Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.

Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs.

Drug-induced liver injury (DILI), although rare, is a frequent cause of adverse drug reactions resulting in warnings and withdrawals of numerous medications. Despite the research community's best efforts, current testing strategies aimed at identifying hepatotoxic drugs prior to human trials are not sufficiently powered to predict the complex mechanisms leading to DILI. In our previous studies, we demonstrated lipophilicity and dose to be associated with increased DILI risk and, and in our latest work, we factored reactive metabolites into the algorithm to predict DILI. Given the inconsistency in determining the potential for drugs to cause DILI, the present study comprehensively assesses the relationship between DILI risk and lipophilicity and the extent of metabolism using a large published dataset of 1036 Food and Drug Administration (FDA)-approved drugs by considering five independent DILI annotations. We found that lipophilicity and the extent of metabolism alone were associated with increased risk for DILI. Moreover, when analyzed in combination with high daily dose (≥100 mg), lipophilicity was statistically significantly associated with the risk of DILI across all datasets (p < 0.05). Similarly, the combination of extensive hepatic metabolism (≥50%) and high daily dose (≥100 mg) was also strongly associated with an increased risk of DILI among all datasets analyzed (p < 0.05). Our results suggest that both lipophilicity and the extent of hepatic metabolism can be considered important risk factors for DILI in humans, and that this relationship to DILI risk is much stronger when considered in combination with dose. The proposed paradigm allows the convergence of different published annotations to a more uniform assessment.

Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction.

Drug-induced liver injury (DILI) remains an important, yet challenging diagnosis for physicians. Each year, additional drugs are implicated in DILI and this year was no different, with more than 1400 articles published on the subject. This review examines some of the most significant highlights and controversies in DILI-related research over the past year and their implications for clinical practice. Several new drugs were approved by the US Food and Drug Administration including a number of drugs implicated in causing DILI, particularly among the chemotherapeutic classes. The COVID-19 pandemic was also a major focus of attention in 2020 and we discuss some of the notable aspects of COVID-19-related liver injury and its implications for diagnosing DILI. Updates in diagnostic and causality assessments related to DILI such as the Roussel Uclaf Causality Assessment Method are included, mindful that there is still no single biomarker or diagnostic tool to unequivocally diagnose DILI. Glutamate dehydrogenase received renewed attention as being more specific than alanine aminotransferase. There were a few new reports of previously unrecognized hepatotoxins, including immune modulators and novel gene therapy drugs that we highlight. Updates and new developments of previously described hepatotoxins, such as immune checkpoint inhibitors and anti-tuberculosis drugs are reviewed. Finally, novel technologies such as organoid culture systems to better predict DILI preclinically may be coming of age and determinants of hepatocyte loss, such as calculating PALT are poised to improve our current means of estimating DILI severity and the risk of acute liver failure.

Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS-DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

Acetaminophen (APAP) overdose causes hepatotoxicity and even acute liver failure. Recent studies indicate that sterile inflammation and innate immune cells may play important roles in damage-induced hepatocytes regeneration and liver repair. The scavenger receptor CD36 has its crucial functions in sterile inflammation. However, the roles of CD36 in APAP induced acute liver injury remain unclear and warrant further investigation.

Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random Forest and 2D molecular descriptors on a dataset of 966 compounds. In addition, predicted transporter inhibition profiles were also included into the models. The initially compiled dataset of 1773 compounds was reduced via a 2-step approach to 966 compounds, resulting in a significant increase (p-value<0.05) in model performance. The models have been validated via 10-fold cross-validation and against three external test sets of 921, 341 and 96 compounds, respectively. The final model showed an accuracy of 64% (AUC 68%) for 10-fold cross-validation (average of 50 iterations) and comparable values for two test sets (AUC 59%, 71% and 66%, respectively). In the study we also examined whether the predictions of our in-house transporter inhibition models for BSEP, BCRP, P-glycoprotein, and OATP1B1 and 1B3 contributed in improvement of the DILI mode. Finally, the model was implemented with open-source 2D RDKit descriptors in order to be provided to the community as a Python script.

