A novel in vitro high-content imaging assay for the prediction of drug-induced lung toxicity

The development of inhaled drugs for respiratory diseases is frequently impacted by lung pathology in non-clinical safety studies. To enable design of novel candidate drugs with the right safety profile, predictive in vitro lung toxicity assays are required that can be applied during drug discovery for early hazard identification and mitigation. Here, we describe a novel high-content imaging-based screening assay that allows for quantification of the tight junction protein occludin in A549 cells, as a model for lung epithelial barrier integrity. We assessed a set of compounds with a known lung safety profile, defined by clinical safety or non-clinical in vivo toxicology data, and were able to correctly identify 9 of 10 compounds with a respiratory safety risk and 9 of 9 compounds without a respiratory safety risk (90% sensitivity, 100% specificity). The assay was sensitive at relevant compound concentrations to influence medicinal chemistry optimization programs and, with an accessible cell model in a 96-well plate format, short protocol and application of automated imaging analysis algorithms, this assay can be readily integrated in routine discovery safety screening to identify and mitigate respiratory toxicity early during drug discovery. Interestingly, when we applied physiologically-based pharmacokinetic (PBPK) modelling to predict epithelial lining fluid exposures of the respiratory tract after inhalation, we found a robust correlation between in vitro occludin assay data and lung pathology in vivo, suggesting the assay can inform translational risk assessment for inhaled small molecules.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.

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.

Oxidative-stress and long-term hepatotoxicity: comparative study in Upcyte human hepatocytes and hepaRG cells

Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions and a major cause of drug development failure and withdrawal. Although different molecular mechanisms are implicated in DILI, enhanced ROS levels have been described as a major mechanism. Human-derived cell models are increasingly used in preclinical safety assessment because they provide quick and relatively inexpensive information in early stages of drug development. We have analyzed and compared the phenotype and functionality of two liver cell models (Upcyte human hepatocytes and HepaRG cells) to demonstrate their suitability for long-term hepatotoxicity assessments and mechanistic studies. The transcriptomic and functional analysis revealed the maintenance of phase I and phase II enzymes, and antioxidant enzymes along time in culture, although the differences found between both test systems underlie the differential sensitivity to hepatotoxins. The evaluation of several mechanisms of cell toxicity, including oxidative stress, by high-content screening, demonstrated that, by combining the stable phenotype of liver cells and repeated-dose exposure regimes to 12 test compounds at clinically relevant concentrations, both Upcyte hepatocytes and HepaRG offer suitable properties to be used in routine screening assays for toxicological assessments during drug preclinical testing.

Fibrinogen improves liver function via promoting cell aggregation and fibronectin assembly in hepatic spheroids

Many key functions performed by the liver depend on the interaction between parenchymal cells and the microenvironment comprised of neighboring cells and extracellular matrix. The biological macromolecules in the matrix, which are dynamically changing, participate in various physiological processes through interactions with cell surface receptors, antigens, and ion channels. We found the rat liver biomatrix scaffold (LBS) prepared from adult rats is more effective in enhancing the function of hepatic spheroids than those derived from newborn or senile rats. Combined with matrisome and bioinformatics analyses, we further found that the glycoproteins, fibronectin and fibrinogen may have special potential for improving hepatocyte function. Human primary hepatocyte organoids and HepaRG spheroids showed more mature hepatocyte phenotype after adding fibronectin and fibrinogen to the culture system. During the cultivation of hepatic spheroids, fibrinogen resulted in an increase in cell-cell junction by promoting cell aggregation and helping fibronectin to assemble on cell surface, which resulted in activation of Wnt/β-catenin pathway. Fibronectin-integrin αVβ1-Wnt/β-catenin may be the axis of signal transduction in parenchymal cell microenvironment. Importantly, fibrinogen enhances the signal transduction. These results suggest that the addition of fibronectin and fibrinogen to the 3D culture system is a new strategy for inducing parenchymal cell functional maturation.

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.

