Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Control compounds for preclinical drug-induced liver injury assessment: Consensus-driven systematic review by the ProEuroDILI network

BACKGROUND & AIMS

Idiosyncratic drug-induced liver injury (DILI) is a complex and unpredictable event caused by drugs, and herbal or dietary supplements. Early identification of human hepatotoxicity at preclinical stages remains a major challenge, in which the selection of validated in vitro systems and test drugs has a significant impact. In this systematic review, we analyzed the compounds used in hepatotoxicity assays and established a list of DILI-positive and -negative control drugs for validation of in vitro models of DILI, supported by literature and clinical evidence and endorsed by an expert committee from the COST Action ProEuroDILI Network (CA17112).

METHODS

Following 2020 PRISMA guidelines, original research articles focusing on DILI which used in vitro human models and performed at least one hepatotoxicity assay with positive and negative control compounds, were included. Bias of the studies was assessed by a modified 'Toxicological Data Reliability Assessment Tool'.

RESULTS

A total of 51 studies (out of 2,936) met the inclusion criteria, with 30 categorized as reliable without restrictions. Although there was a broad consensus on positive compounds, the selection of negative compounds lacked clarity. 2D monoculture, short exposure times and cytotoxicity endpoints were the most tested, although there was no consensus on drug concentrations.

CONCLUSIONS

Extensive analysis highlighted the lack of agreement on control compounds for in vitro DILI assessment. Following comprehensive in vitro and clinical data analysis together with input from the expert committee, an evidence-based consensus-driven list of 10 positive and negative control drugs for validation of in vitro models of DILI is proposed.

IMPACT AND IMPLICATIONS

Prediction of human toxicity early in the drug development process remains a major challenge, necessitating the development of more physiologically relevant liver models and careful selection of drug-induced liver injury (DILI)-positive and -negative control drugs to better predict the risk of DILI associated with new drug candidates. Thus, this systematic study has crucial implications for standardizing the validation of new in vitro models of DILI. By establishing a consensus-driven list of positive and negative control drugs, the study provides a scientifically justified framework for enhancing the consistency of preclinical testing, thereby addressing a significant challenge in early hepatotoxicity identification. Practically, these findings can guide researchers in evaluating safety profiles of new drugs, refining in vitro models, and informing regulatory agencies on potential improvements to regulatory guidelines, ensuring a more systematic and efficient approach to drug safety assessment.

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.

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

BACKGROUND AND AIMS

DILI frequently contributes to the attrition of new drug candidates and is a common cause for the withdrawal of approved drugs from the market. Although some noncytochrome P450 (non-CYP) metabolism enzymes have been implicated in DILI development, their association with DILI outcomes has not been systematically evaluated.

APPROACH AND RESULTS

In this study, we analyzed a large data set comprising 317 drugs and their interactions in vitro with 42 non-CYP enzymes as substrates, inducers, and/or inhibitors retrieved from historical regulatory documents using multivariate logistic regression. We examined how these in vitro drug-enzyme interactions are correlated with the drugs' potential for DILI concern, as classified in the Liver Toxicity Knowledge Base database. Our study revealed that drugs that inhibit non-CYP enzymes are significantly associated with high DILI concern. Particularly, interaction with UDP-glucuronosyltransferases (UGT) enzymes is an important predictor of DILI outcomes. Further analysis indicated that only pure UGT inhibitors and dual substrate inhibitors, but not pure UGT substrates, are significantly associated with high DILI concern.

CONCLUSIONS

Drug interactions with UGT enzymes may independently predict DILI, and their combined use with the rule-of-two model further improves overall predictive performance. These findings could expand the currently available tools for assessing the potential for DILI in humans.

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

INTRODUCTION

Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited.

AREAS COVERED

This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites.

EXPERT OPINION

Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity

Drug-induced renal injury (DIRI) causes >1.5 million adverse events annually in the USA alone. Although standard biomarkers exist for DIRI, they lack the sensitivity or specificity to detect nephrotoxicity before the significant loss of renal function. In this study, we describe the creation of DIRIL - a list of drugs associated with DIRI and nephrotoxicity - from two literature datasets with DIRI annotation, confirmed using FDA drug labeling. DIRIL comprises 317 orally administered drugs covering all 14 anatomical, therapeutic and chemical (ATC) classification categories. Of the 317 drugs, 171 were DIRI-positive and 146 were DIRI-negative. DIRIL will be a relevant and invaluable resource for discovery of new approach methods (NAMs) to predict the occurrence and possible severity of DIRI earlier in drug development.

