Use of transcriptomics in understanding mechanisms of drug-induced toxicity

Multifaceted therapeutic potentials of catalpol, an iridoid glycoside: an updated comprehensive review

Catalpol, classified as an iridoid glucoside, is recognized for its significant role in medicine, particularly in the treatment of various conditions such as diabetes mellitus, neuronal disorders, and inflammatory diseases. This review aims to evaluate the biological implications of catalpol and the mechanisms underlying its diverse pharmacological effects. A thorough exploration of existing literature was conducted utilizing the keyword "Catalpol" across prominent public domains like Google Scholar, PubMed, and EKB. Catalpol has demonstrated a diverse array of pharmacological effects in experimental models, showcasing its anti-diabetic, cardiovascular-protective, neuroprotective, anticancer, hepatoprotective, anti-inflammatory, and antioxidant properties. In summary, catalpol manifests a spectrum of biological effects through a myriad of mechanisms, prominently featuring its anti-inflammatory and antioxidant capabilities. Its diverse pharmacological profile underscores its potential for therapeutic applications across a range of conditions. Further research is warranted to fully elucidate the clinical implications of catalpol and optimize its use in medical practice.

Multi-omics analysis reveals Jianpi formula-derived bioactive peptide-YG-22 potentially inhibited colorectal cancer via regulating epigenetic reprogram and signal pathway regulation

Introduction

Colorectal cancer (CRC) is a prevalent malignancy worldwide, often treated with chemotherapy despite its limitations, including adverse effects and resistance. The traditional Chinese medicine (TCM) Jianpi formula has been demonstrated to improve efficacy of chemotherapy, however the underlying mechanisms still need to be explored. In this study, we aim to screen bioactive peptides derived from the blood of CRC patients through peptidomics and explore the molecular mechanisms of the candidate peptides in the inhibition of CRC using multi-omics analysis.

Methods

In this study, we recruited 10 patients with CRC who had received either adjuvant chemotherapy or adjuvant chemotherapy combined with the traditional Chinese medicine Jianpi formula after surgery. We collected plasma samples at 2 cycles of adjuvant therapy and performed peptidomic analysis on these samples. The differentially bioactive peptides were screened using a model of HCT116 cells in vitro. To investigate the molecular mechanism underlying YG-22's inhibition of the colorectal cancer cell line HCT116, we performed a multi-omics analysis, including transcriptome, metabolome, chromatin accessibility, H3K4Me3 histone methylation, and NF-κB binding site analyses.

Results

Differential peptides were identified in plasma samples from patients treated with adjuvant chemotherapy combined with the Jianpi formula. Among these peptides, YG-22 exhibited the strongest cytotoxic effect on HCT116 cells, reducing cell viability in a dose- and time-dependent manner. Transcriptome analysis highlighted that YG-22 treatment in CRC modulates key pathways associated with lysosome-mediated degradation and apoptosis. Metabolomic profiling further indicated disruptions in tumor-supportive metabolic pathways. Chromatin accessibility and histone modification analyses suggested that YG-22 induces epigenetic reprogramming. Additionally, treatment with YG-22 resulted in significant changes in NF-κB binding and pathway activation.

Conclusions

This study demonstrates that combining chemotherapy with TCM Jianpi formula enriches the molecular landscape and generates bioactive peptides with strong antitumor activity. Furthermore, this study also lays the foundation for further development of peptide-based therapies and highlights the value of combining traditional and modern therapeutic strategies for CRC management.

Urinary bioorthogonal reporters for the monitoring of the efficacy of chemotherapy for lung cancer and of associated kidney injury

The utility of urinary tests for the monitoring of the treatment efficacy and adverse events of anticancer therapies is constrained by the low concentration of relevant urinary biomarkers. Here we report, using mice with lung cancer and treated with chemotherapy, of a urinary fluorescence test for the concurrent monitoring of the levels of a tumour biomarker (cathepsin B) and of a biomarker of chemotherapy-induced kidney injury (N-acetyl-β-D-glucosaminidase). The test involves two intratracheally administered urinary reporters leveraging caged bioorthogonal click handles for the biomarker-dependent activation of 'clickability' and renal clearance, and the bioorthogonal click reaction of each renally cleared reporter with paired fluorescence indicators in the collected urine. In mouse models of chemotherapy-treated orthotopic lung cancer and of cisplatin-induced kidney injury, lower urinary fluorescence signals (which can be measured by a smartphone camera) for tumour and kidney injury levels positively correlated with animal weight gain and survival time. Biomarker-activated bioorthogonal click reactivity and renal clearance combined with bioorthogonally triggered fluorescence in vitro may enable specific, sensitive and rapid urinary assays for the monitoring of other physiopathological processes.

Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules

BACKGROUND/OBJECTIVES

Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction.

METHODS

We compiled a dataset of 360 FDA-classified compounds (231 non-nephrotoxic and 129 nephrotoxic) and predicted 6064 off-target interactions, 59% of which were validated in vitro. We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). These models were then combined into an ensemble model for enhanced performance.

RESULTS

The ensemble model achieved an ROC-AUC of 0.86, with a sensitivity and specificity of 0.79 and 0.78, respectively. The key predictive features included 38 off-target interactions and physicochemical properties such as the number of metabolites, polar surface area (PSA), pKa, and fraction of Sp3-hybridized carbons (fsp3). These features effectively distinguished DIKI from non-DIKI compounds.

