Computational Toxicology最新文献

Multiple single-walled carbon nanotubes, nestled in tandem as concentric cylinders, constitute the multi-walled carbon nanotubes. Due to their unique physical and chemical characteristics, the multi-walled carbon nanotubes find applications over diverse fields. Investigational studies in the literature reveal toxic nature of multi-walled carbon nanotubes. Hence, it is important to sense and predict their genotoxicity profile for public safety. Deep learning-based toxicity profile prediction, would hasten the research in the alleviation of toxicity in the products build using the multi-walled carbon nanotubes. The proposed hybrid-deep learning framework predicts the genotoxicity of variants of multi-walled carbon nanotubes with higher accuracy and precision. The proposed Ensemble Super Learner (ESL) is a hybrid model, built as a cascade combination of three machine learning models and deep autoencoder. The model achieves cent-percent accuracy when trained over the sparse data available on the genotoxic profile of variants of multi-walled carbon nanotubes.

Estrogen receptor (ER) mediated endocrine disruption and blood–brain barrier (BBB) permeability are two crucial pharmacological endpoints that must be assessed for any drug candidate. However, experimental testing is expensive and time-consuming, and in recent years, Quantitative Structure-Property Relationships (QSPRs) have emerged as a viable in silico alternative. However, most QSPR models reported on ER toxicity and BBB permeability have been carried out using 2D descriptors, whereas it has been established that ER binding and BBB permeability are stereoselective processes in which the spatial arrangement of atoms in the molecule plays a key role. The current study addresses this problem using a chirality-sensitive 3D-QSPR methodology entitled ‘EigenValue ANalysiS (EVANS). The EVANS approach merges information from 3D molecular structure with 2D physicochemical properties to generate eigenvalues which are used as descriptors in QSPR modelling. For chiral compounds, EVANS computes descriptors by considering distance attributes from a plethora of enantiomeric states, thereby accounting for the contributions of multiple conformers towards a particular biological endpoint. We deploy the EVANS methodology with machine learning algorithms to build predictive QSPR models for estrogen receptor (ER) mediated endocrine disruption and BBB permeability. Regression analyses of ER binding on a dataset of 132 chemical entities returned a robust and predictive model, with the support vector machine model having $r_{\mathrm{train}}^2=0.84$ and $r_{\mathrm{test}}^2=0.70$. Classification models for BBB permeability on a dataset of 607 chemicals also showed high prediction accuracy, with the artificial neural network model showing the best performance (Accuracy = 0.85, AUC = 0.82, precision = 0.85, F1 score = 0.89). For comparison, conventional 2D-QSPR models were also built for these endpoints, and it was observed that EVANS generates eigenvalues that are superior to descriptors used in standard 2D-QSPR. Overall, our results demonstrate that EVANS is a powerful 3D-QSPR methodology that offers several advantages over existing QSAR/QSPR methods, and can be a useful computational tool in the pharmacological and toxicological evaluation of new and existing drugs.

To address the need for rapid assessment of neurotoxicity from potential exposure to molecules of unknown toxicity, we developed an in silico tool that employs reverse molecular docking to identify receptor targets for molecules and deep-learning models that predict activity on the neurological targets. A selection of human neurologic receptors were obtained from the Protein Data Bank (PDB), then curated and prepared for docking. In total we docked thousands of molecules onto multiples sites on multiple different neurological receptor structures, generating millions of docked poses and scores. With this data we identified protein and ligand interactions and compared that to previously described experimental results. The data was transformed to an image representation and used to generate 2D convolutional deep-learning models. We have generated 19 deep-learning models, of which 17 are over 90% accurate on validation data and the remaining two are 84% and 87% accurate. We have developed a reverse docking GUI and pipeline to identify potential neurological targets for toxins and predict activity of toxins with deep-learning models based on docking identified interactions as an input. As an example, we have applied this pipeline to toluene, a molecule with known toxicity, and correctly predicted it as a GABA(B) agonist. The GUI has been tested on Ubuntu 20.04LTS and Windows 10, and the code, models and GUI are available under GPLv3 on github at https://github.com/mmccarthy1/Autodock_deeplearning_toxicology_GUI.

Context

Relationships among asbestos exposure, lung burden, and mesothelioma risks have been previously evaluated, but it would be useful to validate published epidemiological observations with a mathematical model describing deposition and elimination of various mineral types of fibers.

Objective

(a) To develop a mechanistical model demonstrating uptake and removal of fibers from human lungs, (b) To test the model on the results of a British case-control study, (c) To quantify the updated values for elimination coefficient of various mineral types of asbestos fibers.

Methods

A mechanistic model utilizing the first-order kinetic relationship is proposed that relates levels of exposure to mineral fibers, elimination coefficients, and lung burden at certain points of time. The behaviour of the model was explored for different exposure scenarios. Elimination coefficients for various mineral types were estimated based on the observed proportion of asbestos minerals in exposure vs observed lung burden.

