Hepatotoxicity in drug development: detection, significance and solutions

Synthesis, structural, multitargeted molecular docking analysis of anti-cancer, anti-tubercular, DNA interactions of benzotriazole based macrocyclic ligand

Biologically important macromolecule 1, 1', 3, 3' Bis - [2,3,5,6-Tetramethyl-p-phenylenebis(methylene)] dibenzotriazlinium dibromide hydrate (BTD) was synthesized and characterized using FT-IR, NMR and single-crystal XRD (SCXRD). SCXRD revealed that the compound was crystallized as a monoclinic system and associated through weak intermolecular interactions like H-bonding and π- π stacking interactions. These weak intermolecular interactions in BTD were studied using Crystal Explorer and Gaussian. The calculated energies for the Highest Occupied Molecular Orbital (HOMO) and the Lowest Unoccupied Molecular Orbital (LUMO) showed the stability and reactivity of the title compound. Molecular electrostatic potential (MEP) surface analysis was used to investigate the crystal's nucleophilic and electrophilic reactive sites. The molecular shape and intermolecular interactions in the crystal structure were determined using Hirshfeld surface analysis and fingerprint plots. Anticancer, anti-bacterial and DNA binding ability of BTD were investigated by experimental and theoretical techniques. The obtained results suggest that BTD possesses better anti-cancer, anti-bacterial and DNA binding abilities. The mode of action of antibiotic and anticancer approach was discussed. This provides promising therapeutic advantages for further development.

Liver organoids and their application in liver cancer research

Liver cancer, a common and intractable liver-related disease, is a malignant tumor with a high morbidity, which needs a high treatment cost but still lacks perfect clinical treatment methods. Looking for an effective platform for liver cancer study and drug screening is urgent and important. Traditional analytical methods for liver disease studies mainly rely on the 2D cell culture and animal experiments, which both cannot fully recapitulate physiological and pathological processes of human liver. For example, cell culture can only show basic functions of cells in vitro, while animal models always hold the problem of species divergence. The organoids, a 3D invitro culture system emerged in recent years, is a cell-bound body with different cell types and has partial tissue functions. The organoid technology can reveal the growth state, structure, function and characteristics of the tissue or organ, and plays an important role in reconstructing invitro experimental models that can truly simulate the human liver. In this paper, we will give a brief introduction of liver organoids and review their applications in liver cancer research, especially in liver cancer pathogenesis, drug screening, precision medicine, regenerative medicine, and other fields. We have also discussed advantages and disadvantages of organoids, as well as future directions and perspectives towards liver organoids.

Identification of inhibitors from a functional food-based plant Perillae Folium against hyperuricemia via metabolomics profiling, network pharmacology and all-atom molecular dynamics simulations

Introduction

Hyperuricemia (HUA) is a metabolic disorder caused by purine metabolism dysfunction in which the increasing purine levels can be partially attributed to seafood consumption. Perillae Folium (PF), a widely used plant in functional food, has been historically used to mitigate seafood-induced diseases. However, its efficacy against HUA and the underlying mechanism remain unclear.

Methods

A network pharmacology analysis was performed to identify candidate targets and potential mechanisms involved in PF treating HUA. The candidate targets were determined based on TCMSP, SwissTargetPrediction, Open Targets Platform, GeneCards, Comparative Toxicogenomics Database, and DrugBank. The potential mechanisms were predicted via Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analyses. Molecular docking in AutoDock Vina and PyRx were performed to predict the binding affinity and pose between herbal compounds and HUA-related targets. A chemical structure analysis of PF compounds was performed using OSIRIS DataWarrior and ClassyFire. We then conducted virtual pharmacokinetic and toxicity screening to filter potential inhibitors. We further performed verifications of these inhibitors' roles in HUA through molecular dynamics (MD) simulations, text-mining, and untargeted metabolomics analysis.

Results

We obtained 8200 predicted binding results between 328 herbal compounds and 25 potential targets, and xanthine dehydrogenase (XDH) exhibited the highest average binding affinity. We screened out five promising ligands (scutellarein, benzyl alpha-D-mannopyranoside, elemol, diisobutyl phthalate, and (3R)-hydroxy-beta-ionone) and performed MD simulations up to 50 ns for XDH complexed to them. The scutellarein-XDH complex exhibited the most satisfactory stability. Furthermore, the text-mining study provided laboratory evidence of scutellarein's function. The metabolomics approach identified 543 compounds and confirmed the presence of scutellarein. Extending MD simulations to 200 ns further indicated the sustained impact of scutellarein on XDH structure.

Conclusion

Our study provides a computational and biomedical basis for PF treating HUA and fully elucidates scutellarein's great potential as an XDH inhibitor at the molecular level, holding promise for future drug design and development.

The cytotoxicity of gefitinib on patient‑derived induced pluripotent stem cells reflects gefitinib‑induced liver injury in the clinical setting

Gefitinib is a key drug used in the treatment of non-small cell lung cancer (NSCLC) with EGFR mutations. Gefitinib therapy is superior to conventional chemotherapy for the progression-free survival rate of patients with EGFR mutations. However, 10-26% of patients develop grade 3 or higher hepatotoxicity during gefitinib treatment; therefore, the development of preclinical tests for hepatotoxicity prior to clinical use is desirable. The present study evaluated the use of induced pluripotent stem cells (iPSCs) and iPSC-derived hepatocytes (iPSC-heps), as a platform for preclinical test development. Patient-derived iPSCs were generated by reprogramming peripheral blood mononuclear cells obtained from two groups of gefitinib-treated patients with severe hepatotoxicity [toxicity group (T group)] or mild hepatotoxicity [no clinical toxicity group (N group)]. To examine the hepatotoxicity, the iPSCs from both T and N groups were differentiated into hepatocytes to obtain iPSC-heps. Differentiation was confirmed by measuring the expression levels of hepatocyte markers, such as albumin or α-fetoprotein, via western blotting and quantitative PCR analyses. Cytotoxicity in iPSCs and iPSC-heps after gefitinib treatment was evaluated using a lactate dehydrogenase release assay. The gefitinib-induced cytotoxicity in iPSCs from the T group was significantly higher than that from the N group, whereas there were no significant differences between the groups of iPSC-heps. These results suggested that using iPSCs in preclinical assessment may be a good indicator for the prediction of gefitinib-induced cytotoxicity in clinical use.

