Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling

Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood-brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania ( https://enaloscloud.novamechanics.com/EnalosWebApps/titania/ ), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.

Evaluating applicability domain of acute toxicity QSAR models for military and industrial chemical risk assessment

Quantitative Structure-Activity Relationship (QSAR) models can be used to predict the risk of novel and emergent chemicals causing adverse health outcomes, avoidance of which is crucial for military operations. While QSAR modeling approaches have been proposed for military and industry risk assessment, the applicability of peer-reviewed tissue-specific QSAR models in military and industrial contexts remain largely unexplored, particularly with respect to specific organ toxicity. We investigated the applicability domain (AD) of acute and sub-chronic tissue-specific QSAR models to evaluate the coverage of military- and industrial-relevant chemicals. Our analysis reveals that military-relevant compounds occupy a similar chemical space as industrial compounds. However, published models for acute target organ toxicity had minimal coverage of the military and industrial chemicals. The published Collaborative Acute Toxicity Modeling Suite (CATMoS) acute oral toxicity model was the notable exception, as it covers a broad range of military and industrial chemicals. Our study underscores the urgent need for development of novel tissue-specific QSAR models, or modification of existing models, to improve chemical risk prediction in both industrial and military applications.

Network toxicology and molecular docking to investigative the non-acetylcholinesterase mechanisms and targets of cardiotoxicity injury induced by organophosphorus pesticides

BACKGROUND

Organophosphorus pesticides (OPPs) are widely used in the world, however, OPP poisoning often occurs because of improper use and lack of protective measures. Cardiotoxicity injury induced by OPPs is insidious, and it does not receive attention until the end stage of OPP poisoning. Heart failure or arrhythmia gradually becomes the main lethal cause of OPP poisoning patients.

METHODS

In this study, network toxicology and molecular docking were employed to investigate the non-acetylcholinesterase targets and mechanisms of cardiotoxicity injury induced by OPPs.

RESULTS

One hundred twenty-three targets of dichlorvos, 205 targets of methidathion, and 337 targets of malathion were searched from SwissTargetPreict, STITCH and PharmMapper database. Additionally, 1379 targets related to cardiotoxicity injury were acquired from GeneCards and OMIM database. Ninety-six mutual targets between OPPs and cardiotoxicity injury were considered as the potential cardiotoxicity injury targets induced by OPPs. The protein-protein interaction (PPI) network was constructed using STING database, and 21 core targets were identified by Cytoscape software, such as AKT1, ESR1, HSP90AA1, MAPK1, MMP9, and MAPK8. Gene ontology and KEGG enrichment analysis revealed that cell migration, apoptotic process, protein phosphorylation and signal transduction were the major biological functions associated with OPPs-induced cardiotoxicity injury, and OPPs-induced cardiotoxicity injury might be regulated by MAPK, PI3K-Akt, VEGF signaling pathway. Docking results manifested that the best binding target for dichlorvos, methidathion and malathion were MAPK9 (-7.1 kcal/mol), MAPK1 (-8.1 kcal/mol) and HSP90AA1 (-8.6 kcal/mol) with the lowest affinity, respectively.

CONCLUSION

The core targets and non-AchE mechanisms were explored by network toxicology and molecular docking, providing a theoretical basis for the treatment of OPP-induced cardiotoxicity injury.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Chemical-induced liver cancer: an adverse outcome pathway perspective

INTRODUCTION

The evaluation of the potential carcinogenicity is a key consideration in the risk assessment of chemicals. Predictive toxicology is currently switching toward non-animal approaches that rely on the mechanistic understanding of toxicity.

AREAS COVERED

Adverse outcome pathways (AOPs) present toxicological processes, including chemical-induced carcinogenicity, in a visual and comprehensive manner, which serve as the conceptual backbone for the development of non-animal approaches eligible for hazard identification. The current review provides an overview of the available AOPs leading to liver cancer and discusses their use in advanced testing of liver carcinogenic chemicals. Moreover, the challenges related to their use in risk assessment are outlined, including the exploitation of available data, the need for semantic ontologies, and the development of quantitative AOPs.

EXPERT OPINION

To exploit the potential of liver cancer AOPs in the field of risk assessment, 3 immediate prerequisites need to be fulfilled. These include developing human relevant AOPs for chemical-induced liver cancer, increasing the number of AOPs integrating quantitative toxicodynamic and toxicokinetic data, and developing a liver cancer AOP network. As AOPs and other areas in the field continue to evolve, liver cancer AOPs will progress into a reliable and robust tool serving future risk assessment and management.