Hepatotoxicity of AKR1C3 Inhibitor BAY1128688: Findings from an Early Terminated Phase IIa Trial for the Treatment of Endometriosis

INTRODUCTION

BAY1128688 is a selective inhibitor of aldo-keto reductase family 1 member C3 (AKR1C3), an enzyme implicated in the pathology of endometriosis and other disorders. In vivo animal studies suggested a potential therapeutic application of BAY1128688 in treating endometriosis. Early clinical studies in healthy volunteers supported the start of phase IIa.

OBJECTIVE

This manuscript reports the results of a clinical trial (AKRENDO1) assessing the effects of BAY1128688 in adult premenopausal women with endometriosis-related pain symptoms over a 12-week treatment period.

METHODS

Participants in this placebo-controlled, multicenter phase IIa clinical trial (NCT03373422) were randomized into one of five BAY1128688 treatment groups: 3 mg once daily (OD), 10 mg OD, 30 mg OD, 30 mg twice daily (BID), 60 mg BID; or a placebo group. The efficacy, safety, and tolerability of BAY1128688 were investigated.

RESULTS

Dose-/exposure-dependent hepatotoxicity was observed following BAY1128688 treatment, characterized by elevations in serum alanine transferase (ALT) occurring at around 12 weeks of treatment and prompting premature trial termination. The reduced number of valid trial completers precludes conclusions regarding treatment efficacy. The pharmacokinetics and pharmacodynamics of BAY1128688 among participants with endometriosis were comparable with those previously found in healthy volunteers and were not predictive of the subsequent ALT elevations observed.

CONCLUSIONS

The hepatotoxicity of BAY1128688 observed in AKRENDO1 was not predicted by animal studies nor by studies in healthy volunteers. However, in vitro interactions of BAY1128688 with bile salt transporters indicated a potential risk factor for hepatotoxicity at higher doses. This highlights the importance of in vitro mechanistic and transporter interaction studies in the assessment of hepatoxicity risk and suggests further mechanistic understanding is required.

CLINICAL TRIAL REGISTRATION

NCT03373422 (date registered: November 23, 2017).

Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data

Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.

Pluripotent Stem Cell-Derived Hepatocyte-like Cells: Induction Methods and Applications

The development of regenerative medicine provides new options for the treatment of end-stage liver diseases. Stem cells, such as bone marrow mesenchymal stem cells, embryonic stem cells, and induced pluripotent stem cells (iPSCs), are effective tools for tissue repair in regenerative medicine. iPSCs are an appropriate source of hepatocytes for the treatment of liver disease due to their unlimited multiplication capacity, their coverage of the entire range of genetics required to simulate human disease, and their evasion of ethical implications. iPSCs have the ability to gradually produce hepatocyte-like cells (HLCs) with homologous phenotypes and physiological functions. However, how to induce iPSCs to differentiate into HLCs efficiently and accurately is still a hot topic. This review describes the existing approaches for inducing the differentiation of iPSCs into HLCs, as well as some challenges faced, and summarizes various parameters for determining the quality and functionality of HLCs. Furthermore, the application of iPSCs for in vitro hepatoprotective drug screening and modeling of liver disease is discussed. In conclusion, iPSCs will be a dependable source of cells for stem-cell therapy to treat end-stage liver disease and are anticipated to facilitate individualized treatment for liver disease in the future.

Sublethal necroptosis signaling promotes inflammation and liver cancer

It is currently not well known how necroptosis and necroptosis responses manifest in vivo. Here, we uncovered a molecular switch facilitating reprogramming between two alternative modes of necroptosis signaling in hepatocytes, fundamentally affecting immune responses and hepatocarcinogenesis. Concomitant necrosome and NF-κB activation in hepatocytes, which physiologically express low concentrations of receptor-interacting kinase 3 (RIPK3), did not lead to immediate cell death but forced them into a prolonged "sublethal" state with leaky membranes, functioning as secretory cells that released specific chemokines including CCL20 and MCP-1. This triggered hepatic cell proliferation as well as activation of procarcinogenic monocyte-derived macrophage cell clusters, contributing to hepatocarcinogenesis. In contrast, necrosome activation in hepatocytes with inactive NF-κB-signaling caused an accelerated execution of necroptosis, limiting alarmin release, and thereby preventing inflammation and hepatocarcinogenesis. Consistently, intratumoral NF-κB-necroptosis signatures were associated with poor prognosis in human hepatocarcinogenesis. Therefore, pharmacological reprogramming between these distinct forms of necroptosis may represent a promising strategy against hepatocellular carcinoma.