Synthesis, Computational, and Anticancer In Vitro Investigations of Aminobenzylnaphthols Derived from 2-Naphtol, Benzaldehydes, and α-Aminoacids via the Betti Reaction

Multicomponent reactions have emerged as an important approach for the synthesis of diverse and complicated chemical compounds. They have various advantages over two-component reactions, including the convenience of one-pot procedures and the ability to modify the structure of agents. Here, we employed in vitro and in silico studies to explore the anticancer potential of novel aminobenzylnaphthols derived from the Betti reaction (MMZ compounds). MTT assay was used to explore the cytotoxic activity of the compounds in pancreatic (BxPC-3 cells) and colorectal (HT-29) cancer cell lines or normal human lung fibroblasts (WI-38 cells). Proapoptotic properties of two derivatives MMZ-45AA and MMZ-140C were explored using AO/EB and annexin V-FITC/PI staining. In silico studies including ADMET profiling, molecular target prediction, docking, and dynamics were employed. The compounds exhibited cytotoxic properties and showed proapoptotic properties in respective IC50 concentrations. As indicated by in silico investigations, anticancer activity of MMZs can be attributed to the inhibition of ADORA1, CDK2, and TRIM24. Furthermore, compounds exhibited favorable ADMET properties. MMZs constitute an interesting scaffold for the potential development of new anticancer agents.

In Vitro and In Silico Investigation of BCI Anticancer Properties and Its Potential for Chemotherapy-Combined Treatments

BACKGROUND

DUSP6 phosphatase serves as a negative regulator of MAPK kinases involved in numerous cellular processes. BCI has been identified as a potential allosteric inhibitor with anticancer activity. Our study was designed to test the anticancer properties of BCI in colon cancer cells, to characterize the effect of this compound on chemotherapeutics such as irinotecan and oxaliplatin activity, and to identify potential molecular targets for this inhibitor.

METHODS

BCI cytotoxicity, proapoptotic activity, and cell cycle distribution were investigated in vitro on three colon cancer cell lines (DLD1, HT-29, and Caco-2). In silico investigation was prepared to assess BCI drug-likeness and identify potential molecular targets.

RESULTS

The exposure of colorectal cancer cells with BCI resulted in antitumor effects associated with cell cycle arrest and induction of apoptosis. BCI exhibited strong cytotoxicity on DLD1, HT-29, and Caco-2 cells. BCI showed no significant interaction with irinotecan, but strongly attenuated the anticancer activity of oxaliplatin when administered together. Analysis of synergy potential further confirmed the antagonistic interaction between these two compounds. In silico investigation indicated CDK5 as a potential new target of BCI.

CONCLUSIONS

Our studies point to the anticancer potential of BCI but note the need for a precise mechanism of action.

Prediction of drug-induced hepatotoxicity based on histopathological whole slide images

Liver is an important metabolic organ in human body and is sensitive to toxic chemicals or drugs. Adverse reactions caused by drug hepatotoxicity will damage the liver and hepatotoxicity is the leading cause of removal of approved drugs from the market. Therefore, it is of great significance to identify liver toxicity as early as possible in the drug development process. In this study, we developed a predictive model for drug hepatotoxicity based on histopathological whole slide images (WSI) which are the by-product of drug experiments and have received little attention. To better represent the WSIs, we constructed a graph representation for each WSI by dividing it into small patches, taking sampled patches as nodes and calculating the correlation coefficients between node features as the edges of the graph structure. Then a WSI-level graph convolutional network (GCN) was built to effectively extract the node information of the graph and predict the toxicity. In addition, we introduced a gated attention global context vector (gaGCV) to combine the global context to make node features to contain more comprehensive information. The results validated on rat liver in vivo data from the Open TG-GATES show that the use of WSI for the prediction of toxicity is feasible and effective.

Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning

An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice.

Patient-derived Tumour Organoids: A Bridge between Cancer Biology and Personalised Therapy

Patient-derived tumour organoids (PDOs) have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly. Here, we summarise recent advancements in PDOs from the perspective of cancer biology and clinical applications, focusing on the current challenges and opportunities of reconstructing and standardising more sophisticated PDO models. STATEMENT OF SIGNIFICANCE: Patient-derived tumour organoids (PDOs), three-dimensional (3D) self-assembled organotypic structures, have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly.

Optimisation of the HepaRG cell line model for drug toxicity studies using two different cultivation conditions: advantages and limitations

