Liver Injury Associated with Metamizole Exposure: Features of an Underestimated Adverse Event

Organ-specific immune-mediated reactions to polyethylene glycol and polysorbate excipients: three case reports

Drug-related acute pancreatitis (AP), acute interstitial nephritis (AIN) and drug-induced liver injury (DILI) are rare but serious adverse drug reactions (ADRs) that can have life-threatening consequences. Although the diagnosis of these ADRs can be challenging, causality algorithms and the lymphocyte transformation test (LTT) can be employed to help with the diagnosis. In this report, we present 3 cases of drug-related AP, AIN and DILI. The first case involved a patient with AP to lacosamide and to the excipient polysorbate 80 in pantoprazole. The second case involved a patient with DILI secondary to polyethylene glycol (PEG) excipients and amoxicillin-clavulanate. In case 3, AIN was considered to be the result of sensitization to excipients. Diagnoses were made using causality algorithms and the LTT. The LTT is a useful tool for helping diagnose drug-related AP and DILI, and it can be used to identify the specific drug or excipient causing the ADR. These cases highlight the importance of considering PEG and polysorbate excipients in the causality diagnosis of ADRs.

A new framework for advancing in drug-induced liver injury research. The Prospective European DILI Registry

BACKGROUND & AIMS

No multi-national prospective study of drug-induced liver injury (DILI) has originated in Europe. The design of a prospective European DILI registry, clinical features and short-term outcomes of the cases and controls is reported.

METHODS

Patients with suspected DILI were prospectively enrolled in the United Kingdom, Spain, Germany, Switzerland, Portugal and Iceland, 2016-2021. DILI cases or non-DILI acute liver injury controls following causality assessment were enrolled.

RESULTS

Of 446 adjudicated patients, 246 DILI patients and 100 had acute liver injury due to other aetiologies, mostly autoimmune hepatitis (n = 42) and viral hepatitis (n = 34). DILI patients (mean age 56 years), 57% women, 60% with jaundice and 3.6% had pre-existing liver disease. DILI cases and non-DILI acute liver injury controls had similar demographics, clinical features and outcomes. A single agent was implicated in 199 (81%) DILI cases. Amoxicillin-clavulanate, flucloxacillin, atorvastatin, nivolumab/ipilimumab, infliximab and nitrofurantoin were the most commonly implicated drugs. Multiple conventional medications were implicated in 37 (15%) and 18 cases were caused by herbal and dietary supplements. The most common single causative drug classes were antibacterials (40%) and antineoplastic/immunomodulating agents (27%). Overall, 13 (5.3%) had drug-induced autoimmune-like hepatitis due to nitrofurantoin, methyldopa, infliximab, methylprednisolone and minocycline. Only six (2.4%) DILI patients died (50% had liver-related death), and another six received liver transplantation.

CONCLUSIONS

In this first multi-national European prospective DILI Registry study, antibacterials were the most commonly implicated medications, whereas antineoplastic and immunomodulating agents accounted for higher proportion of DILI than previously described. This European initiative provides an important opportunity to advance the study on DILI.

Metamizole-Associated Risks in Decompensated Hepatic Cirrhosis

BACKGROUND

Because of the increased risk of acute renal failure (ARF), the use of cyclooxygenase (COX) inhibitors is not recommended in patients with decompensated hepatic cirrhosis. Metamizole is not a classic COX inhibitor, but there are insufficient data to support its safe use. In this study, we investigate the effect of metamizole on the risk of ARF in these patients.

METHODS

Metamizole use, ARF incidence, and patient mortality were examined in a large, retrospective, exploratory cohort and validated with data from a prospective registry.

RESULTS

523 patients were evaluated in the exploratory cohort. Metamizole use at baseline was documented in 110 cases (21%) and was independently associated with the development of ARF, severe (grade 3) ARF, and lower survival without liver transplantation at follow-up on day 28 (HR: 2.2, p < 0.001; HR: 2.8, p < 0.001; and HR: 2.6, p < 0.001, respectively). Interestingly, the risk of ARF depended on the dose of metamizole administered (HR: 1.038, p < 0.001). Compared to patients who were treated with opioids, the rate of ARF was higher in the metamizole group (49% vs. 79%, p = 0.014). An increased risk of ARF with metamizole use was also demonstrated in the independent validation cohort (p < 0.001).

CONCLUSION

Metamizole therapy, especially at high doses, should only be used with a high level of caution in patients with decompensated cirrhosis.

Liver Transplantation and the Use of Metamizole to Treat Drug-induced Liver Injury

Metamizole, or dipyrone, is a frequently prescribed analgetic drug that can cause drug-induced liver injury (DILI). Still, there are only a few metamizole-associated DILI cases (n = 61, including our study) described in the literature. So far liver transplantation has been reported in 6 patients with metamizole-induced acute liver failure. In 2020, a German group described a bigger cohort (n = 23) of metamizole-related DILI. Shortly thereafter, this issue gained wider attention as the German Federal Institute for Drugs and Medical Devices published a Direct Healthcare Professional Communication, emphasizing DILI as a potential adverse event caused by metamizole. We herein report 2 patients that were admitted to our liver transplant center due to acute liver failure (ALF) in April and May 2021. Both patients reported intake of metamizole as pain medication over a few weeks. After ruling out alternative reasons for ALF and fulfilling the King's College criteria both patients received emergency liver transplantations in our center. Pathology assessment of both explants were consistent with metamizole-associated DILI. As illustrated by our 2 cases of metamizole-induced liver failure with subsequent liver transplantation, this rare but presumably often overlooked adverse drug effect of metamizole should be considered as differential diagnosis in cases of cryptogenic liver failure.

