Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles,Journal of Medicinal Chemistry

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Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles
Journal of Medicinal Chemistry ( IF 6.8 ) Pub Date : 2017-05-05 00:00:00 , DOI: 10.1021/acs.jmedchem.6b01577
John M. Sanders ₁ , Douglas C. Beshore ₁ , J. Christopher Culberson ₁ , James I. Fells ₁ , Jason E. Imbriglio ₁ , Hakan Gunaydin ₁ , Andrew M. Haidle ₁ , Marc Labroli ₁ , Brian E. Mattioni ₁ , Nunzio Sciammetta ₁ , William D. Shipe ₁ , Robert P. Sheridan ₁ , Linda M. Suen ₁ , Andreas Verras ₁ , Abbas Walji ₁ , Elizabeth M. Joshi ₁ , Tjerk Bueters ₁

Affiliation

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure–activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.

中文翻译：

借助硅胶吸收，分布，代谢，排泄和毒性概况，告知筛选热门系列的选择

高通量筛选（HTS）使数百万种化合物的生物活性得以评估，但对命中系列进行优先排序仍然面临挑战。虽然通常使用生物学，吸收，分布，代谢，排泄和毒性（ADMET），纯度和结构数据来选择化学物质以进行进一步跟踪，但用于筛选命中物的历史ADMET数据的稀缺性限制了我们对早期命中的了解化合物。本文中，我们描述了一种利用一系列内部定量结构-活性关系（QSAR）模型为命中序列生成计算机模拟ADMET分布图的过程，以实现对HTS化学物质的更完整表征。这些配置文件使团队可以快速评估命中序列，以获得所需的ADMET属性或可能需要重大优化的可疑负债。因此，这些计算机模拟数据可以指导ADMET实验，并深刻影响命中系列的进展。提出了一些预期的例子，以证实这种方法的价值。

更新日期：2017-05-05

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