药物引起的肝损伤预测:扩展清除模型及其用于前瞻性转运体和酶为基础的肝细胞应激分级

G. Camenisch
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引用次数: 1

摘要

许多参与药物肝脏清除的酶和转运体也在内源性化合物转运中发挥重要作用。一些活性机制的抑制经常被证明与药物性肝损伤(DILI)有关。扩展清除模型(ECM)描述了驱动肝脏清除的不同过程之间复杂的相互作用,即正弦摄取和外排、小管分泌和细胞内代谢。基于ECM,我们导出了一个整体概念(称为1/ r值方法)来定量描述潜在候选药物对内源性和安全相关化合物的运输和代谢的活性过程的总体抑制效力。对于具有基本完整的体外抑制和体内暴露数据的小型内部化合物训练集,实现了基于ecm的DILI准确预测。此外,通过外部化合物的综合验证集预测了几例DILI,没有出现主要的假阳性结果。然而,由于公共空间中可用的所需输入信息普遍不完整(这是测试集中大量假阴性的最可能原因),ECM用于大规模预测细胞应激介导的DILI的总体合法性仍有待证明。因此,为了推进和加速这一令人兴奋但复杂的领域的科学,迫切需要并应鼓励更加透明和开放的数据共享。
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Drug-Induced Liver Injury Predictions: Extended Clearance Model and Its Use for Prospective Transporter and Enzyme-Based Hepatic Cell Stress Grading
Many enzymes and transporters involved in the hepatic clearance of drugs also play an important role in endogenous compound transport. Inhibition of some of these active mechanisms has frequently been shown to be associated with Drug-Induced Liver Injury (DILI). The Extended Clearance Model (ECM) describes the complex interplay between the different processes driving hepatic clearance, namely sinusoidal uptake and efflux, canalicular secretion and intracellular metabolism. Based on the ECM, we have derived an integral concept (referred as 1/R-value approach) to quantitatively describe the overall inhibition potency of potential drug candidates on active processes involved in the transport and metabolism of endogenous and safety-relevant compounds. For a small training set of in-house compounds with largely complete in vitro inhibition and in vivo exposure data, accurate ECM-based prediction of DILI was realized. Additionally, prediction of several cases of DILI for a comprehensive validation set of external compounds was achieved with no major false-positive results. However, due to general incompleteness of the required input information available in the public space (the most probable reason for the large number of false-negatives in the test set) the overall legitimacy of ECM for large-scale prediction of cell stress mediated DILI still needs to be demonstrated. In order to advance and accelerate science in this exciting but complex field, a more transparent and open sharing of data is therefore urgently needed and should be encouraged.
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