Quantitative Systems Pharmacology: An Overview

Abstract

Quantitative systems pharmacology (QSP) attempts to quantitatively resolve the complex mechanisms and interactions between physiology and medications. Systems biology-based mechanistic models describe biological pathways and their regulatory networks at molecular, cellular, tissue, organ, patient, and/or population levels. QSP models are developed at various biological scales to present various multiscale mechanisms of pharmacological responses. PK PD (Pharmacokinetic pharmacodynamic) and QSP modeling differ in technical aspects such as data requirements, model implementation, and model evaluation/qualification methods. Previous evaluations have not examined the technical differences between PKPD and QSP modeling. Data needs, model implementation, and model evaluation/qualification are key distinctions. Our review addresses the gap by comparing PKPD modeling to QSP modeling, revealing QSP's superiority. The final gap left by previous evaluations is a limited examination of the applications of QSP for comprehending combination cancer therapies and systems-level analysis. Not enough has been said about pathway analysis, network modeling, logical modeling, virtual patient simulations, multi-scale systems pharmacology platforms, and identifying non-identifiable models. Our review fills in these voids and offers valuable insights into the significance of these approaches for elucidating the complex behavior of biological systems and their responses to medications. The aim and objective of this review are to clarify QSP's unique contributions and correct earlier assessments' lack of comparability. Secondly, we study QSP modeling computational approaches such as molecular docking, molecular dynamics modeling, machine learning, and similarity analysis. These methods can model drug-target interactions, filling the gap left by earlier studies and improving pharmacological knowledge. Pathway analysis and network modeling are vital to understanding the complicated regulatory processes that develop from biological component interactions. We examine how network and route analysis might enhance cancer combination treatment, transcending previous evaluations' limited scope. This review improves on earlier reviews by analyzing QSP more thoroughly. It illuminates QSP's potential for personalized medicine, treatment optimization, and illness management