Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans

Hepatic spheroid-on-a-chip: Fabrication and characterization of a spheroid-based in vitro model of the human liver for drug screening applications

The integration of microfabrication and microfluidics techniques into cell culture technology has significantly transformed cell culture conditions, scaffold architecture, and tissue biofabrication. These tools offer precise control over cell positioning and enable high-resolution analysis and testing. Culturing cells in 3D systems, such as spheroids and organoids, enables recapitulating the interaction between cells and the extracellular matrix, thereby allowing the creation of human-based biomimetic tissue models that are well-suited for pre-clinical drug screening. Here, we demonstrate an innovative microfluidic device for the formation, culture, and testing of hepatocyte spheroids, which comprises a large array of patterned microwells for hosting hepatic spheroid culture in a reproducible and organized format in a dynamic fluidic environment. The device allows maintaining and characterizing different spheroid sizes as well as exposing to various drugs in parallel enabling high-throughput experimentation. These liver spheroids exhibit physiologically relevant hepatic functionality, as evidenced by their ability to produce albumin and urea at levels comparable to in vivo conditions and the capability to distinguish the toxic effects of selected drugs. This highlights the effectiveness of the microenvironment provided by the chip in maintaining the functionality of hepatocyte spheroids. These data support the notion that the liver-spheroid chip provides a favorable microenvironment for the maintenance of hepatocyte spheroid functionality.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or in vitro and in vivo assay endpoints; however, these approaches have not accounted for liver-expressed proteins and drug molecules. To address this gap, we have developed an integrated artificial intelligence/machine learning (AI/ML) model to predict DILI severity for small molecules using a combination of physicochemical properties and off-target interactions predicted in silico. We compiled a data set of 603 diverse compounds from public databases. Among them, 164 were categorized as Most DILI (M-DILI), 245 as Less DILI (L-DILI), and 194 as No DILI (N-DILI) by the FDA. Six machine learning methods were used to create a consensus model for predicting the DILI potential. These methods include k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), weighted average ensemble learning (WA) and penalized logistic regression (PLR). Among the analyzed ML methods, SVM, RF, LR, WA, and PLR identified M-DILI and N-DILI compounds, achieving a receiver operating characteristic area under the curve of 0.88, sensitivity of 0.73, and specificity of 0.9. Approximately 43 off-targets, along with physicochemical properties (fsp³, log S, basicity, reactive functional groups, and predicted metabolites), were identified as significant factors in distinguishing between M-DILI and N-DILI compounds. The key off-targets that we identified include: PTGS1, PTGS2, SLC22A12, PPARγ, RXRA, CYP2C9, AKR1C3, MGLL, RET, AR, and ABCC4. The present AI/ML computational approach therefore demonstrates that the integration of physicochemical properties and predicted on- and off-target biological interactions can significantly improve DILI predictivity compared to chemical properties alone.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development

Drug-induced liver injury (DILI) is a major factor influencing new drug withdrawal; therefore, an appropriate toxicity assessment at the preclinical stage is required. Previous in silico models have been established using compound information listed in large data sources, thereby limiting the DILI risk prediction for new drugs. Herein, we first constructed a model to predict DILI risk based on a molecular initiating event (MIE) predicted by quantitative structure-activity relationships, admetSAR parameters (e.g. cytochrome P450 reactivity, plasma protein binding, and water-solubility), and clinical information (maximum daily dose [MDD] and reactive metabolite [RM]) for 186 compounds. The accuracy of the models using MIE, MDD, RM, and admetSAR alone were 43.2%, 47.3%, 77.0%, and 68.9%, while the "predicted MIE + admetSAR + MDD + RM" model's accuracy was 75.7%. The contribution of MIE to the overall prediction accuracy was little effect or rather worsening it. However, it was considered that MIE was a valuable parameter and that it contributed to detect high DILI risk compounds in the early development stage. We next examined the effect of stepwise changes in MDD on altering the DILI risk and estimating the maximum safety dose (MSD) for clinical use based on structural information, admetSAR, and MIE parameters because it is important to estimate the dose that could prevent the DILI onset in clinical conditions. Low-MSD compounds might increase the DILI risk, as these compounds were classified as "most-DILI concern" at low doses. In conclusion, MIE parameters were especially useful to check the DILI concern compounds and to prevent the underestimation of DILI risk in the early stage of drug development.