The HepaRG cell line represents a successful model for hepatotoxicity studies. These cells are of human origin and are differentiated in vitro into mature and functional hepatocyte-like cells. The objective of this research was to compare two different culture protocols, Sison-Young et al. 2017 (hereinafter referred as Sison) and Gripon et al. 2002 (hereinafter referred as Biopredic) for HepaRG cells in order to optimise this model for drug metabolism and toxicity testing studies. HepaRG cells obtained from the same batch were cultured according to the described protocols. Using both protocols, differentiated HepaRG cells retained their drug metabolic capacity (major phase I/II enzymes) and transporters, as well as their morphological characteristics. Morphologically, HepaRG cells cultured after the Biopredic protocol formed more apical membranes and small ductular-like structures, than those cultivated using the Sison protocol. Also, the efflux activity of multidrug resistance protein 1 (MDR1) and multidrug resistance-associated protein 1 (MRP1) as well as the activity of uridine-glucuronosyltransferase (UGT) and glutathione S-transferase (GST) were significantly reduced in HepaRG cultured using the Sison protocol. Applying well-established drug cocktails to measure cytochrome P450 (CYPs) activity, we found that production of the corresponding metabolites was hampered in Sison-cultured HepaRG cells, indicating that the activity of CYP1A2, CYP2C9, CYP3A4, CYP2B6 and CYP2C19 was significantly reduced. Moreover, HepaRG sensitivity to well-known drugs, namely diclofenac, amiodarone, imipramine and paracetamol, revealed some differences between the two culture protocols. Furthermore, the HepaRG cells can be maintained with higher viability and sufficient CYPs activity and expression (i.e. CYP3A4, CYP1A2 and CYP2B6) as well as liver-specific functions, using Biopredic compared with the Sison culture protocol. These maintained liver-specific functions might be dependent on the prolongation of the culture conditions in the case of the Biopredic protocol. In conclusion, based on the metabolic activity of HepaRG cells using the standard protocol from Biopredic, we believe that this protocol is optimal for investigating drug metabolism and pharmacokinetic screening studies.

Identifying potential natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach

Background

Brucellosis is an infectious disease caused by bacteria of the genus Brucella. Although it is the most common zoonosis worldwide, there are increasing reports of drug resistance and cases of relapse after long term treatment with the existing drugs of choice. This study therefore aims at identifying possible natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach.

Methods

Using PyRx 0.8 virtual screening software, the target was docked against a library of natural compounds obtained from edible African plants. The compound, 2-({3-[(3,5-dichlorobenzyl) amino] propyl} amino) quinolin-4(1H)-one (OOU) which is a co-crystallized ligand with the target was used as the reference compound. Screening of the molecular descriptors of the compounds for bioavailability, pharmacokinetic properties, and bioactivity was performed using the SWISSADME, pkCSM, and Molinspiration web servers respectively. The Fpocket and PLIP webservers were used to perform the analyses of the binding pockets and the protein ligand interactions. Analysis of the time-resolved trajectories of the Apo and Holo forms of the target was performed using the Galaxy and MDWeb servers.

Results

The lead compounds, Strophanthidin and Isopteropodin are present in Corchorus olitorius and Uncaria tomentosa (Cat’s-claw) plants respectively. Isopteropodin had a binding affinity score of -8.9 kcal / ml with the target and had 17 anti-correlating residues in Pocket 1 after molecular dynamics simulation. The complex formed by Isopteropodin and the target had a total RMSD of 4.408 and a total RMSF of 9.8067. However, Strophanthidin formed 3 hydrogen bonds with the target at ILE21, GLY262 and LEU294, and induced a total RMSF of 5.4541 at Pocket 1.

Conclusion

Overall, Isopteropodin and Strophanthidin were found to be better drug candidates than OOU and they showed potentials to inhibit the Brucella melitensis Methionyl-tRNA synthetase at Pocket 1, hence abilities to treat brucellosis. In-vivo and in-vitro investigations are needed to further evaluate the efficacy and toxicity of the lead compounds.

The cure for brucellosis involves a long course of treatment with a combination of antibiotics. However, some of the drugs are not recommended for very young children and pregnant women. Moreover, cases of relapse and resistance to these drugs are reported. With the Brucella Methionyl-tRNA synthetase as a target, molecular docking and virtual screening was used to identify possible drug candidates from a library of 1524 compounds obtained from edible African plants. Two lead compounds, Strophanthidin and Isopteropodin usually present in Corchorus olitorius and Uncaria tomentosa (Cat’s claw) plants showed potentials to inhibit the Brucella melitensis Methionyl-tRNA synthetase. Their bioactivities were also confirmed in their molecular dynamic simulation with the target protein. Consequently, both compounds have potentials for safety and efficacy in the treatment of brucellosis.

The horizon of bone organoid: A perspective on construction and application

Bone defects repair and regeneration by various causes such as tumor resection, trauma, degeneration, etc. have always been a key issue in the clinics. As one of the few organs that can regenerate after adulthood, bone itself has a strong regenerative ability. In recent decades, bone tissue engineering technology provides various types of functional scaffold materials and seed cells for bone regeneration and repair, which significantly accelerates the speed and quality of bone regeneration, and many clinical problems are gradually solved. However, the bone metabolism mechanism is complicated, the research duration is long and difficult, which significantly restricts the progress of bone regeneration and repair research. Organoids as a new concept, which is built in vitro with the help of tissue engineering technology based on biological theory, can simulate the complex biological functions of organs in vivo. Once proposed, it shows broad application prospects in the research of organ development, drug screening, mechanism study, and so on. As a complex and special organ, bone organoid construction itself is quite challenging. This review will introduce the characteristics of bone microenvironment, the concept of organoids, focus on the research progress of bone organoids, and propose the strategies for bone organoid construction, study direction, and application prospects.

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This review introduces the concept and recent progress of bone organoids.

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This review proposes the study focus and strategies for constructing bone organoids.

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This review summarizes the potential applications of bone organoids.

Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

Application of Ovarian Cancer Organoids in Precision Medicine: Key Challenges and Current Opportunities

Ovarian cancer (OC) is the leading cause of death among gynecologic malignances. Over the past decades, human-derived models have advanced from monolayer cell cultures to three-dimensional (3D) organoids that could faithfully recapitulate biological characteristics and tumor heterogeneity of primary tissues. As a complement of previous studies based on cell lines or xenografts, organoids provide a 3D platform for mutation–carcinogenesis modeling, high-throughput drug screening, genetic engineering, and biobanking, which might fulfill the gap between basic research and clinical practice. Stepwise, cutting-edge bioengineering techniques of organoid-on-a-chip and 3D bioprinting might converge current challenges and contribute to personalized therapy. We comprehensively reviewed the advantages, challenges, and translational potential of OC organoids. Undeniably, organoids represent an excellent near-physiological platform for OC, paving the way for precision medicine implementation. Future efforts will doubtlessly bring this innovative technique from bench to bedside.