Microphysiological Models for Mechanistic-Based Prediction of Idiosyncratic DILI

Drug-induced liver injury (DILI) is a major contributor to high attrition rates among candidate and market drugs and a key regulatory, industry, and global health concern. While acute and dose-dependent DILI, namely, intrinsic DILI, is predictable and often reproducible in preclinical models, the nature of idiosyncratic DILI (iDILI) limits its mechanistic understanding due to the complex disease pathogenesis, and recapitulation using in vitro and in vivo models is extremely challenging. However, hepatic inflammation is a key feature of iDILI primarily orchestrated by the innate and adaptive immune system. This review summarizes the in vitro co-culture models that exploit the role of the immune system to investigate iDILI. Particularly, this review focuses on advancements in human-based 3D multicellular models attempting to supplement in vivo models that often lack predictability and display interspecies variations. Exploiting the immune-mediated mechanisms of iDILI, the inclusion of non-parenchymal cells in these hepatoxicity models, namely, Kupffer cells, stellate cells, dendritic cells, and liver sinusoidal endothelial cells, introduces heterotypic cell-cell interactions and mimics the hepatic microenvironment. Additionally, drugs recalled from the market in the US between 1996-2010 that were studies in these various models highlight the necessity for further harmonization and comparison of model characteristics. Challenges regarding disease-related endpoints, mimicking 3D architecture with different cell-cell contact, cell source, and the underlying multi-cellular and multi-stage mechanisms are described. It is our belief that progressing our understanding of the underlying pathogenesis of iDILI will provide mechanistic clues and a method for drug safety screening to better predict liver injury in clinical trials and post-marketing.

The Precision Toxicology Initiative

The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions, which are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expect to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals.

Health risk assessment of cadmium exposure by integration of an in silico physiologically based toxicokinetic model and in vitro tests

Cadmium (Cd) is a common environmental pollutant that can damage multiple organs, including the kidney. To prevent renal effects, international authorities have set health-based guidance values of Cd from epidemiological studies. To explore the health risk of Cd exposure and whether human equivalent doses (HEDs) derived from in vitro tests match the current guidance values, we integrated renal tubular epithelial cell-based assays with a physiologically based toxicokinetic model combined with the Monte Carlo method. For females, the HEDs (μg/kg/week) derived from KE2 (DNA damage), KE3 (cell cycle arrest), and KE4 (apoptosis) were 0.20 (2.5th-97.5th percentiles: 0.09-0.48), 0.52 (0.24-1.26), and 2.73 (1.27-6.57), respectively; for males the respective HEDs were 0.23 (0.10-0.49), 0.60 (0.27-1.30), and 3.11 (1.39-6.78). Among them, HED_KE4 (female) was close to the tolerable weekly intake (2.5 μg/kg/week) set by the European Food Safety Authority. The margin of exposure (MOE) derived from HED_KE4 (female) indicated that risks of renal toxicity for populations living in cadmium-contaminated regions should be of concern. This study provided a new approach methodology (NAM) for environmental chemical risk assessment using in silico and in vitro methods.

In vitro to in vivo acetaminophen hepatotoxicity extrapolation using classical schemes, pharmacodynamic models and a multiscale spatial-temporal liver twin

In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical (CL) homogeneous compartment pharmacodynamic (PD) models and a spatial-temporal (ST), multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmacokinetic curve (AUC) of the drug blood concentration. We find that an CL-model based on a well-mixed blood compartment is sufficient to correctly predict the in vivo toxicity from in vitro data. However, the ST-model that integrates more experimental information requires a change of at least one parameter to obtain the same prediction, indicating that spatial compartmentalization may indeed be an important factor.

Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation

BACKGROUND

The initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus.

METHODS

By combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n = 150) and validation cohorts (n = 97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n = 40, ChiCTR2100050288).

FINDINGS

Our model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10 ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p = 0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p = 0.025) with a more variable individualized dose (0.023-0.096 mg/kg/day vs 0.045-0.057 mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75 ± 2.01 vs 6.05 ± 3.35, p = 0.001).

INTERPRETATION

Our genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients.

FUNDING

National Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology

BACKGROUND

Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported.

METHODS

870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows.

RESULTS

Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity.

CONCLUSIONS

The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

UV-B filter octylmethoxycinnamate-induced vascular endothelial disruption on rat aorta: In silico and in vitro approach

