Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix® database

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.

Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure

Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BACKGROUND

The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.

RESULTS

To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.

CONCLUSION

The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

ELIXIR and Toxicology: a community in development

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver

Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Short-term in vivo testing to discriminate genotoxic carcinogens from non-genotoxic carcinogens and non-carcinogens using next-generation RNA sequencing, DNA microarray, and qPCR

Next-generation RNA sequencing (RNA-Seq) has identified more differentially expressed protein-coding genes (DEGs) and provided a wider quantitative range of expression level changes than conventional DNA microarrays. JEMS·MMS·Toxicogenomics group studied DEGs with targeted RNA-Seq on freshly frozen rat liver tissues and on formalin-fixed paraffin-embedded (FFPE) rat liver tissues after 28 days of treatment with chemicals and quantitative real-time PCR (qPCR) on rat and mouse liver tissues after 4 to 48 h treatment with chemicals and analyzed by principal component analysis (PCA) as statics. Analysis of rat public DNA microarray data (Open TG-GATEs) was also performed. In total, 35 chemicals were analyzed [15 genotoxic hepatocarcinogens (GTHCs), 9 non-genotoxic hepatocarcinogens (NGTHCs), and 11 non-genotoxic non-hepatocarcinogens (NGTNHCs)]. As a result, 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) were proposed to discriminate GTHCs from NGTHCs and NGTNHCs. U.S. Environmental Protection Agency studied DEGs induced by 4 known GTHCs in rat liver using DNA microarray and proposed 7 biomarker genes, Bax, Bcmp1, Btg2, Ccng1, Cdkn1a, Cgr19, and Mgmt for GTHCs. Studies involving the use of whole-transcriptome RNA-Seq upon exposure to chemical carcinogens in vivo have also been performed in rodent liver, kidney, lung, colon, and other organs, although discrimination of GTHCs from NGTHCs was not examined. Candidate genes published using RNA-Seq, qPCR, and DNA microarray will be useful for the future development of short-term in vivo studies of environmental carcinogens using RNA-Seq.

TOXRIC: a comprehensive database of toxicological data and benchmarks

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

Repurposing the Antiplatelet Agent Ticlopidine to Counteract the Acute Phase of ER Stress Condition: An Opportunity for Fighting Coronavirus Infections and Cancer

Different pathological conditions, including viral infections and cancer, can have a massive impact on the endoplasmic reticulum (ER), causing severe damage to the cell and exacerbating the disease. In particular, coronavirus infections, including SARS coronavirus-2 (SARS-CoV-2), responsible for COVID-19, cause ER stress as a consequence of the enormous amounts of viral glycoproteins synthesized, the perturbation of ER homeostasis and the modification of ER membranes. Therefore, ER has a central role in the viral life cycle, thus representing one of the Achilles’ heels on which to focus therapeutic intervention. On the other hand, prolonged ER stress has been demonstrated to promote many pro-tumoral attributes in cancer cells, having a key role in tumor growth, metastasis and response to therapies. In this report, adopting a repurposing approach of approved drugs, we identified the antiplatelet agent ticlopidine as an interferent of the unfolded protein response (UPR) via sigma receptors (SRs) modulation. The promising results obtained suggest the potential use of ticlopidine to counteract ER stress induced by viral infections, such as COVID-19, and cancer.

Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation

Epidemiology studies have indicated that environmental cadmium exposure, even at low levels, will result in chronic cadmium accumulation in the kidney with profound adverse consequences and that the diabetic population is more susceptible. However, the underlying mechanisms are yet not fully understood. In the present study, we applied an animal model to study chronic cadmium exposure-induced renal injury and performed whole transcriptome profiling studies. Repetitive CdCl₂ exposure resulted in cadmium accumulation and remarkable renal injuries in the animals. The diabetic ob/ob mice manifested increased severity of renal injury compared with the wild type C57BL/6 J littermate controls. RNA-Seq data showed that cadmium treatment induced dramatic gene expression changes in a dose-dependent manner. Among the differentially expressed genes include the apoptosis hallmark genes which significantly demarcated the treatment effects. Pathway enrichment and network analyses revealed biological oxidation (mainly glucuronidation) as one of the major stress responses induced by cadmium treatment. We next implemented a deep learning algorithm in conjunction with cloud computing and discovered a gene signature that can predict the degree of renal injury induced by cadmium treatment. The present study provided, for the first time, a comprehensive mechanistic understanding of chronic cadmium-induced nephrotoxicity in normal and diabetic populations at the whole genome level.