Microenvironment Activatable Nanoprodrug Based on Gripper-like Cyclic Phenylboronic Acid to Precisely and Effectively Alleviate Drug-induced Hepatitis

Drug-induced hepatitis (DIH), which seriously interferes with disease treatment, is one of the most common reasons for termination of new drugs during preclinical studies or post-marketing surveillance. Although antioxidants and anti-inflammatory agents are promising, their nonspecific distribution and insolubility limit their application. Therefore, precise drug release at the disease site is an important way to alleviate DIH and avoid side effects.

Methods: A gripper-like hydrophilic cyclic phenylboronic acid (cPBA) was synthesized and a nanoprodrug (cPBA-BE) was established by coupling cPBA with hydrophobic baicalein (BE). The stimuli-responsive release properties and therapeutic effect of cPBA-BE on drug-injured hepatocyte were investigated. The biodistribution and therapeutic effect of cPBA-BE both in acetaminophen-induced acute hepatitis model and rifampicin-induced chronic hepatitis model were further evaluated.

Results: cPBA-BE conjugate could self-assemble into nanoprodrug with cPBA as the hydrophilic external layer and BE as the hydrophobic core. In HepaRG cells, cPBA-BE showed stronger cellular uptake. Due to the H₂O₂- and acid-sensitivity, cPBA-BE could achieve adequate BE release, significantly resist the depletion of GSH, mitochondrial dysfunction, downregulation of inflammation and cell apoptosis in the acetaminophen injured HepaRG cells. Biodistribution showed that cPBA-BE specifically increased the concentration of BE in the liver of DIH mice. cPBA-BE could alleviate acetaminophen-induced acute hepatitis or rifampicin-induced chronic hepatitis more effectively through relieving the oxidative stress, inflammation and block the neutrophil infiltration in liver.

Conclusions: cPBA is expected to be a good platform for constructing injectable nanoprodrug with both H₂O₂ and pH-responsive properties by coupling a wide range of drugs containing o-diol. In this study, the nanoprodrug cPBA-BE was determined to be effective for alleviating the DIH.

Organoid models of the tumor microenvironment and their applications

A small percentage of data obtained from animal/2D culture models can be translated to humans. Therefore, there is a need to using native tumour microenvironment mimicking models to improve preclinical screening and reduce this attrition rate. For this purpose, currently, the utilization of organoids is expanding. Tumour organoids can recapitulate tumour microenvironment that is including cancer cells and non-neoplastic host components. Indeed, tumour organoids, both phenotypically and genetically, resemble the tumour tissue that originated from it. The unique properties of the tumour microenvironment can significantly affect drug response and cancer progression. In this review, we will discuss about various organoid culture strategies for modelling the tumour immune microenvironment, their applications and advantages in cancer research such as testing cancer immunotherapeutics, developing novel approaches for personalized medicine, testing drug toxicity, drug screening, study cancer initiation and progression, and we will also review the limitations of organoid culture systems.

Cell-based high-throughput screening for the evaluation of reactive metabolite formation potential

Here, we established a high-throughput in vitro assay system to predict reactive metabolite (RM) formation. First, we performed the glutathione (GSH) consumption assay to monitor GSH levels as an index of RM formation potential using HepaRG cells pretreated with 500 μM D,L-buthionine-(S,R)-sulfoximine (BSO) and then treated with ticlopidine and diclofenac. Both drugs, under GSH-reduced conditions, significantly decreased relative cellular GSH content by 70% and 34%, respectively, compared with that in cells not pretreated with BSO. Next, we examined the correlation between GSH consumption and covalent binding assays; the results showed good correlation (correlation coefficient = 0.818). We then optimized the test compound concentration for evaluating RM formation potential using 76 validation compound sets, and the highest sensitivity (53%) was observed at 100 μM. Finally, using HepG2 cells, PXB-cells, and human primary hepatocytes, we examined the cell types suitable for evaluating RM formation potential. The expression of CYP3A4 was highest in HepaRG cells, suggesting the highest sensitivity (56.4%) of the GSH consumption assay. Moreover, a co-culture model of PXB-cells and HepaRG cells showed high sensitivity (72.7%) with sufficient specificity (85.7%). Thus, the GSH consumption assay can be used to effectively evaluate RM formation potential in the early stages of drug discovery.

Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches

Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity prediction of organic-type chemicals. Here, classifiers for the prediction of DILI were developed by using QuBiLS-MAS 0-2.5D molecular descriptors and shallow machine learning techniques, on a training set composed of 1075 molecules. The best ensemble model build, E13, was obtained with good statistical parameters for the learning series, namely, the following: accuracy = 0.840, sensibility = 0.890, specificity = 0.761, Matthew's correlation coefficient = 0.660, and area under the ROC curve = 0.904. The model was also satisfactorily evaluated with Y-scrambling test, and repeated k-fold cross-validation and repeated k-holdout validation. In addition, an exhaustive external validation was also carried out by using two test sets and five external test sets, with an average accuracy value equal to 0.854 (±0.062) and a coverage equal to 98.4% according to its applicability domain. A statistical comparison of the performance of the E13 model, with regard to results and tools (e.g., Padel DDPredictor Software, Deep Learning DILIserver, and Vslead) reported in the literature, was also performed. In general, E13 presented the best global performance in all experiments. The sum of the ranking differences procedure provided a very similar grouping pattern to that of the M-ANOVA statistical analysis, where E13 was identified as the best model for DILI predictions. A noncommercial and fully cross-platform software for the DILI prediction was also developed, which is freely available at http://tomocomd.com/apps/ptoxra. This software was used for the screening of seven data sets, containing natural products, leads, toxic materials, and FDA approved drugs, to assess the usefulness of the QSAR models in the DILI labeling of organic substances; it was found that 50-92% of the evaluated molecules are positive-DILI compounds. All in all, it can be stated that the E13 model is a relevant method for the prediction of DILI risk in humans, as it shows the best results among all of the methods analyzed.