Throughout human life, an extensive and varied range of emerging environmental contaminants, called endocrine disruptors (EDCs), cause adverse health effects, including in the cardiovascular (CV) system. Cardiovascular diseases (CVD) are worryingly one of the leading causes of all mortality and mobility worldwide. The UV-B filter octylmethoxycinnamate (also designated octinoxate, or ethylhexyl methoxycinnamate (CAS number: 5466-77-3)) is an EDC widely present in all personal care products. However, to date, there are no studies evaluating the OMC-induced effects on vasculature using animal models to improve human cardiovascular health. This work analysed the effects of OMC on rat aorta vasculature and explored the modes of action implicated in these effects. Our results indicated that OMC relaxes the rat aorta by endothelium-dependent mechanisms through the signaling pathways of cyclic nucleotides and by endothelium-independent mechanisms involving inhibition of L-Type voltage-operated Ca²⁺ channels (L-Type VOCC). Overall, OMC toxicity on rat aorta may produce hypotension via vasodilation due to excessive NO release and blockade of L-Type VOCC. Moreover, the OMC-induced endothelial dysfunction may also occur by promoting the endothelial release of endothelin-1. Therefore, our findings demonstrate that exposure to OMC alters the reactivity of the rat aorta and highlight that long-term OMC exposure may increase the risk of human CV diseases.

In vitro/in silico prediction of drug induced steatosis in relation to oral doses and blood concentrations by the Nile Red assay

The accumulation of lipid droplets in hepatocytes is a key feature of drug-induced liver injury (DILI) and can be induced by a subset of hepatotoxic compounds. In the present study, we optimized and evaluated an in vitro technique based on the fluorescent dye Nile Red, further named Nile Red assay to quantify lipid droplets induced by the exposure to chemicals. The Nile Red assay and a cytotoxicity test (CTB assay) were then performed on cells exposed concentration-dependently to 60 different compounds. Of these, 31 were known to induce hepatotoxicity in humans, and 13 were reported to also cause steatosis. In order to compare in vivo relevant blood concentrations, pharmacokinetic models were established for all compounds to simulate the maximal blood concentrations (C_max) at therapeutic doses. The results showed that several hepatotoxic compounds induced an increase in lipid droplets at sub-cytotoxic concentrations. To compare how well (1) the cytotoxicity test alone, (2) the Nile Red assay alone, and (3) the combination of the cytotoxicity test and the Nile Red assay (based on the lower EC₁₀ of both assays) allow the differentiation between hepatotoxic and non-hepatotoxic compounds, a previously established performance metric, the Toxicity Separation Index (TSI) was calculated. In addition, the Toxicity Estimation Index (TEI) was calculated to determine how well blood concentrations that cause an increased DILI risk can be estimated for hepatotoxic compounds. Our findings indicate that the combination of both assays improved the TSI and TEI compared to each assay alone. In conclusion, the study demonstrates that inclusion of the Nile Red assay into in vitro test batteries may improve the prediction of DILI compounds.

Classification of Developmental Toxicants in a Human iPSC Transcriptomics-Based Test

Despite the progress made in developmental toxicology, there is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration (Cmax) and at 20-fold Cmax for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold Cmax) were used, in comparison to testing within the realistic clinical range (1-fold Cmax). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants.

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.

Research Progress in the Pharmacological Activities, Toxicities, and Pharmacokinetics of Sophoridine and Its Derivatives

Sophoridine is a natural quinolizidine alkaloid and a bioactive ingredient that can be isolated and identified from certain herbs, including Sophora flavescens Alt, Sophora alopecuroides L, and Sophora viciifolia Hance. In recent years, this quinolizidine alkaloid has gained widespread attention because of its unique structure and minimal side effects. Modern pharmacological investigations have uncovered sophoridine's multiple wide range biological activities, such as anti-cancer, anti-inflammatory, anti-viral, anti-arrhythmia, and analgesic functions, among others. These pharmacological activities and beneficial effects point to sophoridine as a strong potential therapeutic candidate for the treatment of various diseases, including several cancer types, hepatitis B virus, enterovirus 71, coxsackievirus B3, cerebral edema, cancer pain, heart failure, acute myocardial ischemia, arrhythmia, inflammation, acute lung injury, and osteoporosis. The data showed that sophoridine had adverse reactions, including hepatotoxicity and neurotoxicity. Additionally, analyses of sophoridine's safety, bioavailability, and pharmacokinetic parameters in animal models of research have been limited, especially in the clinic, as have been investigations on its structure-activity relationship. In this article, we comprehensively summarize the biological activities, toxicity, and pharmacokinetic characteristics of sophoridine and its derivatives, as currently reported in publications, as we attempt to provide an overall perspective on sophoridine analogs and the prospects of its application clinically.

