Elevations in serum bilirubin during drug treatment may indicate global liver dysfunction and a high risk of liver failure. However, drugs also can increase serum bilirubin in the absence of hepatic injury by inhibiting specific enzymes/transporters. We constructed a mechanistic model of bilirubin disposition based on known functional polymorphisms in bilirubin metabolism/transport. Using physiologically based pharmacokinetic (PBPK) model‐predicted drug exposure and enzyme/transporter inhibition constants determinedin vitro, our model correctly predicted indinavir‐mediated hyperbilirubinemia in humans and rats. Nelfinavir was predicted not to cause hyperbilirubinemia, consistent with clinical observations. We next examined a new drug candidate that caused both elevations in serum bilirubin and biochemical evidence of liver injury in rats. Simulations suggest that bilirubin elevation primarily resulted from inhibition of transporters rather than global liver dysfunction. We conclude that mechanistic modeling of bilirubin can help elucidate underlying mechanisms of drug‐induced hyperbilirubinemia, and thereby distinguish benign from clinically important elevations in serum bilirubin.

Incorporating inter‐individual differences in drug disposition and responses is essential for ensuring the safe and effective use of drugs in real‐world patients. Despite ongoing efforts, lower participation of children, older individuals, pregnant and breastfeeding women, postmenopausal women, and people with disease states and disabilities in drug clinical trials is frequent, and it requires multifaceted strategies and tools to evaluate drug exposure and responses in broad populations. The availability of modeling and simulation tools, such as physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology/toxicology (QSP/QST) modeling, enables the application of virtual populations that reflect the differences in drug disposition and responses for disease states and different stages of the lifespan. These models integrate clinical trial and real‐world data (RWD) to predict drug exposure, efficacy, and safety. Additionally, machine learning (ML) and artificial intelligence (AI) offer powerful tools for analyzing large datasets and identifying key physiological determinants of drug response across the lifespan. This review discusses the application of in silico and AI models to advance the prediction of drug exposure and responses across the lifespan, including examples of virtual populations in PBPK and QSP/QST models. A case study on QST modeling for drug‐induced liver injury (DILI) in postmenopausal women is presented, along with opportunities and challenges in applying AI for modeling physiological determinants of drug dosing in individuals ranging in age from 12 to > 80 years old in drug development.

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.

Clinical investigation of emvododstat for the treatment of solid tumors was halted after two patients who were heavily treated with other anticancer therapies experienced drug‐induced liver failure. However, preclinical investigations supported that emvododstat at lower doses might be effective in treating acute myeloid leukemia (AML) and against severe acute respiratory syndrome‐coronavirus 2 as a dihydroorotate dehydrogenase inhibitor. Therefore, a quantitative systems toxicology model, DILIsym, was used to predict liver safety of the proposed dosing of emvododstat in AML clinical trials. In vitro mechanistic toxicity data of emvododstat and its desmethyl metabolite were integrated with in vivo exposure within DILIsym to predict hepatotoxicity responses in a simulated human population. DILIsym simulations predicted alanine aminotransferase elevations observed in prior emvododstat clinical trials in patients with solid tumors, but not in the prospective AML clinical trial with the proposed dosing regimens. Exposure predictions based on physiologically‐based pharmacokinetic modeling suggested that reduced doses of emvododstat would produce clinical exposures that would be efficacious to treat AML. In the AML clinical trial, only eight patients experienced aminotransferase elevations, all of which were mild (grade 1), all resolving within a short period of time, and no patient showed symptoms of hepatotoxicity, confirming the prospective prediction of liver safety. Overall, retrospective DILIsym simulations adequately predicted the liver safety liabilities of emvododstat in solid tumor trials and prospective simulations predicted the liver safety of reduced doses in an AML clinical trial. The modeling was critical to enabling regulatory approval to proceed with the AML clinical trial wherein the predicted liver safety was confirmed.

Acetaminophen (APAP) has been formulated as immediate‐, modified‐, and extended‐release tablets (APAP‐IR, ‐MR, and ‐ER, respectively). However, there was concern that APAP‐MR previously available in Europe could form a bezoar after a large overdose, leading to delayed absorption and atypical pharmacokinetics (PK) compared to APAP‐IR, and that current treatment guidelines developed for APAP overdose to prevent severe hepatotoxicity are inappropriate for APAP‐MR. In contrast, APAP‐ER caplets available in the United States are designed with an IR layer and an erodible ER layer. Using modeling and simulation, predicted PK and hepatotoxicity biomarkers following various acute overdose and repeated supratherapeutic ingestion (RSTI) scenarios with APAP‐IR and APAP‐ER were compared to investigate the differences between these two formulations. The existing APAP‐IR representation within DILIsym v8A, a quantitative systems toxicology model of drug‐induced liver injury, was updated, and an APAP‐ER model was developed, using newly acquired in vitro (e.g., tiny‐TIMsg) and clinical data. The model and simulated populations (SimPops) representing healthy adults were extensively validated, before simulating PK and three clinically useful hepatic biomarkers after various overdose scenarios. On average, APAP exposure after acute overdose and RSTI in healthy adults was predicted to be slightly lower for APAP‐ER compared to APAP‐IR, partially due to lower APAP absorption for APAP‐ER, while not markedly impacting the expected time course of APAP plasma concentrations. Similar hepatic biomarker profiles were predicted for both APAP formulations. Based on these results, the APAP overdose consensus treatment guidelines updated in 2023 are not further impacted by this report.

Cystic fibrosis (CF) is a chronic hereditary disease that affects tens of thousands of people worldwide. The introduction of CFTR modulator therapies such as elexacaftor/tezacaftor/ivacaftor (ETI) has significantly improved the quality of life of people with CF. However, ETI has been shown in clinical trials to cause elevations in liver enzymes, and real‐world cases of drug‐induced liver injury (DILI) have also been reported. The mechanism of ETI‐mediated DILI is currently unknown, hindering the development of more effective mitigation strategies for this adverse reaction. Through in vitro assays and quantitative systems toxicology modeling using DILIsym, this study revealed that ivacaftor contributed most significantly to ETI‐mediated DILI, primarily via reactive oxygen species production, resulting in mitochondrial dysfunction due to electron transport chain inhibition. DILIsym modeling also predicted liver enzyme elevations following daily dosing of ETI at a comparable frequency (6.0%) to that of clinical data (8.0%). Simulations of the therapeutic effects of DILI mitigation strategies for ETI showed that dose reduction and antioxidant administration may significantly reduce the frequency of liver enzyme elevations due to ETI.