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This Expert Insights eBook hopes to provide scientists with comprehensive strategies for overcoming biological and manufacturing challenges in ADC development, allowing you to advance precision oncology therapeutics through rational design, efficient production, and rigorous quality characterization.
Key Learning Points:
- Machine learning can rationally design optimized cytotoxic payloads with enhanced bystander effects to target antigen-heterogeneous tumors
- One-pot synthesis enables direct ADC preparation from unpurified antibodies, dramatically simplifying manufacturing workflows
- Advanced peptide mapping with high-resolution mass spectrometry provides precise site-specific characterization essential for quality control
- Integration of computational design, process engineering, and analytical precision accelerates next-generation ADC development
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More Information
Antibody-drug conjugates (ADCs) represent a precision medicine breakthrough in cancer therapy, combining the targeting specificity of monoclonal antibodies with potent cytotoxic drugs. This three-component system—antibody, linker, and payload—delivers chemotherapy directly to tumor cells while minimizing systemic toxicity. Despite over 15 FDA-approved ADCs showing clinical success, two critical challenges remain: tumor heterogeneity, where variable antigen expression limits efficacy, and complex manufacturing processes that increase costs and development timelines. This eBook presents innovations addressing both challenges through AI-guided payload design for enhanced bystander killing, streamlined one-pot synthesis, and advanced analytical characterization methods.
Articles contained in the collection:
- Guo, Y. et al. (2024). Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors. Advanced Science. https://doi.org/10.1002/advs.202306309.
- Lu, M. et al. (2025). Direct Preparation of Site-Specific Antibody-Drug Conjugates with Unpurified Antibodies in Culture Medium. Chembiochem. https://doi.org/10.1002/cbic.202401082.
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