CONCLUSIONS

The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.

The Antioxidant and Hepatoprotective Potential of Berberine and Silymarin on Acetaminophen Induced Toxicity in Cyprinus carpio L

Berberine (BBR) and silymarin (SM) are natural compounds extracted from plants known for their antioxidant and chemoprotective effects on the liver. The present study aimed to investigate the beneficial properties of BBR and SM and the association of BBR with SM on liver function using fish as "in vivo" models. Moreover, the study investigated their hepatoprotective role after acetaminophen (APAP) exposure. For this purpose, the fish (N = 360; 118.4 ± 11.09 g) were fed with control or experimental diets for 9 weeks. In the experimental diets, the feed was supplemented with either SM (1 g/kg feed), BBR (100 and 200 mg/kg feed), or a combination of BBR with SM (SM 1 g/kg feed + BBR 100 mg/kg feed and, respectively, SM 1 g/kg feed + BBR 200 mg/kg feed). After the feeding trial, seven fish from each tank were randomly selected and exposed to a single APAP dose. The selected serum biochemical markers, oxidative stress markers, and lysozyme activity were used to evaluate the efficiency of the supplements on carp's health profile, particularly regarding the hepatopancreas function. Our results showed that the inclusion of SM and BBR (either as a single or in combination) reduced the serum contents of total cholesterol, triglyceride, and alanine transaminase. An increase in the high-density cholesterol was observed after the administration of BBR or BBR in association with SM. Both supplements showed hepatoprotective activity against APAP-induced hepatotoxicity, especially BBR. The ameliorative effects of SM (1 g) in association with BBR (100 mg) were highlighted by the modulation of the nonspecific immune system and oxidative stress alleviation after APAP exposure.

Next-generation sequencing reveals altered gene expression and enriched pathways in triple-negative breast cancer cells treated with oleuropein and oleocanthal

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis and limited treatment options. Oleuropein and oleocanthal are bioactive chemicals found in extra-virgin olive oil; they have been shown to have anti-cancer potential. In this study, we examined the inhibitory effects of these two natural compounds, on MDA-MB-231 and MDA-MB-468 TNBC cell lines. The human TNBC MDA-MB-231 and MDA-MB-468 cell lines were treated with oleuropein or oleocanthal at ranging concentrations for 48 h. After determining the optimum concentration to reach IC50, using the sulforhodamine B assay, total RNA was extracted after 12, 24, and 48 h from treated and untreated cells. Poly(A)-RNA selection was conducted, followed by library construction and RNA sequencing. Differential gene expression (DEG) analysis was performed to identify DEGs between treated and untreated cells. Pathway analysis was carried out using the KEGG and GO databases. Oleuropein and oleocanthal considerably reduced the proliferation of TNBC cells, with oleocanthal having a slightly stronger effect than oleuropein. Furthermore, multi-time series RNA sequencing showed that the expression profile of TNBC cells was significantly altered after treatment with these compounds, with temporal dynamics and groups of genes consistently affected at all time points. Pathway analysis revealed several significant pathways associated with TNBC, including cell death, apoptotic process, programmed cell death, response to stress, mitotic cell cycle process, cell division, and cancer progression. Our findings suggest that oleuropein and oleocanthal have potential therapeutic benefits for TNBC and can be further investigated as alternative treatment options.

Integrated lipidomic and transcriptomic analysis reveals clarithromycin-induced alteration of glycerophospholipid metabolism in the cerebral cortex of mice

Clarithromycin (CLA) has been widely used in the treatment of bacterial infection. Research reveals the adverse effects on the central nervous system among patients receiving CLA treatment; whereas, a relevant underlying mechanism remains considerably unclear. According to our research, an integrated lipidomic and transcriptomic analysis was applied to explore the effect of CLA on neurobehavior. CLA treatment caused anxiety-like behaviors dose-dependently during open field as well as elevated plus maze trials on mice. Transcriptomes and LC/MS-MS-based metabolomes were adopted for investigating how CLA affected lipidomic profiling as well as metabolic pathway of the cerebral cortex. CLA exposure greatly disturbed glycerophospholipid metabolism and the carbon chain length of fatty acids. By using whole transcriptome sequencing, we found that CLA significantly downregulated the mRNA expression of CEPT1 and CHPT1, two key enzymes involved in the synthesis of glycerophospholipids, supporting the findings from the lipidomic profiling. Also, CLA causes changes in neuronal morphology and function in vitro, which support the existing findings concerning neurobehavior in vivo. We speculate that altered glycerophospholipid metabolism may be involved in the neurobehavioral effect of CLA. Our findings contribute to understanding the mechanisms of CLA-induced adverse effects on the central nervous system. 1. Clarithromycin treatment caused anxiety-like behavior with dose-dependent response both in the open field and elevated plus maze test in mice; 2. Clarithromycin exposing predominately disturbed the metabolism of glycerophospholipids in the cerebral cortex of mice; 3. Clarithromycin application remarkably attenuated CEPT1 and CHPT1 gene expression, which participate in the last step in the synthesis of glycerophospholipids; 4. The altered glycerophospholipid metabolomics may be involved in the abnormal neurobehavior caused by clarithromycin.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