Long noncoding RNA LINC00844-mediated molecular network regulates expression of drug metabolizing enzymes and nuclear receptors in human liver cells

Noncoding RNAs, such as long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), regulate gene expression in many physiological and pathological processes, including drug metabolism. Drug metabolizing enzymes (DMEs) are critical components in drug-induced liver toxicity. In this study, we used human hepatic HepaRG cells treated with 5 or 10 mM acetaminophen (APAP) as a model system and identified LINC00844 as a toxicity-responsive lncRNA. We analyzed the expression profiles of LINC00844 in different human tissues. In addition, we examined the correlations between the levels of LINC00844 and those of key DMEs and nuclear receptors (NRs) for APAP metabolism in humans. Our results showed that lncRNA LINC00844 is enriched in the liver and its expression correlates positively with mRNA levels of CYP3A4, CYP2E1, SULT2A1, pregnane X receptor (PXR), and hepatocyte nuclear factor (HNF) 4α. We demonstrated that LINC00844 regulates the expression of these five genes in HepaRG cells using gain- and loss-of-function assays. Further, we discovered that LINC00844 is localized predominantly in the cytoplasm and acts as an hsa-miR-486-5p sponge, via direct binding, to protect SULT2A1 from miRNA-mediated gene silencing. Our data also demonstrated a functional interaction between LINC00844 and hsa-miR-486-5p in regulating DME and NR expression in HepaRG cells and primary human hepatocytes. We depicted a LINC00844-mediated regulatory network that involves miRNA and NRs and influences DME expression in response to APAP toxicity.

Ganoderic acid A/DM-induced NDRG2 over-expression suppresses high-grade meningioma growth

PURPOSE

N-myc downstream-regulated gene 2 (NDRG2) is down-regulated in grade-III meningioma [anaplastic meningioma (AM)] and associated with clinically aggressive behavior. Current therapies in the treatment of high-grade meningioma are lacking with limited success. This study aims to validate the effect of NDRG2-targeted therapy using structurally related bioactive triterpene compounds derived from the edible mushroom Ganoderma lucidum (ganoderic acid A:GA-A/ganoderic acid DM:GA-DM) in human AM in relevant pre-clinical models.

METHODS

Tissue samples from the AM tumor regions of three human patients and control non-tumor samples were used to analyze the expression pattern of NDRG2. In vitro cell culture and in vivo cell-line-derived orthotopic xenograft animal models of AM were utilized to assess efficacy of treatment with GA-A/DM.

RESULTS

Downregulation of NDRG2 expression was observed in surgically resected high-grade meningiomas compared to normal brain. These results prompt us to use NDRG2-targeting agents GA-A/DM. In vitro results showed that 72-h treatments of 25 µM GA-A/DM induced AM cell death, upregulate NDRG2 protein expression, downregulate NDRG2 promoter methylation in meningioma cells as compared to azacitidine and decitabine, the most commonly used demethylating agents. Our results also demonstrated that GA-A/DM does not have any detrimental effect on normal human neurons and arachnoid cells. GA-A/DM promoted apoptotic factors (Bax) while suppressing MMP-9, p-P13K, p-AKT, p-mTOR, and Wnt-2 protein expression. RNAi-mediated knockdown of NDRG2 protein expression increased tumor proliferation, while forced expression of wt-NDRG2 decreased proliferation in an in vitro model. Magnetic resonance (MR) imaging and Hematoxylin (H&E) staining demonstrated gross reduction of tumor volume in GA-A/DM treated mice at 5 weeks when compared with saline-treated orthotopic AM xenografted controls. There was an overall decrease in tumor cell proliferation with increased survival in GA-A/DM-treated animals. Enzyme assays showed that GA-A/DM did not negatively impact hepatic function.

CONCLUSION

GA-A/DM may be a promising natural therapeutic reagent in the treatment of AM by suppressing growth via NDRG2 modulation and altering of intracellular signal pathways. We have shown it could potentially be an effective treatment for AM with decreased cellular proliferation in vitro, decreased tumor volume and increased survival in vivo.

Using Quantitative Systems Toxicology to Investigate Observed Species Differences in CKA-Mediated Hepatotoxicity

CKA, a chemokine receptor antagonist intended for treating inflammatory conditions, produced dose-dependent hepatotoxicity in rats but advanced into the clinic where single doses of CKA up to 600 mg appeared safe in humans. Because existing toxicological platforms used during drug development are not perfectly predictive, a quantitative systems toxicology model investigated the hepatotoxic potential of CKA in humans and rats through in vitro assessments of CKA on mitochondrial respiration, oxidative stress, and bile acid transporters. DILIsym predicted that single doses of CKA caused serum ALT >3xULN in a subset of the simulated rat population, while single doses in a simulated human population did not produce serum ALT elevations. Species differences were largely attributed to differences in liver exposure, but increased sensitivity to inhibition of mitochondrial respiration in the rat also contributed. We conclude that mechanistic modeling can elucidate species differences in the hepatotoxic potential of drug candidates.

In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.