Acute effects of the imidacloprid metabolite desnitro-imidacloprid on human nACh receptors relevant for neuronal signaling

Several neonicotinoids have recently been shown to activate the nicotinic acetylcholine receptor (nAChR) on human neurons. Moreover, imidacloprid (IMI) and other members of this pesticide family form a set of diverse metabolites within crops. Among these, desnitro-imidacloprid (DN-IMI) is of special toxicological interest, as there is evidence (i) for human dietary exposure to this metabolite, (ii) and that DN-IMI is a strong trigger of mammalian nicotinic responses. We set out here to quantify responses of human nAChRs to DN-IMI and an alternative metabolite, IMI-olefin. To evaluate toxicological hazards, these data were then compared to those of IMI and nicotine. Ca2+-imaging experiments on human neurons showed that DN-IMI exhibits an agonistic effect on nAChRs at sub-micromolar concentrations (equipotent with nicotine) while IMI-olefin activated the receptors less potently (in a similar range as IMI). Direct experimental data on the interaction with defined receptor subtypes were obtained by heterologous expression of various human nAChR subtypes in Xenopus laevis oocytes and measurement of the transmembrane currents evoked by exposure to putative ligands. DN-IMI acted on the physiologically important human nAChR subtypes α7, α3β4, and α4β2 (high-sensitivity variant) with similar potency as nicotine. IMI and IMI-olefin were confirmed as nAChR agonists, although with 2-3 orders of magnitude lower potency. Molecular docking studies, using receptor models for the α7 and α4β2 nAChR subtypes supported an activity of DN-IMI similar to that of nicotine. In summary, these data suggest that DN-IMI functionally affects human neurons similar to the well-established neurotoxicant nicotine by triggering α7 and several non-α7 nAChRs.

Dose dependent safety implications and acute intravenous toxicity of aminocellulose-grafted-polycaprolactone coated gelatin nanoparticles in mice

Polymeric nanoparticles (NPs) are the most widely researched nanoformulations and gained broad acceptance in nanotherapeutics for targeted drug delivery and theranostics. However, lack of regulations, guidelines, harmonized standards, and limitations with their employability in clinical circumstances necessitates an in-depth understanding of their toxicology. Here, we examined the in-vivo toxicity of core-shell polymeric NPs made up of gelatin core coated with an outer layer of aminocellulose-grafted polycaprolactone (PCL-AC) synthesized for drug delivery purposes in inflammatory disorders. Nanoparticles were administered intravenously in Swiss albino mice, in multiple dosing (10, 25, and 50 mg/kg body weight) and outcomes of serum biochemistry analysis and histopathology evaluation exhibited that the highest 50 mg/kg administration of NPs altered biochemistry and histopathology aspects of vital organs, while doses of 10 and 25 mg/kg were safe and biocompatible. Further, mast cell (toluidine blue) staining confirmed that administration of the highest dose enhanced mast cell infiltration in tissues of vital organs, while lower doses did not exhibit any of these alterations. Therefore, the results of the present study establish that the NPs disposal in-vivo culminates into alterations in organ structure and function consequences such that lower doses are quite biocompatible and do not demonstrate any structural or functional toxicity while some toxicological effects start appearing at the highest dose.

The hepatocyte export carrier inhibition assay improves the separation of hepatotoxic from non-hepatotoxic compounds

An in vitro/in silico method that determines the risk of human drug induced liver injury in relation to oral doses and blood concentrations of drugs was recently introduced. This method utilizes information on the maximal blood concentration (C_max) for a specific dose of a test compound, which can be estimated using physiologically-based pharmacokinetic modelling, and a cytotoxicity test in cultured human hepatocytes. In the present study, we analyzed if the addition of an assay that measures the inhibition of bile acid export carriers, like BSEP and/or MRP2, to the existing method improves the differentiation of hepatotoxic and non-hepatotoxic compounds. Therefore, an export assay for 5-chloromethylfluorescein diacetate (CMFDA) was established. We tested 36 compounds in a concentration-dependent manner for which the risk of hepatotoxicity for specific oral doses and the capacity to inhibit hepatocyte export carriers are known. Compared to the CTB cytotoxicity test, substantially lower EC₁₀ values were obtained using the CMFDA assay for several known BSEP and/or MRP2 inhibitors. To quantify if the addition of the CMFDA assay to our test system improves the overall separation of hepatotoxic from non-hepatotoxic compounds, the toxicity separation index (TSI) was calculated. We obtained a better TSI using the lower alert concentration from either the CMFDA or the CTB test (TSI: 0.886) compared to considering the CTB test alone (TSI: 0.775). In conclusion, the data show that integration of the CMFDA assay with an in vitro test battery improves the differentiation of hepatotoxic and non-hepatotoxic compounds in a set of compounds that includes bile acid export carrier inhibitors.

