Validation of Multivariate Outlier Detection Analyses Used to Identify Potential Drug-Induced Liver Injury in Clinical Trial Populations
Validation of Multivariate Outlier Detection Analyses Used to Identify Potential Drug-Induced Liver Injury in Clinical Trial Populations
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DOI：
10.2165/11632670-000000000-00000
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发表时间：
2012-01-01
期刊：
影响因子：
4.2
通讯作者：
Lee, Kwan
中科院分区：
文献类型：
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作者：
Lin, Xiwu;Parks, Daniel;Lee, Kwan
背景：在临床试验中，ALT>；3 x正常值上限(ULN)和总胆红素2 x ULN被确定为潜在的严重肝损伤，并被美国FDA称为‘Hy定律’。然而，在临床试验人群中，这些阈值的证据或验证有限。使用来自临床试验的肝脏化学数据，以统计稳健的方式，通过截断稳健的多变量离群值检测(TRMOD)经验地建立决策边界，然后与这些固定阈值进行比较。目的：本研究的目的是检验和验证在一般健康的临床试验人群(即没有潜在肾脏疾病、血液疾病或肝脏疾病的人群)中严重肝损伤的固定阈值和经验推导阈值的性能。方法：使用II-IV期临床试验数据，使用异常值检测方法对ALT和总胆红素数据进行分析，以与FDA Hy定律的经验推导阈值和固定阈值进行比较，然后用FDA的药物所致严重肝毒性评估(EDISH)评估Fold-ULN以及改进的eDISH(MDISH)来评估Fold-Baseline肝脏化学反应，并以图形方式进行评估。GlaxoSmithKline进行的28项II-IV期临床试验的数据由TRMOD算法汇总和分析，以创建决策边界。数据包括18672名女性受试者，她们的平均年龄为44岁，并且没有已知的肝病。结果：在一般健康的临床试验受试者中，经验性地得出的TRMOD界限近似等于‘Hy’s定律‘。识别异常值的TRMOD界限是ALT极限3.4×ULN和胆红素极限2.1×ULN，而FDA的‘Hy定律’是3×ULN和2×ULN。在28项研究中观察到了实验室间的数据差异，并通过使用基线校正的数据来减小这些差异。通过将TRMOD应用于基线校正的数据，这些界限成为3.8x基线的ALT界限和4.8x基线的胆红素界限。检查了随时间确定的肝脏信号的累积关联曲线图。TRMOD分析确定了标准边界和异常值，为在类似试验人群中检测肝脏信号提供了比较数据。结论：对一般健康受试者的临床试验数据进行的TRMOD肝化学分析证实，FDA的Hy定律阈值是检测肝脏安全异常值的可靠手段。TRMOD对肝脏化学数据的评估，通过折叠-ULN和折叠-基线，提供了补充分析和有价值的标准化数据，用于在相似的患者群体中进行比较。当来自相似患者群体的新的临床试验数据位于这些标准边界内时，没有肝脏信号。基线校正数据的使用减少了实验室间的差异，并可能对可能的药物影响更加敏感。我们建议使用ULN校正数据(EDISH)和基线校正数据(MDISH)的图形描述来检查肝脏化学，作为补充方法。
Background: Potential severe liver injury is identified in clinical trials by ALT >3 x upper limits of normal (ULN) and total bilirubin >2 x ULN, and termed 'Hy's Law' by the US FDA. However, there is limited evidence or validation of these thresholds in clinical trial populations. Using liver chemistry data from clinical trials, decision boundaries were built empirically with truncated robust multivariate outlier detection (TRMOD), in a statistically robust manner, and then compared with these fixed thresholds. Additionally, as the analysis of liver chemistry change from baseline has been recently suggested for the identification of liver signals, fold-baseline data was also assessed.Objective: The aim of the study was to examine and validate the performance of fixed and empirically derived thresholds for severe liver injury in generally healthy clinical trial populations (i.e. populations without underlying renal, haematological or liver disease).Methods: Using phase II-IV clinical trial data, ALT and total bilirubin data were analysed using outlier detection methods to compare with empirically derived and fixed thresholds of the FDA's Hy's Law limits, which were then assessed graphically with the FDA's evaluation of Drug-Induced Serious Hepatotoxicity (eDISH) assessing fold-ULN, as well as a modified eDISH (mDISH) to assess fold-baseline liver chemistries. Data from 28 phase II-IV clinical trials conducted by GlaxoSmithKline were aggregated and analysed by the TRMOD algorithm to create decision boundaries. The data consisted of 18 672 predominantly female subjects with a mean age of 44 years and without known liver disease.Results: Among generally healthy clinical trial subjects, the empirically-derived TRMOD boundaries were approximately equivalent to 'Hy's Law'. TRMOD boundaries for identifying outliers were an ALT limit of 3.4 x ULN and a bilirubin limit of 2.1 x ULN, compared with the FDA's 'Hy's Law' of 3 x ULN and bilirubin 2 x ULN. Inter-laboratory data variations were observed across the 28 studies, and were diminished by use of baseline-corrected data. By applying TRMOD to baseline-corrected data, these boundaries became ALT limit of 3.8 x baseline and bilirubin limit of 4.8 x baseline. Cumulative incidence plots of liver signals identified over time were examined. TRMOD analyses identified normative boundaries and outliers that provide comparative data to detect liver signals in similar trial populations.Conclusions: TRMOD liver chemistry analyses of clinical trial data in generally healthy subjects have confirmed the FDA's Hy's Law threshold as a robust means of detecting liver safety outliers. TRMOD evaluation of liver chemistry data, by both fold-ULN and fold-baseline, provides complementary analyses and valuable normative data for comparison in similar patient populations. No liver signal is present when new clinical trial data from similar patient populations lies within these normative boundaries. Use of baseline-corrected data diminishes inter-laboratory variation and may be more sensitive to possible drug effects. We suggest examining liver chemistries using graphical depictions of both ULN-corrected data (eDISH) and baseline-corrected data (mDISH), as complementary methods.