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.

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

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.

The application of cytokeratin-18 as a biomarker for drug-induced liver injury

Drug-induced liver injury (DILI) is a frequent and dangerous adverse effect faced during preclinical and clinical drug therapy. DILI is a leading cause of candidate drug attrition, withdrawal and in clinic, is the primary cause of acute liver failure. Traditional diagnostic markers for DILI include alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP). Yet, these routinely used diagnostic markers have several noteworthy limitations, restricting their sensitivity, specificity and accuracy in diagnosing DILI. Consequently, new biomarkers for DILI need to be identified.A potential biomarker for DILI is cytokeratin-18 (CK18), an intermediate filament protein highly abundant in hepatocytes and cholangiocytes. Extensively researched in a variety of clinical settings, both full length and cleaved forms of CK18 can diagnose early-stage DILI and provide insight into the mechanism of hepatocellular injury compared to traditionally used diagnostic markers. However, relatively little research has been conducted on CK18 in preclinical models of DILI. In particular, CK18 and its relationship with DILI is yet to be characterised in an in vivo rat model. Such characterization of CK18 and ccCK18 responses may enable their use as translational biomarkers for hepatotoxicity and facilitate management of clinical DILI risk in drug development. The aim of this review is to discuss the application of CK18 as a biomarker for DILI. Specifically, this review will highlight the properties of CK18, summarise clinical research that utilised CK18 to diagnose DILI and examine the current challenges preventing the characterisation of CK18 in an in vivo rat model of DILI.

Dynamic Modeling of Mitochondrial Membrane Potential Upon Exposure to Mitochondrial Inhibitors

Mitochondria are the main bioenergetic organelles of cells. Exposure to chemicals targeting mitochondria therefore generally results in the development of toxicity. The cellular response to perturbations in cellular energy production is a balance between adaptation, by reorganisation and organelle biogenesis, and sacrifice, in the form of cell death. In homeostatic conditions, aerobic mitochondrial energy production requires the maintenance of a mitochondrial membrane potential (MMP). Chemicals can perturb this MMP, and the extent of this perturbation depends both on the pharmacokinetics of the chemicals and on downstream MMP dynamics. Here we obtain a quantitative understanding of mitochondrial adaptation upon exposure to various mitochondrial respiration inhibitors by applying mathematical modeling to partially published high-content imaging time-lapse confocal imaging data, focusing on MMP dynamics in HepG2 cells over a period of 24 h. The MMP was perturbed using a set of 24 compounds, either acting as uncoupler or as mitochondrial complex inhibitor targeting complex I, II, III or V. To characterize the effect of chemical exposure on MMP dynamics, we adapted an existing differential equation model and fitted this model to the observed MMP dynamics. Complex III inhibitor data were better described by the model than complex I data. Incorporation of pharmacokinetic decay into the model was required to obtain a proper fit for the uncoupler FCCP. Furthermore, oligomycin (complex V inhibitor) model fits were improved by either combining pharmacokinetic (PK) decay and ion leakage or a concentration-dependent decay. Subsequent mass spectrometry measurements showed that FCCP had a significant decay in its PK profile as predicted by the model. Moreover, the measured oligomycin PK profile exhibited only a limited decay at high concentration, whereas at low concentrations the compound remained below the detection limit within cells. This is consistent with the hypothesis that oligomycin exhibits a concentration-dependent decay, yet awaits further experimental verification with more sensitive detection methods. Overall, we show that there is a complex interplay between PK and MMP dynamics within mitochondria and that data-driven modeling is a powerful combination to unravel such complexity.

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.