A Model-Based Workflow to Benchmark the Clinical Cholestasis Risk of Drugs

We present a generic workflow combining physiology-based computational modeling and in vitro data to assess the clinical cholestatic risk of different drugs systematically. Changes in expression levels of genes involved in the enterohepatic circulation of bile acids were obtained from an in vitro assay mimicking 14 days of repeated drug administration for 10 marketed drugs. These changes in gene expression over time were contextualized in a physiology-based bile acid model of glycochenodeoxycholic acid. The simulated drug-induced response in bile acid concentrations was then scaled with the applied drug doses to calculate the cholestatic potential for each compound. A ranking of the cholestatic potential correlated very well with the clinical cholestasis risk obtained from medical literature. The proposed workflow allows benchmarking the cholestatic risk of novel drug candidates. We expect the application of our workflow to significantly contribute to the stratification of the cholestatic potential of new drugs and to support animal-free testing in future drug development.

Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets

Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew's correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.

Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes

Using in vitro data to estimate point of departure (POD) values is an essential component of new approach methodologies (NAM)-based chemical risk assessments. In this case study, we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI), and toxicokinetic modeling. First, we treated rat primary hepatocytes with 10 concentrations (0.2 to 100 µM) of 51 chemicals that produced hepatotoxicity in repeat-dose subchronic and chronic exposures. Second, we used HCI to measure endoplasmic reticulum stress, mitochondrial function, lysosomal mass, steatosis, apoptosis, DNA texture, nuclear size, and cell number at 24, 48, and 72 h and calculated concentrations at 50% maximal activity (AC50). Third, we estimated administered equivalent doses (AEDs) from AC50 values using toxicokinetic modeling. AEDs using physiologically-based toxicokinetic models were 4.1-fold (SD 6.3) and 8.1-fold (SD 15.5) lower than subchronic and chronic lowest observed adverse effect levels (LOAELs), respectively. In contrast, AEDs from ToxCast and Tox21 assays were 89.8-fold (SD 149.5) and 168-fold (SD 323.7) lower than subchronic and chronic LOAELs. Individual HCI end-points also estimated AEDs for specific hepatic lesions that were lower than in vivo PODs. Lastly, AEDs were similar for different in vitro exposure durations, but steady-state toxicokinetic models produced 7.6-fold lower estimates than dynamic physiologically-based ones. Our findings suggest that NAMs from diverse cell types provide conservative estimates of PODs. In contrast, NAMs based on the same species and cell type as the adverse outcome may produce estimates closer to the traditional in vivo PODs.

Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.

Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.

Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

Subcellular spatio-temporal intravital kinetics of aflatoxin B1 and ochratoxin A in liver and kidney

Local accumulation of xenobiotics in human and animal tissues may cause adverse effects. Large differences in their concentrations may exist between individual cell types, often due to the expression of specific uptake and export carriers. Here we established a two-photon microscopy-based technique for spatio-temporal detection of the distribution of mycotoxins in intact kidneys and livers of anesthetized mice with subcellular resolution. The mycotoxins ochratoxin A (OTA, 10 mg/kg b.w.) and aflatoxin B1 (AFB1, 1.5 mg/kg b.w.), which both show blue auto-fluorescence, were analyzed after intravenous bolus injections. Within seconds after administration, OTA was filtered by glomeruli, and enriched in distal tubular epithelial cells (dTEC). A striking feature of AFB1 toxicokinetics was its very rapid uptake from sinusoidal blood into hepatocytes (t1/2 ~ 4 min) and excretion into bile canaliculi. Interestingly, AFB1 was enriched in the nuclei of hepatocytes with zonal differences in clearance. In the cytoplasm of pericentral hepatocytes, the half-life (t1/2~ 63 min) was much longer compared to periportal hepatocytes of the same lobules (t1/2 ~ 9 min). In addition, nuclear AFB1 from periportal hepatocytes cleared faster compared to the pericentral region. These local differences in AFB1 clearance may be due to the pericentral expression of cytochrome P450 enzymes that activate AFB1 to protein- and DNA-binding metabolites. In conclusion, the present study shows that large spatio-temporal concentration differences exist within the same tissues and its analysis may provide valuable additional information to conventional toxicokinetic studies.

A Decade of Organs-on-a-Chip Emulating Human Physiology at the Microscale: A Critical Status Report on Progress in Toxicology and Pharmacology

Organ-on-a-chip technology has the potential to accelerate pharmaceutical drug development, improve the clinical translation of basic research, and provide personalized intervention strategies. In the last decade, big pharma has engaged in many academic research cooperations to develop organ-on-a-chip systems for future drug discoveries. Although most organ-on-a-chip systems present proof-of-concept studies, miniaturized organ systems still need to demonstrate translational relevance and predictive power in clinical and pharmaceutical settings. This review explores whether microfluidic technology succeeded in paving the way for developing physiologically relevant human in vitro models for pharmacology and toxicology in biomedical research within the last decade. Individual organ-on-a-chip systems are discussed, focusing on relevant applications and highlighting their ability to tackle current challenges in pharmacological research.

