Exploring Experimental and In Silico Approaches for Antibody-Drug Conjugates in Oncology Therapies

Antibody-drug conjugates are a rapidly evolving class of cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic drugs. This review explores experimental and computational advances in ADC design, focusing on structural elements and optimization strategies.

We examined recent developments in the mechanisms of action, antibody engineering, linker chemistries, and payload selection. Emphasis was placed on experimental strategies and computational tools, including molecular modeling and AI-driven structure prediction.

ADCs function through both internalization-dependent and -independent mechanisms, enabling targeted drug delivery and bystander effects. The therapeutic efficacy of ADCs depends on key factors: antigen specificity, linker stability, and payload potency. Linkers are categorized as cleavable or non-cleavable, each with distinct advantages. Payloads-mainly tubulin inhibitors and DNA-damaging agents-require extreme potency to be effective. Computational methods have become essential for antibody modeling, developability assessment, and in silico optimization of ADC components, accelerating candidate selection and reducing experimental labor.

The integration of experimental and in silico approaches enhances ADC design by improving selectivity, stability, and efficacy. These strategies are critical for advancing next-generation ADCs with broader applicability and improved therapeutic indices.