Nextcast: A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

Tox-GAN: An AI Approach Alternative to Animal Studies-a Case Study with Toxicogenomics

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology towards "reducing, refining and replacing" (3Rs) animal use. Here, we proposed a deep generative adversarial network (GAN)-based framework capable of deriving new animal results from existing animal studies without additional experiments. To prove the concept, we employed this Tox-GAN framework to generate both gene activities and expression profiles for multiple doses and treatment durations in toxicogenomics (TGx). Using the pre-existing rat liver TGx data from the Open TG-GATEs, we generated Tox-GAN transcriptomic profiles with high similarity (0.997 ± 0.002 in intensity and 0.740 ± 0.082 in fold change) to the corresponding real gene expression profiles. Consequently, Tox-GAN showed an outstanding performance in two critical TGx applications, gaining a molecular understanding of underlying toxicological mechanisms and gene expression-based biomarker development. For the former, over 87% agreement in Gene Ontology was found between Tox-GAN results and real gene expression data. For the latter, the concordance of biomarkers between real and generated data was high in both predictive performance and biomarker genes. We also demonstrated that the Tox-GAN models constructed with TG-GATEs data were capable of generating transcriptomic profiles reported in DrugMatrix. Finally, we demonstrated potential utility for Tox-GAN in aiding chemical-based read-across. To the best of our knowledge, the proposed Tox-GAN model is novel in its ability to generate in vivo transcriptomic profiles at different treatment conditions from chemical structures. Overall, Tox-GAN holds great promise for generating high-quality toxicogenomic profiles without animal experimentation.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

A network-based systems biology approach for identification of shared Gene signatures between male and female in COVID-19 datasets

The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17-5p, mir-20a-5p, mir-93-5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.

CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans could cause coronavirus disease 2019 (COVID-19). Since its first discovery in Dec 2019, SARS-CoV-2 has become a global pandemic and caused 3.3 million direct/indirect deaths (2021 May). Amongst the scientific community's response to COVID-19, data sharing has emerged as an essential aspect of the combat against SARS-CoV-2. Despite the ever-growing studies about SARS-CoV-2 and COVID-19, to date, only a few databases were curated to enable access to gene expression data. Furthermore, these databases curated only a small set of data and do not provide easy access for investigators without computational skills to perform analyses. To fill this gap and advance open-access to the growing gene expression data on this deadly virus, we collected about 1,500 human bulk RNA-seq datasets from publicly available resources, developed a database and visualization tool, named CovidExpress (https://stjudecab.github.io/covidexpress). This open access database will allow research investigators to examine the gene expression in various tissues, cell lines, and their response to SARS-CoV-2 under different experimental conditions, accelerating the understanding of the etiology of this disease to inform the drug and vaccine development. Our integrative analysis of this big dataset highlights a set of commonly regulated genes in SARS-CoV-2 infected lung and Rhinovirus infected nasal tissues, including OASL that were under-studied in COVID-19 related reports. Our results also suggested a potential FURIN positive feedback loop that might explain the evolutional advantage of SARS-CoV-2.

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.

Multi-omics phenotyping of the gut-liver axis reveals metabolic perturbations from a low-dose pesticide mixture in rats

Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data

'Artificial Intelligence' (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.

ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets

In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes

The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median -42% (25th to 75% percentile -65 to 6) and -29% (25th to 75% percentile -37 to -1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles -58% (25th to 75% percentile -78 to 33), -28% (25th to 75% percentile -46 to 8), -40% (25th to 75% percentile -52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was -54% (25th to 75% percentile -65 to -50), -41 (25th to 75% percentile -46% to 30), and -17% (25th to 75% percentile -36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

The EDCMET Project: Metabolic Effects of Endocrine Disruptors

Endocrine disruptors (EDs) are defined as chemicals that mimic, block, or interfere with hormones in the body's endocrine systems and have been associated with a diverse array of health issues. The concept of endocrine disruption has recently been extended to metabolic alterations that may result in diseases, such as obesity, diabetes, and fatty liver disease, and constitute an increasing health concern worldwide. However, while epidemiological and experimental data on the close association of EDs and adverse metabolic effects are mounting, predictive methods and models to evaluate the detailed mechanisms and pathways behind these observed effects are lacking, thus restricting the regulatory risk assessment of EDs. The EDCMET (Metabolic effects of Endocrine Disrupting Chemicals: novel testing METhods and adverse outcome pathways) project brings together systems toxicologists; experimental biologists with a thorough understanding of the molecular mechanisms of metabolic disease and comprehensive in vitro and in vivo methodological skills; and, ultimately, epidemiologists linking environmental exposure to adverse metabolic outcomes. During its 5-year journey, EDCMET aims to identify novel ED mechanisms of action, to generate (pre)validated test methods to assess the metabolic effects of Eds, and to predict emergent adverse biological phenotypes by following the adverse outcome pathway (AOP) paradigm.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