Characterization of primary mouse hepatocyte spheroids as a model system to support investigations of drug-induced liver injury

Primary mouse hepatocytes isolated from genetically defined and/or diverse lines and disease models are a valuable resource for studying the impact of genetic and environmental factors on drug response and disease. However, standard monolayer cultures result in a rapid decline in mouse hepatocyte viability and functionality. Therefore, we evaluated 3D spheroid methodology for long-term culture of primary mouse hepatocytes, initially to support investigations of drug-induced liver injury (DILI). Primary hepatocytes isolated from male and female C57BL/6J mice were used to generate spheroids by spontaneous self-aggregation in ultra-low attachment plates. Spheroids with well-defined perimeters were observed within 5 days after seeding and retained morphology, ATP, and albumin levels for an additional 2 weeks in culture. Global microarray profiling and quantitative targeted proteomics assessing 10 important drug metabolizing enzymes and transporters demonstrated maintenance of mRNA and protein levels in spheroids over time. Activities for 5 major P450 enzymes were also stable and comparable to activities previously reported for human hepatocyte spheroids. Time- and concentration-dependent decreases in ATP and albumin were observed in response to the DILI-causing drugs acetaminophen, fialuridine, AMG-009, and tolvaptan. Collectively, our results demonstrate successful long-term culture of mouse hepatocytes as spheroids and their utility to support investigations of DILI.

Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method

Cardiac and hepatic toxicity result from induced disruption of the functioning of cardiomyocytes and hepatocytes, respectively, which is tightly related to the organization of their subcellular structures. Cellular structure can be analyzed from microscopy imaging data. However, subtle or complex structural changes that are not easily perceived may be missed by conventional image-analysis techniques. Here we report the evaluation of PhenoTox, an image-based deep-learning method of quantifying drug-induced structural changes using human hepatocytes and cardiomyocytes derived from human induced pluripotent stem cells. We assessed the ability of the deep learning method to detect variations in the organization of cellular structures from images of fixed or live cells. We also evaluated the power and sensitivity of the method for detecting toxic effects of drugs by conducting a set of experiments using known toxicants and other methods of screening for cytotoxic effects. Moreover, we used PhenoTox to characterize the effects of tamoxifen and doxorubicin-which cause liver toxicity-on hepatocytes. PhenoTox revealed differences related to loss of cytochrome P450 3A4 activity, for which it showed greater sensitivity than a caspase 3/7 assay. Finally, PhenoTox detected structural toxicity in cardiomyocytes, which was correlated with contractility defects induced by doxorubicin, erlotinib, and sorafenib. Taken together, the results demonstrated that PhenoTox can capture the subtle morphological changes that are early signs of toxicity in both hepatocytes and cardiomyocytes.

Synthesis, biological activity, molecular docking studies of a novel series of 3-Aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives as the acetylcholinesterase inhibitors

The acetylcholinesterase inhibitors play a critical role in the drug therapy for Alzheimer's disease. In this study, twenty-nine novel 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives were synthesized and assayed for their human acetylcholinesterase (hAChE) inhibitory activities. Inhibitory ratio values of seventeen compounds were above 55% with 4c having the highest value as 77.19%. The compounds with the halogen atoms in the aromatic ring, and N,N-diethylamino or N,N-dimethylamino groups in the side chains at C-3 positions exhibited good inhibitory activity. SAR study was carried out by means of molecular docking technique. According to molecular docking results, the common interacting site for all compounds were found to be peripheral anionic site whereas highly active compounds were interacting with the catalytic active site too. HIGHLIGHTSA novel series of 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives were synthesized and assayed for their human acetylcholinesterase (hAChE) inhibitory activities.The SAR study of the target 3-aryl-7H-thiazolo[3,2-b]-1,2,4-triazin-7-one derivatives was summarized.The active sites in the acetylcholinesterase were analyzed by molecular docking technique.Communicated by Ramaswamy H. Sarma.

Predictability of drug-induced liver injury by machine learning

Background

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.

Methods and results

The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance.

Discussion

We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction.

Reviewers

This article was reviewed by Maciej Kandula and Paweł P. Labaj.