Results

Based on the proposed model, the average elimination coefficient was estimated for crocidolite as 0.099 vs average published value of 0.092, for amosite as 0.169 vs 0.19, and for chrysotile as 6.45 vs average published value of 6.36 (years⁻¹). Lung burden level was demonstrated to change linearly with exposure intensity, and supra-linearly with exposure duration. The simulation of three separate exposure events during three decades showed that lung burden level prevailingly depends on the most recent event (R = 0.967, p < 0.05) and only weakly correlates with the most remote event (R = 0.032, p < 0.05).

Conclusion

In spite of potential limitations, mechanistical modelling of asbestos exposure can serve as an effective tool for risk assessment purposes.

Bioinformatics is inherently an innovative field that is situated at the limit of life and computer sciences that allowed new technological advances in genome sequencing, data processing, predication and simplified the treatment of complex and huge data. This field is related on two common approaches namely; in silico and molecular docking-dynamic experimentations to improve and clarify the scientific perception of ligand-receptor interactions, especially of those molecules involved in the drug elaboration process. This discipline has emerged to replace the traditional approach of drug discovery which was very limited, very expensive, and didn’t always provide the expected results. The objective of this review is to report the key events that have marked the bioinformatics sector during these last few years but also to underline the key elements that have contributed to its success especially in the sectors of pharmacy, biotechnology, bioengineering, and teaching but also on scientific community cooperation. This review will also discuss cutting-edge technology and bioinformatics characteristics in order to clarify some ambiguities in this area.

The Predictive Analytics Toolkit (PAT) was developed to facilitate use of new approach methodologies (NAMs) to predict health hazards and risks from chemicals. PAT is a user-friendly web application that integrates many R packages to enable development and testing of prediction models without any programming. We drew from the work of Ring et al. 2021 (https://doi.org/10.1016/j.comtox.2021.100166), who used random forest models to predict in vivo transcriptomic responses in rat liver from in vitro Tox21 AC50 values for a set of 221 chemicals. Gene ontologies helped identify 735 biological pathways based on differential in vivo expression of specific gene sets. Ring et al. used 12 models that varied in use of toxicokinetics to predict in vivo activity using 5000 random forest iterations for each chemical/pathway combination (the area under the receiver-operator characteristic curve (AUC-ROC) was the measure of model performance). The highest-ranking model (Model 10) used Tox21 AC50 nominal concentrations converted to media concentrations and in vivo doses converted to circulating plasma concentrations; the lowest ranking model (Model 2) used nominal in vitro concentrations and administered in vivo dose levels. Using a subset of 10 pathways from the Ring et al. data, we used PAT to predict the AUC-ROC and to compare the best (Model 10) and worst (Model 2) performing models with only 100 random forest iterations. Using the results from PAT, Model 10 “won” in 60% of the comparisons, a value similar to that calculated for the identical set of comparisons using the supplemental data from Ring et al. (52.2%). Hence, PAT can provide a useful alternative to programming in R for prediction modeling and model performance evaluation, even for extensive genomic data sets.

Mucormycosis or “black fungus” has been currently observed in India, as a secondary infection in COVID-19 infected patients in the post-COVID-stage. Fungus is an uncommon opportunistic infection that affects people who have a weak immune system. In this study, 158 antifungal phytochemicals were screened using molecular docking against glucoamylase enzyme of Rhizopus oryzae to identify potential inhibitors. The docking scores of the selected phytochemicals were compared with Isomaltotriose as a positive control. Most of the compounds showed lower binding energy values than Isomaltotriose (-6.4 kcal/mol). Computational studies also revealed the strongest binding affinity of the screened phytochemicals was Dioscin (-9.4 kcal/mol). Furthermore, the binding interactions of the top ten potential phytochemicals were elucidated and further analyzed. In-silico ADME and toxicity prediction were also evaluated using SwissADME and admetSAR online servers. Compounds Piscisoflavone C, 8-O-methylaverufin and Punicalagin exhibited positive results with the Lipinski filter and drug-likeness and showed mild to moderate of toxicity. Molecular dynamics (MD) simulation (at 300 K for 100 ns) was also employed to the docked ligand-target complex to explore the stability of ligand-target complex, improve docking results, and analyze the molecular mechanisms of protein-target interactions.

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for > 2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure–activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85–98 %) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71 % with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81 % using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

The present study offers the work on the hydrazone derivatives of dehydroacetic acid to be considered for computational and synthetic studies. The hydrazone derivatives of dehydroacetic acid were designed with different electron-withdrawing and electron-releasing substituents. The hydrazones and the parent compound (dehydroacetic acid) were subjected to computational studies to evaluate their pharmacological properties. The compounds were assessed by applying Lipinski’s rule followed by ADMET predictions. Among all the derivatives under studies, 4-hydroxy-6-methyl-3-(1-(2-(2-nitrophenyl) hydrazineylidene) ethyl)-2H-pyran-2-one was found to be the most effective derivative which was further evaluated against BSA, trypsin, amylase, lipase and cathepsins (B and H) by using docking studies. The computational results were also verified experimentally by synthesizing the derivative as well as performing enzyme inhibition studies on the synthesized hydrazone and the parent compound.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD₅₀) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD₅₀-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD₅₀, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.