Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

Effects of Chemicals in Reporter Gene Bioassays with Different Metabolic Activities Compared to Baseline Toxicity

High-throughput cell-based bioassays are used for chemical screening and risk assessment. Chemical transformation processes caused by abiotic degradation or metabolization can reduce the chemical concentration or, in some cases, lead to the formation of more toxic transformation products. Unaccounted loss processes may falsify the bioassay results. Capturing the formation and effects of transformation products is important for relating the in vitro effects to in vivo. Reporter gene cell lines are believed to have low metabolic activity, but inducibility of cytochrome P450 (CYP) enzymes has been reported. Baseline toxicity is the minimal toxicity a chemical can have and is caused by the incorporation of the chemical into cell membranes. In the present study, we improved an existing baseline toxicity model based on a newly defined critical membrane burden derived from freely dissolved effect concentrations, which are directly related to the membrane concentration. Experimental effect concentrations of 94 chemicals in three bioassays (AREc32, ARE-bla and GR-bla) were compared with baseline toxicity by calculating the toxic ratio (TR). CYP activities of all cell lines were determined by using fluorescence-based assays. Only ARE-bla showed a low basal CYP activity and inducibility and AREc32 showed a low inducibility. Overall cytotoxicity was similar in all three assays despite the different metabolic activities indicating that chemical metabolism is not relevant for the cytotoxicity of the tested chemicals in these assays. Up to 28 chemicals showed specific cytotoxicity with TR > 10 in the bioassays, but baseline toxicity could explain the effects of the majority of the remaining chemicals. Seven chemicals showed TR < 0.1 indicating inaccurate physicochemical properties or experimental artifacts like chemical precipitation, volatilization, degradation, or other loss processes during the in vitro bioassay. The new baseline model can be used not only to identify specific cytotoxicity mechanisms but also to identify potential problems in the experimental performance or evaluation of the bioassay and thus improve the quality of the bioassay data.

Exploring NCATS in-house biomedical data for evidence-based drug repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.

Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

BACKGROUND

Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

OBJECTIVES

Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

METHODS

We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

RESULTS

Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

CONCLUSIONS

Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling

The U.S. Tox21 program has developed in vitro assays to test large collections of environmental chemicals in a quantitative high-throughput screening (qHTS) format, using triplicate 15-dose titrations to generate over 100 million data points to date. Counterscreens are also employed to minimize interferences from non-target-specific assay artifacts, such as compound autofluorescence and cytotoxicity. New data analysis approaches are needed to integrate these data and characterize the activities observed from these assays. Here, we describe a complete analysis pipeline that evaluates these qHTS data for technical quality in terms of signal reproducibility. We integrate signals from repeated assay runs, primary readouts and counterscreens to produce a final call on on-target compound activity.

Discrimination of active and inactive substances in cytotoxicity based on Tox21 10K compound library: Structure alert and mode of action

In vitro cytotoxicity assay is an ideal alternative method for the in vivo toxicity in the risk assessment of pollutants in environment. However, modes of action (MOAs) of cytotoxicity have not been investigated for a wide range of compounds. In this paper, binomial and recursive partitioning analysis were carried out between the cytotoxicity and molecular descriptors for 8981 compounds. The results showed that cytotoxicity is strongly related to the chemical hydrophobicity and excess molar refraction, indicating the bio-uptake and chemical-receptor interaction through π and n electron pair play important roles in the cytotoxicity. The decision tree derived from recursive partitioning analysis revealed that the studied compounds could be divided into 25 groups and their structural characteristics could be used as structure alert to identify active and inactive compounds in cytotoxicity. The descriptors used in the decision tree revealed that chemical ionization and bioavailability could affect the cytotoxicity for ionizable and highly hydrophobic compounds. Comparison of MOAs based on Verhaar's classification scheme showed that many inert or less inert compounds were inactive substance, and many reactive or specifically-acting compounds were active substances in the cytotoxicity. In vitro toxicity assay instead of in vivo toxicity assay can be used in the environmental hazard and risk assessment of organic pollutants. The descriptors used in the binomial equation and decision tree reveal that chemical hydrophobicity, ionization and solubility play very important roles for identification of active and inactive compounds. The results obtained in this paper are valuable for understanding the modes of action in cytotoxicity and in vivo-in vitro toxicity relationship.

