Finding a needle in the haystack: ADME and pharmacokinetics/pharmacodynamics characterization and optimization towards 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 the underlying principles and to derive rational optimization strategies. In this review, the authors summarize currently 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–rug 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 further enrich the field and offer interesting alternative approaches.

降解剂（Degraders）是一类日益重要的小分子药物子模态，这一点可从持续增长的相关学术文献以及进入人体临床试验的临床候选分子中得到佐证。然而，针对这类分子的临床前吸收-分布-代谢-排泄（ADME）以及药代动力学-药效动力学（PK/PD）特性优化仍颇具挑战。当前已有大量研究工作致力于阐明其内在作用机制，并推导合理的优化策略。在本综述中，作者总结了当前体外实验（in vitro assays）与体内实验（in vivo experiments）领域的最佳实践规范。此外，作者还梳理并评述了当前学界对于最优理化特性及其对ADME特性的影响的认知，涵盖目前已知的药物-药物相互作用相关知识。最后，作者阐述了降解剂特有的药代动力学预测以及PK/PD相关概念，并介绍了如何在研究项目中最优地应用这些理念。尽管该领域近年来已取得诸多进展，但持续的研究将进一步深化我们对降解剂优化相关合理设计的认知。一旦获得规模更大、可靠性更强的数据集，机器学习与计算方法的重要性将愈发凸显。此外，组织靶向策略（尤其是针对中枢神经系统的相关策略）将得到更多研究，以阐明能够利用催化作用模式的高效药物治疗方案。最后，更多专业化策略（如共价降解剂、LOVdegs）可进一步丰富该领域的研究方向，并提供极具前景的替代研究路径。