Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs.
Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs.
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DOI：
10.3390/ph14050472
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发表时间：
2021-05-17
期刊：
影响因子：
--
通讯作者：
Norris RA
中科院分区：
文献类型：
--
作者：
Beck TC;Beck KR;Morningstar J;Benjamin MM;Norris RA
在美国，每年约有2.8%的住院治疗是由于药物不良相互作用造成的，代表超过245，000例住院治疗。药物间相互作用通常由主要的细胞色素P450（CYP 450）抑制引起。为了减少不良相互作用的发生率，常规采用各种方法，例如改变药物给药方案和/或最小化处方药物的数量;然而，通常，在不影响治疗结果的情况下，无法实现药物数量的减少。近80%的药物由于药代动力学问题而在开发中失败，概述了在临床前药物设计期间检查细胞色素相互作用的重要性。在这篇综述中，我们研究了CYP 3A 4，2D 6，2C 19，2C 9和1A 2的小分子抑制剂的理化和结构特性。尽管细胞色素抑制剂往往具有不同的理化性质和结构特征，但这些描述符本身不足以预测主要的细胞色素抑制概率和亲和力。基于计算机模拟方法的机器学习可以用作预测抑制的更稳健和准确的方式。审查中强调了这些不同的方法。
Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.
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影响因子：
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