PPubMed28 Jun 2026

Advances and Future Directions in Antibody–Drug Conjugates: From Paradigm Shifts to Data-Driven Design

Smita Kumari, Lillian M. Cool, Elizabeth Howard, Jogendra Singh Pawar, Georg Hempel

Abstract

Title: Simple Summary Antibody-drug conjugates are an important and rapidly evolving class of targeted cancer treatments that combine the precision of antibodies with the killing power of anticancer drugs. This review highlights how recent advances in target selection, antibody engineering, linker design, payload choice, and conjugation methods are improving the effectiveness and safety of these therapies. It also discusses current challenges such as toxicity, resistance, and manufacturing complexity, as well as the growing role of computational and artificial intelligence-based methods in guiding future development. By summarizing these advances, this review aims to provide a clear framework for designing the next generation of antibody-drug conjugates and to support future research across oncology and beyond.

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The review outlines recent ADC advances—target selection, engineering, linkers, payloads, and AI‑driven design—while highlighting safety, resistance, and manufacturing challenges.

What’s new

i.Comprehensive synthesis of recent advances in target selection, antibody engineering, linker design, payload choice, and conjugation methods for ADCs.
ii.Identification of emerging challenges such as toxicity, resistance, formulation stability, and manufacturing complexity.
iii.Discussion of the growing role of artificial intelligence and data‑driven approaches in ADC design and optimization.
iv.Provision of a framework to guide next‑generation ADC development across oncology and beyond.

How they did it

The authors performed an extensive literature review of recent preclinical and clinical studies, summarizing advances in ADC components and technologies. They integrated data from clinical trial tables, mechanistic studies, and computational research to construct a cohesive overview of the field.

What they found

Over 30 ADC candidates are in Phase III trials targeting HER2, HER3, EGFR, CD276, B7‑H4, FRα, DLL3, and PD‑L1.
Reported payload‑to‑antibody ratios (PAR) for these candidates range from 1.9 to 8.
AI‑based design tools are increasingly used to predict optimal target‑antibody‑payload combinations, accelerating candidate selection.

Caveats

✕Persistent toxicity and off‑target effects limit therapeutic windows.
✕Resistance mechanisms to ADCs remain incompletely understood.
✕Manufacturing and formulation challenges, especially for high‑concentration and subcutaneous products.
✕AI models require larger, high‑quality datasets to improve predictive accuracy.

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