Proteomic Comparison of Various Hepatic Cell Cultures for Preclinical Safety Pharmacology

Experimental drugs need to be screened for safety within time constraints. Hepatotoxicity is one concerning contributor to the failure of investigational new drugs and a major rationale for postmarketing withdrawal decisions. Ethical considerations in preclinical research force the requirement for highly predictive in vitro assays using human tissue which retains functionality reflective of primary tissue. Here, the proteome of cells commonly used to assess preclinical hepatotoxicity was compared. Primary human hepatocytes (PHHs), hepatocyte-like cells (HLCs) differentiated from human pluripotent stem cells, HepG2 cell monolayers and HepG2 cell 3D spheroids were cultured and collected as whole cell lysates. Over 6000 proteins were identified and quantified in terms of relative abundance in replicate proteomic experiments using isobaric tagging methods. Comparison of these quantitative data provides biological insight into the feasibility of using HLCs, HepG2 monolayers, and HepG2 3D spheroids for hepatotoxicity testing. Collectively these data reveal how HLCs differentiated for 35 days and HepG2 cells proteomes differ from one another and that of PHHs. HepG2 cells possess a strong cancer cell signature and do not adequately express key metabolic proteins which mark the hepatic phenotype, this was not substantially altered by culturing as 3D spheroids. These data suggest that while no single hepatic model reflects the diverse array of outcomes required to mimic the in vivo liver functions, that HLCs are the most suitable investigational avenue for replacing PHHs in vitro.

Three-Dimensional Organoids in Cancer Research: The Search for the Holy Grail of Preclinical Cancer Modeling

Most solid tumors become therapy resistant and will relapse, with no durable treatment option available. One major impediment to our understanding of cancer biology and finding innovative approaches to cancer treatment stems from the lack of better preclinical tumor models that address and explain tumor heterogeneity and person-to-person differences in therapeutic and toxic responses. Past cancer research has been driven by inadequate in vitro assays utilizing two-dimensional monolayers of cancer cells and animal models. Additionally, animal models do not truly mimic the original human tumor, are time consuming, and usually costly. New preclinical models are needed for innovation in cancer translational research. Hence, it is time to welcome the three-dimensional (3D) organoids: self-organizing cells grown in 3D culture systems mimicking the parent tissues from which the primary cells originate. The 3D organoids offer deeper insights into the crucial cellular processes in tissue and organ formation and pathological processes. Generation of near-perfect physiological microenvironments allow 3D organoids to couple with gene editing tools, such as the clustered regularly interspersed short palindromic repeat (CRISPR)/CRISPR-associated 9 and the transcription activator-like effector nucleases to model human diseases, offering distinct advantages over current models. We explain in this expert review that through recapitulating patients' normal and tumor tissues, organoid technology can markedly advance personalized medicine and help reveal once hidden aspects of cancers. The use of defined tissue- or organ-specific matrices, among other factors, will likely allow organoid technology to realize its potential in innovating many fields of life sciences.

Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals

Recent efforts aimed at integrating in vitro high-throughput screening (HTS) data into chemical toxicity assessments are necessitating increased understanding of concordance between chemical-induced responses observed in vitro versus in vivo. This investigation set out to (1) measure concordance between in vitro HTS data and transcriptomic responses observed in vivo, focusing on the liver, and (2) identify attributes that can influence concordance. Signal response profiles from 130 substances were compared between in vitro data produced through Tox21 and liver transcriptomic data through DrugMatrix, collected from rats exposed to a chemical for ≤5 days. A global in vitro-to-in vivo comparative analysis based on pathway-level responses resulted in an overall average percent agreement of 79%, ranging on a per-chemical basis between 41% and 100%. Whereas concordance amongst inactive chemicals was high (89%), concordance amongst chemicals showing in vitro activity was only 13%, suggesting that follow-up in vivo and/or orthogonal in vitro assays would improve interpretations of in vitro activity. Attributes identified to influence concordance included experimental design attributes (eg, cell type), target pathways, and physicochemical properties (eg, logP). The attribute that most consistently increased concordance was dose applicability, evaluated by filtering for experimental doses administered to rats that were within 10-fold of those related to likely bioactivity, derived using Tox21 data and high-throughput toxicokinetic modeling. Together, findings suggest that in vitro screening approaches to predict in vivo toxicity are viable particularly when certain attributes are considered, including whether activity versus inactivity is observed, experimental design, chemical properties, and dose applicability.

In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis

Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.

A Standardized Collagen-Based Scaffold Improves Human Hepatocyte Shipment and Allows Metabolic Studies over 10 Days

Due to pronounced species differences, hepatotoxicity of new drugs often cannot be detected in animal studies. Alternatively, human hepatocytes could be used, but there are some limitations. The cells are not always available on demand or in sufficient amounts, so far there has been only limited success to allow the transport of freshly isolated hepatocytes without massive loss of function or their cultivation for a long time. Since it is well accepted that the cultivation of hepatocytes in 3D is related to an improved function, we here tested the Optimaix-3D Scaffold from Matricel for the transport and cultivation of hepatocytes. After characterization of the scaffold, we shipped cells on the scaffold and/or cultivated them over 10 days. With the evaluation of hepatocyte functions such as urea production, albumin synthesis, and CYP activity, we showed that the metabolic activity of the cells on the scaffold remained nearly constant over the culture time whereas a significant decrease in metabolic activity occurred in 2D cultures. In addition, we demonstrated that significantly fewer cells were lost during transport. In summary, the collagen-based scaffold allows the transport and cultivation of hepatocytes without loss of function over 10 days.

Models of Drug Induced Liver Injury (DILI) - Current Issues and Future Perspectives

Background:

Drug-induced Liver Injury (DILI) is an important cause of acute liver failure cases in the United States, and remains a common cause of withdrawal of drugs in both preclinical and clinical phases.

Methods:

A structured search of bibliographic databases – Web of Science Core Collection, Scopus and Medline for peer-reviewed articles on models of DILI was performed. The reference lists of relevant studies was prepared and a citation search for the included studies was carried out. In addition, the characteristics of screened studies were described.

Results:

One hundred and six articles about the existing knowledge of appropriate models to study DILI in vitro and in vivo with special focus on hepatic cell models, variations of 3D co-cultures, animal models, databases and predictive modeling and translational biomarkers developed to understand the mechanisms and pathophysiology of DILI are described.