Zebrafish toxicological screening could aid Leishmaniosis drug discovery

BACKGROUND

Recently a screen from a library of 1.8 million compounds identified in vitro a potent activity of the 2-aminobenzimidazoles series against Leishmania infantum, the etiological agent responsible by over 20.000 deaths each year. Several analogs were synthesized and in vitro tested through an optimization program, leading to a promising 2-aminobenzimidazoles derived compound (2amnbzl-d) that was progressed to in vivo mice studies. However, the not expected toxic effects prevented its progression to more advanced preclinical and clinical phases of drug development. Due to limitations of cell models in detecting whole organism complex interactions, 90% of the compounds submitted to pre-clinical tests are reproved. The use of Zebrafish embryo models could improve this rate, saving mammals, time and costs in the development of new drugs. To test this hypothesis, we compared 2amnbzl-d with two compounds with already established safety profile: carbamazepine and benznidazole, using an embryo Zebrafish platform based on acute toxicity, hepatotoxicity, neurotoxicity and cardiotoxicity assays (Pltf-AcHpNrCd).

RESULTS

Tests were performed blindly, and the results demonstrated the presence of lethal and teratogenic effects (CL50%: 14.8 µM; EC50%: 8.6 µM), hepatotoxic in concentrations above 7.5 µM and neurotoxic in embryos exposed to 15 µM of 2amnbzl-d. Nevertheless, benznidazole exposition showed no toxicity and only the 100 µM of carbamazepine induced a bradycardia.

CONCLUSIONS

Results using Pltf-AcHpNrCd with zebrafish reproduced that found in the toxicological tests with mammals to a portion of the costs and time of experimentation.

Latest impact of engineered human liver platforms on drug development

Drug-induced liver injury (DILI) is a leading cause of drug attrition, which is partly due to differences between preclinical animals and humans in metabolic pathways. Therefore, in vitro human liver models are utilized in biopharmaceutical practice to mitigate DILI risk and assess related mechanisms of drug transport and metabolism. However, liver cells lose phenotypic functions within 1–3 days in two-dimensional monocultures on collagen-coated polystyrene/glass, which precludes their use to model the chronic effects of drugs and disease stimuli. To mitigate such a limitation, bioengineers have adapted tools from the semiconductor industry and additive manufacturing to precisely control the microenvironment of liver cells. Such tools have led to the fabrication of advanced two-dimensional and three-dimensional human liver platforms for different throughput needs and assay endpoints (e.g., micropatterned cocultures, spheroids, organoids, bioprinted tissues, and microfluidic devices); such platforms have significantly enhanced liver functions closer to physiologic levels and improved functional lifetime to >4 weeks, which has translated to higher sensitivity for predicting drug outcomes and enabling modeling of diseased phenotypes for novel drug discovery. Here, we focus on commercialized engineered liver platforms and case studies from the biopharmaceutical industry showcasing their impact on drug development. We also discuss emerging multi-organ microfluidic devices containing a liver compartment that allow modeling of inter-tissue crosstalk following drug exposure. Finally, we end with key requirements for engineered liver platforms to become routine fixtures in the biopharmaceutical industry toward reducing animal usage and providing patients with safe and efficacious drugs with unprecedented speed and reduced cost.

Exploratory Study Using Urinary Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91-1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48-0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC-IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.

The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.

SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. In the current preclinical drug combination screening, the top combinations for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested in Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler prioritized as top hits those synergistic drug pairs that showed higher selective efficacy (difference between efficacy and toxicity), which offers an improved likelihood for clinical success.

High-throughput combinatorial screening is an established approach to identify candidate drug combinations to be further developed as safe and effective treatment options for many diseases, such as various types of cancers, bacterial, malarial, and viral infections. The selection of top performing drug combinations for further development is an important step for the success of the screen, where not only the synergy but also selective efficacy and potential toxicity of the drug pairs should be critically assessed. Currently, there is no method available for this; therefore, we developed SynToxProfiler tool, which was demonstrated in two different application cases to prioritize synergistic drug pairs with higher efficacy and lower toxicity as top hits, providing thus an increased likelihood for their clinical success.

Comparative analysis of urban road dust compositions in relation to their potential human health impacts

This study investigated the chemical components of fine urban road dust from seven sampling sites, based on which we could predict potential human health effects. The elemental compositions, including the contents of metals and volatile or semivolatile organic compounds, were determined to establish comprehensive chemical profiles of solid road dust. The chemical profiles, consisting of C: H ratio, metal contents, and relative abundances of organic compounds, provided a chemical signature for road dust. To overall cytotoxicity values ranging between 7 and 58%, water extracts contributed less than 15%, and cell death mainly occurred via direct contact with solid-phase components, which possibly indicates that the selected chemical profile of solid-phase road dust components could serve as a strong predictor for BJ and WI-38 cytotoxicity. Pure metal oxides (Cr₂O₃, CuO, Fe₂O₃, MnO₂, NiO, or ZnO) exhibited a positive dose-response, and the corresponding metal contents in solid road dust were well correlated with cell viability. The principal component analysis (PCA) results suggested that the metal contents were stronger predictors of cytotoxicity than the benzene derivative or hydrocarbon contents. The chemical profiles established in this study could be further utilized to identify candidate health hazard factors in road dust.

Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity

Drug-induced liver injury (DILI) is of significant concern to drug development and regulatory review because of the limited success with existing preclinical models. For developing alternative methods, a large drug list is needed with known DILI severity and toxicity. We augmented the DILIrank data set [annotated using US Food and Drug Administration (FDA) drug labeling)] with four literature datasets (N >350 drugs) to generate the largest drug list with DILI classification, called DILIst (DILI severity and toxicity). DILIst comprises 1279 drugs, of which 768 were DILI positives (increase of 65% from DILIrank), whereas 511 were DILI negatives (increase of 65%). The investigation of DILI positive-negative distribution across various therapeutic categories revealed the most and least frequent DILI categories. Thus, we consider DILIst to be an invaluable resource for the community to improve DILI research.

Predicting Drug-Induced Liver Injury with Bayesian Machine Learning

Drug induced liver injury (DILI) can require significant risk management in drug development and on occasion can cause morbidity or mortality, leading to drug attrition. Optimizing candidates preclinically can minimize hepatotoxicity risk, but it is difficult to predict due to multiple etiologies encompassing DILI, often with multifactorial and overlapping mechanisms. In addition to epidemiological risk factors, physicochemical properties, dose, disposition, lipophilicity, and hepatic metabolic function are also relevant for DILI risk. Better human-relevant, predictive models are required to improve hepatotoxicity risk assessment in drug discovery. Our hypothesis is that integrating mechanistically relevant hepatic safety assays with Bayesian machine learning will improve hepatic safety risk prediction. We present a quantitative and mechanistic risk assessment for candidate nomination using data from in vitro assays (hepatic spheroids, BSEP, mitochondrial toxicity, and bioactivation), together with physicochemical (cLogP) and exposure (Cmax_total) variables from a chemically diverse compound set (33 no/low-, 40 medium-, and 23 high-severity DILI compounds). The Bayesian model predicts the continuous underlying DILI severity and uses a data-driven prior distribution over the parameters to prevent overfitting. The model quantifies the probability that a compound falls into either no/low-, medium-, or high-severity categories, with a balanced accuracy of 63% on held-out samples, and a continuous prediction of DILI severity along with uncertainty in the prediction. For a binary yes/no DILI prediction, the model has a balanced accuracy of 86%, a sensitivity of 87%, a specificity of 85%, a positive predictive value of 92%, and a negative predictive value of 78%. Combining physiologically relevant assays, improved alignment with FDA recommendations, and optimal statistical integration of assay data leads to improved DILI risk prediction.

Prediction of Drug-Induced Hepatotoxicity Using Long-Term Stable Primary Hepatic 3D Spheroid Cultures in Chemically Defined Conditions

High failure rates of drug candidates in the clinics, restricted-use warnings as well as withdrawals of drugs in postmarketing stages are of substantial concern for the pharmaceutical industry and drug-induced liver injury (DILI) constitutes one of the most frequent reasons for such safety failures. Importantly, as DILI cannot be accurately predicted using animal models, animal safety tests are commonly complemented with assessments in human in vitro systems. 3D spheroid cultures of primary human hepatocytes in chemically defined conditions, hereafter termed CD-spheroids, have recently emerged as a microphysiological model system in which hepatocytes retain their molecular phenotypes and hepatic functions for multiple weeks in culture. However, their predictive power for the detection of hepatotoxic liabilities has not been systematically assessed. Therefore, we here evaluated the hepatotoxicity of 123 drugs with or without direct implication in clinical DILI events. Importantly, using ATP quantifications as the single endpoint, the model accurately distinguished between hepatotoxic and nontoxic structural analogues and exceeded both sensitivity and specificity of all previously published in vitro assays at substantially lower exposure levels, successfully detecting 69% of all hepatotoxic compounds without producing any false positive results (100% specificity). Furthermore, the platform supports the culture of spheroids of primary hepatocytes from preclinical animal models, thereby allowing the identification of animal-specific toxicity events. We anticipate that CD-spheroids represent a powerful and versatile tool in drug discovery and preclinical drug development that can reliably flag hepatotoxic drug candidates and provide guidance for the selection of the most suitable animal models.

