SuperCYPsPred-a web server for the prediction of cytochrome activity
SuperCYPsPred-a web server for the prediction of cytochrome activity
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DOI：
10.1093/nar/gkaa166
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发表时间：
2020-07-02
影响因子：
14.9
通讯作者：
Preissner, Robert
中科院分区：
文献类型：
--
作者：
Banerjee, Priyanka;Dunkel, Mathias;Preissner, Robert
细胞色素P450酶（CYP）介导的药物代谢影响药物的药代动力学，并通过药物相互作用（DDI）导致患者的不良结局。吸收、分布、代谢、排泄和毒性（ADMET）问题是药物临床试验失败的主要原因。由于只有一半的批准药物的代谢细节是已知的，因此需要一种可靠预测CYP特异性的工具。SuperCYPsPred网络服务器目前专注于五种主要的CYP同工酶，包括CYP 1A 2，CYP 2C 19，CYP 2D 6，CYP 2C 9和CYP 3A 4，它们负责超过80%的临床药物代谢。CYP抑制分类的预测模型基于成熟的机器学习方法。该模型在交叉验证和外部验证集上进行了验证，并取得了良好的性能。网络服务器将2D化学结构作为输入，并使用不同的分子指纹、沿着的置信度评分、相似化合物、文献中发表的已知药物CYP信息、单个细胞色素的详细相互作用特征（包括DDI表和总体CYP预测雷达图）报告10种模型的化学品的CYP抑制特征（http：//insilico-cyp.charite.de/SuperCYPsPred/）。细胞色素P450酶（CYP）介导的药物代谢影响药物的药代动力学，并通过药物相互作用（DDI）导致患者的不良结局。吸收、分布、代谢、排泄和毒性（ADMET）问题是药物临床试验失败的主要原因。由于只有一半的批准药物的代谢细节是已知的，因此需要一种可靠预测CYP特异性的工具。SuperCYPsPred网络服务器目前专注于五种主要的CYP同工酶，包括CYP 1A 2，CYP 2C 19，CYP 2D 6，CYP 2C 9和CYP 3A 4，它们负责超过80%的临床药物代谢。CYP抑制分类的预测模型基于成熟的机器学习方法。该模型在交叉验证和外部验证集上进行了验证，并取得了良好的性能。网络服务器将2D化学结构作为输入，并使用不同的分子指纹、沿着的置信度评分、相似化合物、文献中发表的已知药物CYP信息、单个细胞色素的详细相互作用特征（包括DDI表和总体CYP预测雷达图）报告10种模型的化学品的CYP抑制特征（http：//insilico-cyp.charite.de/SuperCYPsPred/）。Web服务器不需要登录或注册，可以免费使用。
Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug-drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs-published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.