Dioxin-elicited decrease in cobalamin redirects propionyl-CoA metabolism to the β-oxidation-like pathway resulting in acrylyl-CoA conjugate buildup

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that induces diverse biological and toxic effects, including reprogramming intermediate metabolism, mediated by the aryl hydrocarbon receptor. However, the specific reprogramming effects of TCDD are unclear. Here, we performed targeted LC-MS analysis of hepatic extracts from mice gavaged with TCDD. We detected an increase in S-(2-carboxyethyl)-L-cysteine, a conjugate from the spontaneous reaction between the cysteine sulfhydryl group and highly reactive acrylyl-CoA, an intermediate in the cobalamin (Cbl)-independent β-oxidation-like metabolism of propionyl-CoA. TCDD repressed genes in both the canonical Cbl-dependent carboxylase and the alternate Cbl-independent β-oxidation-like pathways as well as inhibited methylmalonyl-CoA mutase (MUT) at lower doses. Moreover, TCDD decreased serum Cbl levels and hepatic cobalt levels while eliciting negligible effects on gene expression associated with Cbl absorption, transport, trafficking, or derivatization to 5'-deoxy-adenosylcobalamin (AdoCbl), the required MUT cofactor. Additionally, TCDD induced the gene encoding aconitate decarboxylase 1 (Acod1), the enzyme responsible for decarboxylation of cis-aconitate to itaconate, and dose-dependently increased itaconate levels in hepatic extracts. Our results indicate MUT inhibition is consistent with itaconate activation to itaconyl-CoA, a MUT suicide inactivator that forms an adduct with adenosylcobalamin. This adduct in turn inhibits MUT activity and reduces Cbl levels. Collectively, these results suggest the decrease in MUT activity is due to Cbl depletion following TCDD treatment, which redirects propionyl-CoA metabolism to the alternate Cbl-independent β-oxidation-like pathway. The resulting hepatic accumulation of acrylyl-CoA likely contributes to TCDD-elicited hepatotoxicity and the multihit progression of steatosis to steatohepatitis with fibrosis.

A Multi-Omics Approach to Evaluate the Toxicity Mechanisms Associated with Silver Nanoparticles Exposure

Silver nanoparticles (AgNPs) are currently used in many different industrial, commercial and health fields, mainly due to their antibacterial properties. Due to this widespread use, humans and the environment are increasingly exposed to these types of nanoparticles, which is the reason why the evaluation of the potential toxicity associated with AgNPs is of great importance. Although some of the toxic effects induced by AgNPs have already been shown, the elucidation of more complete mechanisms is yet to be achieved. In this sense, and since the integration of metabolomics and transcriptomics approaches constitutes a very useful strategy, in the present study targeted and untargeted metabolomics and DNA microarrays assays have been combined to evaluate the molecular mechanisms involved in the toxicity induced by 10 nm AgNPs. The results have shown that AgNPs induce the synthesis of glutathione as a cellular defense mechanism to face the oxidative environment, while inducing the depletion of relevant molecules implicated in the synthesis of important antioxidants. In addition, it has been observed that AgNPs completely impair the intracellular energetic metabolism, especially affecting the production of adenosine triphosphate (ATP) and disrupting the tricarboxylic acids cycle. It has been demonstrated that AgNPs exposure also affects the glycolysis pathway. The effect on such pathway differs depending on the step of the cycle, which a significant increase in the levels of glucose as way to counterbalance the depleted levels of ATP.

GADD45 in Stress Signaling, Cell Cycle Control, and Apoptosis

GADD45 is a gene family consisting of GADD45A, GADD45B, and GADD45G that is often induced by DNA damage and other stress signals associated with growth arrest and apoptosis. Many of these roles are carried out via signaling mediated by p38 mitogen-activated protein kinases (MAPKs). The GADD45 proteins can contribute to p38 activation either by activation of upstream kinase(s) or by direct interaction, as well as suppression of p38 activity in certain cases. In vivo, there are important tissue and cell type specific differences in the roles for GADD45 in MAPK signaling. In addition to being p53-regulated, GADD45A has also been found to contribute to p53 activation via p38. Like other stress and signaling proteins, GADD45 proteins show complex regulation and numerous effectors. More recently, aberrant GADD45 expression has been found in several human cancers, but the mechanisms behind these findings largely remain to be understood.

Treatment with Hydroxyurea Leads to Fetal Hemoglobin Reactivation through CA1 and LIN28B Genes: An In Vitro Study

Hydroxyurea (HU) is an effective drug to increase fetal γ-globin gene (Hb F) expression, replacing the missing adult β-globin gene. The mechanism of Hb F induction by HU and improvement in clinical symptoms are still poorly understood. The current study aimed to improve the molecular understanding of drug-induced alterations and reveals genes related to HU treatment responsiveness in β-thalassemia (β-thal). We analyzed the GSE109186 dataset using system biology and weighted gene coexpression network analysis (WGCNA) to identify and quantify gene expression changes reflected in the HU-treated human erythroblastic leukemia cells. The K562 cell line was treated in 50, 100, and 150 µM concentrations of HU for 24, 48, and 72 hours with three replications. The alteration of CA1, LIN28B and Hb F gene expression in HU-treated cells was evaluated using the real-time polymerase chain (real-time PCR) technique. The results showed that LIN28B has an increase of 4.27-fold on the first day of HU-treatment in 50 µM (p < 0.01). The CA1 expression showed a decrease at all times and doses of treatment, and the most decrease happened in 48 hours and 50 µM (p < 0.04). Hb F also showed the highest increase in 100 µM after 24 hours of treatment (5.18-fold). In summary, the data suggest that alteration of LIN28B and CA1 gene expression is associated with γ-globin increasing in HU-treated cells.

