Design to Dose: Integrating Quantitative Insights Across the Antibody Drug Conjugate ( ADC ) Development Lifecycle

ABSTRACT

Antibody‐drug conjugates (ADCs) are among the most pharmacologically complex therapeutics in clinical development. Their behavior emerges from the interplay of antibody disposition, linker stability, payload pharmacology, and host physiology—interdependencies that render qualitative descriptors such as “stronger affinity” or “more potent payload” insufficient for reliable prediction of preclinical and clinical outcomes. Despite growing investment in quantitative medicine across drug development, no unified framework exists to guide quantitative decision‐making across the ADC design–development–dose‐optimization continuum. In this review we survey how fit‐for‐purpose quantitative approaches inform decisions at each stage of the development path, built on multi‐analyte data pipelines that capture the coupled pharmacokinetics (PK) of antibody, linker‐payload, and released payload. We highlight the utility of PBPK‐driven (physiologically‐based pharmacokinetics) feasibility and mechanistic sensitivity analyses for candidate triage and allometric and mechanism‐based translation for human exposure predictions. We also present a stepwise construction of exposure–response relationships bridging preclinical and clinical data and integration of efficacy and safety into a clinical utility index for dose optimization. Throughout, we argue that the central challenge for quantitative ADC development is not the absence of sophisticated models, but the disciplined integration of fit‐for‐purpose approaches into the decisions that shape the development path. We synthesize these ideas in the proposition of a preclinical clinical utility index (pCUI) as an efficacious lens for model‐informed ADC development: an integrative benefit–risk framework expanding on the established clinical utility index (CUI) that quantitatively assesses whether a therapeutic window plausibly exists before first‐in‐human dosing.