Croitoru Anastasia, Orr Asuka A, MacKerell Alexander D
Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
SilcsBio LLC, Baltimore, MD, USA.
Nat Chem Biol. 2025 Jun 27. doi: 10.1038/s41589-025-01950-z.
Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known chemical agents with the specificity of monoclonal antibodies, thereby maximizing efficacy while minimizing adverse side effects. Although multiple ADCs have made it to the marketplace, their development remains a challenge in part owing to the lack of three-dimensional (3D) structural information that must account for the inherent flexibility of monoclonal antibodies as well as that of the drug payloads. This Perspective discusses computational methods, including machine learning and physics-based approaches, that could facilitate the interpretation of experimental data, make predictions on optimal solutions concerning drug conjugate linker type, conjugation sites and drug/antibody ratios and minimize the number of design iterations during ADC development. We explore examples of how the information content from physics-based 3D molecular modeling and simulations on model ADCs may facilitate ADC design.
抗体药物偶联物（ADCs）是治疗多种癌症的一种有效治疗方法。它们将已知化学药剂的毒性与单克隆抗体的特异性相结合，从而在将副作用降至最低的同时最大限度地提高疗效。尽管多种ADC已进入市场，但它们的开发仍然是一项挑战，部分原因是缺乏三维（3D）结构信息，而这些信息必须考虑单克隆抗体以及药物payloads的固有灵活性。本观点讨论了计算方法，包括机器学习和基于物理的方法，这些方法可以促进对实验数据的解释，对关于药物偶联物连接子类型、偶联位点和药物/抗体比例的最佳解决方案进行预测，并在ADC开发过程中尽量减少设计迭代的次数。我们探讨了基于物理的3D分子建模和对模型ADC的模拟所提供的信息内容如何促进ADC设计的示例。
