Center of Excellence for Bioinformatics, Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States.
Chem Res Toxicol. 2012 Jan 13;25(1):122-9. doi: 10.1021/tx200320e. Epub 2011 Dec 13.
Around 40% of drug-induced liver injury (DILI) cases are not detected in preclinical studies using the conventional indicators. It has been hypothesized that genomic biomarkers will be more sensitive than conventional markers in detecting human hepatotoxicity signals in preclinical studies. For example, it has been hypothesized and demonstrated in some cases that (1) genomic biomarkers from the rat liver can discriminate drug candidates that have a greater or lesser potential to cause DILI in susceptible patients despite no conventional indicators of liver toxicity being observed in preclinical studies, and (2) more sensitive biomarkers for early detection of DILI can be derived from a "subtoxic dose" at which the injury in the liver occurs at the molecular but not the phenotypic level. With a public TGx data set derived from short-term in vivo studies using rats, we divided drugs exhibiting human hepatotoxicity into three groups according to whether elevated alanine aminotransferase (ALT) or total bilirubin (TBL) were observed in the treated rats: (A) The elevation was observed in the treated rats, (B) no elevation was observed for all of the treated rats, and (C) no elevation could be observed at a lower dose and shorter duration but occur when a higher or longer treatment was applied. A control group (D) was comprised of drugs known not to cause human hepatotoxicity and for which no rats exhibited elevated ALT or TBL. We developed classifiers for groups A, B, and C against group D and found that the gene signature from scenario A could achieve 83% accuracy for human hepatotoxicity potential of drugs in a leave-one-compound-out cross-validation process, much higher than scenarios B (average 45%) and C (61%). Furthermore, the signature derived from scenario A exhibited relevance to hepatotoxicity in a pathway-based analysis and performed well on two independent public TGx data sets using different chemical treatments and profiled with different microarray platforms. Our study implied that the human hepatotoxicity potential of a drug can be reasonably assessed using TGx analysis of short-term in vivo studies only if it produces significant elevation of ALT or TBL in the treated rats. The study further revealed that the value of "sensitive" biomarkers derived from scenario C was not promising as expected for DILI assessment using the reported TGx design. The study will facilitate further research to understand the role of genomic biomarkers from rats for assessing human hepatotoxicity.
大约 40%的药物性肝损伤(DILI)病例在使用传统指标的临床前研究中未被发现。有人假设,基因组生物标志物在临床前研究中检测人类肝毒性信号方面将比传统标志物更敏感。例如,在某些情况下已经假设并证明了(1)来自大鼠肝脏的基因组生物标志物可以区分那些在易感患者中具有更大或更小引起 DILI 潜力的药物候选物,尽管在临床前研究中没有观察到任何肝毒性的传统指标,以及(2)可以从“亚毒性剂量”中得出更敏感的生物标志物,以早期检测 DILI,其中损伤发生在分子水平而不是表型水平。使用源自大鼠短期体内研究的公开 TGx 数据集,我们根据在处理大鼠中观察到的丙氨酸氨基转移酶(ALT)或总胆红素(TBL)升高与否,将表现出人类肝毒性的药物分为三组:(A)在处理大鼠中观察到升高,(B)所有处理大鼠均未观察到升高,以及(C)在较低剂量和较短时间内无法观察到升高,但在较高或较长的治疗时会发生。对照组(D)由已知不会引起人类肝毒性且无大鼠表现出 ALT 或 TBL 升高的药物组成。我们针对组 D 开发了针对组 A、B 和 C 的分类器,并发现情景 A 的基因特征在化合物逐个排除的交叉验证过程中可以实现 83%的药物人类肝毒性潜力的准确性,远高于情景 B(平均 45%)和 C(61%)。此外,从情景 A 获得的特征在基于途径的分析中与肝毒性相关,并且在使用不同化学处理和不同微阵列平台进行分析的两个独立的公开 TGx 数据集上表现良好。我们的研究表明,如果药物在处理大鼠中导致 ALT 或 TBL 显著升高,则可以合理地使用 TGx 分析短期体内研究来评估药物的人类肝毒性潜力。该研究进一步表明,对于使用报告的 TGx 设计进行 DILI 评估,来自情景 C 的“敏感”生物标志物的价值并不如预期的那样有希望。该研究将有助于进一步研究,以了解大鼠基因组生物标志物在评估人类肝毒性方面的作用。