Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.

Background/Objectives: Infliximab, ipilimumab, and nivolumab are three monoclonal antibodies that have been associated with hepatotoxicity. Three separate physiologically based pharmacokinetic (PBPK) models were developed in GastroPlus^® to simulate plasma and liver concentrations in patient populations after administration of either infliximab, ipilimumab, or nivolumab. Methods: The models include distribution and clearance mechanisms specific to large molecules, FcRn binding dynamics, and target-mediated drug disposition (TNF-α for infliximab, CTLA-4 for ipilimumab, and PD-1 for nivolumab). Results: The PBPK model for each large molecule was able to reproduce observed plasma concentration data in patient populations, including patients with rheumatoid arthritis and patients with solid tumors. Liver concentrations were predicted to be between 10% and 23% of the plasma concentrations for each of the three drugs, aligning with previously reported results. This lends further validity to the PBPK models and their ability to accurately predict hepatic concentrations in the absence of direct tissue measurements. Conclusions: These results can be used to drive liver toxicity predictions using the quantitative systems toxicology model, BIOLOGXsym™, which integrates hepatic interstitial concentrations with in vitro mechanistic toxicity data to predict the extent of liver toxicity for biologics.