The cellular response to drug perturbation is limited: comparison of large-scale chemogenomic fitness signatures

Background

Chemogenomic profiling is a powerful approach for understanding the genome-wide cellular response to small molecules. First developed in Saccharomyces cerevisiae, chemogenomic screens provide direct, unbiased identification of drug target candidates as well as genes required for drug resistance. While many laboratories have performed chemogenomic fitness assays, few have been assessed for reproducibility and accuracy. Here we analyze the two largest independent yeast chemogenomic datasets comprising over 35 million gene-drug interactions and more than 6000 unique chemogenomic profiles; the first from our own academic laboratory (HIPLAB) and the second from the Novartis Institute of Biomedical Research (NIBR).

Results

Despite substantial differences in experimental and analytical pipelines, the combined datasets revealed robust chemogenomic response signatures, characterized by gene signatures, enrichment for biological processes and mechanisms of drug action. We previously reported that the cellular response to small molecules is limited and can be described by a network of 45 chemogenomic signatures. In the present study, we show that the majority of these signatures (66%) are also found in the companion dataset, providing further support for their biological relevance as conserved systems-level, small molecule response systems.

Conclusions

Our results demonstrate the robustness of chemogenomic fitness profiling in yeast, while offering guidelines for performing other high-dimensional comparisons including parallel CRISPR screens in mammalian cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08395-x.

A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease

Drug-drug interactions (DDIs) and adverse drug reactions (ADR) are experienced by many patients, especially by elderly population due to their multiple comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text mining approaches are required to retrieve DDI and ADR information from PubMed. Recently we developed a hybrid approach for predicting DDI and ADR information from PubMed. There are many other existing approaches for retrieving DDI and ADR information from PubMed. However, none of the approaches are meant for retrieving DDI and ADR specific to patient population, gender, pharmacokinetics, and pharmacodynamics. Here, we present a text mining protocol which is based on our recent work for retrieving DDI and ADR information specific to patient population, gender, pharmacokinetics, and pharmacodynamics from PubMed.

Investigation of Ifosfamide Toxicity Induces Common Upstream Regulator in Liver and Kidney

Ifosfamide is an alkylating agent, a synthetic analogue of cyclophosphamide, used to treat various solid cancers. In this study, the toxicity of ifosfamide was evaluated using single-and multiple-dose intraperitoneal administration in rats under Good Laboratory Practice guidelines, and an additional microarray experiment was followed to support toxicological findings. A single dose of ifosfamide (50 mg/kg) did not induce any pathological changes. Meanwhile, severe renal toxicity was observed in the 7 and 28 days consecutively administered groups, with significant increases in blood urea nitrogen and creatinine levels. In the tox-list analysis, cholesterol synthesis-related genes were mostly affected in the liver and renal failure-related genes were affected in the kidney after ifosfamide administration. Moreover, interferon regulatory factor 7 was selected as the main upstream regulator that changed in both the liver and kidney, and was found to interact with other target genes, such as ubiquitin specific peptidase 18, radical S-adenosyl methionine domain containing 2, and interferon-stimulated gene 15, which was further confirmed by real-time RT-PCR analysis. In conclusion, we confirmed kidney-biased ifosfamide organ toxicity and identified identically altered genes in both the liver and kidney. Further comprehensive toxicogenomic studies are required to reveal the exact relationship between ifosfamide-induced genes and organ toxicity.

A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data

BACKGROUND

Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines.

METHODS

Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action.

RESULTS

The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks.

CONCLUSION

The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization's International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals

New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

Appended Aromatic Moieties in Flexible Bis-3-chloropiperidines Confer Tropism against Pancreatic Cancer Cells

Nitrogen mustards (NMs) are an old but still largely diffused class of anticancer drugs. However, spreading mechanisms of resistance undermine their efficacy and therapeutic applicability. To expand their antitumour value, we developed bis-3-chloropiperidines (B-CePs), a new class of mustard-based alkylating agent, and we recently reported the striking selectivity for BxPC-3 pancreatic tumour cells of B-CePs bearing aromatic moieties embedded in the linker. In this study, we demonstrate that such tropism is shared by bis-3-chloropiperidines bearing appended aromatic groups in flexible linkers, whereas esters substituted by aliphatic groups or by efficient DNA-interacting groups are potent but nonselective cytotoxic agents. Besides, we describe how the critical balance between water stability and DNA reactivity can affect the properties of bis-3-chloropiperidines. Together, these findings support the exploitation of B-CePs as potential antitumour clinical candidates.