Treating Cells as Reagents to Design Reproducible Assays

The reproducibility of high-throughput cell-based assays is dependent on having a consistent source of cells for each experiment. Developing an understanding of the nature of cells growing in vitro and factors that influence their responsiveness to test compounds will contribute to the development of reproducible cell-based assays. Using good cell culture practices and establishing standard operating procedures (SOPs) for handling cultures can eliminate several potential contributors to variability in the responsiveness and performance of cells. The SOPs for handling each cell type must have clear and detailed instructions that can be understood and followed among different laboratories. The SOPs should include documenting the source of cells and authenticating their identity, both of which have become required to achieve peer acceptance of experimental data. Variability caused by biological issues such as phenotypic drift can be reduced by using standardized subculture procedures or using cryopreserved cells to set up experiments. Variability caused by inconsistent dispensing of cells per well and edge effects can be identified by measuring how many cells are present and whether they are alive or dead. Multiplex methods for real-time measurement of viable or dead cell number in each sample can be used for normalizing data and determining if proliferation or cytotoxicity has occurred during the experiment. Following good cell culture practices will go a long way toward executing reproducible cell-based assays. Resources will be included describing good cell culture practices, cell line authentication, and multiplex determination of cell number as an internal control.

Nuisance compounds in cellular assays

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.

Decrease in membrane fluidity and traction force induced by silica-coated magnetic nanoparticles

Background

Nanoparticles are being increasingly used in biomedical applications owing to their unique physical and chemical properties and small size. However, their biophysical assessment and evaluation of side-effects remain challenging. We addressed this issue by investigating the effects of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate [MNPs@SiO₂(RITC)] on biophysical aspects, such as membrane fluidity and traction force of human embryonic kidney 293 (HEK293) cells. We further extended our understanding on the biophysical effects of nanoparticles on cells using a combination of metabolic profiling and transcriptomic network analysis.

Results

Overdose (1.0 μg/µL) treatment with MNPs@SiO₂(RITC) induced lipid peroxidation and decreased membrane fluidity in HEK293 cells. In addition, HEK293 cells were morphologically shrunk, and their aspect ratio was significantly decreased. We found that each traction force (measured in micropillar) was increased, thereby increasing the total traction force in MNPs@SiO₂(RITC)-treated HEK293 cells. Due to the reduction in membrane fluidity and elevation of traction force, the velocity of cell movement was also significantly decreased. Moreover, intracellular level of adenosine triphosphate (ATP) was also decreased in a dose-dependent manner upon treatment with MNPs@SiO₂(RITC). To understand these biophysical changes in cells, we analysed the transcriptome and metabolic profiles and generated a metabotranscriptomics network, which revealed relationships among peroxidation of lipids, focal adhesion, cell movement, and related genes and metabolites. Furthermore, in silico prediction of the network showed increment in the peroxidation of lipids and suppression of focal adhesion and cell movement.

Conclusion

Taken together, our results demonstrated that overdose of MNPs@SiO₂(RITC) impairs cellular movement, followed by changes in the biophysical properties of cells, thus highlighting the need for biophysical assessment of nanoparticle-induced side-effects.

