Cherkas Yauheniya, McMillian Michael K, Amaratunga Dhammika, Raghavan Nandini, Sasaki Jennifer C
Janssen Research and Development, LLC, Spring House, PA, USA.
Janssen Research and Development, LLC, Raritan, NJ 08869, USA.
Toxicol Rep. 2016 Jan 18;3:252-261. doi: 10.1016/j.toxrep.2016.01.009. eCollection 2016.
As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel "aggregating bundles of clusters" (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs--indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3'-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.
随着传统毒理基因组学数据库的出现，改进的数据挖掘方法现已成为提取和可视化毒物与基因表达之间微妙关系的关键。在本研究中，一种新颖的“聚类束聚合”（ABC）程序被应用于强生（杨森）大鼠肝脏毒理基因组学数据库 [3] 中区分胆汁淤积性药物与非胆汁淤积性药物及模型毒物。药物性胆汁淤积是一个重要问题，特别是当一种新化合物带着这种不良反应进入市场时，标准的临床前模型常常会错误预测这种毒性。从先前杨森内部的基因表达特征中选择了三个特征明确的胆汁淤积反应基因（Cyp7a1、Mrp3 和 Bsep）；这三个基因在我们数据库中的 90 多种典型化合物中表现出不同的、非冗余的反应。使用 ABC 程序，在比较化合物基因反应时将无关贡献最小化。所有基因都被赋予与其与 Cyp7a1、Mrp3 和 Bsep 的相关性成比例的权重，并使用重采样技术得出化合物相似性的稳定度量。已知与大鼠胆汁淤积相关的化合物相对于彼此通常具有较小的该度量值，但相对于非胆汁淤积性化合物也具有较大的该度量值。用 ABC 衍生特征对数据进行可视化显示，有一组紧密的、行为基本相同的人类胆汁淤积性药物和实验性胆汁淤积性毒物（乙炔雌二醇、脂多糖、α-萘异硫氰酸酯和亚甲基二苯胺、双硫仑、纳曲酮、美吡拉敏、非那西丁、α-甲基多巴、氟他胺、非甾体抗炎药——吲哚美辛、氟比洛芬、双氯芬酸、氟芬那酸、舒林酸和尼美舒利、丁基羟基甲苯、胡椒基丁醚和溴苯），一些活性稍低的化合物（3'-乙酰氨基芴、安吖啶、肼苯哒嗪、鞣酸），一些行为非常不同且与非胆汁淤积性和胆汁淤积性药物都不同的药物（酮康唑、双嘧达莫、赛庚啶和苯胺），以及许多推测的人类胆汁淤积性药物，它们在大鼠中未显示出胆汁淤积的证据（氯丙嗪、红霉素、烟酸、卡托普利、氨苯砜、利福平、格列本脲、辛伐他汀、呋塞米、他莫昔芬和磺胺甲恶唑）。其他研究小组之前也指出，这些药物中的大多数仅在人类中表现出胆汁淤积。这项工作的结果表明，ABC 程序和类似的统计方法有助于整合数据，以便在毒理基因组学数据库之间比较毒物，提取反应之间的相似性并减少无法解释的数据变异。