Today's Challenges to De-Risk and Predict Drug Safety in Human "Mind-the-Gap"

Current gaps in drug safety sciences can result from the inability (1) to identify hazard across multiple target organs, (2) to predict and risk assess with certainty against drug safety liabilities for the major target organs, (3) to optimally manage and mitigate against drug safety liabilities, and (4) to apply principles of governance on the generation, integration, and use of experimental data. Translational safety assessment to evaluate several target-organ drug toxicities can only be partially achieved by use of current in vitro and in vivo test systems. What remains to be tackled necessitates the deployment of in vitro-human-relevant test systems to address human specific or selective forms of toxicities. Nevertheless, such models may only address in part some of the requirements in today's armament of biomedical tools essential for improving the discovery of drug candidates. Refinement of in silico tools, Target Safety Assessment and a greater understanding of mechanistic insights of toxicities might provide future opportunities to better identify drug safety liabilities. The increasing diversity of drug modalities present further challenges for nonclinical and clinical development requiring further research to develop suitable test systems and technologies. Our ability to optimally manage and mitigate safety risk will come from the greater refinement of safety margin estimates, provision and use of human-relevant safety biomarkers, and understanding of the translation from in silico, in vitro, and in vivo studies to human. An improvement of governance frameworks and standards at all levels within organizations, national, and international, can only help facilitate drug discovery and development programs.

CXCL9 regulates acetaminophen-induced liver injury via CXCR3

Drug-induced liver injury has become a serious public health problem. Although the mechanism of acetaminophen (APAP)-induced liver injury has been studied for decades it has not been fully elucidated. In-depth study into the mechanisms underlying APAP-induced liver injury may provide useful information for more effective prevention and treatment. In the present study, the role of C-X-C motif chemokine ligand-9 (CXCL9) in APAP-induced liver injury was investigated thus providing a novel direction for the prevention and treatment of drug hepatitis. A total of 20 fasting male patients ingested APAP tablets at Nanjing First Hospital. In addition, wild type (WT) mice were treated with 250 mg/kg APAP or isodose PBS for 1, 3, 6 and 12 h, respectively. Results from reverse-transcription-quantitative polymerase chain reaction analyses demonstrated that CXCL9 mRNA levels were increased in the blood of patients who took APAP in a fasting state and in the livers of APAP-treated WT mice, compared with their respective controls. Hepatocyte apoptosis in the liver tissue of APAP-treated mice decreased following administration of a CXCL9 neutralizing antibody. Caspase-3, caspase-8 and phosphorylated-AKT (S437) were activated in primary hepatocytes isolated from WT mice following CXCL9 treatment. However, no significant differences in expression of caspase-3, caspase-8 and p-AKT (S437) were detected in hepatocytes isolated from C-X-C motif chemokine receptor 3 (CXCR3)^−/− mice following CXCL9 treatment. After CXCL9 administration, WT mice exhibited higher serum levels of aspartate transaminase and increased caspase-3 and caspase-8 activity in liver tissue compared with controls. The same trends were not observed in CXCR3^−/− mice. In conclusion, CXCL9 regulated APAP-induced liver injury through stimulation of hepatocyte apoptosis via binding to CXCR3. These findings provide a novel prevention and treatment strategy for DILI.

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Second exposure to acetaminophen overdose is associated with liver fibrosis in mice

Repeated administration of hepatotoxicants is usually accompanied by liver fibrosis. However, the difference in response as a result of repeated exposures of acetaminophen (APAP) compared to a single dose is not well-studied. Therefore, in the current study, the liver response after a second dose of APAP was investigated. Adult fasted Balb/C mice were exposed to two toxic doses of 300 mg/kg APAP, which were administered 72 h apart from each other. Subsequently, blood and liver from the treated mice were collected 24 h and 72 h after both APAP administrations. Liver transaminase, i.e. alanine amino transferase (ALT) and aspartate amino transferase (AST) levels revealed that the fulminant liver damage was reduced after the second APAP administration compared to that observed at the same time point after the first treatment. These results correlated with the necrotic areas as indicated by histological analyses. Surprisingly, Picro Sirius Red (PSR) staining showed that the accumulation of extracellular matrix after the second dose coincides with the upregulation of some fibrogenic signatures, e.g., alpha smooth muscle actin. Non-targeted liver tissue metabolic profiling indicates that most alterations occur 24 h after the first dose of APAP. However, the levels of most metabolites recover to basal values over time. This organ adaptation process is also confirmed by the upregulation of antioxidative systems like e.g. superoxide dismutase and catalase. From the results, it can be concluded that there is a different response of the liver to APAP toxic doses, if the liver has already been exposed to APAP. A necroinflammatory process followed by a liver regeneration was observed after the first APAP exposure. However, fibrogenesis through the accumulation of extracellular matrix is observed after a second challenge. Therefore, further studies are required to mechanistically understand the so called "liver memory".

