Drug Safety

, Volume 35, Issue 10, pp 865–875

Validation of Multivariate Outlier Detection Analyses Used to Identify Potential Drug-Induced Liver Injury in Clinical Trial Populations


    • Medical AnalyticsGlaxo-SmithKline
  • Daniel Parks
    • Medical AnalyticsGlaxo-SmithKline
  • Jeffery Painter
    • Medical AnalyticsGlaxoSmithKline
  • Christine M. Hunt
    • Global Clinical Safety and PharmacovigilanceGlaxoSmithKline
  • Heide A. Stirnadel-Farrant
    • Worldwide EpidemiologyGlaxoSmithKline
  • Jie Cheng
    • Medical AnalyticsGlaxo-SmithKline
  • Alan Menius
    • Medical AnalyticsGlaxoSmithKline
  • Kwan Lee
    • Medical AnalyticsGlaxo-SmithKline
Original Research Article

DOI: 10.1007/BF03261982

Cite this article as:
Lin, X., Parks, D., Painter, J. et al. Drug Saf (2012) 35: 865. doi:10.1007/BF03261982


Background: Potential severe liver injury is identified in clinical trials by ALT >3 × upper limits of normal (ULN) and total bilirubin >2 × 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 × ULN and a bilirubin limit of 2.1× ULN, compared with the FDA’s ‘Hy’s Law’ of 3 × ULN and bilirubin 2 × 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 × baseline and bilirubin limit of 4.8 × 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.

Copyright information

© Springer International Publishing AG 2012