Review highlights the evolution of ADC conjugation techniques and proposes a practical selection framework for optimizing efficacy and safety.
Antibody–drug conjugates (ADCs) have received renewed attention in oncology following the clinical and commercial success of agents such as trastuzumab deruxtecan (Enhertu®), which underscored the clinical importance of coordinated optimization of the antibody, linker, payload, and conjugation strategy. Among these parameters, conjugation strategy strongly influences drug-to-antibody ratio (DAR) distribution, pharmacokinetics, manufacturability, and safety. Over the past two decades, ADC conjugation technologies have evolved from first-generation stochastic modification approaches to second-generation site-specific engineering strategies and, more recently, to third-generation site-selective platforms applicable to native antibodies. First-generation approaches remain the most clinically validated and have the strongest regulatory precedent despite their intrinsic heterogeneity. Second-generation approaches improve positional control and DAR homogeneity, but often increase demands on antibody engineering, expression, and process complexity. Third-generation approaches aim to preserve controlled conjugation while reducing sequence-engineering requirements, yet remain limited by a restricted number of accessible sites and less extensive clinical and regulatory experience. In this review, we summarize the evolution of ADC conjugation technologies, highlight representative clinical and translational examples, and discuss the major trade-offs of each platform. We also propose a practical selection framework based on payload properties, required DAR control, antibody stability, manufacturability, and regulatory considerations, with relevance to next-generation ADCs including dual-payload and bispecific formats. Overall, conjugation strategy should be selected according to the scientific, manufacturing, and regulatory requirements of each ADC program rather than on the basis of a presumed technological hierarchy.
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Kim et al. (2026) studied this question.
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