Enantioselectivity in ecotoxicity of pharmaceuticals, illicit drugs, and industrial persistent pollutants in aquatic and terrestrial environments: A review

At present, there is a serious concern about the alarming number of recalcitrant contaminants that can negatively affect biodiversity threatening the ecological status of marine, estuarine, freshwater, and terrestrial ecosystems (e.g., agricultural soils and forests). Contaminants of emerging concern (CEC) such as pharmaceuticals (PHAR), illicit drugs (ID), industrial persistent pollutants, such as polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) and chiral ionic solvents are globally spread and potentially toxic to non-target organisms. More than half of these contaminants are chiral and have been measured at different enantiomeric proportions in diverse ecosystems. Enantiomers can exhibit different toxicodynamics and toxicokinetics, and thus, can cause different toxic effects. Therefore, the enantiomeric distribution in occurrence cannot be neglected as the toxicity and other adverse biological effects are expected to be enantioselective. Hence, this review aims to reinforce the recognition of the stereochemistry in environmental risk assessment (ERA) of chiral CEC and gather up-to-date information about the current knowledge regarding the enantioselectivity in ecotoxicity of PHAR, ID, persistent pollutants (PCBs and PBDEs) and chiral ionic solvents present in freshwater and agricultural soil ecosystems. We performed an online literature search to obtain state-of-the-art research about enantioselective studies available for assessing the impact of these classes of CEC. Ecotoxicity assays have been carried out using organisms belonging to different trophic levels such as microorganisms, plants, invertebrates, and vertebrates, and considering ecologically relevant aquatic and terrestrial species or models organisms recommended by regulatory entities. A battery of ecotoxicity assays was also reported encompassing standard acute toxicity to sub-chronic and chronic assays and different endpoints as biomarkers of toxicity (e.g., biochemical, morphological alterations, reproduction, behavior, etc.). Nevertheless, we call attention to the lack of knowledge about the potential enantioselective toxicity of many PHAR, ID, and several classes of industrial compounds. Additionally, several questions regarding key species, selection of most appropriate toxicological assays and ERA of chiral CEC are addressed and critically discussed.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds

Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.

Evaluation of the Toxicity Potential of the Metabolites of Di-Isononyl Phthalate and of Their Interactions with Members of Family 1 of Sulfotransferases-A Computational Study

Di-isononyl phthalates are chemicals that are widely used as plasticizers. Humans are extensively exposed to these compounds by dietary intake, through inhalation and skin absorption. Sulfotransferases (SULTs) are enzymes responsible for the detoxification and elimination of numerous endogenous and exogenous molecules from the body. Consequently, SULTs are involved in regulating the biological activity of various hormones and neurotransmitters. The present study considers a computational approach to predict the toxicological potential of the metabolites of di-isononyl phthalate. Furthermore, molecular docking was considered to evaluate the inhibitory potential of these metabolites against the members of family 1 of SULTs. The metabolites of di-isononyl phthalate reveal a potency to cause liver damage and to inhibit receptors activated by peroxisome proliferators. These metabolites are also usually able to inhibit the activity of the members of family 1 of SULTs, except for SULT1A3 and SULT1B1. The outcomes of this study are important for an enhanced understanding of the risk of human exposure to di-isononyl phthalates.

Predictions of the Biological Effects of Several Acyclic Monoterpenes as Chemical Constituents of Essential Oils Extracted from Plants

Acyclic terpenes are biologically active natural products having applicability in medicine, pharmacy, cosmetics and other practices. Consequently, humans are exposed to these chemicals, and it is necessary to assess their pharmacokinetics profiles and possible toxicity. The present study considers a computational approach to predict both the biological and toxicological effects of nine acyclic monoterpenes: beta-myrcene, beta-ocimene, citronellal, citrolellol, citronellyl acetate, geranial, geraniol, linalool and linalyl acetate. The outcomes of the study emphasize that the investigated compounds are usually safe for humans, they do not lead to hepatotoxicity, cardiotoxicity, mutagenicity, carcinogenicity and endocrine disruption, and usually do not have an inhibitory potential against the cytochromes involved in the metabolism of xenobiotics, excepting CYP2B6. The inhibition of CYP2B6 should be further analyzed as this enzyme is involved in both the metabolism of several common drugs and in the activation of some procarcinogens. Skin and eye irritation, toxicity through respiration and skin-sensitization potential are the possible harmful effects revealed by the investigated compounds. These outcomes underline the necessity of in vivo studies regarding the pharmacokinetics and toxicological properties of acyclic monoterpenes so as to better establish the clinical relevance of their use.