Use of a non-hepatic cell line highlights limitations associated with cell-based assessment of metabolically induced toxicity

Metabolically induced drug-toxicity is a major cause of drug failure late in drug optimization phases. Accordingly, in vitro metabolic profiling of compounds is being introduced at earlier stages of the drug discovery pipeline. An increasingly common method to obtain these profiles is through overexpression of key CYP450 metabolic enzymes in immortalized liver cells, to generate competent hepatocyte surrogates. Enhanced cytotoxicity is presumed to be due to toxic metabolite production via the overexpressed enzyme. However, metabolically induced toxicity is a complex multi-parameter phenomenon and the potential background contribution to metabolism arising from the use of liver cells which endogenously express CYP450 isoforms is consistently overlooked. In this study, we sought to reduce the potential background interference by applying this methodology in kidney-derived HEK293 cells which lack endogenous CYP450 expression. Overexpression of CYP3A4 resulted in increased HEK293 proliferation, while exposure to four compounds with reported metabolically induced cytotoxicity in liver-derived cells overexpressing CYP3A4 resulted in no increase in cytotoxicity. Our results indicate that overexpression of a single CYP450 isoform in hepatic cell lines may not be a reliable method to discriminate which enzymes are responsible for metabolic induced cytotoxicity.

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.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Cell-based assay using glutathione-depleted HepaRG and HepG2 human liver cells for predicting drug-induced liver injury

Immortalized liver cells have been used for evaluating the toxicity of compounds; however, excessive glutathione is considered to lessen cytotoxicity. In this study, we compared the effects of glutathione depletion on cytotoxicities of drugs using HepaRG and HepG2 cells, which express and lack drug-metabolizing enzymes, respectively, for predicting drug-induced liver injury (DILI) risks. These cells were pre-incubated with L-buthionine-S,R-sulfoximine (BSO) and then exposed to 34 test compounds with various DILI risks for 24 h. ATP level exhibited the highest predictability of DILI among tested parameters. BSO treatment rendered cells susceptible to drug-induced cytotoxicity when evaluated by cell viability and caspase 3/7 activity with the sensitivity of cell viability from 50% in non-treated HepaRG cells to 71% in BSO-treated HepaRG cells. These results indicate that cytotoxicity assays using GSH-depleted HepaRG cells improve the predictability of DILI risks. However, HepaRG cells were not always superior to HepG2 cells when assessed by ATP level. The combination of HepG2 and HepaRG cells index produced the best prediction in the cases of caspase 3/7 acitivity and ATP level. In conclusions, the developed highly sensitive cell-based assay using GSH-reduced cells would be useful for predicting potential DILI risks at an early stage of drug development.

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.

Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays

Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC₅₀ values obtained from the chip platform were correlated with rat LD₅₀ values, human C _max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC₅₀ and C _max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.

Customised in vitro model to detect human metabolism-dependent idiosyncratic drug-induced liver injury

Drug-induced liver injury (DILI) has a considerable impact on human health and is a major challenge in drug safety assessments. DILI is a frequent cause of liver injury and a leading reason for post-approval drug regulatory actions. Considerable variations in the expression levels of both cytochrome P450 (CYP) and conjugating enzymes have been described in humans, which could be responsible for increased susceptibility to DILI in some individuals. We herein explored the feasibility of the combined use of HepG2 cells co-transduced with multiple adenoviruses that encode drug-metabolising enzymes, and a high-content screening assay to evaluate metabolism-dependent drug toxicity and to identify metabolic phenotypes with increased susceptibility to DILI. To this end, HepG2 cells with different expression levels of specific drug-metabolism enzymes (CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, GSTM1 and UGT2B7) were exposed to nine drugs with reported hepatotoxicity. A panel of pre-lethal mechanistic parameters (mitochondrial superoxide production, mitochondrial membrane potential, ROS production, intracellular calcium concentration, apoptotic nuclei) was used. Significant differences were observed according to the level of expression and/or the combination of several drug-metabolism enzymes in the cells created ad hoc according to the enzymes implicated in drug toxicity. Additionally, the main mechanisms implicated in the toxicity of the compounds were also determined showing also differences between the different types of cells employed. This screening tool allowed to mimic the variability in drug metabolism in the population and showed a highly efficient system for predicting human DILI, identifying the metabolic phenotypes associated with increased DILI risk, and indicating the mechanisms implicated in their toxicity.

