2011;Wong et al, 2012a,b). In addition, PK-PD modeling is a useful tool that can facilitate the translation of preclinical data to humans.…”

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions

“…Systems modeling provides a means to integrate existing knowledge about these processes as a sequence of events, and is used to elucidate complex and dynamic crosstalk between multiple biological processes. Therefore, the systems modeling approach is being used to investigate the molecular and cellular mechanisms involved in the pathophysiology of complex multietiological diseases; it is increasingly being used to better characterize, understand, and predict pharmacological modulation of biological targets in a quantitative manner 5, 6, 7. Furthermore, pharmaceutical industries rigorously prioritize a model‐informed drug discovery and development (MID3) framework, for prediction and extrapolation, aimed at improving the quality, efficiency, and cost‐effectiveness of decision‐making.…”