Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model that provides a preliminary estimate of the dose for a given exposure target based on chemical structure. First, we construct machine learning models to estimate rat pharmacokinetics, which are subsequently allometrically scaled to estimate human pharmacokinetics. Second, we establish a 10 nM free concentration target for early HDP where potency data are not yet available. Finally, we assess the uncertainty associated with each model and propagate these into the final estimate, providing us with actionable guidance on the level of accuracy of these estimates. We find that this strategy can reduce preparation of compounds with poor properties relative to an unstructured approach, but extensive experimental testing remains required.

中文翻译：

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在设计点进行剂量预测


人体剂量预测 （HDP） 是临床前药物发现中化合物优化的有用工具。我们在这里描述了我们独家的计算机 HDP 策略，用于对化合物设计进行分类以进行合成和实验分析。我们的目标是一个模型，根据化学结构为给定的暴露目标提供剂量的初步估计。首先，我们构建机器学习模型来估计大鼠药代动力学，随后对其进行异速生长缩放以估计人类药代动力学。其次，我们为尚无效力数据的早期 HDP 建立了一个 10 nM 的游离浓度目标。最后，我们评估与每个模型相关的不确定性，并将其传播到最终估计中，从而为我们提供关于这些估计准确性水平的可操作指导。我们发现，相对于非结构化方法，这种策略可以减少性能较差的化合物的制备，但仍需要进行广泛的实验测试。