In silico modeling to optimize interpretation of liver safety biomarkers in clinical trials

Abstract

Current strategies to delineate the risk of serious drug-induced liver injury associated with drugs rely on assessment of serum biomarkers that have been utilized for many decades. In particular, serum alanine aminotransferase and total bilirubin levels are typically used to assess hepatic integrity and function, respectively. Parallel measurement of these biomarkers is utilized to identify patients with drug-induced hepatocellular jaundice (“Hy’s Law” cases) which carries at least a 10% risk of death or liver transplant. However, current guidelines regarding use of these biomarkers in clinical trials can put study subjects at risk for life-threatening drug-induced liver injury, or result in over estimation of risk that may halt development of safe drugs. In addition, pharmaceutical companies are increasingly being required to conduct large and expensive clinical trials to “defend” the safety of their new drug when results from smaller trials are inconclusive. Innovative approaches and some novel biomarkers are now being employed to maximize the value of traditional biochemical tests. DILIsym®, a product of the DILIsim Initiative, utilizes serial serum alanine aminotransferase values, along with serum biomarkers of apoptosis vs necrosis, to estimate percent hepatocyte loss and total bilirubin elevations resulting from loss of global liver function. The results from analyses conducted with DILIsym have been reported to the FDA to support the safety of entolimod and cimaglermin alfa after elevations in serum alanine aminotransferase and/or bilirubin halted clinical development. DILIsym can also be utilized to determine whether rises in serum conjugated and unconjugated bilirubin are consistent with mechanisms unrelated to toxicity (i.e. inhibition of bilirubin transport or metabolism). In silico modeling of traditional and novel drug-induced liver injury biomarker data obtained in clinical trials may be the most efficient and accurate way to define the liver safety profile of new drug candidates.

Impact statement

Blood tests used in clinical trials to detect and monitor drug-induced liver injury (DILI) have not changed in half a century. These tests have several shortcomings: their use has not completely prevented clinical trial participants from risk of life-threatening DILI, they can give false positive results that halt the development of safe drug candidates, and they can create liver safety “concerns” that require large additional clinical trials to accurately define DILI risk. This review highlights the use of in silico modeling to improve interpretation of the blood tests currently available to detect DILI risk in new drug candidates. This approach is increasingly being applied in clinical trials to more precisely assess the degree of hepatocellular injury and its functional impact. This new approach holds the promise of more accurately defining DILI risk in smaller clinical trials.