背景：血清转氨酶，碱性磷酸酶和胆红素是用于DILI诊断的常用参数，分类，和预后。然而,临床检查的相关性，组织病理学和药物化学性质尚未得到充分研究。由于胆汁淤积是一种常见而复杂的DILI表现，我们的目标是研究临床特征和药物特性对药物引起的胆汁淤积（DIC）患者进行分层的相关性，并建立一个预后模型来识别高危患者和高度关注的药物。
方法：在七个数据库中通过关键字和布尔运算符搜索与DIC相关的文章。相关文章被上传到Sysrev，基于机器学习的平台，用于文章评论和数据提取。人口统计,临床,生物化学，收集肝脏组织病理学数据。从数据库或QSAR建模获得药物性质。进行统计分析和逻辑回归。
结果：收集了与52种药物相关的432例DIC患者的数据。纤维化与死亡密切相关，而小管缺乏和ALP与慢性相关。引起胆汁淤积的药物分为三个主要群体。纯胆汁淤积型分为两种亚型，预后不同，小管缺乏，纤维化，ALP和胆红素。建立了基于非侵入性参数和药物特性的DIC结果预测模型。结果表明，物理化学(pKa-a)和药代动力学(生物利用度，CYP2C9）属性影响DIC表型，并允许鉴定高度关注的药物。
结论：我们发现了DIC表现之间的新关联，并揭示了具有特定临床和化学特征的新DIC亚型。开发的预测DIC结果模型可以促进临床实践和药物分类中的DIC预后。
BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs.
METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article
review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed.
RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs.
CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.
