Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures

Drug-induced impairment of mitochondrial fatty acid oxidation and steatosis: assessment of causal relationship with 45 pharmaceuticals

Drug-induced liver injury (DILI) represents a major issue for pharmaceutical companies, being a potential cause of black-box warnings on marketed pharmaceuticals, or drug withdrawal from the market. Lipid accumulation in the liver also referred to as steatosis, may be secondary to impaired mitochondrial fatty acid oxidation (mtFAO). However, an overall causal relationship between drug-induced mtFAO inhibition and the occurrence of steatosis in patients has not yet been established with a high number of pharmaceuticals. Hence, 32 steatogenic and 13 nonsteatogenic drugs were tested for their ability to inhibit mtFAO in isolated mouse liver mitochondria. To this end, mitochondrial respiration was measured with palmitoyl-l-carnitine, palmitoyl-CoA + l-carnitine, or octanoyl- l-carnitine. This mtFAO tri-parametric assay was able to predict the occurrence of steatosis in patients with a sensitivity and positive predictive value above 88%. To get further information regarding the mechanism of drug-induced mtFAO impairment, mitochondrial respiration was also measured with malate/glutamate or succinate. Drugs such as diclofenac, methotrexate, and troglitazone could inhibit mtFAO secondary to an impairment of the mitochondrial respiratory chain, whereas dexamethasone, olanzapine, and zidovudine appeared to impair mtFAO directly. Mitochondrial swelling, transmembrane potential, and production of reactive oxygen species were also assessed for all compounds. Only the steatogenic drugs amiodarone, ketoconazole, lovastatin, and toremifene altered all these 3 mitochondrial parameters. In conclusion, our tri-parametric mtFAO assay could be useful in predicting the occurrence of steatosis in patients. The combination of this assay with other mitochondrial parameters could also help to better understand the mechanism of drug-induced mtFAO inhibition.

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets

Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.

In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review

New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.

ToxSTAR: drug-induced liver injury prediction tool for the web environment

SUMMARY

Drug-induced liver injury (DILI) is a challenging endpoint in predictive toxicology because of the complex reactive metabolites that cause liver damage and the wide range of mechanisms involved in the development of the disease. ToxSTAR provides structural similarity-based DILI analysis and in-house DILI prediction models that predict four DILI subtypes (cholestasis, cirrhosis, hepatitis and steatosis) based on drug and drug metabolite molecules.

AVAILABILITY AND IMPLEMENTATION

ToxSTAR is freely available at https://toxstar.kitox.re.kr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation

Drug-induced liver injury (DILI) is one of the major causes of post-approval withdrawal of therapeutics. As a result, there is an increasing need for accurate predictive in vitro assays that reliably detect hepatotoxic drug candidates while reducing drug discovery time, costs, and the number of animal experiments. In vitro hepatocyte-based research has led to an improved comprehension of the underlying mechanisms of chemical toxicity and can assist the prioritization of therapeutic choices with low hepatotoxicity risk. Therefore, several in vitro systems have been generated over the last few decades. This review aims to comprehensively present the development and validation of 2D (two-dimensional) and 3D (three-dimensional) culture approaches on hepatotoxicity screening of compounds and highlight the main factors affecting predictive power of experiments. To this end, we first summarize some of the recognized hepatotoxicity mechanisms and related assays used to appraise DILI mechanisms and then discuss the challenges and limitations of in vitro models.

Ebselen prevents cigarette smoke-induced cognitive dysfunction in mice by preserving hippocampal synaptophysin expression

Background

Cigarette smoking (CS) is the leading cause of chronic obstructive pulmonary disease (COPD). The “spill-over” of pulmonary inflammation into the systemic circulation may damage the brain, leading to cognitive dysfunction. Cessation of CS can improve pulmonary and neurocognitive outcomes, however, its benefit on the neuroinflammatory profile remains uncertain. Here, we investigate how CS exposure impairs neurocognition and whether this can be reversed with CS cessation or an antioxidant treatment.

Methods

Male BALB/c mice were exposed to CS (9 cigarettes/day for 8 weeks) followed by 4 weeks of CS cessation. Another cohort of CS-exposed mice were co-administrated with a glutathione peroxidase mimetic, ebselen (10 mg/kg) or vehicle (5% CM-cellulose). We assessed pulmonary inflammation, spatial and working memory, and the hippocampal microglial, oxidative and synaptic profiles.

Results

CS exposure increased lung inflammation which was reduced following CS cessation. CS caused spatial and working memory impairments which were attributed to hippocampal microglial activation and suppression of synaptophysin. CS cessation did not improve memory deficits or alter microglial activation. Ebselen completely prevented the CS-induced working and spatial memory impairments, which was associated with restored synaptophysin expression without altering microglial activation.

Conclusion

We were able to model the CS-induced memory impairment and microglial activation seen in human COPD. The preventative effects of ebselen on memory impairment is likely to be dependent on a preserved synaptogenic profile. Cessation alone also appears to be insufficient in correcting the memory impairment, suggesting the importance of incorporating antioxidant therapy to help maximising the benefit of cessation.

Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs

Assessment of structure-activity relationships (SARs) for predicting severe drug-induced liver injury (DILI) is essential since in vivo and in vitro preclinical methods cannot detect many druglike compounds disrupting liver functions. To date, plenty of SAR models for the prediction of DILI have been developed; however, none of them considered the route of drug administration and daily dose, which may introduce significant bias into prediction results. We have created a dataset of 617 drugs with parenteral and oral administration routes and consistent information on DILI severity. We have found a clear relationship between route, dose, and DILI severity. According to SAR, nearly 40% of moderate- and non-DILI-causing drugs would cause severe DILI if they were administered at high oral doses. We have proposed the following approach to predict severe DILI. New compounds recommended to be used at low oral doses (<∼10 mg daily), or parenterally, can be considered not causing severe DILI. DILI for compounds administered at medium oral doses (∼10-100 mg daily; 22.2% of drugs under consideration) can be considered unpredictable because reasonable SAR models were not obtained due to the small size and heterogeneity of the corresponding dataset. The DILI potential of the compounds recommended to be used at high oral doses (more than ∼100 mg daily) can be estimated using SAR modeling. The balanced accuracy of the approach calculated by a 10-fold cross-validation procedure is 0.803. The developed approach can be used to estimate severe DILI for druglike compounds proposed to use at low and high oral doses or parenterally at the early stages of drug development.

Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors

A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher's exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

A Multiple Logistic Regression Model Based on Gamma-Glutamyl Transferase as a Biomarker for Early Prediction of Drug-Induced Liver Injury in Vietnamese Patients

The discovery of new biomarkers and the causality of drug-induced liver injury (DILI) is a major focus in modern medicine. Alcoholism is considered a risk factor for DILI. However, the extraction and assessment of alcohol history are difficult due to noncooperation by patients and intermittent management. Therefore, we conducted a case-control study of 1277 patients diagnosed with DILI according to the Roussel Uclaf Causality Assessment Method scale to evaluate gamma-glutamyl transferase (GGT) as a biomarker for predicting DILI in Vietnamese patients, where the proportion of alcoholism is quite high. Further, we built and validated a logistic regression model to predict the risk of DILI in hospitalized patients. The risk of DILI increased by 10% for 1 UI/L higher levels of GGT before prescription (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.00-1.01). A history of alcoholism was not a risk factor for DILI occurrence (OR, 1.83; 95%CI, 0.99-3.04; P = .057). A logistic regression model was successfully built and validated based on age; sex; initial levels of alanine aminotransferase, alkaline phosphatate, GGT, likelihood score of the suspected drug, and history of liver disease; the area under the receiver operating characteristic curve of the model was 0.883 (95%CI, 0.868-0.897). Our results thus suggest the necessity of exercising caution when prescribing to patients without a history of alcoholism but having high GGT levels. This model can be applied clinically to assess the risk of DILI before prescribing to reduce the risk of DILI in the patient.

Research advances in pathogenesis and diagnostic markers of drug-induced liver injury

The pathogenesis of drug-induced liver injury (DILI) is complex, involving a variety of factors; so far, it has not been very clear yet. In recent years, scholars have carried out many studies on the pathogenesis of DILI. The diversity of clinical manifestations and the lack of specific and unified diagnostic criteria for DILI increase the complexity of diagnosis and treatment of DILI. In order to strengthen the understanding of DILI, this paper summarizes the recent research progress on the pathogenesis and diagnostic markers of DILI.

Combining In Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable in silico models for predicting hepatic steatosis on the basis of an in vivo data set of 1041 compounds measured in rodent studies with repeated oral exposure. The imbalanced nature of the data set (1:8, with the "steatotic" compounds belonging to the minority class) required the use of meta-classifiers-bagging with stratified under-sampling and Mondrian conformal prediction-on top of the base classifier random forest. One major goal was the investigation of the influence of different descriptor combinations on model performance (tested by predicting an external validation set): physicochemical descriptors (RDKit), ToxPrint features, as well as predictions from in silico nuclear receptor and transporter models. All models based upon descriptor combinations including physicochemical features led to reasonable balanced accuracies (BAs between 0.65 and 0.69 for the respective models). Combining physicochemical features with transporter predictions and further with ToxPrint features gave the best performing model (BAs up to 0.7 and efficiencies of 0.82). Whereas both meta-classifiers proved useful for this highly imbalanced toxicity data set, the conformal prediction framework also guarantees the error level and thus might be favored for future studies in the field of predictive toxicology.

A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis

Owing to the primary role which it holds within metabolism of xenobiotics, the liver stands at heightened risk of exposure to, and injury from, potentially hazardous substances. A principal manifestation of liver dysfunction is cholestasis-the impairment of physiological bile circulation from its point of origin within the organ to the site of action in the small intestine. The capacity for early identification of compounds liable to exert cholestatic effects is of particular utility within the field of pharmaceutical development, where contribution toward candidate attrition is great. Shortcomings associated with the present in vitro methodologies forecasting cholestasis render their predictivity questionable, permitting scope for the adoption of computational toxicology techniques. As such, the intention of this study has been to construct an in silico profiler, founded upon clinical data, highlighting structural motifs most reliably associated with the end point. Drawing upon a list of >1500 small molecular drugs, compiled and annotated by Kotsampasakou, E. and Ecker, G. F. (J. Chem. Inf. Model. 2017, 57, 608-615), we have formulated a series of 15 structural alerts. These describe fragments intrinsic within distinct pharmaceutical classes including psychoactive tricyclics, β-lactam antimicrobials, and estrogenic/androgenic steroids. Description of the coverage and selectivity of each are provided, alongside consideration of the underlying reactive mechanisms and relevant structure-activity concerns. Provision of mechanistic anchoring ensures that potential exists for framing within the adverse outcome pathway paradigm-the chemistry conveyed through the alert, in particular enabling rationalization at the level of the molecular initiating event.

Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.