Conclusion:

Besides descriptions of current applications of existing modeling systems, associated advantages and limitations of each modeling system and future directions for research development are discussed as well.

Organoids in cancer research

The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.

Predicting drug-induced liver injury: The importance of data curation

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

Prediction of hepatotoxicity for drugs using human pluripotent stem cell-derived hepatocytes

Drug-induced liver toxicity is a main reason for withdrawals of new drugs in late clinical phases and post-launch of the drugs. Thus, hepatotoxicity screening of drug candidates in pre-clinical stage is important for reducing drug attrition rates during the clinical development process. Here, we show commercially available hepatocytes that could be used for early toxicity evaluation of drug candidates. From our hepatic differentiation technology, we obtained highly pure (≥98%) hepatocytes from human embryonic stem cells (hESCs) having mature phenotypes and similar gene expression profiles with those of primary human tissues. Furthermore, we optimized 96-well culture condition of hESC-derived hepatocytes suitable for toxicity tests in vitro. To this end, we demonstrated the efficacy of our optimized hepatocyte model for predicting hepatotoxicity against the Chinese herbal medicines and showed that toxicity patterns from our hepatocyte model was similar to those of human primary cultured hepatocytes. We conclude that toxicity test using our hepatocyte model could be a good alternative cell source for pre-clinical study to predict potential hepatotoxicity in drug discovery industries.

Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach

Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).

Spontaneous and Drug-Induced Hepatic Pathology of the Laboratory Beagle Dog, the Cynomolgus Macaque and the Marmoset

This review focuses on the background hepatic pathology present in three of the most commonly used species in the safety assessment of drugs, namely the beagle dog, the marmoset and the cynomolgus macaque. Both the nonneoplastic and neoplastic pathology are reviewed with a discussion on the potential impact that significant background pathology might have on the interpretation of any drug-induced pathology during subsequent testing. Although specific instances, such as parasitological infection in wild-caught primates can pose problems of interpretation, in general the background pathology in both the dog and the nonhuman primates, is not significantly different from that seen in the liver of laboratory rodents and with experience should not pose significant problems for the experienced pathologist. The relative merits of the primate versus the dog as a choice of second species are also considered in some detail. Although there is an inbuilt prejudice that the primate will more closely mimic subsequent effects that might occur in man in the clinic, insofar as the liver is concerned, there are many instances where the dog has been more representative of human exposure and metabolism and there is little evidence to show that the nonhuman primate is consistently better than dog in predicting human liver toxicity. As with most areas of science, comparative toxicology would dictate that the more information gained, from as wide a range of species as is practical, will give the best assessment for any subsequent problems in the clinic. This pragmatic approach should prove to be more successful than one based entirely upon an assumption, and in many cases the assumption is incorrect, that the primate always predicts human toxicity better than the nonprimate, including the dog.

Investigating the Mechanistic Basis for Hepatic Toxicity Induced by an Experimental Chemokine Receptor 5 (CCR5) Antagonist Using a Compendium of Gene Expression Profiles

A compendium of hepatic gene expression signatures was used to identify a mechanistic basis for the hepatic toxicity of an experimental CCR5 antagonist (MrkA). Development of MrkA, a potential HIV therapeutic, was discontinued due to hepatotoxicity in preclinical studies. Rats were treated with MrkA at 3 dose levels (50, 250, and 500 mg/kg) for 1, 3, or 7 days. Hepatic toxicity (vacuolation, consistent with steatosis, and elevated serum transaminase levels) was observed at 250 and 500 mg/kg, but not at 50 mg/kg. Hepatic gene expression profiles were compared to a compendium of hepatic expression profiles. MrkA was similar to 3 β-oxidation inhibitors (valproate, cyclopropane carboxylate, pivalate), 8 PPARα agonists (fenofibrate, bezafibrate and 6 fibrate analogues), and 3 other diverse compounds (diethylnitrosamine, microcystin LR & actinomycin D). These data indicate MrkA to be a mitochondrial inhibitor, and activation of PPARα-regulated transcription was thought to be due to an accumulation of endogenous ligands. While mitochondrial inhibition was likely responsible for steatosis, canonical pathway analysis revealed that progression to liver injury may be mediated by activation of the innate immune system primarily through NF-kB pathways. These results demonstrate the utility of a gene expression response compendium in developing transcriptional biomarkers and identifying the mechanistic basis for toxicity.

Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

Target organ profiles in toxicity studies supporting human dosing: Does severity progress with longer duration of exposure?

We have previously reported the profile of target organs (defined as organs showing histopathological changes) in rodent and non-rodent toxicity studies conducted prior to first-time-in-man (FTiM) for 77 AstraZeneca candidate drugs (CDs). Here, we test the assumption that toxicity is exacerbated by dosing duration by comparing the incidence and severity of target organ toxicities in these ≤ 6 week FTiM studies with those observed in subsequent subchronic/chronic (≥ 3 month) studies. Looking at the effect of dosing duration on severity (pathological score) and incidence (percentage of animals within the group) for the 39 CDs that met the criteria for inclusion (comparable doses between FTiM and subchronic/chronic studies), new toxicities appeared for 31 target organs but existing ones resolved for 29 target organs. Increased severity was more frequent for rodent (16 target organs) than for non-rodent (4 target organs). Most notable changes were a large increase in severity/incidence in liver and in non-rodent lung in contrast to a large decrease in severity and incidence for kidneys/ureter and for the non-rodent thymus. Overall this analysis shows that, even with continued exposure, target organ toxicities of CDs are as likely to show partial or complete recovery as they are to progress in severity.