Association of CYP1A1 and CYP1B1 inhibition in in vitro assays with drug-induced liver injury

Drug-induced liver injury (DILI) is one of the major causes for the discontinuation of drug development and withdrawal of drugs from the market. Since it is known that reactive metabolite formation and being substrates or inhibitors of cytochrome P450s (P450s) are associated with DILI, we systematically investigated the association between human P450 inhibition and DILI. The inhibitory activity of 266 DILI-positive drugs (DILI drugs) and 92 DILI-negative drugs (no-DILI drugs), which were selected from Liver Toxicity Knowledge Base (US Food and Drug Administration), against 8 human P450 forms was assessed using recombinant enzymes and luminescent substrates, and the threshold values showing the highest balanced accuracy for DILI discrimination were determined for each P450 enzyme using receiver operating characteristic analyses. The results showed that among the P450s tested, CYP1A1 and CYP1B1 were inhibited by DILI drugs more than no-DILI drugs with a statistical significance. We found that 91% of drugs that showed inhibitory activity greater than the threshold values against CYP1A1 or CYP1B1 were DILI drugs. The results of internal 5-fold cross-validation confirmed the usefulness of CYP1A1 and CYP1B1 inhibition data for the threshold-based discrimination of DILI drugs. Although the contribution of these P450s to drug metabolism in the liver is considered minimal, our present findings suggest that the assessment of CYP1A1 and CYP1B1 inhibition is useful for screening DILI risk of drug candidates at the early stage of drug development.

Biomarkers of drug-induced liver injury: a mechanistic perspective through acetaminophen hepatotoxicity

Introduction: Liver injury induced by drugs is a serious clinical problem. Many circulating biomarkers for identifying and predicting drug-induced liver injury (DILI) have been proposed.Areas covered: Biomarkers are mainly predicated on the mechanistic understanding of the underlying DILI, often in the context of acetaminophen overdose. New panels of biomarkers have emerged that are related to recovery/regeneration rather than injury following DILI. We explore the clinical relevance and limitations of these new biomarkers including recent controversies. Extracellular vesicles have also emerged as a promising vector of biomarkers, although the biological role for EVs may limit their clinical usefulness. New technological approaches for biomarker discovery are also explored.Expert opinion: Recent clinical studies have validated the efficacy of some of these new biomarkers, cytokeratin-18, macrophage colony-stimulating factor receptor, and osteopontin for DILI prognosis. Low prevalence of DILI is an inherent limitation to DILI biomarker development. Furthering mechanistic understanding of DILI and leveraging technological advances (e.g. machine learning/omics) is necessary to improve upon the newest generation of biomarkers. The integration of omics approaches with machine learning has led to novel insights in cancer research and DILI research is poised to leverage these technologies for biomarker discovery and development.

Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling

The risk of drug-induced liver injury (DILI) poses a major challenge for development of natural products derived from traditional Chinese medicines (NP-TCMs). It is urgent to find a new method for the safety assessment of the NP-TCMs. Recent study has reported an in vitro/in silico method to estimate the acceptable daily intake of hepatotoxic compounds using support vector machine (SVM) classifier and physiologically based pharmacokinetic (PBPK) modeling. However, this method is not suitable for estimating the dosing schedule of compounds which are administered in multiple daily doses. Thus, in this study, the method mentioned above was in particular optimized, and used to estimate the hepatotoxic plasma concentrations of 17 NP-TCMs. Additionally, the oral dosing schedules of the triptolide, emodin, matrine and oxymatrine were also predicted by the SVM classifier and PBPK modeling. The optimization included that: (1) in vitro cytotoxicity data of 28 training set compounds was optimized using benchmark concentrations (BMC) modeling; (2) AUC of the training set compound was used as the in vivo metric instead of C_max to better reflect the total daily exposure of compounds which are administered in multiple daily doses; (3) using the mean AUC in plasma as in vivo metric and BMC value as in vitro metric could achieve the better toxicity separation index (0.962 vs. 0.938); (4) The TSI for C_max and BMC values was 0.985 calculated in this study, and the results indicated that BMC modeling improved the separation performance. This optimized in vitro-in vivo extrapolation (IVIVE) workflow could extrapolate in vitro BMC to blood concentrations and the oral dosing schedule which are corresponding to certain risk of hepatotoxicity. The estimated safe dosing schedule of oxymatrine by this optimized method was close to the clinical recommended dosing regimen. The results indicate that the optimized method could be used to predict the dosing schedule of compounds administered in multiple daily doses, and our optimized workflow could be helpful for the safety assessment as well as the research and development on NP-TCMs.

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

BACKGROUND

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

RESULTS

As part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.

CONCLUSION

Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction

MOTIVATION

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

METHODS

We used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation.