Transcriptomic analyses of livers from mice exposed to 1,4-dioxane for up to 90 days to assess potential mode(s) of action underlying liver tumor development

1,4-Dioxane is a volatile organic compound with industrial and commercial applications as a solvent and in the manufacture of other chemicals. 1,4-Dioxane has been demonstrated to induce liver tumors in chronic rodent bioassays conducted at very high doses. The available evidence for 1,4-dioxane-induced liver tumors in rodents aligns with a threshold-dependent mode of action (MOA), with the underlying mechanism being less clear in the mouse than in rats. To gain a better understanding of the underlying molecular mechanisms related to liver tumor development in mice orally exposed to 1,4-dioxane, transcriptomics analysis was conducted on liver tissue collected from a 90-day drinking water study in female B6D2F₁/Crl mice (Lafranconi et al., 2020). Using tissue samples from female mice exposed to 1,4-dioxane in the drinking water at concentrations of 0, 40, 200, 600, 2,000 or 6,000 ppm for 7, 28, and 90 days, transcriptomic analyses demonstrate minimal treatment effects on global gene expression at concentrations below 600 ppm. At higher concentrations, genes involved in phase II metabolism and mitotic cell cycle checkpoints were significantly upregulated. There was an overall lack of enrichment of genes related to DNA damage response. The increase in mitotic signaling is most prevalent in the livers of mice exposed to 1,4-dioxane at the highest concentrations for 90 days. This finding aligns with phenotypic changes reported by Lafranconi et al. (2020) after 90-days of exposure to 6,000 ppm 1,4-dioxane in the same tissues. The transcriptomics analysis further supports overarching study findings demonstrating a non-mutagenic, threshold-based, mitogenic MOA for 1,4-dioxane-induced liver tumors.

Toxicogenomic Analysis

The biological functions of a cell may change in response to exposure to toxic agents. Toxicogenomics employs the recent developments in genomics, transcriptomics, and proteomics to study how a chemical impacts gene/protein expression and cell functions. We describe a method for transcriptomic analysis by RNA sequencing based on Illumina HiSeq, NextSeq, or NovaSeq Systems followed by real-time qPCR validation. We also depict a method for proteomic analysis by "one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis" (1D SDS-PAGE) and a sample preparation procedure for "liquid chromatography in tandem with mass spectrometry" (LC-MS/MS), and we present some generic points to consider during LC-MS/MS.

Low-Dose Silver Nanoparticle Surface Chemistry and Temporal Effects on Gene Expression in Human Liver Cells

Silver nanoparticles (AgNPs) are widely incorporated into consumer and biomedical products for their antimicrobial and plasmonic properties with limited risk assessment of low-dose cumulative exposure in humans. To evaluate cellular responses to low-dose AgNP exposures across time, human liver cells (HepG2) are exposed to AgNPs with three different surface charges (1.2 µg mL-1 ) and complete gene expression is monitored across a 24 h period. Time and AgNP surface chemistry mediate gene expression. In addition, since cells are fed, time has marked effects on gene expression that should be considered. Surface chemistry of AgNPs alters gene transcription in a time-dependent manner, with the most dramatic effects in cationic AgNPs. Universal to all surface coatings, AgNP-treated cells responded by inactivating proliferation and enabling cell cycle checkpoints. Further analysis of these universal features of AgNP cellular response, as well as more detailed analysis of specific AgNP treatments, time points, or specific genes, is facilitated with an accompanying application. Taken together, these results provide a foundation for understanding hepatic response to low-dose AgNPs for future risk assessment.

Global Transcriptional Analysis of Nontransformed Human Intestinal Epithelial Cells (FHs 74 Int) after Exposure to Selected Drinking Water Disinfection By-Products

BACKGROUND

Drinking water disinfection inadvertently leads to the formation of numerous disinfection by-products (DBPs), some of which are cytotoxic, mutagenic, genotoxic, teratogenic, and potential carcinogens both in vitro and in vivo.

OBJECTIVES

We investigated alterations to global gene expression (GE) in nontransformed human small intestine epithelial cells (FHs 74 Int) after exposure to six brominated and two chlorinated DBPs: bromoacetic acid (BAA), bromoacetonitrile (BAN), 2,6-dibromo-p-benzoquinone (DBBQ), bromoacetamide (BAM), tribromoacetaldehyde (TBAL), bromate (BrO3-), trichloroacetic acid (TCAA), and trichloroacetaldehyde (TCAL).

METHODS

Using whole-genome cDNA microarray technology (Illumina), we examined GE in nontransformed human cells after 4h exposure to DBPs at predetermined equipotent concentrations, identified significant changes in gene expression (p≤0.01), and investigated the relevance of these genes to specific toxicity pathways via gene and pathway enrichment analysis.

RESULTS

Genes related to activation of oxidative stress-responsive pathways exhibited fewer alterations than expected based on prior work, whereas all DBPs induced notable effects on transcription of genes related to immunity and inflammation.

DISCUSSION

Our results suggest that alterations to genes associated with immune and inflammatory pathways play an important role in the potential adverse health effects of exposure to DBPs. The interrelationship between these pathways and the production of reactive oxygen species (ROS) may explain the common occurrence of oxidative stress in other studies exploring DBP toxicity. Finally, transcriptional changes and shared induction of toxicity pathways observed for all DBPs caution of additive effects of mixtures and suggest further assessment of adverse health effects of mixtures is warranted. https://doi.org/10.1289/EHP4945.