Quantitative structure-activity relationship models for predicting apparent rate constants of organic compounds with ferrate (VI)

Ferrate (VI) (Fe (VI)) is a promising, environmentally friendly multifunctional oxidant widely applied in organic compound degradation. Oxidative kinetics of the apparent second-order rate constants (k_app) of Fe (VI) with organic compounds are critical for modeling oxidation processes. Herein, a quantitative structure-activity relationship (QSAR) model was developed using particle swarm optimization and an extreme learning machine to better understand the laws of the k_app values of organic compounds, including 33 aliphatic and aromatic hydrocarbon derivatives, during degradation by Fe (VI). Seven components-electronic hardness (H), electronic softness (S), ratio of oxygen to carbon atoms (O_n/C_n), energy of the highest occupied molecular orbital (E_HOMO), vertical ionization potential (VIP), maximum nucleophilic reaction index (f(+)_x), and minimum relative electrophilicity index (RE_n) constitute the critical molecular parameters. The developed QSAR model was verified on the basis of the coefficient of determination (R²) and the root mean square error (RMSE): for the training set, R² = 0.924 and RMSE = 1.186, whereas for the test set, R² = 0.996, and RMSE = 0.352. The applicability, reliability, and predictability of the model were verified by estimating the applicability domain (AD) of the model. Furthermore, QSAR models constructed using different methods were compared, and the main impact descriptors and conclusions obtained from previous studies were theoretically analyzed. Results indicate that constructing the QSAR model facilitates k_app prediction for Fe (VI) in the degradation of various organic compounds, improves the understanding of the degradation mechanism, and reduces the pressure on human and material resources caused by experiments.

In Silico Investigation of Selected Pesticides and Their Determination in Agricultural Products Using QuEChERS Methodology and HPLC-DAD

In this study, we considered some pesticides as active substances within formulations for the protection of plant-based food in the Republic of Serbia in silico, because these pesticides have not often been investigated in this way previously, and in an analytical way, because there are not very many available fast, cheap, and easy methods for their determination in real agricultural samples. Seven pesticides were detected in selected agricultural products (tomatoes, cucumbers, peppers, and grapes) using the QuEChERS methodology and HPLC-DAD. Standard curves for the investigated pesticides (chlorantraniliprole, methomyl, metalaxyl, thiacloprid, acetamiprid, emamectin benzoate, and cymoxanil) show good linearity, with R² values from 0.9785 to 0.9996. The HPLC-DAD method is fast, and these pesticides can be determined in real spiked samples in less than 15 min. We further characterized the pesticides we found in food based on physicochemical properties and molecular descriptors to predict the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of the compounds. We summarized the data supporting their effects on humans using various computational tools to determine their potential adverse effects. The results of our prediction study show that all of the selected pesticides considered in this study have good oral bioavailability, and those with high toxicity, therefore, could be harmful to human health. Chlorantraniliprole was shown in a molecular docking study as a good starting point for a new Alzheimer's disease drug candidate.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

In silico Study of Antiviral Activity of Polyphenol Compounds from Ocimum basilicum by Molecular Docking, ADMET, and Drug-Likeness Analysis

Aim

The SARS-CoV-2 virus is a disease that has mild to severe effects on patients, which can even lead to death. One of the enzymes that act as DNA replication is the main protease, which becomes the main target in the inhibition of the SARS-CoV-2 virus. In finding effective drugs against this virus, Ocimum basilicum is a potential herbal plant because it has been tested to have high phytochemical content and bioactivity. Apigenin-7-glucuronide, dihydrokaempferol-3-glucoside, and aesculetin are polyphenolic compounds found in Ocimum basilicum.

Purpose

The purpose of this study was to analyze the mechanism of inhibition of the three polyphenolic compounds in Ocimum basilicum against the main protease and to predict pharmacokinetic activity and the drug-likeness of a compound using the Lipinski Rule of Five.

