Design to Dose: Integrating Quantitative Insights Across the Antibody Drug Conjugate (ADC) Development Lifecycle
4 affiliations
- 1Global PK/PD and Pharmacometrics Eli Lilly and Company Bracknell UK
- 2PK Sciences, Translational Medicine Novartis Biomedical Research Cambridge Massachusetts USA
- 3Clinical Pharmacology, Pharmacometrics, & Translational Sciences MapLight Therapeutics, Inc. Redwood City California USA
- 4Immunology Development Novartis Pharmaceuticals UK Ltd London UK
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.
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BibTeX
@article{bending2026design,
title = {{Design to Dose: Integrating Quantitative Insights Across the Antibody Drug Conjugate (ADC) Development Lifecycle}},
author = {Daniel Bending and Eshita Khera and Inez Lam and Innocent G. Asiimwe and Venkatesh Pilla Reddy},
journal = {Clinical and Translational Science},
year = {2026},
month = nov,
doi = {10.1111/cts.70768},
url = {https://doi.org/10.1111/cts.70768},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Design to Dose: Integrating Quantitative Insights Across the Antibody Drug Conjugate (ADC) Development Lifecycle
AU - Daniel Bending
AU - Eshita Khera
AU - Inez Lam
AU - Innocent G. Asiimwe
AU - Venkatesh Pilla Reddy
PY - 2026
DA - 2026/11/03
JO - Clinical and Translational Science
DO - 10.1111/cts.70768
UR - https://doi.org/10.1111/cts.70768
AB - 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.
SN - 1752-8054
SN - 1752-8062
ER -