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.

A new approach for the assessment of the toxicity of polyphenol-rich compounds with the use of high content screening analysis

The toxicity of in vitro tested compounds is usually evaluated based on AC₅₀ values calculated from dose-response curves. However, there is a large group of compounds for which a standard four-parametric sigmoid curve fitting may be inappropriate for estimating AC₅₀. In the present study, 22 polyphenol-rich compounds were prioritized from the least to the most toxic based on the total area under and over the dose-response curves (AUOC) in relation to baselines. The studied compounds were ranked across three key cell indicators (mitochondrial membrane potential, cell membrane integrity and nuclear size) in a panel of five cell lines (HepG2, Caco-2, A549, HMEC-1, and 3T3), using a high-content screening (HCS) assay. Regarding AUOC score values, naringin (negative control) was the least toxic phenolic compound. Aronox, spent hop extract and kale leaf extract had very low cytotoxicity with regard to mitochondrial membrane potential and cell membrane integrity, as well as nuclear morphology (nuclear area). Kaempferol (positive control) exerted strong cytotoxic effects on the mitochondrial and nuclear compartments. Extracts from buckthorn bark, walnut husk and hollyhock flower were highly cytotoxic with regard to the mitochondrion and cell membrane, but not the nucleus. We propose an alternative algorithm for the screening of a large number of agents and for identifying those with adverse cellular effects at an early stage of drug discovery, using high content screening analysis. This approach should be recommended for series of compounds producing a non-sigmoidal cell response, and for agents with unknown toxicity or mechanisms of action.

Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury

Drug-induced liver injury (DILI) continues to be a major source of clinical attrition, precautionary warnings, and post-market withdrawal of drugs. Accordingly, there is a need for more predictive tools to assess hepatotoxicity risk in drug discovery. Three-dimensional (3D) spheroid hepatic cultures have emerged as promising tools to assess mechanisms of hepatotoxicity, as they demonstrate enhanced liver phenotype, metabolic activity, and stability in culture not attainable with conventional two-dimensional hepatic models. Increased sensitivity of these models to drug-induced cytotoxicity has been demonstrated with relatively small panels of hepatotoxicants. However, a comprehensive evaluation of these models is lacking. Here, the predictive value of 3D human liver microtissues (hLiMT) to identify known hepatotoxicants using a panel of 110 drugs with and without clinical DILI has been assessed in comparison to plated two-dimensional primary human hepatocytes (PHH). Compounds were treated long-term (14 days) in hLiMT and acutely (2 days) in PHH to assess drug-induced cytotoxicity over an 8-point concentration range to generate IC₅₀ values. Regardless of comparing IC₅₀ values or exposure-corrected margin of safety values, hLiMT demonstrated increased sensitivity in identifying known hepatotoxicants than PHH, while specificity was consistent across both assays. In addition, hLiMT out performed PHH in correctly classifying hepatotoxicants from different pharmacological classes of molecules. The hLiMT demonstrated sufficient capability to warrant exploratory liver injury biomarker investigation (miR-122, HMGB1, α-GST) in the cell-culture media. Taken together, this study represents the most comprehensive evaluation of 3D spheroid hepatic cultures up to now and supports their utility for hepatotoxicity risk assessment in drug discovery.

Model Systems for Studying the Role of Canalicular Efflux Transporters in Drug-Induced Cholestatic Liver Disease

Bile formation is a key function of the liver. Disturbance of bile flow may lead to liver disease and is called cholestasis. Cholestasis may be inherited, for example, in progressive familial intrahepatic cholestasis or acquired, for example, by drug-mediated inhibition of bile salt export from hepatocytes into the canaliculi. The key transport system for exporting bile salts into the canaliculi is the bile salt export pump. Inhibition of the bile salt export pump by drugs is a well-established cause of drug-induced cholestasis. Investigation of the role of the multidrug resistance protein 3, essential for biliary phospholipid secretion, is emerging now. This overview summarizes current concepts and methods with an emphasis on in vitro model systems for the investigation of drug-induced cholestasis in the general context of drug-induced liver injury.

