A Rapid Method to Estimate Hepatocyte Loss Due to Drug‐Induced Liver Injury

An In Silico Platform to Predict Cardiotoxicity Risk of Anti-tumor Drug Combination with hiPSC-CMs Based In Vitro Study

OBJECTIVE

Antineoplastic agent-induced systolic dysfunction is a major reason for interruption of anticancer treatment. Although targeted anticancer agents infrequently cause systolic dysfunction, their combinations with chemotherapies remarkably increase the incidence. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a potent in vitro model to assess cardiovascular safety. However, quantitatively predicting the reduction of ejection fraction based on hiPSC-CMs is challenging due to the absence of the body's regulatory response to cardiomyocyte injury.

METHODS

Here, we developed and validated an in vitro-in vivo translational platform to assess the reduction of ejection fraction induced by antineoplastic drugs based on hiPSC-CMs. The translational platform integrates drug exposure, drug-cardiomyocyte interaction, and systemic response. The drug-cardiomyocyte interaction was implemented as a mechanism-based toxicodynamic (TD) model, which was then integrated into a quantitative system pharmacology-physiological-based pharmacokinetics (QSP-PBPK) model to form a complete translational platform. The platform was validated by comparing the model-predicted and clinically observed incidence of doxorubicin and trastuzumab-induced systolic dysfunction.

RESULTS

A total of 33,418 virtual patients were incorporated to receive doxorubicin and trastuzumab alone or in combination. For doxorubicin, the QSP-PBPK-TD model successfully captured the overall trend of systolic dysfunction incidences against the cumulative doses. For trastuzumab, the predicted incidence interval was 0.31-2.7% for single-agent treatment and 0.15-10% for trastuzumab-doxorubicin sequential treatment, covering the observations in clinical reports (0.50-1.0% and 1.5-8.3%, respectively).

CONCLUSIONS

In conclusion, the in vitro-in vivo translational platform is capable of predicting systolic dysfunction incidence almost merely depend on hiPSC-CMs, which could facilitate optimizing the treatment protocol of antineoplastic agents.

Applications of In Silico Models to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

Clinical Significance of Transient Asymptomatic Elevations in Aminotransferase (TAEAT) in Oncology

Monitoring for liver injury remains an important aspect of drug safety assessment, including for oncotherapeutics. When present, drug-induced liver injury may limit the use or result in the discontinuation of these agents. Drug-induced liver injury can exhibit with a wide spectrum of clinical and biochemical manifestations, ranging from transient asymptomatic elevations in aminotransferases (TAEAT) to acute liver failure. Numerous oncotherapeutics have been associated with TAEAT, with published reports indicating a phenomenon in which patients may be asymptomatic without overt liver injury despite the presence of grade ≥3 aminotransferase elevations. In this review, we discuss the occurrence of TAEAT in the context of oncology clinical trials and clinical practice, as well as the clinical relevance of this phenomenon as an adverse event in response to oncotherapeutics and the related cellular and molecular mechanisms that may underlie its occurrence. We also identify several gaps in knowledge relevant to the diagnosis and the management of TAEAT in patients receiving oncotherapeutics, and identify areas warranting further study to enable the future development of consensus guidelines to support clinical decision-making.

Drug-Induced Liver Injury: Highlights and Controversies in the Recent Literature

Drug-induced liver injury (DILI) remains an important, yet challenging diagnosis for physicians. Each year, additional drugs are implicated in DILI and this year was no different, with more than 1400 articles published on the subject. This review examines some of the most significant highlights and controversies in DILI-related research over the past year and their implications for clinical practice. Several new drugs were approved by the US Food and Drug Administration including a number of drugs implicated in causing DILI, particularly among the chemotherapeutic classes. The COVID-19 pandemic was also a major focus of attention in 2020 and we discuss some of the notable aspects of COVID-19-related liver injury and its implications for diagnosing DILI. Updates in diagnostic and causality assessments related to DILI such as the Roussel Uclaf Causality Assessment Method are included, mindful that there is still no single biomarker or diagnostic tool to unequivocally diagnose DILI. Glutamate dehydrogenase received renewed attention as being more specific than alanine aminotransferase. There were a few new reports of previously unrecognized hepatotoxins, including immune modulators and novel gene therapy drugs that we highlight. Updates and new developments of previously described hepatotoxins, such as immune checkpoint inhibitors and anti-tuberculosis drugs are reviewed. Finally, novel technologies such as organoid culture systems to better predict DILI preclinically may be coming of age and determinants of hepatocyte loss, such as calculating P_ALT are poised to improve our current means of estimating DILI severity and the risk of acute liver failure.

A New Paradigm for Safety Data Signal Detection and Evaluation Using Open-Source Software Created by an Interdisciplinary Working Group

Techniques to evaluate large amounts of safety data continue to evolve based on a greater understanding of how the brain processes visual information and the advancement of programing tools. The Interactive Safety Graphics Task Force of the American Statistical Association Biopharmaceutical Safety Working Group has assembled a multidisciplinary team of experts in a variety of domains to develop the next generation of open-source visual analytical tools for safety data based on these advances. The multidisciplinary approach resulted in the rapid development of the first tool, a novel interactive version of the familiar Evaluation of Drug-Induced Serious Hepatotoxicity (eDISH) graphic along with a unique clinical workflow to guide the reviewer through the data analysis. This now serves as the model for the team to expand the open-source platform into a suite of other interactive safety analysis tools.

Strategies for Early Prediction and Timely Recognition of Drug-Induced Liver Injury: The Case of Cyclin-Dependent Kinase 4/6 Inhibitors

The idiosyncratic nature of drug-induced liver injury (DILI) represents a current challenge for drug developers, regulators and clinicians. The myriad of agents (including medications, herbals, and dietary supplements) with recognized DILI potential not only strengthens the importance of the post-marketing phase, when urgent withdrawal sometimes occurs for rare unanticipated liver toxicity, but also shows the imperfect predictivity of pre-clinical models and the lack of validated biomarkers beyond traditional, non-specific liver function tests. After briefly reviewing proposed key mechanisms of DILI, we will focus on drug-related risk factors (physiochemical and pharmacokinetic properties) recently proposed as predictors of DILI and use cyclin-dependent kinase 4/6 inhibitors, relatively novel oral anticancer medications approved for breast cancer, as a case study to discuss the feasibility of early detection of DILI signals during drug development: published data from pivotal clinical trials, unpublished post-marketing reports of liver adverse events, and pharmacokinetic properties will be used to provide a comparative evaluation of their liver safety and gain insight into drug-related risk factors likely to explain the observed differences.