Drug interaction with Udp-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury.

DILI frequently contributes to the attrition of new drug candidates and is a common cause for the withdrawal of approved drugs from the market. Although some noncytochrome P450 (non-CYP) metabolism enzymes have been implicated in DILI development, their association with DILI outcomes has not been systematically evaluated. In this study, we analyzed a large data set comprising 317 drugs and their interactions in vitro with 42 non-CYP enzymes as substrates, inducers, and/or inhibitors retrieved from historical regulatory documents. We examined how these in vitro drug-enzyme interactions are correlated with the drugs' potential for DILI concern, as classified in the Liver Toxicity Knowledge Base database. Our study revealed that drugs that inhibit non-CYP enzymes are significantly associated with high DILI concern. Particularly, interaction with UDP-glucuronosyltransferases (UGT) enzymes is an important predictor of DILI outcomes. Further analysis indicated that only pure UGT inhibitors and dual substrate inhibitors, but not pure UGT substrates, are significantly associated with high DILI concern. Notably, drug interactions with UGT enzymes may independently predict DILI, and their combined use with the rule-of-two model further improves overall predictive performance. These findings could expand the currently available tools for assessing the potential for DILI in humans.

Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects.

Post-market medical device-associated failures and patient problems are reported in Medical Device Reports (MDRs) to the US Food and Drug Administration. Reports are accessible through Manufacturer and User Facility Device Experience (MAUDE), a database including both required and voluntary submissions. We present an overview of >10 million MDRs received from 2011 to 2021. Approximately 92% of reporting issues represent medical device physical or functional failures, categorized from 1704 codes related to medical device integrity or function. ∼8% were coded adverse events (AEs). Patient outcomes are reported via 998 patient codes in 19 medical specialties (cardiovascular, orthopedic, etc.). ∼40% of patient reports indicated "no health consequences"; however, a small number of devices had consistently high AE reports. While overall reports did not exhibit a sex-based dichotomy, ∼9% of the reported AEs occurred more frequently in females, many of which were related to immune effects. The analyses are subject to uncertainties and potential bias based on data available and data selected for analysis. However, such an overview of post-market MDR data, not previously published, fills a gap in understanding medical device issues and patient-based outcomes related to medical device use. Trends identified may be subjects of additional hypotheses, analysis, and research.

Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.

Drug-induced liver injury (DILI) poses a significant challenge for the pharmaceutical industry and regulatory bodies. Despite extensive toxicological research aimed at mitigating DILI risk, the effectiveness of these techniques in predicting DILI in humans remains limited. Consequently, researchers have explored novel approaches and procedures to enhance the accuracy of DILI risk prediction for drug candidates under development. In this study, we leveraged a large human dataset to develop machine learning models for assessing DILI risk. The performance of these prediction models was rigorously evaluated using a 10-fold cross-validation approach and an external test set. Notably, the random forest (RF) and multilayer perceptron (MLP) models emerged as the most effective in predicting DILI. During cross-validation, RF achieved an average prediction accuracy of 0.631, while MLP achieved the highest Matthews Correlation Coefficient (MCC) of 0.245. To validate the models externally, we applied them to a set of drug candidates that had failed in clinical development due to hepatotoxicity. Both RF and MLP accurately predicted the toxic drug candidates in this external validation. Our findings suggest that in silico machine learning approaches hold promise for identifying DILI liabilities associated with drug candidates during development.

Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values.

