Prior drug allergies are associated with worse outcome in patients with idiosyncratic drug-induced liver injury: A machine learning approach for risk stratification

Drug metabolizing enzymes and transporters, and their roles for the development of drug-induced liver injury

INTRODUCTION

Drug-induced liver injury (DILI) poses a significant challenge to drug development and human healthcare. The complex mechanisms underlying DILI make it challenging to accurately predict its occurrence, often leading to substantial financial losses from failed drug development projects and drug withdrawals. Growing evidence suggests that drug-metabolizing enzymes and transporters (DMETs) play a critical role in the development of DILI.

AREAS COVERED

In this review, we explore findings about the contributions of DMETs to DILI, with a focus on the studies examining genetic polymorphisms and their interactions with drugs. Additionally, we highlight the roles of DMETs in the development of predictive models for assessing DILI potential and in uncovering the mechanisms involved in DILI.

EXPERT OPINION

As new approach methods (NAMs) for assessing and predicting drug toxicity gain more prominence, it is imperative to better understand the adverse outcome pathways (AOPs) that underpin these methods. DMETs largely play a pivotal role in the molecular initiating events of DILI-related AOPs. Further research is needed to characterize DILI-related AOP networks and enhance the predictive performance of NAMs for assessing DILI risk.

Risk factors and prediction for DILI in clinical practice

INTRODUCTION

Drug-induced liver injury is an important adverse effect and can be caused by various medications, including novel therapeutic agents. The risk stratification of patients susceptible to DILI is a growing field.

AREAS COVERED

The current article highlights new studies on risk stratification regarding risk factors of DILI, prediction of liver injury, and predictors of severe outcomes. Studies on patient demographic and genetic risk factors are discussed, in addition to the potential role of concomitant medications that may affect the risk of DILI.

EXPERT OPINION

Although much is known about patient risk factors for DILI, a better combination of these factors into risk scores is needed to predict which patients are particularly susceptible. Knowledge of these risk factors might determine drug treatment in the near future, as well as the need for routine monitoring of liver tests.

Drug Induced Liver Injury: Highlights and Controversies in the 2023 Literature

Drug-induced liver injury (DILI) remains an active field of clinical research and investigation with more than 4700 publications appearing in 2023 relating to hepatotoxicity of all causes and injury patterns. As in years past, we have attempted to identify and summarize highlights and controversies from the past year's literature. Several new and novel therapeutic agents were approved by the US Food and Drug Administration (FDA) in 2023, a number of which were associated with significant hepatotoxicity. Updates in the diagnosis and management of DILI using causality scores as well as newer artificial intelligence-based methods were published. Details of newly established hepatotoxins as well as updated information on previously documented hepatotoxic drugs is presented. Significant updates in treatment of DILI were also included as well as reports related to global DILI registries.

Epidemiology and Risk Determinants of Drug-Induced Liver Injury: Current Knowledge and Future Research Needs

AIMS

Drug-induced liver injury (DILI) is a major global health concern resulting from adverse reactions to medications, supplements or herbal medicines. The relevance of DILI has grown with an aging population, the rising prevalence of chronic diseases and the increased use of biologics, including checkpoint inhibitors. This article aims to summarise current knowledge on DILI epidemiology and risk factors.

METHODS

This review critically appraises available evidence on DILI frequency, outcomes and risk determinants, focusing on drug properties and non-genetic host factors that may influence susceptibility.

RESULTS

DILI incidence varies across populations, with hospitalised patients experiencing notably higher rates than outpatients or the general population. Increased medication use, particularly among older adults and women, may partly explain age- and sex-based disparities in DILI incidence and reporting. Physiological changes associated with aging likely increase susceptibility to DILI in older adults, though further exposure-based studies are needed for definitive conclusions. Current evidence does not strongly support that women are inherently more susceptible to DILI than men; rather, susceptibility appears to depend on specific drugs. However, once DILI occurs, older age and female sex are associated with greater severity and poorer outcomes. Other less-studied host-related risk factors are also discussed based on available evidence.

CONCLUSIONS

This article summarises existing data on DILI frequency, outcomes, drug properties affecting hepatotoxicity and non-genetic host risk factors while identifying critical knowledge gaps. Addressing these gaps through future research could enhance understanding and support preventive measures.

Artificial Intelligence: An Emerging Tool for Studying Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a complex and potentially severe adverse reaction to drugs, herbal products or dietary supplements. DILI can mimic other liver diseases clinical presentation, and currently lacks specific diagnostic biomarkers, which hinders its diagnosis. In some cases, DILI may progress to acute liver failure. Given its public health risk, novel methodologies to enhance the understanding of DILI are crucial. Recently, the increasing availability of larger datasets has highlighted artificial intelligence (AI) as a powerful tool to construct complex models. In this review, we summarise the evidence about the use of AI in DILI research, explaining fundamental AI concepts and its subfields. We present findings from AI-based approaches in DILI investigations for risk stratification, prognostic evaluation and causality assessment and discuss the adoption of natural language processing (NLP) and large language models (LLM) in the clinical setting. Finally, we explore future perspectives and challenges in utilising AI for DILI research.

Predictive models and applicability of artificial intelligence-based approaches in drug allergy

PURPOSE OF REVIEW

Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings.

RECENT FINDINGS

Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy.

SUMMARY

This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.