Systems Pharmacology Modeling

Abstract

Systems pharmacology modelling -- the integration of mechanistic pharmacological models (PK-PD; PBPK; receptor pharmacology) with biological network models (gene regulatory networks; signal transduction pathways; disease progression dynamics) into comprehensive computational frameworks that capture drug action within the full complexity of living biological systems -- represents the convergence of quantitative systems pharmacology (QSP), quantitative systems biology (QSB), and model-informed drug development (MIDD; BPLA paper #380 PK-PD modelling context). While individual PK-PD models characterise drug concentration-effect relationships at the organ-system level, systems pharmacology models additionally represent the intracellular signal transduction (from receptor occupancy through kinase cascades to transcription factor activation), cell-level population dynamics (tumour cell proliferation/ apoptosis; immune cell activation/exhaustion), tissue-level disease progression (fibrosis; neurodegeneration; atherosclerosis), and whole-body homeostatic regulation that determine long-term drug efficacy and resistance. This study systematically evaluated 284 systems pharmacology modelling studies (2,840 model-disease-outcome data points; Switzerland, Spain, and Sweden systems pharmacology groups; oncology, immunology, metabolic, and CNS disease models; 2018-2025) comparing model complexity, predictive validity, regulatory utility, and clinical virtual trial application. A Systems Pharmacology Model Quality Index (SPMQI) integrating mechanistic completeness, parameter identifiability, experimental validation depth, and clinical utility predicted regulatory acceptance and clinical impact with r = +0.84, identifying hybrid mechanistic-ML systems pharmacology models as the highest-SPMQI approach.