The drug development landscape is expanding to include drug modalities such as PROteolysis-TArgeting Chimeras (PROTACs) and peptides, offering possibilities for previously intractable biological targets. However, with their size and chemical nature, they diverge from established frameworks for the prediction of oral bioavailability. This evolution to larger and more complex molecules necessitates new methodologies and prediction models to continuously expand on bioavailability guidelines. We describe the high-capacity adoption of two chromatographic physicochemical assays and their application for iterative compound optimization to achieve oral bioavailability. We further describe how these data underpin the continuous refinement of internal machine learning models, which guide compound synthesis decisions in the molecular design phase. Based on data for a set of 691 PROTACs, and two project examples, we confirm a sweet spot for oral bioavailability at log D values higher than the norm for small molecules and show how experimental data and prediction models synergize to effectively drive chemistry optimization.

中文翻译：

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闭合设计-制造-测试-分析循环：实验和预测之间的相互作用推动 PROTAC 的生物利用度


药物开发领域正在扩大，包括蛋白水解-TArgeting Chimeras （PROTAC） 和肽等药物模式，为以前难以处理的生物靶点提供了可能性。然而，由于它们的大小和化学性质，它们与预测口服生物利用度的既定框架不同。这种向更大、更复杂的分子的演变需要新的方法和预测模型，以不断扩展生物利用度指南。我们描述了两种色谱物理化学测定的高容量采用及其在迭代化合物优化中实现口服生物利用度的应用。我们进一步描述了这些数据如何支持内部机器学习模型的持续改进，这些模型指导分子设计阶段的化合物合成决策。根据一组 691 个 PROTAC 的数据和两个项目示例，我们确认了 log D 值高于小分子标准值时口服生物利用度的最佳点，并展示了实验数据和预测模型如何协同作用以有效推动化学优化。