In silico approaches to predicting drug metabolism, toxicology and beyond
In silico approaches to predicting drug metabolism, toxicology and beyond
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DOI：
10.1042/bst0310611
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发表时间：
2003-06-01
影响因子：
3.9
通讯作者：
Ekins, S
中科院分区：
文献类型：
--
作者：
Ekins, S
新候选药物的发现和优化越来越依赖于与药物代谢、毒理学和一般生物制药特性相关的实验和计算方法的结合。随着细胞色素- p450介导的药物-药物相互作用、代谢稳定性和毒理学分析的高通量分析的大量产出，我们有了更多的数据，这将有助于模型的建立。基于800多种结构不同分子的人肝微粒体代谢稳定性递归分配模型用于预测具有已知对数体外清除数据的分子（Spearman's rho -0.64, P < 0.0001）。此外，根据单独发表的数据，已经产生了66种钾通道人乙醚-甲gogo （hERG）抑制剂的定量结构-活性关系，这些抑制剂与最近一些药物的失败有关。该模型已通过进一步发表的25个分子的数据得到验证（Spearman's rho 0.83, P < 0.0001）。如果要从这些类型的计算模型中实现持续的价值，需要在新数据的验证和优化方面进行一些应用研究。一些相对简单的方法可能有价值，当涉及到结合多个模型的数据，以改善和集中在最有可能成功的分子上的药物发现。
The discovery and optimization of new drug candidates is becoming increasingly reliant upon the combination of experimental and computational approaches related to drug metabolism, toxicology and general biopharmaceutical properties. With the considerable output of high-throughput assays for cytochrome-P450-mediated drug-drug interactions, metabolic stability and assays for toxicology, we have orders of magnitude more data that will facilitate model building. A recursive partitioning model for human liver microsomal metabolic stability based on over 800 structurally diverse molecules was used to predict molecules with known log in vitro clearance data (Spearman's rho -0.64, P < 0.0001). in addition, with solely published data, a quantitative structure-activity relationship for 66 inhibitors of the potassium channel human ether-a-gogo (hERG) that has been implicated in the failure of a number of recent drugs has been generated. This model has been validated with further published data for 25 molecules (Spearman's rho 0.83, P < 0.0001). if continued value is to be realized from these types of computational models, there needs to be some applied research on their validation and optimization with new data. Some relatively simple approaches may have value when it comes to combining data from multiple models in order to improve and focus drug discovery on the molecules most likely to succeed.