SNV identification from single-cell RNA sequencing data

Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

Metabolomic-transcriptomic landscape of 8-epidiosbulbin E acetate -a major diterpenoid lactone from Dioscorea bulbifera tuber induces hepatotoxicity

Studies have shown that 8-epidiosbulbin E acetate (EEA), a major diterpenoid lactone in the tuber of Dioscorea bulbifera, can induce hepatotoxicity in vivo. However, the underlying mechanisms remain unknown. Using the integrated transcriptomic and metabolomics method, in this study we investigated the global effect of EEA exposure on the transcriptomic and metabolomic profiles in mice. The abundance of 7131 genes and 42 metabolites in the liver, as well as 43 metabolites in the serum were altered. It should be noted that EEA mainly damaged hepatic cells through the aberrant regulation of multiple systems primarily including bile acid metabolism, and taurine and hypotaurine metabolism. In addition, an imbalance of bile acid metabolism was found to play a key pat in response to EEA-triggered hepatotoxicity. In summary, these findings contributed to understanding the underlying mechanisms of EEA hepatotoxicity.

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

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

Pharmacologic normalization of pathogenic dosage underlying genetic diseases: an overview of the literature and path forward

Most monogenic disorders are caused by a pathologic deficit or excess of a single transcript and/or protein. Given that small molecules, including drugs, can affect levels of mRNA and protein, the pharmacologic normalization of such pathogenic dosage represents a possible therapeutic approach for such conditions. Here, we review the literature exploring pharmacologic modulation of mRNA and/or protein levels for disorders with paralogous modifier genes, for haploinsufficient disorders (insufficient gene-product), as well as toxic gain-of-function disorders (surplus or pathologic gene-product). We also discuss challenges facing the development of rare disease therapy by pharmacologic modulation of mRNA and protein. Finally, we lay out guiding principles for selection of disorders which may be amenable to this approach.

SureSelectXT RNA Direct: A Technique for Expression Analysis Through Sequencing of Target-Enriched FFPE Total RNA

Gene expression profiling of samples from biobanks requires a method that can be used with intact as well as partially degraded RNA. High throughput applications can benefit from reducing the number of processing steps including eliminating the poly(A) selection and ribosomal depletion steps. When performing targeted capture, we have found that we can eliminate the upfront poly(A) selection/ribosomal depletion steps that cause bias in standard mRNA-Seq workflows. This target enrichment solution allows for whole transcriptome or customized content to characterize differential gene expression patterns (especially for mid/low level transcripts). Protocol modifications to the Agilent Strand-Specific RNA Library Prep kit resulted in a new workflow called "RNA Direct" that generates RNA-Seq data with minimal ribosomal contamination and good sequencing coverage. Using RNA isolated from a set of matched samples including fresh frozen (FF) or formalin-fixed, paraffin-embedded (FFPE) from tumor/normal tissues we generated high-quality data using a protocol that does not require upfront ribosomal depletion or poly(A) selection. Using SureSelect^XT RNA Direct protocol (RNA Direct) workflow, we found transcripts to be upregulated or downregulated to similar degrees with similar confidence levels in both the FF and FFPE samples, demonstrating the utility for meaningful gene expression studies with biobank samples of variable quality.

Pharmacology behind Common Drug Nephrotoxicities

Patients are exposed to numerous prescribed and over-the-counter medications. Unfortunately, drugs remain a relatively common cause of acute and chronic kidney injury. A combination of factors including the innate nephrotoxicity of drugs, underlying patient characteristics that increase their risk for kidney injury, and the metabolism and pathway of excretion by the kidneys of the various agents administered enhance risk for drug-induced nephrotoxicity. This paper will review these clinically relevant aspects of drug-induced nephrotoxicity for the clinical nephrologist.

Extracellular vesicles as potential biomarkers for alcohol- and drug-induced liver injury and their therapeutic applications

Extracellular vesicles (EVs) are small membranous vesicles originating from various cells and tissues, including the liver parenchymal hepatocytes and nonparenchymal cells such as Kupffer and stellate cells. Recently, the pathophysiological role of EVs, such as exosomes and microvesicles, has been increasingly recognized based on their properties of intercellular communications. These EVs travel through the circulating blood and interact with specific cells and then deliver their cargos such as nucleic acids and proteins into recipient cells. In addition, based on their stabilities, circulating EVs from body fluids such as blood, cerebrospinal fluid, urine, saliva, semen, breast milk and amniotic fluids are being studied as a valuable source of potential biomarkers for providing information about the physiological status of original cells or tissues. In addition, EVs are considered potential therapeutic agents due to their ability for intercellular communications between different cell types within the liver and between various organs through transfer of their cargos. In this review, we have briefly described recent advances in the characteristics and pathophysiological roles of EVs in alcoholic liver disease (ALD) or drug-induced liver injury (DILI) and discuss their advantages in the discovery of potential biomarkers and therapeutic agents.