An Automated Multiplexed Hepatotoxicity and CYP Induction Assay Using HepaRG Cells in 2D and 3D

Drug-induced liver injury (DILI) and drug-drug interactions (DDIs) are concerns when developing safe and efficacious compounds. We have developed an automated multiplex assay to detect hepatotoxicity (i.e., ATP depletion) and metabolism (i.e., cytochrome P450 1A [CYP1A] and cytochrome P450 3A4 [CYP3A4] enzyme activity) in two-dimensional (2D) and three-dimensional (3D) cell cultures. HepaRG cells were cultured in our proprietary micromold plates and produced spheroids. HepaRG cells, in 2D or 3D, expressed liver-specific proteins throughout the culture period, although 3D cultures consistently exhibited higher albumin secretion and CYP1A/CYP3A4 enzyme activity than 2D cultures. Once the spheroid hepatic quality was assessed, 2D and 3D HepaRGs were challenged to a panel of DILI- and CYP-inducing compounds for 7 days. The 3D HepaRG model had a 70% sensitivity to liver toxins at 7 days, while the 2D model had a 60% sensitivity. In both the 2D and 3D HepaRG models, 83% of compounds were predicted to be CYP inducers after 7 days of compound exposure. Combined, our results demonstrate that an automated multiplexed liver spheroid system is a promising cell-based method to evaluate DILI and DDI for early-stage drug discovery.

Human biology-based drug safety evaluation: scientific rationale, current status and future challenges

INTRODUCTION

Animal toxicity studies used to assess the safety of new candidate pharmaceuticals prior to their progression into human clinical trials are unable to assess the risk of non-pharmacologically mediated idiosyncratic adverse drug reactions (ADRs), the most frequent of which are drug-induced liver injury and cardiotoxicity. Idiosyncratic ADRs occur only infrequently and in certain susceptible humans, but are caused by many hundreds of different drugs and may lead to serious illness. Areas covered: Idiosyncratic ADRs are initiated by drug-related chemical insults, which cause toxicity due to susceptibility factors that manifest only in certain patients. The chemical insults can be detected using in vitro assays. These enable useful discrimination between drugs that cause high versus low levels of idiosyncratic ADR concern. Especially promising assays, which have been described recently in peer-reviewed scientific literature, are highlighted. Expert opinion: Effective interpretation of in vitro toxicity data requires integration of endpoints from multiple assays, which each address different mechanisms, and must also take account of human systemic and tissue drug exposure in vivo. Widespread acceptance and use of such assays has been hampered by the lack of correlation between idiosyncratic human ADR risk and toxicities observed in vivo in animals.

Idiosyncratic Drug-Induced Liver Injury (IDILI): Potential Mechanisms and Predictive Assays

Idiosyncratic drug-induced liver injury (IDILI) is a significant source of drug recall and acute liver failure (ALF) in the United States. While current drug development processes emphasize general toxicity and drug metabolizing enzyme- (DME-) mediated toxicity, it has been challenging to develop comprehensive models for assessing complete idiosyncratic potential. In this review, we describe the enzymes and proteins that contain polymorphisms believed to contribute to IDILI, including ones that affect phase I and phase II metabolism, antioxidant enzymes, drug transporters, inflammation, and human leukocyte antigen (HLA). We then describe the various assays that have been developed to detect individual reactions focusing on each of the mechanisms described in the background. Finally, we examine current trends in developing comprehensive models for examining these mechanisms. There is an urgent need to develop a panel of multiparametric assays for diagnosing individual toxicity potential.

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

Hepatic 3D spheroid models for the detection and study of compounds with cholestatic liability

Drug-induced cholestasis (DIC) is poorly understood and its preclinical prediction is mainly limited to assessing the compound's potential to inhibit the bile salt export pump (BSEP). Here, we evaluated two 3D spheroid models, one from primary human hepatocytes (PHH) and one from HepaRG cells, for the detection of compounds with cholestatic liability. By repeatedly co-exposing both models to a set of compounds with different mechanisms of hepatotoxicity and a non-toxic concentrated bile acid (BA) mixture for 8 days we observed a selective synergistic toxicity of compounds known to cause cholestatic or mixed cholestatic/hepatocellular toxicity and the BA mixture compared to exposure to the compounds alone, a phenomenon that was more pronounced after extending the exposure time to 14 days. In contrast, no such synergism was observed after both 8 and 14 days of exposure to the BA mixture for compounds that cause non-cholestatic hepatotoxicity. Mechanisms behind the toxicity of the cholestatic compound chlorpromazine were accurately detected in both spheroid models, including intracellular BA accumulation, inhibition of ABCB11 expression and disruption of the F-actin cytoskeleton. Furthermore, the observed synergistic toxicity of chlorpromazine and BA was associated with increased oxidative stress and modulation of death receptor signalling. Combined, our results demonstrate that the hepatic spheroid models presented here can be used to detect and study compounds with cholestatic liability.

