From Biological Mechanisms to Causal Inference: Quantitative Systems Pharmacology and Causal Frameworks in Drug Development

Digital patients and in silico clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision-making by enabling the simulation of treatment effects prior to testing in patients. This tutorial examines the combined use of quantitative systems pharmacology (QSP) and causal inference (CI) — two complementary paradigms for causal reasoning in drug development.

QSP models generate physiologically interpretable predictions by encoding biological knowledge (compartmental flows, receptor kinetics, feedback loops) into mechanistic equations, asking: given specified mechanistic assumptions, how does the system behave under intervention? Causal inference instead starts from observed real-world or trial data and estimates intervention effects using formal causal frameworks, explicitly addressing confounding, multicausality, and clinical heterogeneity — asking: given the data, what are the causal effects of intervening in the real world?

Through worked case studies (a Phase 1b-to-2b acceleration in asthma, an FDA approval built on digital-twin evidence in an ultra-rare disease, portfolio-wide replacement of animal testing for bispecific antibodies, and a target trial emulation that reproduced a landmark cardiovascular RCT), the authors argue that QSP (“causality by construction”) and causal inference (“causality by inference”) are complementary rather than competing, and that their principled integration — including emerging hybrid digital patient models — can support more robust, credible decision-making across the drug development lifecycle.