miR-122 Release in Exosomes Precedes Overt Tolvaptan-Induced Necrosis in a Primary Human Hepatocyte Micropatterned Coculture Model

Idiosyncratic drug-induced liver injury (IDILI) is thought to often result from an adaptive immune attack on the liver. However, it has been proposed that the cascade of events culminating in an adaptive immune response begins with drug-induced hepatocyte stress, release of exosomal danger signals, and innate immune activation, all of which may occur in the absence of significant hepatocelluar death. A micropatterned coculture model (HepatoPac) was used to explore the possibility that changes in exosome content precede overt necrosis in response to the IDILI drug tolvaptan. Hepatocytes from 3 human donors were exposed to a range of tolvaptan concentrations bracketing plasma Cmax or DMSO control continuously for 4, 24, or 72 h. Although alanine aminotransferase release was not significantly affected at any concentration, tolvaptan exposures at approximately 30-fold median plasma Cmax resulted in increased release of exosomal microRNA-122 (miR-122) into the medium. Cellular imaging and microarray analysis revealed that the most significant increases in exosomal miR-122 were associated with programmed cell death and small increases in membrane permeability. However, early increases in exosome miR-122 were more associated with mitochondrial-induced apoptosis and oxidative stress. Taken together, these data suggest that tolvaptan treatment induces cellular stress and exosome release of miR-122 in primary human hepatocytes in the absence of overt necrosis, providing direct demonstration of this with a drug capable of causing IDILI. In susceptible individuals, these early events may occur at pharmacologic concentrations of tolvaptan and may promote an adaptive immune attack that ultimately results in clinically significant liver injury.

Development and Validation of a Test to Identify Drugs That Cause Idiosyncratic Drug-Induced Liver Injury

BACKGROUND & AIMS

Idiosyncratic drug-induced liver injury (iDILI) is one of the most challenging diagnoses in hepatology. It is frequently impossible to identify the agent that has caused iDILI in patients who take multiple medicines. We developed an in vitro method to identify drugs that cause liver injury in patients, based on drug toxicity to monocyte-derived hepatocyte-like (MH) cells from patient blood samples. We then collected data on patients who were re-exposed to drugs found to be toxic in the MH test to validate test performance.

METHODS

We performed a prospective study of patients referred to the University Hospital in Munich, Germany, with acute liver injury believed to be caused by medications (300 patients were enrolled in the study and we present data from 40 patients with iDILI and re-exposure to implicated drugs). We collected data from patients on medical history, laboratory test and imaging results, findings from biopsy analyses, and medications taken. Blood samples were collected from all patients and MH cells were isolated and cultured for 10 days. MH cells were then incubated with drugs to which each patient had been exposed, and toxicity was measured based on release of lactate dehydrogenase. Agents found to be toxic to MH cells were considered as candidates for the cause of liver injury. Patients were followed up for up to 6 months after liver injury and data on drug re-exposures and subsequent liver damage within the following 3 to 24 months were associated with findings from MH tests.

RESULTS

Our test identified 10 drugs that were toxic to MH cells from 13 patients (amoxicillin/clavulanate to cells from 2 patients; diclofenac to cells from 2 patients; methylprednisolone to cells from 2 patients; and atorvastatin, metamizole, pembrolizumab, piperacillin/tazobactam, moxifloxacin, duloxetine, or sertraline each to cells from 1 patient). Thirteen patients had a recurrence of liver injury after inadvertent re-exposure to a single drug, and the MH test correctly identified 12 of the 13 drugs that caused these liver re-injury events. All 86 drugs that were not toxic to MH cells in our assay were safely resumed by patients and were not associated with liver re-injury in 27 patients. Therefore, the MH test identifies drugs that cause liver injury with 92.3% sensitivity and 100% specificity (1 false-negative and 12 true-positive results).

CONCLUSIONS

We developed a test to identify drugs that cause liver injury in patients based on their toxicity to MH cells isolated from patients with DILI. We validated results from the assay and found it to identify drugs that cause DILI with 92.3% sensitivity and 100% specificity. The MH cell test could be a tool to identify causes of iDILI, even in patients taking multiple medications. ClinicalTrials.gov no: NCT 02353455.