RESULTS

We have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ∈(0.55-0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.

Development of a cost-effective automated platform to produce human liver spheroids for basic and applied research

Liver disease represents an increasing cause of global morbidity and mortality. Currently, liver transplant is the only treatment curative for end-stage liver disease. Donor organs cannot meet the demand and therefore scalable treatments and new disease models are required to improve clinical intervention. Pluripotent stem cells represent a renewable source of human tissue. Recent advances in three-dimensional cell culture have provided the field with more complex systems that better mimic liver physiology and function. Despite these improvements, current cell-based models are variable in performance and expensive to manufacture at scale. This is due, in part, to the use of poorly defined or cross-species materials within the process, severely affecting technology translation. To address this issue, we have developed an automated and economical platform to produce liver tissue at scale for modelling disease and small molecule screening. Stem cell derived liver spheres were formed by combining hepatic progenitors with endothelial cells and stellate cells, in the ratios found within the liver. The resulting tissue permitted the study of human liver biology 'in the dish' and could be scaled for screening. In summary, we have developed an automated differentiation system that permits reliable self-assembly of human liver tissue for biomedical application. Going forward we believe that this technology will not only serve as anin vitroresource, and may have an important role to play in supporting failing liver function in humans.

Comparing in vitro human liver models to in vivo human liver using RNA-Seq

The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue.

Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience

In 2016, the European Commission launched the EU-ToxRisk research project to develop and promote animal-free approaches in toxicology. The 36 partners of this consortium used in vitro and in silico methods in the context of case studies (CSs). These CSs included both compounds with a highly defined target (e.g. mitochondrial respiratory chain inhibitors) as well as compounds with poorly defined molecular initiation events (e.g. short-chain branched carboxylic acids). The initial project focus was on developing a science-based strategy for read-across (RAx) as an animal-free approach in chemical risk assessment. Moreover, seamless incorporation of new approach method (NAM) data into this process (= NAM-enhanced RAx) was explored. Here, the EU-ToxRisk consortium has collated its scientific and regulatory learnings from this particular project objective. For all CSs, a mechanistic hypothesis (in the form of an adverse outcome pathway) guided the safety evaluation. ADME data were generated from NAMs and used for comprehensive physiological-based kinetic modelling. Quality assurance and data management were optimized in parallel. Scientific and Regulatory Advisory Boards played a vital role in assessing the practical applicability of the new approaches. In a next step, external stakeholders evaluated the usefulness of NAMs in the context of RAx CSs for regulatory acceptance. For instance, the CSs were included in the OECD CS portfolio for the Integrated Approach to Testing and Assessment project. Feedback from regulators and other stakeholders was collected at several stages. Future chemical safety science projects can draw from this experience to implement systems toxicology-guided, animal-free next-generation risk assessment.

Handling deviating control values in concentration-response curves

In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{20}$$\end{document}20 values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls: (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-effect range, low variances of the replicate measurements, and deviating controls, control values should be omitted before fitting the model. (3) When data are missing in the no-effect range, the Brain-Cousens model sometimes leads to better results than the default model.

An in vitro coculture system of human peripheral blood mononuclear cells with hepatocellular carcinoma-derived cells for predicting drug-induced liver injury

Preventing clinical drug-induced liver injury (DILI) remains a major challenge, because DILI develops via multifactorial mechanisms. Immune and inflammatory reactions are considered important mechanisms of DILI; however, biomarkers from in vitro systems using immune cells have not been comprehensively studied. The aims of this study were (1) to identify promising biomarker genes for predicting DILI in an in vitro coculture model of peripheral blood mononuclear cells (PBMCs) with a human liver cell line, and (2) to evaluate these genes as predictors of DILI using a panel of drugs with different clinical DILI risk. Transcriptome-wide analysis of PBMCs cocultured with HepG2 or differentiated HepaRG cells that were treated with several drugs revealed an appropriate separation of DILI-positive and DILI-negative drugs, from which 12 putative biomarker genes were selected. To evaluate the predictive performance of these genes, PBMCs cocultured with HepG2 cells were exposed to 77 different drugs, and gene expression levels in PBMCs were determined. The MET proto-oncogene receptor tyrosine kinase (MET) showed the highest area under the receiver-operating characteristic curve (AUC) value of 0.81 among the 12 genes with a high sensitivity/specificity (85/66%). However, a stepwise logistic regression model using the 12 identified genes showed the highest AUC value of 0.94 with a high sensitivity/specificity (93/86%). Taken together, we established a coculture system using PBMCs and HepG2 cells and selected biomarkers that can predict DILI risk. The established model would be useful in detecting the DILI potential of compounds, in particular those that involve an immune mechanism.