Capuzzi Stephen J, Muratov Eugene N, Tropsha Alexander
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
J Chem Inf Model. 2017 Mar 27;57(3):417-427. doi: 10.1021/acs.jcim.6b00465. Epub 2017 Feb 25.
The use of substructural alerts to identify Pan-Assay INterference compoundS (PAINS) has become a common component of the triage process in biological screening campaigns. These alerts, however, were originally derived from a proprietary library tested in just six assays measuring protein-protein interaction (PPI) inhibition using the AlphaScreen detection technology only; moreover, 68% (328 out of the 480 alerts) were derived from four or fewer compounds. In an effort to assess the reliability of these alerts as indicators of pan-assay interference, we performed a large-scale analysis of the impact of PAINS alerts on compound promiscuity in bioassays using publicly available data in PubChem. We found that the majority (97%) of all compounds containing PAINS alerts were actually infrequent hitters in AlphaScreen assays measuring PPI inhibition. We also found that the presence of PAINS alerts, contrary to expectations, did not reflect any heightened assay activity trends across all assays in PubChem including AlphaScreen, luciferase, beta-lactamase, or fluorescence-based assays. In addition, 109 PAINS alerts were present in 3570 extensively assayed, but consistently inactive compounds called Dark Chemical Matter. Finally, we observed that 87 small molecule FDA-approved drugs contained PAINS alerts and profiled their bioassay activity. Based on this detailed analysis of PAINS alerts in nonproprietary compound libraries, we caution against the blind use of PAINS filters to detect and triage compounds with possible PAINS liabilities and recommend that such conclusions should be drawn only by conducting orthogonal experiments.
使用亚结构警报来识别泛分析干扰化合物(PAINS)已成为生物筛选活动中分类过程的常见组成部分。然而,这些警报最初源自一个仅在六种使用AlphaScreen检测技术测量蛋白质-蛋白质相互作用(PPI)抑制的分析中测试过的专有库;此外,68%(480个警报中的328个)源自四种或更少的化合物。为了评估这些警报作为泛分析干扰指标的可靠性,我们利用PubChem中的公开可用数据,对PAINS警报对生物分析中化合物混杂性的影响进行了大规模分析。我们发现,所有含有PAINS警报的化合物中,大多数(97%)实际上在测量PPI抑制的AlphaScreen分析中是不常见的命中物。我们还发现,与预期相反,PAINS警报的存在并未反映出PubChem中包括AlphaScreen、荧光素酶、β-内酰胺酶或基于荧光的分析在内的所有分析中的任何增强的分析活性趋势。此外,在3570种经过广泛分析但始终无活性的化合物(称为暗化学物质)中存在109个PAINS警报。最后,我们观察到87种美国食品药品监督管理局(FDA)批准的小分子药物含有PAINS警报,并对它们的生物分析活性进行了分析。基于对非专有化合物库中PAINS警报的详细分析,我们告诫不要盲目使用PAINS过滤器来检测和分类可能具有PAINS风险的化合物,并建议只有通过进行正交实验才能得出这样的结论。