Advances in Engineered Human Liver Platforms for Drug Metabolism Studies

Metabolism in the liver often determines the overall clearance rates of many pharmaceuticals. Furthermore, induction or inhibition of the liver drug metabolism enzymes by perpetrator drugs can influence the metabolism of victim drugs (drug-drug interactions). Therefore, determining liver-drug interactions is critical during preclinical drug development. Unfortunately, studies in animals are often of limited value because of significant differences in the metabolic pathways of the liver across different species. To mitigate such limitations, the pharmaceutical industry uses a continuum of human liver models, ranging from microsomes to transfected cell lines and cultures of primary human hepatocytes (PHHs). Of these models, PHHs provide a balance of high-throughput testing capabilities together with a physiologically relevant cell type that exhibits all the characteristic enzymes, cofactors, and transporters. However, PHH monocultures display a rapid decline in metabolic capacity. Consequently, bioengineers have developed several tools, such as cellular microarrays, micropatterned cocultures, self-assembled and bioprinted spheroids, and perfusion devices, to enhance and stabilize PHH functions for ≥2 weeks. Many of these platforms have been validated for drug studies, whereas some have been adapted to include liver nonparenchymal cells that can influence hepatic drug metabolism in health and disease. Here, we focus on the design features of such platforms and their representative drug metabolism validation datasets, while discussing emerging trends. Overall, the use of engineered human liver platforms in the pharmaceutical industry has been steadily rising over the last 10 years, and we anticipate that these platforms will become an integral part of drug development with continued commercialization and validation for routine screening use.

Do In Vitro Assays Predict Drug Candidate Idiosyncratic Drug-Induced Liver Injury Risk?

In vitro assays are commonly used during drug discovery to try to decrease the risk of idiosyncratic drug-induced liver injury (iDILI). But how effective are they at predicting risk? One of the most widely used methods evaluates cell cytotoxicity. Cytotoxicity assays that used cell lines that are very different from normal hepatocytes, and high concentrations of drug, were not very accurate at predicting idiosyncratic drug reaction risk. Even cytotoxicity assays that use more biologically normal cells resulted in many false-positive and false-negative results. Assays that quantify reactive metabolite formation, mitochondrial injury, and bile salt export pump (BSEP) inhibition have also been described. Although evidence suggests that reactive metabolite formation and BSEP inhibition can play a role in the mechanism of iDILI, these assays are not very accurate at predicting risk. In contrast, inhibition of the mitochondrial electron transport chain appears not to play an important role in the mechanism of iDILI, although other types of mitochondrial injury may do so. It is likely that there are many additional mechanisms by which drugs can cause iDILI. However, simply measuring more parameters is unlikely to provide better predictive assays unless those parameters are actually involved in the mechanism of iDILI. Hence, a better mechanistic understanding of iDILI is required; however, mechanistic studies of iDILI are very difficult. There is substantive evidence that most iDILI is immune mediated; therefore, the most accurate assays may involve those that determine immune responses to drugs. New methods to manipulate immune tolerance may greatly facilitate development of more suitable methods.

In vitro screening of cell bioenergetics to assess mitochondrial dysfunction in drug development

Drug-induced mitochondrial toxicity is considered as a common cellular mechanism that can induce a variety of organ toxicities. In the present manuscript, 17 in vitro mitochondrial toxic drugs, reported to induce Drug-Induced Liver Injury (DILI) and 6 non-mitochondrial toxic drugs (3 with DILI and 3 without DILI concern), were tested in HepG2 cells using a bioenergetics system. The 17 mitochondrial toxic drugs represent a wide variety of mitochondrial dysfunctions as well as DILI and include 4 pairs of drugs which are structurally related but associated with different DILI concerns in human. Cell bioenergetics were measured using the XF96e analyzer which simultaneous monitor oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), indirect measurements of oxidative phosphorylation and glycolysis, respectively. OCR associated with ATP production, maximal respiration, proton leak and spare respiratory capacity, were also assessed. Duplicate experiments resulted in a sensitivity of 82% (14/17) and specificity of 83% (5/6). The addition of stressors improved specificity considerably. Cut-offs, statistics and rules are clearly discussed to facilitate the use of this assay for screening purposes. Overall, the authors consider that this assay should be part of the battery of safety screening assays at early stages of drug development.