Patients and Methods

The method used is to predict the molecular docking inhibition mechanism using Autodock 4.0 tools and use pkcsm and protox online web server to analyze ADMET and Drug-likeness.

Results

The binding affinity for apigenin-7-glucuronide was -8.77 Kcal/mol, dihydrokaempferol-3-glucoside was -8.96 Kcal/mol, and aesculetin was -5.79 Kcal/mol. Then, the inhibition constant values were 375.81 nM, 270.09 nM, and 57.11 µM, respectively. Apigenin-7-glucuronide and dihydrokaempferol-3-glucoside bind to the main protease enzymes on the active sites of CYS145 and HIS41, while aesculetin only binds to the active sites of CYS145. On ADMET analysis, these three compounds met the predicted pharmacokinetic parameters, although there are some specific parameters that must be considered especially for aesculetin compounds. Meanwhile, on drug-likeness analysis, apigenin-7-glucuronide and dihydrokaempferol-3-glucoside compounds have one violation and aesculetin have no violation.

Conclusion

Based on the data obtained, Apigenin-7-glucuronide and dihydrokaempferol-3-glucoside are compounds that have more potential to have an antiviral effect on the main protease enzyme than aesculetin. Based on pharmacokinetic parameters and drug-likeness, three compounds can be used as lead compounds for further research.

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity

Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity.

Assessment of the Effects of Triticonazole on Soil and Human Health

Triticonazole is a fungicide used to control diseases in numerous plants. The commercial product is a racemate containing (R)- and (S)-triticonazole and its residues have been found in vegetables, fruits, and drinking water. This study considered the effects of triticonazole on soil microorganisms and enzymes and human health by taking into account the enantiomeric structure when applicable. An experimental method was applied for assessing the effects of triticonazole on soil microorganisms and enzymes, and the effects of the stereoisomers on soil enzymes and human health were assessed using a computational approach. There were decreases in dehydrogenase and phosphatase activities and an increase in urease activity when barley and wheat seeds treated with various doses of triticonazole were sown in chernozem soil. At least 21 days were necessary for the enzymes to recover the activities. This was consistent with the diminution of the total number of soil microorganisms in the 14 days after sowing. Both stereoisomers were able to bind to human plasma proteins and were potentially inhibitors of human cytochromes, revealing cardiotoxicity and low endocrine disruption potential. As distinct effects, (R)-TTZ caused skin sensitization, carcinogenicity, and respiratory toxicity. There were no significant differences in the interaction energies of the stereoisomers and soil enzymes, but (S)-TTZ exposed higher interaction energies with plasma proteins and human cytochromes.

PreS/MD: Predictor of Sensitization Hazard for Chemical Substances Released From Medical Devices

In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ new approach methodologies, including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (predictor of sensitization for medical devices). To enable model development, we (1) collected, curated, and integrated the largest publicly available dataset for GPMT results; (2) succeeded in developing externally predictive (balanced accuracy of 70%-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) quantitative structure-activity relationships (QSAR) models for GPMT using machine learning algorithms, including deep learning; and (3) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.

Assessment of the Effects of Chitosan, Chitooligosaccharides and Their Derivatives on Lemna minor

Chitosan, chitooligosaccharides and their derivatives’ production and use in many fields may result in their release to the environment, possibly affecting aquatic organisms. Both an experimental and a computational approach were considered for evaluating the effects of these compounds on Lemna minor. Based on the determined EC50 values against L. minor, only D-glucosamine hydrochloride (EC50 = 11.55 mg/L) was considered as “slightly toxic” for aquatic environments, while all the other investigated compounds, having EC50 > 100 mg/L, were considered as “practically non-toxic”. The results obtained in the experimental approach were in good agreement with the predictions obtained using the admetSAR2.0 computational tool, revealing that the investigated compounds were not considered toxic for crustacean, fish and Tetrahymena pyriformis aquatic microorganisms. The ADMETLab2.0 computational tool predicted the values of IGC50 for Tetrahymena pyriformis and the LC50 for fathead minnow and Daphnia magna, with the lowest values of these parameters being revealed by totally acetylated chitooligosaccharides in correlation with their lowest solubility. The effects of the chitooligosaccharides and chitosan on L. minor decreased with increased molecular weight, increased with the degree of deacetylation and were reliant on acetylation patterns. Furthermore, the solubility mainly influenced the effects on the aqueous environment, with a higher solubility conducted to lower toxicity.

