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首页> 美国卫生研究院文献>Drug Design Development and Therapy >Provisional in-silico biopharmaceutics classification (BCS) to guide oral drug product development
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Provisional in-silico biopharmaceutics classification (BCS) to guide oral drug product development

机译：临时硅内生物制药分类（BCS）指导口服药物产品的开发

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The main objective of this work was to investigate in-silico predictions of physicochemical properties, in order to guide oral drug development by provisional biopharmaceutics classification system (BCS). Four in-silico methods were used to estimate LogP: group contribution (CLogP) using two different software programs, atom contribution (ALogP), and element contribution (KLogP). The correlations (r2) of CLogP, ALogP and KLogP versus measured LogP data were 0.97, 0.82, and 0.71, respectively. The classification of drugs with reported intestinal permeability in humans was correct for 64.3%–72.4% of the 29 drugs on the dataset, and for 81.82%–90.91% of the 22 drugs that are passively absorbed using the different in-silico algorithms. Similar permeability classification was obtained with the various in-silico methods. The in-silico calculations, along with experimental melting points, were then incorporated into a thermodynamic equation for solubility estimations that largely matched the reference solubility values. It was revealed that the effect of melting point on the solubility is minor compared to the partition coefficient, and an average melting point (162.7°C) could replace the experimental values, with similar results. The in-silico methods classified 20.76% (±3.07%) as Class 1, 41.51% (±3.32%) as Class 2, 30.49% (±4.47%) as Class 3, and 6.27% (±4.39%) as Class 4. In conclusion, in-silico methods can be used for BCS classification of drugs in early development, from merely their molecular formula and without foreknowledge of their chemical structure, which will allow for the improved selection, engineering, and developability of candidates. These in-silico methods could enhance success rates, reduce costs, and accelerate oral drug products development.
机译：这项工作的主要目的是调查物理化学特性的计算机模拟预测，以便通过临时生物药物分类系统（BCS）指导口服药物的开发。使用四种计算机模拟方法来估计LogP：使用两个不同的软件程序进行的基团贡献（CLogP），原子贡献（ALogP）和元素贡献（KLogP）。 CLogP，ALogP和KLogP与测得的LogP数据的相关性（r 2 ）分别为0.97、0.82和0.71。报告的人类肠道通透性药物的分类对于数据集上29种药物的64.3％–72.4％是正确的，对于使用不同的计算机模拟算法被动吸收的22种药物中的81.82％–90.91％是正确的。使用多种计算机模拟方法可获得相似的渗透性分类。然后将硅内计算以及实验熔点合并到热力学方程式中，以进行溶解度估算，该溶解度估算值与参考溶解度值基本匹配。结果表明，与分配系数相比，熔点对溶解度的影响较小，平均熔点（162.7℃）可以代替实验值，结果相似。硅片内方法将27.66％（±3.07％）归为1类，41.51％（±3.32％）归为2类，30.49％（±4.47％）归为3类和6.27％（±4.39％）归为4类总之，计算机内方法可用于早期开发中的BCS药物分类，仅从分子式出发，无需预知其化学结构，这将改善候选物的选择，工程和可开发性。这些计算机内方法可以提高成功率，降低成本并加快口服药物产品的开发。

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