Proteomics Analysis of Monocyte-Derived Hepatocyte-Like Cells Identifies Integrin Beta 3 as a Specific Biomarker for Drug-Induced Liver Injury by Diclofenac

Idiosyncratic drug-induced liver injury (iDILI) is a major cause of acute liver failure resulting in liver transplantation or death. Prediction and diagnosis of iDILI remain a great challenge, as current models provide unsatisfying results in terms of sensitivity, specificity, and prognostic value. The absence of appropriate tools for iDILI detection also impairs the development of reliable biomarkers. Here, we report on a new method for identification of drug-specific biomarkers. We combined the advantages of monocyte-derived hepatocyte-like (MH) cells, able to mimic individual characteristics, with those of a novel mass spectrometry-based proteomics technology to assess potential biomarkers for Diclofenac-induced DILI. We found over 2,700 proteins differentially regulated in MH cells derived from individual patients. Herefrom, we identified integrin beta 3 (ITGB3) to be specifically upregulated in Diclofenac-treated MH cells from Diclofenac-DILI patients compared to control groups. Finally, we validated ITGB3 by flow cytometry analysis of whole blood and histological staining of liver biopsies derived from patients diagnosed with Diclofenac-DILI. In summary, our results show that biomarker candidates can be identified by proteomics analysis of MH cells. Application of this method to a broader range of drugs in the future will exploit its full potential for the development of drug-specific biomarkers. Data are available via ProteomeXchange with identifier PXD008918.

Species differences in bile acids II. Bile acid metabolism

One of the mechanisms of drug-induced liver injury (DILI) involves alterations in bile acid (BA) homeostasis and elimination, which encompass several metabolic pathways including hydroxylation, amidation, sulfation, glucuronidation and glutathione conjugation. Species differences in BA metabolism may play a major role in the failure of currently used in vitro and in vivo models to predict reliably the DILI during the early stages of drug discovery and development. We developed an in vitro cofactor-fortified liver S9 fraction model to compare the metabolic profiles of the four major BAs (cholic acid, chenodeoxycholic acid, lithocholic acid and ursodeoxycholic acid) between humans and several animal species. High- and low-resolution liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance imaging were used for the qualitative and quantitative analysis of BAs and their metabolites. Major species differences were found in the metabolism of BAs. Sulfation into 3-O-sulfates was a major pathway in human and chimpanzee (4.8%-52%) and it was a minor pathway in all other species (0.02%-14%). Amidation was primarily with glycine (62%-95%) in minipig and rabbit and it was primarily with taurine (43%-81%) in human, chimpanzee, dog, hamster, rat and mice. Hydroxylation was highest (13%-80%) in rat and mice followed by hamster, while it was lowest (1.6%-22%) in human, chimpanzee and minipig. C6-β hydroxylation was predominant (65%-95%) in rat and mice, while it was at C6-α position in minipig (36%-97%). Glucuronidation was highest in dog (10%-56%), while it was a minor pathway in all other species (<12%). The relative contribution of the various pathways involved in BA metabolism in vitro were in agreement with the observed plasma and urinary BA profiles in vivo and were able to predict and quantify the species differences in BA metabolism. In general, overall, BA metabolism in chimpanzee is most similar to human, while BA metabolism in rats and mice is most dissimilar from human.

Understanding drug targets: no such thing as bad news

How can small-to-medium pharma and biotech companies enhance the chances of running a successful drug project and maximise the return on a limited number of assets? Having a full appreciation of the safety risks associated with proposed drug targets is a crucial element in understanding the unwanted side-effects that might stop a project in its tracks. Having this information is necessary to complement knowledge about the probable efficacy of a future drug. However, the lack of data-rich insight into drug-target safety is one of the major causes of drug-project failure today. Conducting comprehensive target-safety reviews early in the drug discovery process enables project teams to make the right decisions about which drug targets to take forward.

Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse

Studies of gene expression are common in toxicology and provide important clues to mechanistic understanding of adverse effects of chemicals. Most prior studies have been performed in a single strain or cell line; however, gene expression is heavily influenced by the genetic background, and these genotype-expression differences may be key drivers of inter-individual variation in response to chemical toxicity. In this study, we hypothesized that the genetically diverse Collaborative Cross mouse population can be used to gain insight and suggest mechanistic hypotheses for the dose- and genetic background-dependent effects of chemical exposure. This hypothesis was tested using a model liver toxicant trichloroethylene (TCE). Liver transcriptional responses to TCE exposure were evaluated 24 h after dosing. Transcriptomic dose-responses were examined for both TCE and its major oxidative metabolite trichloroacetic acid (TCA). As expected, peroxisome- and fatty acid metabolism-related pathways were among the most dose-responsive enriched pathways in all strains. However, nearly half of the TCE-induced liver transcriptional perturbation was strain-dependent, with abundant evidence of strain/dose interaction, including in the peroxisomal signaling-associated pathways. These effects were highly concordant between the administered TCE dose and liver levels of TCA. Dose-response analysis of gene expression at the pathway level yielded points of departure similar to those derived from the traditional toxicology studies for both non-cancer and cancer effects. Mapping of expression-genotype-dose relationships revealed some significant associations; however, the effects of TCE on gene expression in liver appear to be highly polygenic traits that are challenging to positionally map. This study highlights the usefulness of mouse population-based studies in assessing inter-individual variation in toxicological responses, but cautions that genetic mapping may be challenging because of the complexity in gene exposure-dose relationships.

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.

Current nonclinical testing paradigm enables safe entry to First-In-Human clinical trials: The IQ consortium nonclinical to clinical translational database

The contribution of animal testing in drug development has been widely debated and challenged. An industry-wide nonclinical to clinical translational database was created to determine how safety assessments in animal models translate to First-In-Human clinical risk. The blinded database was composed of 182 molecules and contained animal toxicology data coupled with clinical observations from phase I human studies. Animal and clinical data were categorized by organ system and correlations determined. The 2×2 contingency table (true positive, false positive, true negative, false negative) was used for statistical analysis. Sensitivity was 48% with a 43% positive predictive value (PPV). The nonhuman primate had the strongest performance in predicting adverse effects, especially for gastrointestinal and nervous system categories. When the same target organ was identified in both the rodent and nonrodent, the PPV increased. Specificity was 84% with an 86% negative predictive value (NPV). The beagle dog had the strongest performance in predicting an absence of clinical adverse effects. If no target organ toxicity was observed in either test species, the NPV increased. While nonclinical studies can demonstrate great value in the PPV for certain species and organ categories, the NPV was the stronger predictive performance measure across test species and target organs indicating that an absence of toxicity in animal studies strongly predicts a similar outcome in the clinic. These results support the current regulatory paradigm of animal testing in supporting safe entry to clinical trials and provide context for emerging alternate models.

A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

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: recent advances in diagnosis and risk assessment

Idiosyncratic drug-induced liver injury (IDILI) is a rare but potentially severe adverse drug reaction that should be considered in patients who develop laboratory criteria for liver injury secondary to the administration of a potentially hepatotoxic drug. Although currently used liver parameters are sensitive in detecting DILI, they are neither specific nor able to predict the patient's subsequent clinical course. Genetic risk assessment is useful mainly due to its high negative predictive value, with several human leucocyte antigen alleles being associated with DILI. New emerging biomarkers which could be useful in assessing DILI include total keratin18 (K18) and caspase-cleaved keratin18 (ccK18), macrophage colony-stimulating factor receptor 1, high mobility group box 1 and microRNA-122. From the numerous in vitro test systems that are available, monocyte-derived hepatocytes generated from patients with DILI show promise in identifying the DILI-causing agent from among a panel of coprescribed drugs. Several computer-based algorithms are available that rely on cumulative scores of known risk factors such as the administered dose or potential liabilities such as mitochondrial toxicity, inhibition of the bile salt export pump or the formation of reactive metabolites. A novel DILI cluster score is being developed which predicts DILI from multiple complimentary cluster and classification models using absorption-distribution-metabolism-elimination-related as well as physicochemical properties, diverse substructural descriptors and known structural liabilities. The provision of more advanced scientific and regulatory guidance for liver safety assessment will depend on validating the new diagnostic markers in the ongoing DILI registries, biobanks and public-private partnerships.

Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective

The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.