Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.
The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.
Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.
These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.
The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.
Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.
These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

药物性肝损伤（Drug-induced liver injury, DILI）是导致药物研发终止及药物撤市的主要原因，但目前尚无针对DILI风险评估的金标准方法。本研究团队此前已发现DILI与CYP1A1或CYP1B1抑制作用之间存在关联，在此基础上，我们进一步采用决策树分析方法，评估了细胞色素P450（cytochrome P450, P450）抑制试验数据用于DILI风险评估的应用价值。
本研究采用重组酶与发光底物，对存在DILI担忧的药物（DILI药物）及无DILI担忧药物（no-DILI drugs）对10种人源P450的抑制活性进行了评估。同时，采用HepG2细胞对这些药物开展细胞毒性试验与高内涵分析，并通过alvaDesc软件计算分子描述符。
本研究以是否包含P450抑制活性的数据作为变量，开展决策树分析以区分DILI药物与无DILI担忧药物。当纳入P450抑制活性数据时，模型的分类准确率显著提升。在完成决策树初步分类后，再通过P450抑制活性对药物进行二次区分。结果显示，通过引入P450抑制试验数据，可正确区分大量假阳性及假阴性药物。
上述结果表明，P450抑制试验数据可有效用于DILI风险评估。