Finding a needle in the haystack: ADME and pharmacokinetics/pharmacodynamics characterization and optimization toward orally available bifunctional protein degraders

Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate underlying principles and to derive rational optimization strategies. In this review the authors summarize current best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism and excretion properties including the current knowledge of Drug-Drug interactions. Finally, the authors describe the Pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects. Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the Central Nervous System will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can enrich the field further and offer interesting alternative approaches.降解剂（Degraders）是一类日益重要的小分子药物子类型，相关学术出版物数量持续增长，进入人体临床试验的候选药物分子也不断增多，足以印证其领域价值的快速提升。尽管如此，针对降解剂的ADME（吸收、分布、代谢、排泄，Absorption, Distribution, Metabolism, Excretion）与PK/PD（药代动力学/药效动力学，Pharmacokinetics/Pharmacodynamics）性质开展临床前优化，仍是一项极具挑战性的工作。目前已有大量研究投入到阐明降解剂的核心作用机制，并推导合理的优化策略当中。本综述中，作者总结了当前降解剂研究领域中体外实验与体内实验的最佳实践方案。此外，作者梳理并评述了当前学界对降解剂最优理化性质的认知，以及该性质对ADME性质的影响，其中涵盖了当前关于药物相互作用（Drug-Drug Interactions）的研究成果。最后，作者阐述了降解剂所特有的药代动力学预测与药代动力学/药效动力学相关概念，以及如何在研究项目中最佳落地这些理念。尽管该领域近年来已取得诸多进展，但持续开展相关研究仍将进一步加深我们对降解剂优化相关合理设计的认知。一旦可获取规模更大、稳定性更强的数据集，机器学习（Machine Learning）与计算方法的重要性将日益凸显。此外，组织靶向策略（尤其是针对中枢神经系统（Central Nervous System, CNS）的靶向策略）将得到更多研究关注，以阐明可利用催化作用模式的高效药物给药方案。最后，更多专业化策略（如共价降解剂、LOVdegs）将进一步丰富该领域，并提供极具潜力的新型研究方向。