Upgrading HepG2 cells with adenoviral vectors that encode drug-metabolizing enzymes: application for drug hepatotoxicity testing

INTRODUCTION

Drug attrition rates due to hepatotoxicity are an important safety issue considered in drug development. The HepG2 hepatoma cell line is currently being used for drug-induced hepatotoxicity evaluations, but its expression of drug-metabolizing enzymes is poor compared with hepatocytes. Different approaches have been proposed to upgrade HepG2 cells for more reliable drug-induced liver injury predictions. Areas covered: We describe the advantages and limitations of HepG2 cells transduced with adenoviral vectors that encode drug-metabolizing enzymes for safety risk assessments of bioactivable compounds. Adenoviral transduction facilitates efficient and controlled delivery of multiple drug-metabolizing activities to HepG2 cells at comparable levels to primary human hepatocytes by generating an 'artificial hepatocyte'. Furthermore, adenoviral transduction enables the design of tailored cells expressing particular metabolic capacities. Expert opinion: Upgraded HepG2 cells that recreate known inter-individual variations in hepatic CYP and conjugating activities due to both genetic (e.g., polymorphisms) or environmental (e.g., induction, inhibition) factors seems a suitable model to identify bioactivable drug and conduct hepatotoxicity risk assessments. This strategy should enable the generation of customized cells by reproducing human pheno- and genotypic CYP variability to represent a valuable human hepatic cell model to develop new safer drugs and to improve existing predictive toxicity assays.

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

Drug-induced liver injury (DILI) is a major cause of late-stage clinical drug attrition, market withdrawal, black-box warnings, and acute liver failure. Consequently, it has been an area of focus for toxicologists and clinicians for several decades. In spite of considerable efforts, limited improvements in DILI prediction have been made and efforts to improve existing preclinical models or develop new test systems remain a high priority. While prediction of intrinsic DILI has improved, identifying compounds with a risk for idiosyncratic DILI (iDILI) remains extremely challenging because of the lack of a clear mechanistic understanding and the multifactorial pathogenesis of idiosyncratic drug reactions. Well-defined clinical diagnostic criteria and risk factors are also missing. This paper summarizes key data interpretation challenges, practical considerations, model limitations, and the need for an integrated risk assessment. As demonstrated through selected initiatives to address other types of toxicities, opportunities exist however for improvement, especially through better concerted efforts at harmonization of current, emerging and novel in vitro systems or through the establishment of strategies for implementation of preclinical DILI models across the pharmaceutical industry. Perspectives on the incorporation of newer technologies and the value of precompetitive consortia to identify useful practices are also discussed.

A multicenter assessment of single-cell models aligned to standard measures of cell health for prediction of acute hepatotoxicity

Assessing the potential of a new drug to cause drug-induced liver injury (DILI) is a challenge for the pharmaceutical industry. We therefore determined whether cell models currently used in safety assessment (HepG2, HepaRG, Upcyte and primary human hepatocytes in conjunction with basic but commonly used endpoints) are actually able to distinguish between novel chemical entities (NCEs) with respect to their potential to cause DILI. A panel of thirteen compounds (nine DILI implicated and four non-DILI implicated in man) were selected for our study, which was conducted, for the first time, across multiple laboratories. None of the cell models could distinguish faithfully between DILI and non-DILI compounds. Only when nominal in vitro concentrations were adjusted for in vivo exposure levels were primary human hepatocytes (PHH) found to be the most accurate cell model, closely followed by HepG2. From a practical perspective, this study revealed significant inter-laboratory variation in the response of PHH, HepG2 and Upcyte cells, but not HepaRG cells. This variation was also observed to be compound dependent. Interestingly, differences between donors (hepatocytes), clones (HepG2) and the effect of cryopreservation (HepaRG and hepatocytes) were less important than differences between the cell models per se. In summary, these results demonstrate that basic cell health endpoints will not predict hepatotoxic risk in simple hepatic cells in the absence of pharmacokinetic data and that a multicenter assessment of more sophisticated signals of molecular initiating events is required to determine whether these cells can be incorporated in early safety assessment.