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

Drug-induced liver injury (DILI) via metabolic activation by drug-metabolizing enzymes, especially cytochrome P450 (CYP), is a major cause of drug failure and drug withdrawal. In this study, an in vitro model using HepG2 cells in combination with human liver microsomes was developed for the prediction of DILI. The cytotoxicity of cyclophosphamide, a model drug for bioactivation, was augmented in HepG2 cells cultured with microsomes in a manner dependent on exposure time, microsomal protein concentration, and NADPH. Experiments using pan- or isoform-selective CYP inhibitors showed that CYP2B6 and CYP3A4 are responsible for the bioactivation of cyclophosphamide. In a metabolite identification study employing LC-ESI-QTrap and LC-ESI-QTOF, cyclophosphamide metabolites including phosphoramide mustard, a toxic metabolite, were detected in HepG2 cells cultured with microsomes, but not without microsomes. The cytotoxic effects of acetaminophen and diclofenac were also potentiated by microsomes. The potentiation of acetaminophen cytotoxicity was dependent on CYP-dependent metabolism, and the augmentation of diclofenac cytotoxicity was not mediated by either CYP- or UDP-glucuronosyltransferase-dependent metabolism. The cytotoxic effects of leflunomide, nefazodone, and bakuchiol were attenuated by microsomes. The detoxication of leflunomide by microsomes was attributed to mainly CYP3A4-dependent metabolism. The protective effect of microsomes against nefazodone cytotoxicity was dependent on both CYP-mediated metabolism and nonspecific protein binding. Nonspecific protein binding but not CYP-dependent metabolism played a critical role in the attenuation of bakuchiol cytotoxicity. The present study suggests that HepG2 cells cultured with human liver microsomes can be a reliable model in which to predict DILI via bioactivation by drug metabolizing enzymes.

Drug-induced liver injury is the major cause of acute liver failure. However, the underlying mechanisms seem to be multifaceted and remain poorly understood, resulting in few effective therapies. Here, we report a novel mechanism that contributes to acetaminophen-induced hepatotoxicity through the induction of ferroptosis, a distinctive form of programmed cell death. We subsequently identified therapies protective against acetaminophen-induced liver damage and found that (+)-clausenamide ((+)-CLA), an active alkaloid isolated from the leaves of Clausena lansium (Lour.) Skeels, inhibited acetaminophen-induced hepatocyte ferroptosis both in vivo and in vitro. Consistently, (+)-CLA significantly alleviated acetaminophen-induced or erastin-induced hepatic pathological damages, hepatic dysfunctions and excessive production of lipid peroxidation both in cultured hepatic cell lines and mouse liver. Furthermore, treatment with (+)-CLA reduced the mRNA level of prostaglandin endoperoxide synthase 2 while it increased the protein level of glutathione peroxidase 4 in hepatocytes and mouse liver, confirming that the inhibition of ferroptosis contributes to the protective effect of (+)-CLA on drug-induced liver damage. We further revealed that (+)-CLA specifically reacted with the Cys-151 residue of Keap1, which blocked Nrf2 ubiquitylation and resulted in an increased Nrf2 stability, thereby leading to the activation of the Keap1-Nrf2 pathway to prevent drug-induced hepatocyte ferroptosis. Our studies illustrate the innovative mechanisms of acetaminophen-induced liver damage and present a novel intervention strategy to treat drug overdose by using (+)-CLA.

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or 'modules' enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.

