Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced Studies, Kasetsart University, Bangkok, 10900, Thailand.
Mol Inform. 2019 Oct;38(10):e1900059. doi: 10.1002/minf.201900059. Epub 2019 Aug 2.
Volume of distribution (Vd ) is a measure of how effectively a drug molecule is distributed throughout the body. Along with the clearance, it determines the half-life and therefore the drug dosing interval. A number of different pre-clinical approaches are available to predict the Vd in human including quantitative structure activity relationship (QSAR) models. Vd QSAR models have been reported for human and rat, but not important pre-clinical species including dog, mouse and monkey. In this study, we have generated Vd QSAR model on the human and commonly used pre-clinical species, each of which differs in terms of size, chemical diversity and data quality. We discuss the model performance by species, assess the effect the domain of applicability and the relative merits of building chemical series-specific models. In addition, we compare the intrinsic variability of the experimental logVd data (∼1.2 fold error) to in-vivo interspecies differences (∼2 fold error) and in silico based models (∼3 fold error). This prompted us to explore whether one species could be used to predict another, particularly where little data for that species is available. i. e. does the expansion in domain of applicability prove beneficial over and above any deterioration due to the use of response values from an alternative species.
分布容积(Vd)是衡量药物分子在体内分布效率的指标。与清除率一起,它决定了半衰期,从而决定了药物的给药间隔。有许多不同的临床前方法可用于预测人体中的 Vd,包括定量构效关系(QSAR)模型。已经报道了用于人和大鼠的 Vd QSAR 模型,但对于包括狗、鼠和猴在内的重要临床前物种尚未报道。在这项研究中,我们为人类和常用的临床前物种生成了 Vd QSAR 模型,每个模型在大小、化学多样性和数据质量方面都有所不同。我们按物种讨论了模型性能,评估了适用域的影响以及构建化学系列特异性模型的相对优点。此外,我们将实验 logVd 数据的固有可变性(约 1.2 倍误差)与体内种间差异(约 2 倍误差)和基于计算的模型(约 3 倍误差)进行了比较。这促使我们探讨是否可以使用一种物种来预测另一种物种,特别是在该物种的数据很少的情况下。即,适用域的扩展是否比由于使用替代物种的响应值而导致的任何恶化更有益。