Validation of multivariate outlier detection analyses used to identify potential drug-induced liver injury in clinical trial populations.

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.