A systematic comparison of hepatobiliary adverse drug reactions in FDA and EMA drug labeling reveals discrepancies

Background Factors Contributing to Safety Warning Discordance in the Initial Labeling of New Drugs in Japan, the United States, and the European Union

Drug labels summarize essential safety information for healthcare professionals and patients. However, studies have reported a discordance of safety information in drug labeling among countries/regions. We aimed to identify the characteristics of adverse events associated with discordant safety warnings during the initial labeling of new drugs approved at approximately the same time in Japan, the United States, and the European Union. Safety warning discordance/concordance between two countries/regions and the explanatory variables were assessed using multivariable logistic regression. The safety warning concordance rate was 71.0% (152/214) for the United States and the European Union, 59.5% (135/227) for Japan and the United States, and 64.3% (144/224) for Japan and the European Union. A significant association with discordant safety warnings was revealed for "adverse event rate" and "warning status in a similar drug" between the United States and the European Union; "adverse event rate," "adverse event included in important medical event list," and "warning status in a similar drug" between Japan and the United States; and "adverse event included in important medical event list" and "warning status in a similar drug" between Japan and the European Union. Clarifying and publicizing the reasons for safety warnings, along with an awareness of the factors associated with the discordance identified in this study, will help healthcare professionals, patients, marketing authorization holders, and regulatory authorities around the world share the background of country/region-specific warnings, reducing the possibility of confusion among them due to the discrepancies.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost

Introduction

Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources.

Methods

We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token.

Results

The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA).

Discussion

Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

Factors associated with hematological adverse reactions of drugs authorized via the centralized procedure

Serious hematological adverse drug reactions (HADRs) may lead to or prolong hospitalization and even cause death. The aim of this study was to determine the regulatory factors associated with HADRs caused by drugs that were authorized up to July 2023 by the European Medicines Agency (EMA) and to evaluate the frequency of HADRs. Using a cross-sectional approach, the type and frequency of HADRs were collected from the Summaries of Product Characteristics of Drugs Authorized by the EMA and analyzed within proprietary, nonproprietary, and biosimilar/biological frameworks. Multivariate statistical analysis was used to investigate the associations of generic status, biosimilar status, conditional approval, exceptional circumstances, accelerated assessment, orphan drug status, years on the market, administration route, and inclusion on the Essential Medicines List (EML) with HADRs. In total, 54.78% of proprietary drugs were associated with HADRs at any frequency, while anemia, leucopenia, and thrombocytopenia were observed in approximately 36% of the patients. The predictors of any HADR, anemia, and thrombocytopenia of any frequency are generic status, biosimilar status, and inclusion on the EML, while the only protective factor is the administration route. Biosimilars and their originator biologicals have similar frequencies of HADRs; the only exception is somatropin. Knowledge of the regulatory factors associated with HADRs could help clinicians address monitoring issues when new drugs are introduced for the treatment of patients.

Computational models for predicting liver toxicity in the deep learning era

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.

The Evolving Profile of Idiosyncratic Drug-Induced Liver Injury

Idiosyncratic drug-induced liver injury (DILI) is an infrequent but important cause of liver disease. Newly identified causes of DILI include the COVID vaccines, turmeric, green tea extract, and immune checkpoint inhibitors. DILI is largely a clinical diagnosis of exclusion that requires evaluation for more common causes of liver injury and a compatible temporal association with the suspect drug. Recent progress in DILI causality assessment includes the development of the semi-automated revised electronic causality assessment method (RECAM) instrument. In addition, several drug-specific HLA associations have been identified that can help with the confirmation or exclusion of DILI in individual patients. Various prognostic models can help identify the 5%-10% of patients at highest risk of death. Following suspect drug cessation, 80% of patients with DILI fully recover, whereas 10%-15% have persistently abnormal laboratory studies at 6 months of follow-up. Hospitalized patients with DILI with an elevated international normalized ratio or mental status changes should be considered for N-acetylcysteine therapy and urgent liver transplant evaluation. Selected patients with moderate to severe drug reaction with eosinophilia and systemic symptoms or autoimmune features on liver biopsy may benefit from short-term corticosteroids. However, prospective studies are needed to determine the optimal patients and dose and duration of steroids to use. LiverTox is a comprehensive, freely accessible Web site with important information regarding the hepatotoxicity profile of more than 1000 approved medications and 60 herbal and dietary supplement products. It is hoped that ongoing "omics" studies will lead to additional insight into DILI pathogenesis, improved diagnostic and prognostic biomarkers, and mechanism-based treatments.

