Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions

Chemical-induced liver cancer: an adverse outcome pathway perspective

INTRODUCTION

The evaluation of the potential carcinogenicity is a key consideration in the risk assessment of chemicals. Predictive toxicology is currently switching toward non-animal approaches that rely on the mechanistic understanding of toxicity.

AREAS COVERED

Adverse outcome pathways (AOPs) present toxicological processes, including chemical-induced carcinogenicity, in a visual and comprehensive manner, which serve as the conceptual backbone for the development of non-animal approaches eligible for hazard identification. The current review provides an overview of the available AOPs leading to liver cancer and discusses their use in advanced testing of liver carcinogenic chemicals. Moreover, the challenges related to their use in risk assessment are outlined, including the exploitation of available data, the need for semantic ontologies, and the development of quantitative AOPs.

EXPERT OPINION

To exploit the potential of liver cancer AOPs in the field of risk assessment, 3 immediate prerequisites need to be fulfilled. These include developing human relevant AOPs for chemical-induced liver cancer, increasing the number of AOPs integrating quantitative toxicodynamic and toxicokinetic data, and developing a liver cancer AOP network. As AOPs and other areas in the field continue to evolve, liver cancer AOPs will progress into a reliable and robust tool serving future risk assessment and management.

Joint and interactive effects of metal mixtures on liver damage: Epidemiological evidence from repeated-measures study

BACKGROUND

The impact of heavy metals on liver function has been examined in numerous epidemiological studies. However, these findings lack consistency and longitudinal validation.

METHODS

In this study, we conducted three follow-up surveys with 426 participants from Northeast China. Blood and urine samples were collected, along with questionnaire information. Urine samples were analyzed for concentrations of four metals (chromium [Cr], cadmium [Cd], lead [Pb], and manganese [Mn]), while blood samples were used to measure five liver function indicators (alanine aminotransferase [ALT], aspartate aminotransferase [AST], albumin [ALB], globulin [GLB], and total protein [TP]). We utilized a linear mixed-effects model (LME) to explore the association between individual heavy metal exposure and liver function. Joint effects of metal mixtures were investigated using quantile g-computation and Bayesian kernel machine regression (BKMR). Furthermore, we employed BKMR and Marginal Effect models to examine the interaction effects between metals on liver function.

RESULTS

The LME results demonstrated a significant association between urinary heavy metals (Cr, Cd, Pb, and Mn) and liver function markers. BKMR results indicated positive associations between heavy metal mixtures and ALT, AST, and GLB, and negative associations with ALB and TP, which were consistent with the g-comp results. Synergistic effects were observed between Cd-Cr on ALT, Mn-Cr and Cr-Pb on ALB, while an antagonistic effect was found between Mn-Pb and Mn-Cd on ALB. Additionally, synergistic effects were observed between Mn-Cr on GLB and Cd-Cr on TP. Furthermore, a three-way antagonistic effect of Mn-Pb-Cr on ALB was identified.

CONCLUSION

Exposure to heavy metals (Cr, Cd, Mn, Pb) is associated with liver function markers, potentially leading to liver damage. Moreover, there are joint and interaction effects among these metals, which warrant further investigation at both the population and mechanistic levels.

Comprehensive Study of Drug-Induced Pruritus Based on Adverse Drug Reaction Report Database

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one's quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA's Adverse Event Reporting System (FAERS) data. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. A principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin's surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

Identifying Crude Drugs in Kampo Medicines Associated with Drug-Induced Liver Injury Using the Japanese Adverse Drug Event Report Database: A Comprehensive Survey

The current study aimed to identify the crude drugs associated with drug-induced liver injury (DILI) in 148 Kampo medicines prescribed throughout Japan using the Japanese Adverse Drug Event Report (JADER) database, a large-scale spontaneous reporting system in Japan. First, we tabulated the number of DILI reports from the report-based dataset and the background information from the patient-based dataset. Thereafter, we combined the 126 crude drugs into 104 crude drug groups to examine multicollinearity. Finally, the reporting odds ratios (RORs), 95% confidence intervals, p values for Fisher's exact test, and number of reports were calculated for each crude group to identify those associated with DILI. Notably, the number of adverse event reports for DILI (63,955) exceeded that for interstitial lung disease (51,347), the most common adverse event. In total, 78 crude drug groups (90 crude drugs) were reported to have an ROR > 1, a p < 0.05, and ≥10 reported cases. Our results highlight DILI as an essential issue, given that it was among the most frequently reported adverse drug reactions. We were able to clearly identify the crude drugs associated with DILI, which could help manage adverse drug reactions attributed to Kampo medicines and crude drugs.