Advantageous use of HepaRG cells for the screening and mechanistic study of drug-induced steatosis

Only a few in vitro assays have been proposed to evaluate the steatotic potential of new drugs. The present study examines the utility of HepaRG cells as a cell-based assay system for screening drug-induced liver steatosis. A high-content screening assay was run to evaluate multiple toxicity-related cell parameters in HepaRG cells exposed to 28 compounds, including drugs reported to cause steatosis through different mechanisms and non-steatotic compounds. Lipid content was the most sensitive parameter for all the steatotic drugs, whereas no effects on lipid levels were produced by non-steatotic compounds. Apart from fat accumulation, increased ROS production and altered mitochondrial membrane potential were also found in the cells exposed to steatotic drugs, which indicates that all these cellular events contributed to drug-induced hepatotoxicity. These findings are of clinical relevance as most effects were observed at drug concentrations under 100-fold of the therapeutic peak plasmatic concentration. HepaRG cells showed increased lipid overaccumulation vs. HepG2 cells, which suggests greater sensitivity to drug-induced steatosis. An altered expression profile of transcription factors and the genes that code key proteins in lipid metabolism was also found in the cells exposed to drugs capable of inducing liver steatosis. Our results generally indicate the value of HepaRG cells for assessing the risk of liver damage associated with steatogenic compounds and for investigating the molecular mechanisms involved in drug-induced steatosis.

Metabolic activation and drug-induced liver injury: in vitro approaches for the safety risk assessment of new drugs

Drug-induced liver injury (DILI) is a significant leading cause of hepatic dysfunction, drug failure during clinical trials and post-market withdrawal of approved drugs. Many cases of DILI are unexpected reactions of an idiosyncratic nature that occur in a small group of susceptible individuals. Intensive research efforts have been made to understand better the idiosyncratic DILI and to identify potential risk factors. Metabolic bioactivation of drugs to form reactive metabolites is considered an initiation mechanism for idiosyncratic DILI. Reactive species may interact irreversibly with cell macromolecules (covalent binding, oxidative damage), and alter their structure and activity. This review focuses on proposed in vitro screening strategies to predict and reduce idiosyncratic hepatotoxicity associated with drug bioactivation. Compound incubation with metabolically competent biological systems (liver-derived cells, subcellular fractions), in combination with methods to reveal the formation of reactive intermediates (e.g., formation of adducts with liver proteins, metabolite trapping or enzyme inhibition assays), are approaches commonly used to screen the reactivity of new molecules in early drug development. Several cell-based assays have also been proposed for the safety risk assessment of bioactivable compounds. Copyright © 2015 John Wiley & Sons, Ltd.

Evaluation of CYP3A4 inhibition and hepatotoxicity using DMSO-treated human hepatoma HuH-7 cells

A human hepatoma cell line (HuH-7) was evaluated as a metabolically competent cell model to investigate cytochrome P450 3A4 (CYP3A4) inhibition, induction, and hepatotoxicity. First, CYP3A4 gene expression and activity were determined in HuH-7 cells under three culture conditions: 1-week culture, 3-week culture, or 1 % dimethyl sulfoxide (DMSO) treatment. HuH-7 cells treated with DMSO for 2 weeks after confluence expressed the highest CYP3A4 gene expression and activity compared to the other two culture conditions. Furthermore, CYP3A4 activity in DMSO-treated HuH-7 cells was compared to that in a human hepatoma cell line (HepG2/C3A) and human bipotent progenitor cell line (HepaRG), which yielded the following ranking: HepaRG > DMSO-treated HuH-7 >> HepG2/C3A cells. The effects of three known CYP3A4 inhibitors were evaluated using DMSO-treated HuH-7 cells. CYP3A4 enzyme inhibition in HuH-7 cells was further compared to human recombinant CYP3A4, indicating similar potency for reversible inhibitors (IC 50 within 2.5-fold), but different potency for the irreversible inhibitor. Next, induction of CYP3A4 activity was compared between DMSO-treated HuH-7 and HepaRG cells using two known inducers. DMSO-treated HuH-7 cells yielded minimal CYP3A4 induction compared to that in the HepaRG cells after 48-h treatments. Finally, the cytotoxicity of five known hepatotoxicants was evaluated in DMSO-treated HuH-7, HepG2/C3A, and HepaRG cells, and significant differences in cytotoxic sensitivity were observed. Overall, DMSO-treated HuH-7 cells are a valuable model for medium- or high-throughput screening of chemicals for CYP3A4 inhibition and hepatotoxicity.