This study investigates the predictive power of pretreatment (baseline) data in identifying patients at risk of developing liver chemistry signals during clinical trials. Using data from 24 late-stage clinical trials, the researchers developed classification models to predict liver chemistry outcomes based on baseline information, including demographics, medical history, concomitant medications, and baseline laboratory results. The models achieved an average validation accuracy of around 80%, with baseline levels of individual liver chemistry tests being the most important predictors of their own elevations during the trials. High baseline bilirubin levels were associated with a high risk of developing biochemical Hy's law cases, while baseline γ-glutamyltransferase (GGT) levels showed some predictive value but did not significantly improve predictability beyond established liver chemistry tests. The study highlights the potential of using baseline data to predict liver chemistry signals and suggests that such predictions could enable proactive and targeted risk management. It also explores the utility of baseline data in evaluating new biomarkers, showing that while traditional markers like ALT and bilirubin are useful, adding GGT does not significantly enhance their predictive power. The analysis also identifies different temporal profiles of liver chemistry signals in biochemical Hy's law cases, with some cases showing a pattern where bilirubin levels rise before transaminase levels. The study further notes that high baseline bilirubin levels are not uncommon and are strongly associated with an increased risk of developing biochemical Hy's law cases. The findings suggest that baseline liver condition may be an important factor in determining how the liver reacts to treatment, and that patients with high baseline bilirubin levels may be more vulnerable to developing biochemical Hy's law cases during clinical trials. The study concludes that pretreatment data can be used to predict which patients are at higher risk of developing liver chemistry signals, and that such predictions could help determine whether new biomarkers offer improved performance over established ones.This study investigates the predictive power of pretreatment (baseline) data in identifying patients at risk of developing liver chemistry signals during clinical trials. Using data from 24 late-stage clinical trials, the researchers developed classification models to predict liver chemistry outcomes based on baseline information, including demographics, medical history, concomitant medications, and baseline laboratory results. The models achieved an average validation accuracy of around 80%, with baseline levels of individual liver chemistry tests being the most important predictors of their own elevations during the trials. High baseline bilirubin levels were associated with a high risk of developing biochemical Hy's law cases, while baseline γ-glutamyltransferase (GGT) levels showed some predictive value but did not significantly improve predictability beyond established liver chemistry tests. The study highlights the potential of using baseline data to predict liver chemistry signals and suggests that such predictions could enable proactive and targeted risk management. It also explores the utility of baseline data in evaluating new biomarkers, showing that while traditional markers like ALT and bilirubin are useful, adding GGT does not significantly enhance their predictive power. The analysis also identifies different temporal profiles of liver chemistry signals in biochemical Hy's law cases, with some cases showing a pattern where bilirubin levels rise before transaminase levels. The study further notes that high baseline bilirubin levels are not uncommon and are strongly associated with an increased risk of developing biochemical Hy's law cases. The findings suggest that baseline liver condition may be an important factor in determining how the liver reacts to treatment, and that patients with high baseline bilirubin levels may be more vulnerable to developing biochemical Hy's law cases during clinical trials. The study concludes that pretreatment data can be used to predict which patients are at higher risk of developing liver chemistry signals, and that such predictions could help determine whether new biomarkers offer improved performance over established ones.