Predicting liver cytosol stability of small molecules
Predicting liver cytosol stability of small molecules
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DOI：
10.1186/s13321-020-00426-7
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发表时间：
2020-04-07
影响因子：
8.6
通讯作者：
Dac-Trung Nguyen
中科院分区：
文献类型：
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作者：
Shah, Pranav;Siramshetty, Vishal Babu;Dac-Trung Nguyen
在过去的几十年里，化学家们已经熟练地设计出避免细胞色素P（CYP）450介导的代谢的化合物。典型的筛选试验在肝微粒体组分中进行，并且可能忽略胞质酶的贡献，直到药物发现过程的后期。关于胞质酶介导的代谢的数据很少，化学家也没有可靠的工具来帮助设计远离这种责任。在这项研究中，我们筛选了1450种化合物的肝细胞溶质介导的代谢稳定性和提取的转换规则，可能有助于药物化学家在优化这些负债的化合物。通过在小鼠（CD-1雄性）和人（混合性别）胞质溶胶组分中进行内部实验，收集体外半衰期数据。匹配的分子对分析结合定性-构效关系建模进行，以确定影响胞质稳定性的化学结构转化。在测试集上对转换规则进行了前瞻性验证。此外，选定的规则进行了验证不同的化学库和实验测试，以确认是否可以概括所确定的转换得到的对。验证结果包括近250种库化合物和相应的半衰期数据，可供公众使用。数据集还用于基于不同的分子描述符和机器学习方法生成计算机分类模型，以预测胞质溶胶介导的负债。据我们所知，这是第一个系统的计算机模拟的努力，以解决胞质酶介导的负债。
Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities. In this study, we screened 1450 compounds for liver cytosol-mediated metabolic stability and extracted transformation rules that might help medicinal chemists in optimizing compounds with these liabilities. In vitro half-life data were collected by performing in-house experiments in mouse (CD-1 male) and human (mixed gender) cytosol fractions. Matched molecular pairs analysis was performed in conjunction with qualitative-structure activity relationship modeling to identify chemical structure transformations affecting cytosolic stability. The transformation rules were prospectively validated on the test set. In addition, selected rules were validated on a diverse chemical library and the resulting pairs were experimentally tested to confirm whether the identified transformations could be generalized. The validation results, comprising nearly 250 library compounds and corresponding half-life data, are made publicly available. The datasets were also used to generate in silico classification models, based on different molecular descriptors and machine learning methods, to predict cytosol-mediated liabilities. To the best of our knowledge, this is the first systematic in silico effort to address cytosolic enzyme-mediated liabilities.