Antituberculosis (anti-TB) drugs are the most common cause of drug-induced liver injury (DILI). There are numerous studies revealing the associations between the polymorphisms of pharmacogenes and the risk of anti-TB DILI (ATDILI). In the present study, relevant studies regarding the pharmacogenes associated with ATDILI were systematically searched in PubMed and Scopus. A total of 24 genes associated with ATDILI were reported on and the top five reported genes in terms of frequency were revealed to be N-acetyltransferase 2, cytochrome P450 family 2 subfamily E member 1, glutathione S-transferases [glutathione S-transferase mu 1 (GSTM1) and glutathione S-transferase theta 1 (GSTT1)] and solute carrier organic anion transporter family member 1B1. As ATDILI may be the result of direct and indirect interactions, the encoded proteins were further analysed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to observe the protein-protein interactions and the associations amongst these proteins. The results suggested that only GSTT1 and GSTM1 were central proteins associated with all the other analysed proteins. Therefore, the association between GSTT1 or GSTM1 and the risk of developing ATDILI were further analysed. The results revealed that a GSTM1 deletion genotype was significantly associated with risk of ATDILI [odds ratio (OR), 1.28; 95% confidence interval (CI), 1.08-1.51; P=0.004], whereas the GSTT1 deletion genotype and GSTM1/GSTT1 dual-deletion genotype were not significantly associated with risk of ATDILI. Subgroup analysis based on ethnicity was performed and the results demonstrated a significant association between GSTM1 and ATDILI in South Asian individuals (OR, 1.48; 95% CI, 1.12-1.95; P=0.005), which has not been reported previously, to the best of our knowledge. In conclusion, GSTM1 was associated with ATDILI in South Asian individuals.

Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials.

Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.

In preclinical stages of drug development, anticipating potential adverse drug effects such as toxicity is an important issue for both saving resources and preventing public health risks. Current in vitro cytotoxicity tests are restricted by their predictive potential and their ability to provide mechanistic information. This study aimed to develop a metabolomic mass spectrometry-based approach for the detection and classification of drug-induced hepatotoxicity. To this end, the metabolite profiles of human derived hepatic cells (i.e., HepG2) exposed to different well-known hepatotoxic compounds acting through different mechanisms (i.e., oxidative stress, steatosis, phospholipidosis, and controls) were compared by multivariate data analysis, thus allowing us to decipher both common and mechanism-specific altered biochemical pathways. Briefly, oxidative stress damage markers were found in the three mechanisms, mainly showing altered levels of metabolites associated with glutathione and γ-glutamyl cycle. Phospholipidosis was characterized by a decreased lysophospholipids to phospholipids ratio, suggestive of phospholipid degradation inhibition. Whereas, steatosis led to impaired fatty acids β-oxidation and a subsequent increase in triacylglycerides synthesis. The characteristic metabolomic profiles were used to develop a predictive model aimed not only to discriminate between non-toxic and hepatotoxic drugs, but also to propose potential drug toxicity mechanism(s).

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.

In the last few years, the world has had to face the SARS-CoV-2 infection and its multiple effects. Even though COVID-19 was first considered to be a respiratory disease, it has an extended clinical spectrum with symptoms occurring in many tissues, and it is now identified as a systematic disease. Therefore, various drugs are used during the therapy of hospitalized COVID-19 patients. Studies have shown that many of these drugs could have adverse side-effects, including drug-induced liver injury-also known as DILI-which is the focus of our review. Despite the consistent findings, the pathophysiological mechanism behind DILI in COVID-19 disease is still complex, and there are a few risk factors related to it. However, when it comes to the diagnosis, there are specific algorithms (including the RUCAM algorithm) and biomarkers that can assist in identifying DILI and which we will analyze in our review. As indicated by the title, a variety of drugs are associated with this COVID-19-related complication, including systemic corticosteroids, drugs used for the therapy of uncontrolled cytokine storm, as well as antiviral, anti-inflammatory, and anticoagulant drugs. Bearing in mind that hepatotoxicity is very likely to occur during COVID-19, especially in patients treated with multiple medications, we will also refer to the use of other drugs used for DILI therapy in an effort to control and prevent a severe and long-term outcome.

Anti-staphylococcal penicillins (ASPs) are among the most commonly prescribed antibiotics in children and are associated with a risk of drug-induced liver injury (DILI). Despite the frequent use of ASPs in children, there is no consensus on whether liver function tests (LFTs) should be routinely monitored during treatment.