Increased liver-specific proteins in circulating extracellular vesicles as potential biomarkers for drug- and alcohol-induced liver injury

Drug- and alcohol-induced liver injury are a leading cause of liver failure and transplantation. Emerging evidence suggests that extracellular vesicles (EVs) are a source of biomarkers because they contain unique proteins reflecting the identity and tissue-specific origin of the EV proteins. This study aimed to determine whether potentially hepatotoxic agents, such as acetaminophen (APAP) and binge alcohol, can increase the amounts of circulating EVs and evaluate liver-specific EV proteins as potential biomarkers for liver injury. The circulating EVs, isolated from plasma of APAP-exposed, ethanol-fed mice, or alcoholic hepatitis patients versus normal control counterparts, were characterized by proteomics and biochemical methods. Liver specific EV proteins were analyzed by immunoblots and ELISA. The amounts of total and liver-specific proteins in circulating EVs from APAP-treated mice significantly increased in a dose- and time-dependent manner. Proteomic analysis of EVs from APAP-exposed mice revealed that the amounts of liver-specific and/or hepatotoxic proteins were increased compared to those of controls. Additionally, the increased protein amounts in EVs following APAP exposure returned to basal levels when mice were treated with N-acetylcysteine or glutathione. Similar results of increased amounts and liver-specific proteins in circulating EVs were also observed in mice exposed to hepatotoxic doses of thioacetamide or d-galactosamine but not by non-hepatotoxic penicillin or myotoxic bupivacaine. Additionally, binge ethanol exposure significantly elevated liver-specific proteins in circulating EVs from mice and alcoholics with alcoholic hepatitis, compared to control counterparts. These results indicate that circulating EVs in drug- and alcohol-mediated hepatic injury contain liver-specific proteins that could serve as specific biomarkers for hepatotoxicity.

The Opportunities of Metabolomics in Drug Safety Evaluation

Although safety of drug candidates is carefully monitored in preclinical and clinical studies using a variety of approaches, drug toxicity may still occur in clinical practice. Therefore, novel approaches are needed to complement the current drug safety evaluation system. Metabolomics comprehensively analyzes the metabolites altered by drug exposure, which can therefore be used to profile drug metabolism, endobiotic metabolism, and drug-microbiota interactions. The information from metabolomic analysis can be used to determine the off-targets of a drug candidate, and thus provide a mechanistic understanding of drug toxicity. We herein discuss the opportunities of metabolomics in drug safety evaluation.

RNA‑seq profiling of mRNA associated with hypertrophic cardiomyopathy

The mechanisms of hypertrophic cardiomyopathy (HCM) pathogenesis can be investigated by determining the differences between healthy and disease states at the molecular level. In the present study, large‑scale transcriptome sequencing was performed to compare mRNA expression in patients with HCM and control groups using an Illumina sequencing platform. Compared with the genome background, 257 differentially expressed genes (DEGs) were identified in which 62 genes were downregulated and 195 genes were upregulated. Reverse transcription‑quantitative polymerase chain reaction was performed to validate the expression pattern of certain mRNAs. Gene ontology enrichment and KEGG analysis of mRNAs was conducted to identify the biological modules and pathological pathways associated with the DEGs. To the best of our knowledge, this is the first time study to investigate the differences in mRNA between patients with HCM and normal controls at the transcriptome level. The results of the study will contributed to the understanding of the important molecular mechanisms involved in HCM and aid the selection of key genes to investigate in the future.

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Integrative functional transcriptomic analyses implicate specific molecular pathways in pulmonary toxicity from exposure to aluminum oxide nanoparticles

Gene expression profiling has developed rapidly in recent years and it can predict and define mechanisms underlying chemical toxicity. Here, RNA microarray and computational technology were used to show that aluminum oxide nanoparticles (Al2O3 NPs) were capable of triggering up-regulation of genes related to the cell cycle and cell death in a human A549 lung adenocarcinoma cell line. Gene expression levels were validated in Al2O3 NPs exposed A549 cells and mice lung tissues, most of which showed consistent trends in regulation. Gene-transcription factor network analysis coupled with cell- and animal-based assays demonstrated that the genes encoding PTPN6, RTN4, BAX and IER play a role in the biological responses induced by the nanoparticle exposure, which caused cell death and cell cycle arrest in the G2/S phase. Further, down-regulated PTPN6 expression demonstrated a core role in the network, thus expression level of PTPN6 was rescued by plasmid transfection, which showed ameliorative effects of A549 cells against cell death and cell cycle arrest. These results demonstrate the feasibility of using gene expression profiling to predict cellular responses induced by nanomaterials, which could be used to develop a comprehensive knowledge of nanotoxicity.

The global effect of exposing bakers' yeast to 5-fluoruracil and nystatin; a view to Toxichip

A genome-wide screen of a haploid deletion library of bakers' yeast (Saccharomyces cerevisiae) was conducted to document the phenotypic and transcriptional impact of exposure to each of the two pharmaceutical products 5-fluorouracil (an anti-tumor agent) and nystatin (an anti-fungal agent). The combined data set was handled by applying a systems biology perspective. A Gene Ontology analysis identified functional categories previously characterized as likely targets for both compounds. Induced transcription profiles were well correlated in yeast and human HepG2 cells. The identified molecular targets for both compounds were used to suggest a small set of human orthologues as appropriate for testing on human material. The yeast system developed here (denoted "Toxichip") has likely utility for identifying biomarkers relevant for health and environmental risk assessment applications required as part of the development process for novel pharmaceuticals.

A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure

When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.