Highlights of the drug-induced liver injury literature for 2021

INTRODUCTION

In 2021, over 3,000 articles on Drug-Induced Liver Injury (DILI) were published, nearly doubling the annual number compared to 2011. This review selected DILI articles from 2021 we felt held the greatest interest and clinical relevance.

AREAS COVERED

A literature search was conducted using PubMed between 1 March 2021 and 28 February 2022. 86 articles were included. This review discusses new and established cases of hepatotoxins, including new FDA approvals and COVID-19 therapeutics. Developments in biomarkers and causality assessment methods are discussed. Updates from registries are also explored.

EXPERT OPINION

DILI diagnosis and prognostication remain challenging. Roussel Uclaf Causality Assessment Method (RUCAM) is the best option for determining causality and has been increasingly accepted by clinicians. Revised Electronic Causality Assessment Method (RECAM) may be more user-friendly and accurate but requires further validation. Quantitative systems pharmacology methods, such as DILIsym, are increasingly used to predict hepatotoxicity. Oncotherapeutic agents represent many newly approved and described causes of DILI. Such hepatotoxicity is deemed acceptable relative to the benefit these drugs offer. Drugs developed for non-life-threatening disorders may not show a favorable benefit-to-risk ratio and will be more difficult to approve. As the COVID-19 landscape evolves, its effect on DILI deserves further investigation.

Novel predictors for liver transplantation or death in drug-induced acute liver failure

OBJECTIVE

Drug-induced liver injury (DILI) is a leading cause of acute liver failure (ALF). Predictors for orthotopic liver transplantation (OLT) or death in drug-induced ALF (DI-ALF) are scarce.

METHODS

In total 33 of 346 patients recruited for our prospective study on potentially hepatotoxic drugs had ALF. DILI diagnosis was based on Roussel Uclaf Causality Assessment Method (RUCAM) score and expert opinion. Area under the receiver operating characteristic (AUROC) curve, Youden's index and positive and negative likelihood ratios were calculated to identify the best performing predictive markers and scores for OLT or death.

RESULTS

Poor outcome was associated with lower baseline platelet counts and cholinesterase (CHE) levels, higher International Normalized Ratio (INR) levels and Model for End-Stage Liver Disease (MELD) scores. Yet, AUROC reached a maximum of only 0.71-0.75 for either of those laboratory markers or the MELD score. Notably however, combinations of those scores were highly discriminatory, in particular INR/(CHE*platelet count) and MELD/(CHE*platelet count), showing an AUROC of 0.91, a positive likelihood ratios of 13.78 and a negative likelihood ratios of 0.08.

CONCLUSION

While baseline MELD score, INR, CHE, and platelet counts had limited potential to discriminate between DI-ALF with survival or poor outcome, their combinations were highly associated with OLT or death in patients with DI-ALF.

Utility of Lymphocyte Transformation Test for Assisting Updated Roussel Uclaf Causality Assessment Method in Drug-Induced Liver Injury: A Case-Control Study

Background: The Roussel Uclaf Causality Assessment Method (RUCAM) is a validated tool for assessing causality in cases of suspected drug-induced liver injury (DILI). However, RUCAM cannot discriminate between concomitant hepatotoxic drugs with the same temporal sequence.

Objective: To analyse the utility of the lymphocyte transformation test (LTT) for assisting updated RUCAM in 45 patients and 40 controls with a clinical diagnosis of DILI.

Methods: Suspected DILI cases were detected through the Prospective Pharmacovigilance Program from Laboratory Signals in Hospital (PPLSH) or by consultations. The controls completed the drug therapy with no adverse reactions during the study period. A receiver operating characteristics (ROC) curve analysis was performed to calculate the optimal cut-off value for the stimulation index (SI), corresponding to the largest sum for the specificity and sensitivity values of LTT for true DILI cases.

Results: Out of 45 patients diagnosed with DILI, 42 cases were detected by the PPLSH, two cases by consultation and one case by both methods. Most DILI cases (64.4%) arose during hospitalization. According to the biochemical parameters, 24 cases (53.3%) had the hepatocellular phenotype, 14 (31.1%) had the cholestatic phenotype, and 7 cases (15.6%) had the mixed phenotype. Considering the severity criteria, 7 (15.5%) cases were classified as moderate DILI, and 4 (8.9%) were severe DILI; there were no fatal cases. A total of 149 drugs (median/case, 3; IQR, 2–5) were suspected to be involved in the DILI cases (RUCAM score ≥3). In 8 cases, only one drug was suspected, and polypharmacy (≥5 drugs) was identified in 29% of the cases. Of all DILI cases, 46 (30.9%) of the 149 suspected drugs produced positive LTT results, and the LTT was positive in 34 (75.5%) of the 45 patients. No exposed controls produced positive LTT results. The optimal cut-off of 1.95 for the SI was obtained with a sensitivity of 77% and specificity of 100% (area under the curve, 0.91; 95% asymptotic confidence interval 0.84–0.97; p < 0.001). The sensitivity of the hepatocellular phenotype was 92%.

Conclusion: Our results demonstrate that LTT is an add on strengthening causality in cases of suspected idiosyncratic DILI, especially for patients with several suspected drugs and a hepatocellular phenotype.

ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18

Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10 000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.