INTRODUCTION: On-treatment excursions of liver laboratory test values in clinical trials involving subjects with underlying liver disease are relevant for the efficacy and safety assessment of drug products and biologics. Existing visualization and analysis tools do not efficiently provide an integrated view of these excursions when baseline liver tests are abnormal. OBJECTIVE: The aim of this study was to develop a composite plot that enables visualization of on-treatment changes in liver test results both as multiples of the upper limit of normal defined by each laboratory's reference population (×ULN) and multiples of the subjects' baseline (×BLN) values. METHODS: The composite plot approach combines biochemical evaluation for drug-induced severe hepatotoxicity (eDISH) plots sequentially applied to subjects' baseline and peak on-treatment liver test results normalized by ULN and integrates them into a four-panel shift plot of peak on-treatment values normalized by BLN. RESULTS: The composite plot enabled efficient assessment of improvement in liver test values during treatment compared with pretreatment in subjects treated with the investigational drug (or the natural history of placebo-treated subjects) and identified outlier subjects for potential drug-induced liver injury. CONCLUSION: For studies in subjects with abnormal baseline values, the composite plot has potential application in the assessment of beneficial and concerning on-treatment modifications in liver test values in reference to the individual subject's baseline and population threshold values.

A Single-Arm Multi-Center Phase II Clinical Trial of Cadonilimab (anti-PD-1/CTLA-4) in Combination with or without Conventional Second-Line Treatment for Patients with Extensive Stage Small Cell Lung Cancer.

BACKGROUND: Cadonilimab (AK104) is a bispecific IgG-single-chain Fv fragment (ScFv) antibody that binds to PD-1 and CTLA-4. Cadonilimab has shown encouraging anti-tumour activity and a favourable safety profile in several tumour types. In second-line treatment, there is no defined standard of care for patients with extensive-stage small-cell lung cancer (ES-SCLC). Cadonilimab is expected to show substantial clinical efficacy. OBJECTIVE: To assess the antitumor activity and safety of cadonilimab monotherapy or combination with conventional therapy in ES-SCLC patients who failed first-line treatment. METHODS: In this multicenter, open-label, phase II study, ES-SCLC patients who had failed first-line treatment, also aged 18 years to 70 years with histologically or cytologically confirmed ES-SCLC, and an Eastern Cooperative Oncology Group performance status (ECOG-PS) of 0-2 were eligible. Patients will receive cadonilimab 10 mg/kg every three weeks (Q3 W) among 24 months until progressive disease (PD) or adverse events (AE) discovery. The primary endpoint is progression-free survival (PFS). TRIAL REGISTRATION: NCT05901584.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy.

BACKGROUND: Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers. METHODS: To address these gaps, we constructed The Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multicancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune checkpoint blockades (ICBs). RESULTS: TCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy. CONCLUSIONS: TCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology.

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.

A novel function of claudin-5 in maintaining the structural integrity of the heart and its implications in cardiac pathology.

This study aims to investigate the role of claudin-5 (Cldn5) in cardiac structural integrity. Proteomic analysis was performed to screen the protein profiles in enlarged left atrium from atrial fibrillation (AF) patients. Cldn5 shRNA adeno-associated virus (AAV) or siRNA was injected into the mouse left ventricle or added into HL1 cells respectively to knockdown Cldn5 in cardiomyocytes to observe whether the change of Cldn5 influences cardiac morphology and function, and affects those protein expressions stem from the proteomic analysis. Mitochondrial density and membrane potential were also measured by Mitotracker staining and JC-1 staining under the confocal microscope in HL1 cells. Cldn5 was reduced in cardiomyocytes from the left atrial appendage of AF patients compared to non-AF donors. Proteomic analysis showed 83 proteins were less abundant and 102 proteins were more abundant in AF patients. KEGG pathway analysis showed less abundant CACNA2D2, CACNB2, MYL2 and MAP6 were highly associated with dilated cardiomyopathy. Cldn5 shRNA AAV injection caused severe cardiac atrophy, dilation and myocardial dysfunction in mice. The decreases in mitochondrial numbers and mitochondrial membrane potentials in HL1 cells were observed after Cldn5 knockdown. We demonstrated for the first time the mechanism of Cldn5 downregulation-induced myocyte atrophy and myocardial dysfunction might be associated with the downregulation of CACNA2D2, CACNB2, MYL2 and MAP6, and mitochondrial dysfunction in cardiomyocytes.

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.