Examination of Risk Factors and Expression Patterns of Atypical Femoral Fractures Using the Japanese Adverse Drug Event Report Database: A Retrospective Pharmacovigilance Study

Atypical femoral fracture (AFF) is a rare complication related to the use of bisphosphonates (BPs). Herein, we analyzed the risk factors and onset patterns of AFF using the Japanese Adverse Drug Event Report database and reported the findings. First, the independent risk factors for AFF were gender (female), high body mass index, and medical history of osteoporosis, arthritis, and systemic lupus erythematosus (SLE). Drug-related risk factors for AFF included BPs (i.e., alendronic acid, ibandronic acid, etidronic acid, zoledronic acid, minodronic acid, risedronic acid), denosumab, prednisolone, lansoprazole, rabeprazole, exemestane, letrozole, eldecalcitol, and menatetrenone. Therefore, it appears that AFF is influenced by a combination of patient backgrounds and drugs, and that the risk of developing AFF is particularly high in patients with fragile bones (e.g., osteoporosis, arthritis, and SLE). Second, in the analysis of AFF onset patterns, the onset of AFF from BPs and denosumab took a long time (>1 year) to develop. Analysis using a Weibull distribution showed wear-out failure-type AFF onset for BPs and denosumab, and both osteoporosis and cancer patients with long-term administration of these drugs showed a tendency to have an increased risk of onset. AFF developed earlier in osteoporosis patients with long-term administration of BPs and denosumab than in cancer patients.

Sex disparity and drug-induced liver injury

Drug-induced liver injury (DILI) is a potentially serious clinical condition that remains a major problem for patients, physicians and those involved in the development of new drugs. Population and hospital-based studies have reported incidences of DILI varying from 1.4 to 19.1/100.000. Overall, females have a 1.5- to 1.7-fold greater risk of developing adverse drug reactions and the female/male ratio increases after the age of 49 years, suggesting a clear susceptibility of DILI after menopause. Sex differences in pharmacokinetics and pharmacodynamic, sex-specific hormonal effects or interaction with signalling molecules that can influence drug efficacy and safety and differences in abnormal immune response following drug exposure are the main probable causes of the higher vulnerability observed among female patients. A novel phenotype of autoimmune-mediated DILI following the use of check-point inhibitors in oncology and haematology has been recently described. Finally, there have been increasing reports of DILI associated with use of herbal and dietary supplements that is more frequently reported in women.

Development of in silico prediction models for drug-induced liver malignant tumors based on the activity of molecular initiating events: Biologically interpretable features

Liver malignant tumors (LMTs) have recently been reported as severe and life-threatening adverse drug events associated with drug-induced liver injury (DILI). DILIs are the most common adverse drug event and can cause the withdrawal of medicinal products or major regulatory action. To reduce the attrition rate and cost of drug discovery, various quantitative structure-toxicity relationship models have been proposed to predict the probability of a DILI based on the chemical structure of a drug. However, there are many unresolved issues regarding the predictors of LMT-inducing drugs, and biologically interpretable prediction models for LMT have not been developed. Here, we constructed prediction models for whether a drug is LMT-inducing based on the activity of molecular initiating events (MIEs), which are biologically interpretable features and are defined as the initial interaction between a molecule and biosystem. We then constructed five machine learning models (i.e., LightGBM, XGBoost, random forest, neural network, and support vector machine) and evaluated their predictive performances. LightGBM achieved the best performance among the tested models. The MIEs making the highest contribution to the model construction for drug-induced LMT were inducement of Enhanced Level of Genome Instability Gene 1 (human ATAD5), nuclear factor-κ B, and activation of thyrotropin-releasing hormone receptor. These results support the previous literature and can be related to the mechanism onset of drug-induced LMT. Our findings may provide useful knowledge for drug development, research, and regulatory decision-making and will contribute to building more accurate and meaningful DILI prediction models by increasing understanding of biological predictors.

Exploring the Mechanisms Underlying Drug-Induced Fractures Using the Japanese Adverse Drug Event Reporting Database

Fractures occur when bones become fragile and are subjected to external forces as occurring during falls. The use of drugs that increase bone fragility or fall risk increases the risk of fracture. This study investigates drug-induced fractures reported in the Japanese Adverse Drug Event Report (JADER) database in patients using 4892 drugs. Atypical femur fracture was the most frequently reported fracture, and 58 other fractures were also reported. Using Volcano plots and multiple logistic regression analysis, we identified the risk factors for drug-induced fractures as being female, of older age, higher body mass index, and using one of 90 drugs. The drug groups significantly associated with drug-induced fractures included bone resorption inhibitors, antiviral drugs, dopaminergic drugs, corticosteroids, and sleep sedatives. Principal component analysis was used to examine the relationship between the use of specific drugs and the site of drug-induced fracture. Bone resorption inhibitors and corticosteroids were associated with atypical femur fractures, jaw fractures, and ulna fractures through an osteoclast-mediated process. Other drugs were found to increase fracture risk via non-osteoclast-mediated mechanisms. These findings suggest that many drugs can result in drug-induced fractures through a variety of mechanisms.