A CRISP approach to QSP: XAI enabling fit-for-purpose models
Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of clinical outcomes. Realizing this potential requires effectively managing the complexity of mathematical models representing biological systems. Here, we present and validate a novel QSP workflow--CRISP (Contextualized Reduction for Identifiability and Scientific Precision)--that addresses a central challenge in QSP: the problem of complexity and over-parameterization, in which models contain irrelevant parameters that obscure interpretation and hinder predictive reliability. The CRISP workflow begins with a literature-derived model, constructed to be comprehensive and unbiased by integrating prior mechanistic insights. At the core of the workflow is the Manifold Boundary Approximation Method (MBAM), a reduction technique that simplifies models while preserving mechanistic structure and predictive fidelity. By applying MBAM in a context-specific manner, CRISP links parsimonious models directly to predictions of interest, clarifying causal structure and enhancing interpretability. The resulting models are computationally efficient and well-suited to key QSP tasks, including virtual population generation, experimental design, toxicology, and target discovery. We demonstrate the utility of CRISP on case studies involving the coagulation cascade and SHIV infection, and identify promising directions for improving the efficacy of bNAb therapies for HIV. Together, these results establish CRISP as a general-purpose QSP workflow for turning complex mechanistic models into tools for precise scientific reasoning to guide pharmacological and regulatory decision-making.