Identification of 6-(piperazin-1-yl)-1,3,5-triazine as a chemical scaffold with broad anti-schistosomal activities

Background: Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease of considerable importance in resource-poor communities throughout the developing world. In the absence of an immunoprophylactic vaccine and due to over-reliance on a single chemotherapy (praziquantel), schistosomiasis control is at risk should drug insensitive schistosomes develop. In this context, application of in silico virtual screening on validated schistosome targets has proven successful in the identification of novel small molecules with anti-schistosomal activity.   Methods: Focusing on the Schistosoma mansoni histone methylation machinery, we herein have used RNA interference (RNAi), ELISA-mediated detection of H3K4 methylation, homology modelling and in silico virtual screening to identify a small collection of small molecules for anti-schistosomal testing. A combination of low to high-throughput whole organism assays were subsequently used to assess these compounds' activities on miracidia to sporocyst transformation, schistosomula phenotype/motility metrics and adult worm motility/oviposition readouts. Results: RNAi-mediated knockdown of smp_138030/smmll-1 (encoding a histone methyltransferase, HMT) in adult worms (~60%) reduced parasite motility and egg production. Moreover, in silico docking of compounds into Smp_138030/SmMLL-1's homology model highlighted competitive substrate pocket inhibitors, some of which demonstrated significant activity on miracidia, schistosomula and adult worm lifecycle stages together with variable effects on HepG2 cells. Particularly, the effect of compounds containing a 6-(piperazin-1-yl)-1,3,5-triazine core on adult schistosomes recapitulated the results of the smp_138030/smmll-1 RNAi screens. Conclusions: The biological data and the structure-activity relationship presented in this study define the 6-(piperazin-1-yl)-1,3,5-triazine core as a promising starting point in ongoing efforts to develop new urgently needed schistosomicides.

Quorum sensing N-Acyl homoserine lactones are a new class of anti-schistosomal

Background

Schistosomiasis is a prevalent neglected tropical disease that affects approximately 300 million people worldwide. Its treatment is through a single class chemotherapy, praziquantel. Concerns surrounding the emergence of praziquantel insensitivity have led to a need for developing novel anthelmintics.

Methodology/Principle findings

Through evaluating and screening fourteen compounds (initially developed for anti-cancer and anti-viral projects) against Schistosoma mansoni, one of three species responsible for most cases of human schistosomiasis, a racemic N-acyl homoserine (1) demonstrated good efficacy against all intra mammalian lifecycle stages including schistosomula (EC₅₀ = 4.7 μM), juvenile worms (EC₅₀ = 4.3 μM) and adult worms (EC₅₀ = 8.3 μM). To begin exploring structural activity relationships, a further 8 analogues of this compound were generated, including individual (R)- and (S)- enantiomers. Upon anti-schistosomal screening of these analogues, the (R)- enantiomer retained activity, whereas the (S)- lost activity. Furthermore, modification of the lactone ring to a thiolactone ring (3) improved potency against schistosomula (EC₅₀ = 2.1 μM), juvenile worms (EC₅₀ = 0.5 μM) and adult worms (EC₅₀ = 4.8 μM). As the effective racemic parent compound is structurally similar to quorum sensing signaling peptides used by bacteria, further evaluation of its effect (along with its stereoisomers and the thiolactone analogues) against Gram⁺ (Staphylococcus aureus) and Gram^- (Escherichia coli) species was conducted. While some activity was observed against both Gram⁺ and Gram^- bacteria species for the racemic compound 1 (MIC 125 mg/L), the (R) stereoisomer had better activity (125 mg/L) than the (S) (>125mg/L). However, the greatest antimicrobial activity (MIC 31.25 mg/L against S. aureus) was observed for the thiolactone containing analogue (3).

Conclusion/Significance

To the best of our knowledge, this is the first demonstration that N-Acyl homoserines exhibit anthelmintic activities. Furthermore, their additional action on Gram⁺ bacteria opens a new avenue for exploring these molecules more broadly as part of future anti-infective initiatives.

Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease that negatively impacts the lives of approximately 300 million people worldwide. In the absence of a vaccine, it is currently controlled by a single drug, Praziquantel (PZQ). Although incredibly valuable in controlling disease burden, PZQ-mediated chemotherapy is ineffective against juvenile worms and may not be sustainable should resistance develop. The need to identify an alternative or combinatorial drug is, therefore, a priority in contributing to the control of this parasitic disease into the 21^st century. In this study, we have identified a new class of anthelmintic, N-acyl homoserine lactones, which are normally used by bacteria for quorum sensing and population density control. The tested N-acyl homoserine lactones were active against all intra-human schistosome lifecycle stages, in particular, when a thiolactone modification to the core N-acyl homoserine ring was made. Interestingly, these N-acyl homoserine lactones also displayed antimicrobial activities against Gram⁺Staphylococcus aureus. By demonstrating broad activities against schistosomes and bacteria exemplars, this study identified a potential route for the further development of a new anti-infective class.