Reported hepatotoxicity and hepatotoxicity guidance in the product information of protein kinase inhibitors in oncology registered at the European Medicines Agency

Protein kinase inhibitors (PKIs) used in oncology can induce severe and even fatal hepatotoxicity. Several PKIs are registered within a certain class to target a specific kinase. No systematic comparison of the reported hepatotoxicity and clinical guidance for monitoring and management of hepatotoxic events between the various PKI summaries of product characteristics (SmPC) is yet available. A systematic analysis of data on 21 hepatotoxicity parameters obtained from the SmPCs and European public assessment reports (EPARs) of European Medicines Agency-approved antineoplastic PKIs (n = 55) has been conducted. The median reported incidence (range) of all grades of aspartate aminotransferase (AST) elevations was 16.9% (2.0%-86.4%) for PKI monotherapy, with 2.1% (0.0%-10.3%) being grade 3/4 and for all grades alanine aminotransferase (ALT) elevations 17.6% (2.0%-85.5%), with 3.0% (0.0%-25.0%) being grade 3/4. Fatalities due to hepatotoxicity were reported for 22 out of 47 PKIs (monotherapy) and for 5 out of 8 PKIs (combination therapy). A maximum grade of grade 4 and grade 3 hepatotoxicity was reported for 45% (n = 25) and 6% (n = 3), respectively. Liver parameter monitoring recommendations were present in 47 of the 55 SmPCs. Dose reductions were recommended for 18 PKIs. Discontinuation was recommended for patients meeting Hy's law criteria (16 out of 55 SmPCs). Severe hepatotoxic events are reported in approximately 50% of the analyzed SmPCs and EPARs. Differences in the degree of hepatotoxicity are apparent. Although liver parameter monitoring recommendations are present in the vast majority of the analyzed PKI SmPCs, the clinical guidance for hepatotoxicity was not standardized.

Foerster K, Terstegen T, Vogt C, Said A, Schulz M, E. Haefeli W. Oral Drugs Against COVID-19

BACKGROUND

Five-day oral therapies against early COVID-19 infection have recently been conditionally approved in Europe. In the drug combination nirmatrelvir + ritonavir (nirmatrelvir/r), the active agent, nirmatrelvir, is made bioavailable in clinically adequate amounts by the additional administration of a potent inhibitor of its first-pass metabolism by way of cytochrome P450 [CYP] 3A in the gut and liver. In view of the central role of CYP3A in the clearance of many different kinds of drugs, and the fact that many patients with COVID-19 are taking multiple drugs to treat other conditions, it is important to assess the potential for drug interactions when nirmatrelvir/r is given, and to minimize the risks associated with such interactions.

METHODS

We defined the interaction profile of ritonavir on the basis of information derived from two databases (Medline, GoogleScholar), three standard electronic texts on drug interactions, and manufacturer-supplied drug information. We compiled a list of drugs and their potentially relevant interactions, developed a risk min - imization algorithm, and applied it to the substances in question. We also compiled a list of commonly prescribed drugs for which there is no risk of interaction with nirmatrelvir/r.

RESULTS

Out of 190 drugs and drug combinations, 57 do not need any special measures when given in combination with brief, low-dose ritonavir treatment, while 15 require dose modification or a therapeutic alternative, 8 can be temporarily discontinued, 9 contraindicate ritonavir use, and 102 should preferably be combined with a different treatment.

CONCLUSION

We have proposed measures that are simple to carry out for the main types of drug that can interact with ritonavir. These measures can be implemented under quarantine conditions before starting a 5-day treatment with nirmatrelvir/r.