Translating extracellular microRNA into clinical biomarkers for drug-induced toxicity: from high-throughput profiling to validation

Over the past 5 years, extracellular microRNAs (miRNAs) are being vigorously explored as injury biomarkers, including drug-induced cardiotoxicity, hepatotoxicity and nephrotoxicity. Currently, the development of miRNAs as clinical biomarkers has been hindered by the lack of standardization. Therefore, extracellular miRNA-based biomarkers have not been embraced as diagnostic tools. Each platform has its strengths and weaknesses when working with low-input-amount RNA samples from body fluids; the selection of a miRNA quantification approach should be based on the study design. The following review provides a summary of the extracellular miRNA release and stability in body fluids, performances of different miRNA quantification platforms, existing clinical gold standards for drug-induced tissue damage and translation of the miRNA biomarkers from the nonclinical to clinical setting.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Development of novel tools for the in vitro investigation of drug-induced liver injury

INTRODUCTION

Due to its complex mechanisms and unpredictable occurrence, drug-induced liver injury (DILI) complicates drug identification and classification. Since species-specific differences in metabolism and pharmacokinetics exist, data obtained from animal studies may not be sufficient to predict DILI in humans.

AREAS COVERED

Over the last few decades, numerous in vitro models have been developed to replace animal testing. The advantages and disadvantages of commonly used liver-derived in vitro models (e.g., cell lines, hepatocyte models, liver slices, three-dimensional (3D) hepatospheres, etc.) are discussed. Toxicogenomics-based methodologies (genomics, epigenomics, transcriptomics, proteomics and metabolomics) and next-generation sequencing have also been used to enhance the reliability of DILI prediction. This review presents an overview of the currently used alternative toxicological models and of the most advanced approaches in the field of DILI research.

EXPERT OPINION

It seems unlikely that a single in vitro system will be able to mimic the complex interactions in the human liver. Three-dimensional multicellular systems may bridge the gap between conventional 2D models and in vivo clinical studies in humans and provide a reliable basis for hepatic toxicity assay development. Next-generation sequencing technologies, in comparison to microarray-based technologies, may overcome the current limitations and are promising for the development of predictive models in the near future.

Adverse Outcome Pathways and Drug-Induced Liver Injury Testing

Drug-induced liver injury is a prominent reason for premarketing and postmarketing drug withdrawal and can be manifested in a number of ways, such as cholestasis, steatosis, and fibrosis. The mechanisms driving these toxicological processes have been well characterized and have been emdedded in adverse outcome pathway frameworks in recent years. This review evaluates these constructs and simultaneously illustrates their use in the preclinical testing of drug-induced liver injury.

Development of a toxicogenomics signature for genotoxicity using a dose-optimization and informatics strategy in human cells

The development of in vitro molecular biomarkers to accurately predict toxicological effects has become a priority to advance testing strategies for human health risk assessment. The application of in vitro transcriptomic biomarkers promises increased throughput as well as a reduction in animal use. However, the existing protocols for predictive transcriptional signatures do not establish appropriate guidelines for dose selection or account for the fact that toxic agents may have pleiotropic effects. Therefore, comparison of transcriptome profiles across agents and studies has been difficult. Here we present a dataset of transcriptional profiles for TK6 cells exposed to a battery of well-characterized genotoxic and nongenotoxic chemicals. The experimental conditions applied a new dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in preliminary dose-finding studies. The subsequent microarray-based transcriptomic analyses at the optimized dose revealed responses to the test chemicals that were typically complex, often exhibiting substantial overlap in the transcriptional responses between a variety of the agents making analysis challenging. Using the nearest shrunken centroids method we identified a panel of 65 genes that could accurately classify toxicants as genotoxic or nongenotoxic. To validate the 65-gene panel as a genomic biomarker of genotoxicity, the gene expression profiles of an additional three well-characterized model agents were analyzed and a case study demonstrating the practical application of this genomic biomarker-based approach in risk assessment was performed to demonstrate its utility in genotoxicity risk assessment.

Integrating multiple omics to unravel mechanisms of Cyclosporin A induced hepatotoxicity in vitro

In order to improve attrition rates of candidate-drugs there is a need for a better understanding of the mechanisms underlying drug-induced hepatotoxicity. We aim to further unravel the toxicological response of hepatocytes to a prototypical cholestatic compound by integrating transcriptomic and metabonomic profiling of HepG2 cells exposed to Cyclosporin A. Cyclosporin A exposure induced intracellular cholesterol accumulation and diminished intracellular bile acid levels. Performing pathway analyses of significant mRNAs and metabolites separately and integrated, resulted in more relevant pathways for the latter. Integrated analyses showed pathways involved in cell cycle and cellular metabolism to be significantly changed. Moreover, pathways involved in protein processing of the endoplasmic reticulum, bile acid biosynthesis and cholesterol metabolism were significantly affected. Our findings indicate that an integrated approach combining metabonomics and transcriptomics data derived from representative in vitro models, with bioinformatics can improve our understanding of the mechanisms of action underlying drug-induced hepatotoxicity. Furthermore, we showed that integrating multiple omics and thereby analyzing genes, microRNAs and metabolites of the opposed model for drug-induced cholestasis can give valuable information about mechanisms of drug-induced cholestasis in vitro and therefore could be used in toxicity screening of new drug candidates at an early stage of drug discovery.