A Cheminformatics Study Regarding the Human Health Risks Assessment of the Stereoisomers of Difenoconazole

Difenoconazole is a chemical entity containing two chiral centers and having four stereoisomers: (2R,4R)-, (2R,4S)-, (2S,4R)- and (2S,4S)-difenoconazole, the marketed product containing a mixture of these isomers. Residues of difenoconazole have been identified in many agricultural products and drinking water. A computational approach has been used to evaluate the toxicological effects of the difenoconazole stereoisomers on humans. It integrates predictions of absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles, prediction of metabolism sites, and assessment of the interactions of the difenoconazole stereoisomers with human cytochromes, nuclear receptors and plasma proteins by molecular docking. Several toxicological effects have been identified for all the difenoconazole stereoisomers: high plasma protein binding, inhibition of cytochromes, possible hepatotoxicity, neurotoxicity, mutagenicity, skin sensitization potential, moderate potential to produce endocrine disrupting effects. There were small differences in the predicted probabilities of producing various biological effects between the distinct stereoisomers of difenoconazole. Furthermore, there were significant differences between the interacting energies of the difenoconazole stereoisomers with plasma proteins and human cytochromes, the spectra of the hydrogen bonds and aromatic donor-acceptor interactions being quite distinct. Some distinguishing results have been obtained for the (2S,4S)-difenoconazole: it registered the highest value for clearance, exposed reasonable probabilities to produce cardiotoxicity and carcinogenicity and negatively affected numerous nuclear receptors.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity

BACKGROUND

Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points.

METHODS

We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets.

RESULTS

In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays.

CONCLUSIONS

We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

Analysis of Mercury in Skin Lightening Cream by Microwave Plasma Atomic Emission Spectroscopy (MP-AES)

This research aimed at developing an analysis method, which was optimized and validated to determine the content of mercury in skin lightening cream discovered in the market in Bandar Lampung, Indonesia, through the use of microwave plasma atomic emission spectroscopy (MP-AES). The optimization on the analysis method was conducted on pump rate, viewing position, and reductant concentration in order to obtain the highest mercury emission intensity, while the solution stability was optimized to know the stability of mercury in the solution. The result showed that the method developed had precision with a relative standard deviation of 2.67%, recovery value of 92.78%, and linearity with an r value of 0.993, respectively. The sensitivity of the instrument detection had a limit of analysis method detection and quantification of 0.59 and 1.98 µg/L, respectively. The results of the test of the lightening cream (8 of 16 samples) positively contained mercury in the range of 422.61–44,960.79 ng/g. Therefore the method of analysis developed may be used for routine analysis of chemicals in any cosmetics products.

Mining Toxicity Information from Large Amounts of Toxicity Data

Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.

Assessing the calibration in toxicological in vitro models with conformal prediction

Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data’s descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy—exchanging the calibration data only—is convenient as it does not require retraining of the underlying model.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.

In silico Assessment of Pharmacological Profile of Low Molecular Weight Oligo-Hydroxyalkanoates

Polyhydroxyalkanoates (PHAs) are a large class of polyesters that are biosynthesized by microorganisms at large molecular weights (Mw > 80 kDa) and have a great potential for medical applications because of their recognized biocompatibility. Among PHAs, poly(3-hydroxybutyrate), poly(4-hydroxybutyrate), poly(3-hydroxyvalerate), poly(4-hydroxyvalerate), and their copolymers are proposed to be used in biomedicine, but only poly(4-hydroxybutyrate) has been certified for medical application. Along with the hydrolysis of these polymers, low molecular weight oligomers are released typically. In this study, we have used a computational approach to assess the absorption, distribution, metabolism, and excretion (ADME)-Tox profiles of low molecular weight oligomers (≤32 units) consisting of 3-hydroxybutyrate, 4-hydroxybutyrate, 3-hydroxyvalerate, 4-hydroxyvalerate, 3-hydroxybutyrate-co-3-hydroxyvalerate, and the hypothetical PHA consisting of 4-hydroxybutyrate-co-4-hydroxyvalerate. According to our simulations, these oligomers do not show cardiotoxicity, hepatotoxicity, carcinogenicity or mutagenicity, and are neither substrates nor inhibitors of the cytochromes involved in the xenobiotic's metabolism. They also do not affect the human organic cation transporter 2 (OCT2). However, they are considered to be inhibitors of the organic anion transporters OATP1B1, and OATP1B3. In addition, they may produce eye irritation, and corrosion, skin irritation and have a low antagonistic effect on the androgen receptor.

