Targeted protein degraders (TPDs), an emerging therapeutic modality, are attracting considerable interest with the promise to address disease‐related proteins that are not druggable with conventional small molecule inhibitors. Despite their novel mechanism of action, the PK/PD relationship of degraders is still approached with a mindset deeply rooted in inhibitor drugs. Here, we establish how predictive mechanistic modeling specifically tailored to TPDs can significantly enhance the value of the available information during lead generation and optimization. By integrating the results from in vitro assays with routinely collected PK data, modeling accurately predicts degradation in vivo. These predictions transform the prioritization of compounds for in vivo studies as well as the selection of optimal dose schedules and most informative measurement time points with the least number of animals. Moreover, the comprehensive modeling framework (1) identifies the PK/PD driver of targeted protein degradation and subsequent downstream pharmacodynamic effects, and (2) uncovers the fundamental difference between degrader and inhibitor PK/PD relationships. The practical utility of our predictive modeling is demonstrated with relevant use cases. This framework will allow researchers to transition from current, mostly serendipity‐based approaches to more sound model‐informed decision making. Going forward, the presented predictive PK/PD modeling framework lays out a rational path to incorporate inter‐species differences in the pharmacology and thus promises to help with getting the dose right in clinical trials.

中文翻译：

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改变靶向蛋白质降解剂的发现：预测 PK/PD 建模的转化能力

靶向蛋白降解剂（TPD）是一种新兴的治疗方式，有望解决传统小分子抑制剂无法药物化的疾病相关蛋白，因此引起了人们极大的兴趣。尽管降解剂的作用机制新颖，但仍以抑制剂药物中根深蒂固的思维方式来处理降解剂的 PK/PD 关系。在这里，我们确定了专​​门针对 TPD 定制的预测机制建模如何在潜在客户生成和优化过程中显着提高可用信息的价值。通过整合结果体外使用常规收集的 PK 数据进行分析，建模准确预测降解体内。这些预测改变了化合物的优先顺序体内研究以及选择最佳剂量方案和用最少数量的动物提供最多信息的测量时间点。此外，综合建模框架 (1) 确定了靶向蛋白质降解的 PK/PD 驱动因素以及随后的下游药效学效应，(2) 揭示了降解剂和抑制剂 PK/PD 关系之间的根本区别。我们的预测模型的实用性通过相关用例得到了证明。该框架将使研究人员能够从当前主要基于偶然性的方法过渡到更合理的基于模型的决策。展望未来，所提出的预测 PK/PD 建模框架为纳入药理学中的物种间差异奠定了合理的路径，从而有望帮助在临床试验中获得正确的剂量。