Predicting oxidative stress induced by organic chemicals by using quantitative Structure-Activity relationship methods

Cellular exposure to xenobiotic human-made products will lead to oxidative stress that gives rise to DNA damage, as well as chemical or mechanical damage. Distinguishing the chemicals that will induce oxidative stress and predicting their toxicity is necessary. In the present study, 4270 compounds in the ARE-bla assay were investigated to predict active and inactive compounds by using simple algorithms, namely, recursive partitioning (RP) and binomial logistic regression, and to develop the quantitative structure-activity relationship (QSAR) models of chemicals that activate the ARE pathway to induce oxidative stress and exert toxic effects on cells. A decision tree based on scaffold-based fragments obtained through RP analysis showed the best identification accuracy. However, the overall identification accuracy of this model for active compounds was unsatisfactory due to limited fragments. Furthermore, a binomial logistic regression model was developed from 638 active compounds and 3632 inactive chemicals. The model with a cutoff of 0.15 could predict chemicals that were active or inactive with the prediction accuracy of 69.1%. Its area under the receiver operating characteristic (ROC) curve metric (AUROC) was 0.762, which indicated the acceptable predictive ability of this model. The parameters nBM (number of multiple bonds) and H% (percentage of H atom) played dominant roles in the prediction of the activity (inactive or active) of chemicals. A global QSAR model was developed to predict the toxicity of active chemicals. However, the model displayed an unsatisfactory result with R² = 0.316 and R²_ext = 0.090. Active chemicals were then classified on the basis of structure. A total of 79 compounds with carbon chains could be predicted with acceptable performance by using a QSAR model with six descriptors (R² = 0.722, R²_ext = 0.798, Q²_Loo = 0.654, Q²_Boot = 0.755, Q²_ext = 0.721). The simple models established here contribute to efforts on identification compounds inducing oxidative stress and provide the scientific basis for risk assessment to organisms in the environment.

Identification of 6-(piperazin-1-yl)-1,3,5-triazine as a chemical scaffold with broad anti-schistosomal activities

Background: Schistosomiasis, caused by infection with blood fluke schistosomes, is a neglected tropical disease of considerable importance in resource-poor communities throughout the developing world. In the absence of an immunoprophylactic vaccine and due to over-reliance on a single chemotherapy (praziquantel), schistosomiasis control is at risk should drug insensitive schistosomes develop. In this context, application of in silico virtual screening on validated schistosome targets has proven successful in the identification of novel small molecules with anti-schistosomal activity.   Methods: Focusing on the Schistosoma mansoni histone methylation machinery, we herein have used RNA interference (RNAi), ELISA-mediated detection of H3K4 methylation, homology modelling and in silico virtual screening to identify a small collection of small molecules for anti-schistosomal testing. A combination of low to high-throughput whole organism assays were subsequently used to assess these compounds' activities on miracidia to sporocyst transformation, schistosomula phenotype/motility metrics and adult worm motility/oviposition readouts. Results: RNAi-mediated knockdown of smp_138030/smmll-1 (encoding a histone methyltransferase, HMT) in adult worms (~60%) reduced parasite motility and egg production. Moreover, in silico docking of compounds into Smp_138030/SmMLL-1's homology model highlighted competitive substrate pocket inhibitors, some of which demonstrated significant activity on miracidia, schistosomula and adult worm lifecycle stages together with variable effects on HepG2 cells. Particularly, the effect of compounds containing a 6-(piperazin-1-yl)-1,3,5-triazine core on adult schistosomes recapitulated the results of the smp_138030/smmll-1 RNAi screens. Conclusions: The biological data and the structure-activity relationship presented in this study define the 6-(piperazin-1-yl)-1,3,5-triazine core as a promising starting point in ongoing efforts to develop new urgently needed schistosomicides.