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure-triggered testing to in silico-based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure-Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Solubility and ADMET profiles of short oligomers of lactic acid

Polylactic acid (PLA) is a polymer with an increased potential to be used in different medical applications, including tissue engineering and drug-carries. The use of PLA in medical applications implies the evaluation of the human organism's response to the polymer inserting and to its degradation products. Consequently, within this study, we have investigated the solubility and ADMET profiles of the short oligomers (having the molecular weight lower than 3000 Da) resulting in degradation products of PLA. There is a linear decrease of the molar solubility of investigated oligomers with molecular weight. The results that are obtained also reveal that short oligomers of PLA have promising pharmacological profiles and limited toxicological effects on humans. These oligomers are predicted as potential inhibitors of the organic anion transporting peptides OATP1B1 and OATP1B3, they present minor probability to affect the androgen and glucocorticoid receptors, have a weak potential of hepatotoxicity, and may produce eye injuries. These outcomes may be used to guide or to supplement in vitro and/or in vivo toxicity tests such as to enhance the biodegradation properties of the biopolymer.

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Chitosan is a natural polymer revealing an increased potential to be used in different biomedical applications, including drug delivery systems, and tissue engineering. It implies the evaluation of the organism response to the biomaterial implantation. Low-molecular degradation products, the chito-oligomers, are resulting mainly from the influence of enzymes, which are found in the organism fluids. Within this study, we have performed the computational assessment of pharmacological profiles and toxicological effects on human health of small chito-oligomers with distinct molecular weights, deacetylation degrees, and acetylation patterns. Our approach is based on the fact that regulatory agencies and researchers in the drug development field rely on the use of modeling to predict biological effects and to guide decision making. To be considered as valid for regulatory purposes, every model that is used for predictions should be associated with a defined toxicological endpoint and has appropriate robustness and predictivity. Within this context, we have used FAF-Drugs4, SwissADME, and PreADMET tools to predict the oral bioavailability of chito-oligomers and SwissADME, PreADMET, and admetSAR2.0 tools to predict their pharmacokinetic profiles. The organs and genomic toxicities have been assessed using admetSAR2.0 and PreADMET tools but specific computational facilities have been also used for predicting different toxicological endpoints: Pred-Skin for skin sensitization, CarcinoPred-EL for carcinogenicity, Pred-hERG for cardiotoxicity, ENDOCRINE DISRUPTOME for endocrine disruption potential and Toxtree for carcinogenicity and mutagenicity. Our computational assessment showed that investigated chito-oligomers reflect promising pharmacological profiles and limited toxicological effects on humans, regardless of molecular weight, deacetylation degree, and acetylation pattern. According to our results, there is a possible inhibition of the organic anion transporting peptides OATP1B1 and/or OATP1B3, a weak potential of cardiotoxicity, a minor probability of affecting the androgen receptor, and phospholipidosis. Consequently, these results may be used to guide or to complement the existing in vitro and in vivo toxicity tests, to optimize biomaterials properties and to contribute to the selection of prototypes for nanocarriers.

A computational exploration into the structure, antioxidant capacity, toxicity and drug-like activity of the anthocyanidin "Petunidin"

A computational investigation on the structure and antioxidant property of a natural food colorant Petunidin (PT) was performed under DFT/B3LYP/6-31+ G (d, p). PT has a drug score of +0.804 which indicates its drug-like nature. The antioxidant property of PT was well explained by HAT mechanism and it has been found that the electron releasing substituents decreases the BDE value. PT has lowest BDE value at C3 position and is confirmed by the lowest pKa value, high atomic charge and lowest bond order. PT easily donates the hydrogen atom and exists in the deprotonated form in blood as the pKa value at C3 is less than the pH value of blood. PT shows no violation to Lipinski's rule of 5 indicating its nature as an orally admissible drug. More over PT has considerable bioactivity against nuclear receptor ligand while it shows only moderate activity towards GPCR and ion channel modulator. Also it shows moderate activity as an enzyme inhibitor and protease inhibitor but shows considerable activity as a kinase inhibitor. PT is non toxic in nature and all these factors favor its use as a potential antioxidant and a drug.