Drug vector representation: a tool for drug similarity analysis

DrugMatrix is a valuable toxicogenomic dataset, which provides in vivo transcriptome data corresponding to hundreds of chemical drugs. However, the relationships between drugs and how those drugs affect the biological process are still unknown. The high dimensionality of the microarray data hinders its application. The aims of this study are to (1) represent the transcriptome data by lower-dimensional vectors, (2) compare drug similarity, (3) represent drug combinations by adding vectors and (4) infer drug mechanism of action (MoA) and genotoxicity features. We borrowed the latent semantic analysis (LSA) technique from natural language processing to represent treatments (drugs with multiple concentrations and time points) by dense vectors, each dimension of which is an orthogonal biological feature. The gProfiler enrichment tool was used for the 100-dimensional vector feature annotation. The similarity between treatments vectors was calculated by the cosine function. Adding vectors may represent drug combinations, treatment times or treatment doses that are not presented in the original data. Drug-drug interaction pairs had a higher similarity than random drug pairs in the hepatocyte data. The vector features helped to reveal the MoA. Differential feature expression was also implicated for genotoxic and non-genotoxic carcinogens. An easy-to-use Web tool was developed by Shiny Web application framework for the exploration of treatment similarities and drug combinations (https://bioinformatics.fafu.edu.cn/drugmatrix/). We represented treatments by vectors and provided a tool that is useful for hypothesis generation in toxicogenomic, such as drug similarity, drug repurposing, combination therapy and MoA.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

BACKGROUND

Most chemicals in commerce have not been evaluated for their carcinogenic potential. The de facto gold-standard approach to carcinogen testing adopts the 2-y rodent bioassay, a time-consuming and costly procedure. High-throughput in vitro assays are a promising alternative for addressing the limitations in carcinogen screening.

OBJECTIVES

We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in vitro gene expression assays.

METHODS

We generated a large toxicogenomics resource comprising [Formula: see text] expression profiles corresponding to 330 chemicals profiled in HepG2 (human hepatocellular carcinoma cell line) at multiple doses and replicates. Predictive models of carcinogenicity and genotoxicity were built using a random forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external sources, including Drugmatrix and the Connectivity Map.

RESULTS

Among profiles with sufficient bioactivity, our classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, was not significantly associated with long-term carcinogenicity in doses up to [Formula: see text]. However, sufficient bioactivity was necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed pathways consistent with known pathways that drive cancer, including DNA damage and repair. The data is available at https://clue.io/CRCGN_ABC , and a portal for query and visualization of the results is accessible at https://carcinogenome.org .

DISCUSSION

We demonstrated an in vitro screening approach using gene expression profiling to predict carcinogenicity and infer MoAs of chemical perturbations. https://doi.org/10.1289/EHP3986.

Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine multiple samples into a single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq, microarrays, as well as another low-cost highly multiplexed technique known as 3' digital gene expression (3'DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3'DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype per se for non-chemically induced injuries revealed the robust performance of the DNN models across these additional external testing datasets. Thus, the DNN models learned features specific to the injury phenotype contained in the gene expression data.

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.

ChemDIS 2: an update of chemical-disease inference system

Computational inference of affected functions, pathways and diseases for chemicals could largely accelerate the evaluation of potential effects of chemical exposure on human beings. Previously, we have developed a ChemDIS system utilizing information of interacting targets for chemical-disease inference. With the target information, testable hypotheses can be generated for experimental validation. In this work, we present an update of ChemDIS 2 system featured with more updated datasets and several new functions, including (i) custom enrichment analysis function for single omics data; (ii) multi-omics analysis function for joint analysis of multi-omics data; (iii) mixture analysis function for the identification of interaction and overall effects; (iv) web application programming interface (API) for programmed access to ChemDIS 2. The updated ChemDIS 2 system capable of analyzing more than 430 000 chemicals is expected to be useful for both drug development and risk assessment of environmental chemicals.

Database URL: ChemDIS 2 is freely accessible via https://cwtung.kmu.edu.tw/chemdis

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.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.