Pathogenetic analyses of carbamazepine-induced liver injury in F344 rats focused on immune- and inflammation-related factors

Drug-induced liver injury is one of the major reasons for a drug to be withdrawn postmarketing. Carbamazepine (CBZ), an anticonvulsant agent, has been reported rarely to cause liver failure in humans. We recently generated a rat model of CBZ-induced liver injury using F344 rats for five consecutive days of CBZ administration combined with a glutathione (GSH) depletor, L-buthionine S,R-sulfoximine, treatment. The involvement of metabolic activation was demonstrated in developing CBZ-induced liver injury, and a difference in metabolic activation reactions between mice and rats was indicated. In this study, we analyzed the pathogenetic mechanism of CBZ-induced liver injury, primarily focusing on immune- and inflammation-related factors using the rat model for CBZ-induced liver injury. After the last CBZ administration, plasma alanine aminotransfearase (ALT) levels were drastically increased. In the histopathological evaluation, time-dependent hepatocellular degeneration and necrosis were observed in the centrilobular region. Different from mice, although hepatic mRNA expression levels of inflammation-related genes were increased, T-helper cell-related genes were not predominantly changed in rats. The number of ED1- and ED2-positive macrophages was increased in injured centrilobular areas in the liver with CBZ-induced liver injury. Treatment with a Kupffer cell depletor, gadolinium chloride, prevented the elevation of plasma ALT levels and an increase in the hepatic mRNA expression levels of inflammation-related genes. Hepatic adenosine triphosphate (ATP) contents were significantly decreased 24 h after CBZ administration. Therefore, the Kupffer cells-mediated inflammation was predominant in the development of the CBZ-induced liver injury in rats.

Preventing Drug-Induced Liver Injury: How Useful Are Animal Models?

Drug-induced liver injury (DILI) is the most common organ toxicity encountered in regulatory animal toxicology studies required prior to the clinical development of new drug candidates. Very few reports have evaluated the value of these studies for predicting DILI in humans. Indeed, compounds inducing liver toxicity in regulatory toxicology studies are not always correlated with a risk of DILI in humans. Conversely, compounds associated with the occurrence of DILI in phase 3 studies or after market release are often tested negative in regulatory toxicology studies. Idiosyncratic DILI is a rare event that is precipitated in an individual by the simultaneous occurrence of several critical factors. These factors may relate to the host (e.g. human leukocyte antigen polymorphism, inflammation), the drug (e.g. reactive metabolites) or the environment (e.g. diet/microbiota). This type of toxicity therefore cannot be detected in conventional animal toxicology studies. Several animal models have recently been proposed for the identification of drugs with the potential to cause idiosyncratic DILI: rats treated with lipopolysaccharide, Sod2(+/-) mice, panels of inbred mouse strains or chimeric mice with humanized livers. These models are not suitable for use in the prospective screening of new drug candidates. Humans therefore constitute the best model for predicting and assessing idiopathic DILI.

Toward predictive models for drug-induced liver injury in humans: are we there yet?

Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.

Target organ profiles in toxicity studies supporting human dosing: an assessment of recovery and chronic dosing

We have previously reported the profile of toxic effects with respect to target organs (defined as organs showing histopathological changes) observed in rodent and non-rodent toxicity studies conducted prior to first time in man (FTIM) for 77 AstraZeneca candidate drugs (CDs) across a range of therapy areas. The main objectives of the current study were twofold; to determine which target organs observed in the FTIM studies recovered after a dose free recovery period and to determine which additional target organs were observed in subsequent chronic (⩾3month) studies required to support longer term clinical dosing. The analysis showed that ⩾86% of findings in studies supporting FTIM either fully or partially resolved at the end of the recovery period, with profiles of recovery that were similar whether the CD progressed into man or not and across different therapy areas. Compared to observations in FTIM studies, chronic studies identified toxicities in an additional 39% of target organs. Overall these data demonstrate that chronic studies in both rodents and non-rodents provide valuable information for the risk assessment for longer term dosing in humans. In addition, the high levels of recovery demonstrated in this analysis suggest that inclusion of recovery assessments on FTIM studies should be on a case-by-case basis driven by a positive indication of need. This is in line with ICH non-clinical guidance that states that reversibility of severe nonclinical toxicities of potential clinic relevance should be assessed 'when appropriate', but that the evaluation can be based on a study of reversibility or on a scientific assessment.

Multi-targeted DATS prevents tumor progression and promotes apoptosis in ectopic glioblastoma xenografts in SCID mice via HDAC inhibition

Glioblastoma, the most lethal brain tumor, remains incurable despite aggressive chemotherapy and surgical interventions. New chemotherapeutics for glioblastoma have been explored in preclinical models and some agents have reached the clinical setting. However, success rates are not significant. Previous investigations involving diallyl trisulfide (DATS), a garlic compound, indicated significant anti-cancer effects in glioblastoma in vitro. DATS has also been shown to inhibit histone deacetylase activity and impede glioblastoma tumor progression. We hypothesized that DATS would block ectopic U87MG tumor by multiple pro-apoptotic pathways via inhibiting histone deacetylase (HDAC). To prove this, we developed ectopic U87MG tumors in SCID mice and treated them daily with intraperitoneal injections of DATS for 7 days. Results indicated that DATS (10 μg/kg-10 mg/kg) dose-dependently reduced tumor mass and number of mitotic cells within tumors. Histological and biochemical assays demonstrated that DATS reduced mitosis in tumors, decreased HDAC activity, increased acetylation of H3 and H4, inhibited cell cycle progression, decreased pro-tumor markers (e.g., survivin, Bcl-2, c-Myc, mTOR, EGFR, VEGF), promoted apoptotic factors (e.g., bax, mcalpian, active caspase-3), and induced DNA fragmentation. Our data also demonstrated an increase in p21Waf1 expression, which correlated with increased p53 expression and MDM2 degradation following DATS treatment. Finally, histological assessment and enzyme assays showed that even the highest dose of DATS did not negatively impact hepatic function. Collectively, our results clearly demonstrated that DATS could be an effective therapeutic agent in preventing tumor progression and inducing apoptosis in human glioblastoma in vivo, without impairing hepatic function.