Single-Cell Analysis Reveals that Chronic Silver Nanoparticle Exposure Induces Cell Division Defects in Human Epithelial Cells

Multiple organizations have urged a paradigm shift from traditional, whole animal, chemical safety testing to alternative methods. Although these forward-looking methods exist for risk assessment and predication, animal testing is still the preferred method and will remain so until more robust cellular and computational methods are established. To meet this need, we aimed to develop a new, cell division-focused approach based on the idea that defective cell division may be a better predictor of risk than traditional measurements. To develop such an approach, we investigated the toxicity of silver nanoparticles (AgNPs) on human epithelial cells. AgNPs are the type of nanoparticle most widely employed in consumer and medical products, yet toxicity reports are still confounding. Cells were exposed to a range of AgNP doses for both short- and-long term exposure times. The analysis of treated cell populations identified an effect on cell division and the emergence of abnormal nuclear morphologies, including micronuclei and binucleated cells. Overall, our results indicate that AgNPs impair cell division, not only further confirming toxicity to human cells, but also highlighting the propagation of adverse phenotypes within the cell population. Furthermore, this work illustrates that cell division-based analysis will be an important addition to future toxicology studies.

Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs

Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.

Prediction of ADME-Tox properties and toxicological endpoints of triazole fungicides used for cereals protection

Within this study we have considered 9 triazole fungicides that are approved to be used in European Union for protecting cereals: cyproconazole, epoxiconazole, flutriafol, metconazole, paclobutrazole, tebuconazole, tetraconazole, triadimenol and triticonazole. We have summarized the few available data that support their effects on humans and used various computational tools to obtain a widely view concerning their possible harmful effects on humans. The results of our predictive study reflect that all triazole fungicides considered in this study reveal good oral bioavailability, are envisaged as being able to penetrate the blood brain barrier and to interact with P-glycoprotein and with hepatic cytochromes. The predictions concerning the toxicological endpoints for the investigated triazole fungicides reveal that they. reflect potential of skin sensitization, of blockage of the hERG K+ channels and of endocrine disruption, that they have not mutagenic potential and their carcinogenic potential is not clear. Epoxiconazole and triadimenol are predicted to have the highest potentials of producing numerous harmful effects on humans and their use should be avoided or limited.

Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors

Persistent, bioaccumulative and toxic (PBT) chemicals symbolize a group of substances that are not easily degraded; instead, they accumulate in different organisms and exhibit an acute or chronic toxicity. The limited empirical data on PBT chemicals, the high cost of testing together with the regulatory constraints and the international push for reduced animal testing motivate a greater reliance on predictive computational methods like quantitative structure-activity relationship (QSAR) models in PBT assessment. Papa and Gramatica have recently proposed a PBT index that could be computed directly from structural features. In the current study, we have modelled the experimentally derived PBT index data using an extended topological atom (ETA) along with constitutional descriptors to show the usefulness of the ETA indices in modelling the endpoint. The models developed through a double cross-validation (DCV) method gave the best results in terms of both internal and external validation metrics. The developed models were comparable in predictive quality to those previously reported. The current models were further used for consensus predictions of PBT behaviour for a set of pharmaceuticals and a set of synthetic drug-like compounds. The developed models can be used in PBT hazard screening for identification and prioritization of chemicals from the structural information alone.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology

In this chapter we present and discuss, with the aid of several representative case studies from drug discovery and computational toxicology, a new cheminformatics platform, Enalos Suite, that was developed with open source and freely available software. Enalos Suite ( http://enalossuite.novamechanics.com/ ) was designed and developed as a useful tool to address a variety of cheminformatics problems, given that it expedites tasks performed in predictive modeling and allows access, data mining and manipulation for multiple chemical databases (PubChem, UniChem, etc.). Enalos Suite was carefully designed to permit its extension and adjustment to the special field of interest of each user, including, for instance, nanoinformatics, biomedical, and other applications. To demonstrate the functionalities of Enalos Suite that are useful in different cheminformatics applications, we present indicative case studies that include the exploitation of chemical databases within a drug discovery project, the calculation of molecular descriptors, and finally the development of a predictive QSAR model validated according to OECD principles. We aspire that at the end of this chapter, the reader will capture the effectiveness of different functionalities included in the Enalos Suite that could be of significant value in a multitude of cheminformatics applications.

In vitro to in vivo extrapolation for high throughput prioritization and decision making

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.