Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.
Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.
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DOI：
10.1021/acs.molpharmaceut.2c00698
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发表时间：
2023-01-02
影响因子：
4.9
通讯作者：
Mizuguchi, Kenji
中科院分区：
文献类型：
--
作者：
Watanabe, Reiko;Kawata, Toshio;Ueda, Shinya;Shinbo, Takumi;Higashimori, Mitsuo;Natsume-Kitatani, Yayoi;Mizuguchi, Kenji
细胞色素P450等代谢酶对体内清除的贡献率(Fm)是一个药代动力学指标，对于定量评价药物与药物的相互作用尤为重要。由于获得体内实验的FM值具有挑战性，因此从体外实验中获得的FM值经常被交替使用。本研究旨在探索仅利用化学结构信息构建预测体内FM的机器学习模型的可能性。我们从公共数据库中收集了319个化合物的在体FM值和化学结构，并使用几种机器学习方法构建了预测模型。结果表明，仅根据结构信息即可获得体内FM值，其预测性能与体外实验值相当，对参与诱导或抑制CYP的化合物的预测精度显着高于体外预测值。我们在药物发现早期阶段预测体内FM值的新方法应该有助于提高药物优化过程的效率。
The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug–drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.
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影响因子：
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作者：
Kobayashi S;Kane TB;Paton C
通讯作者：
Paton C
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.
影响因子：
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作者：
Paton, Chris;Kobayashi, Shinji
通讯作者：
Kobayashi, Shinji
影响因子：
8.6
作者：
Moriwaki H;Tian YS;Kawashita N;Takagi T
通讯作者：
Takagi T
影响因子：
8.6
作者：
Hinselmann G;Rosenbaum L;Jahn A;Fechner N;Zell A
通讯作者：
Zell A