Predictive models for estimating cytotoxicity on the basis of chemical structures

Cytotoxicity is a critical property in determining the fate of a small molecule in the drug discovery pipeline. Cytotoxic compounds are identified and triaged in both target-based and cell-based phenotypic approaches due to their off-target toxicity or on-target and on-mechanism toxicity for oncology and neurodegenerative targets. It is critical that chemical-induced cytotoxicity be reliably predicted before drug candidates advance to the late stage of development, or more ideally, before compounds are synthesized. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in NCATS annotated libraries against four 'normal' cell lines (HEK 293, NIH 3T3, CRL-7250 and HaCat) using CellTiter-Glo (CTG) technology and constructed highly predictive models to estimate cytotoxicity from chemical structures. There are 5,241 non-redundant compounds having unambiguous activities in the four different cell lines, among which 11.8% compounds exhibited cytotoxicity in two or more cell lines and are thus labelled cytotoxic. The support vector classification (SVC) models trained with 80% randomly selected molecules achieved the area under the receiver operating characteristic curve (AUC-ROC) of 0.88 on average for the remaining 20% compounds in the test sets in 10 repeating experiments. Application of under-sampling rebalancing method further improved the averaged AUC-ROC to 0.90. Analysis of structural features shared by cytotoxic compounds may offer medicinal chemists heuristic design ideas to eliminate undesirable cytotoxicity. The profiling of cytotoxicity of drug-like molecules with annotated primary mechanism of action (MOA) will inform on the roles played by different targets or pathways in cellular viability. The predictive models for cytotoxicity (accessible at https://tripod.nih.gov/web_adme/cytotox.html) provide the scientific community a fast yet reliable way to prioritize molecules with little or no cytotoxicity for downstream development.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Bioactivity Signatures of Drugs vs. Environmental Chemicals Revealed by Tox21 High-Throughput Screening Assays

Humans are exposed to tens of thousands of chemicals over the course of a lifetime, yet there remains inadequate data on the potential harmful effects of these substances on human health. Using quantitative high-throughput screening (qHTS), we can test these compounds for potential toxicity in a more efficient and cost-effective way compared to traditional animal studies. Tox21 has developed a library of ~10,000 chemicals (Tox21 10K) comprising one-third approved and investigational drugs and two-thirds environmental chemicals. In this study, the Tox21 10K was screened in a qHTS format against a panel of 70 cell-based assays with 213 readouts covering a broad range of biological pathways. Activity profiles were compared with chemical structure to assess their ability to differentiate drugs from environmental chemicals, and structure was found to be a better predictor of which chemicals are likely to be drugs. Drugs and environmental chemicals were further analyzed for diversity in structure and biological response space and showed distinguishable, but not distinct, responses in the Tox21 assays. Inclusion of other targets and pathways to further diversify the biological response space covered by these assays is likely required to better evaluate the safety profile of drugs and environmental chemicals to prioritize for in-depth toxicological studies.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

Advantages of evaluating γH2AX induction in non-clinical drug development

γH2AX, the phosphorylated form of a histone variant H2AX at Ser 139, is already widely used as a biomarker to research the fundamental biology of DNA damage and repair and to assess the risk of environmental chemicals, pollutants, radiation, and so on. It is also beginning to be used in the early non-clinical stage of pharmaceutical drug development as an in vitro tool for screening and for mechanistic studies on genotoxicity. Here, we review the available information on γH2AX-based test systems that can be used to develop drugs and present our own experience of practically applying these systems during the non-clinical phase of drug development. Furthermore, the potential application of γH2AX as a tool for in vivo non-clinical safety studies is also discussed.

Correction: Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

[This corrects the article DOI: 10.1371/journal.pone.0177902.].