Computational Toxicology最新文献

{"title":"Salt-driven chromatin remodeling associated with senescence dysregulation plays a crucial role in the carcinogenesis of gastric cancer subtype","authors":"Karthik Balakrishnan","doi":"10.1016/j.comtox.2023.100262","DOIUrl":"10.1016/j.comtox.2023.100262","url":null,"abstract":"<div><p>Many studies previously reported that salt (NaCl) at high concentrations has genotoxicity and carcinogenicity features in human cells. The current study used an integrative functional-genomics approach to identify the effect of high salt-driven dysregulation in gastric cancer subtype carcinogenesis. Therefore, gene sets related to salt, chromatin, and senescence were collected from the molecular signatures database and investigated their expression in the many gastric cancer mRNA expression profile cohorts and its cell lines profile using pathway-focused gene-sets activation scoring methods. In this study, high salt-induced chromatin remodeling associated with senescence-mediated dysregulation are exceedingly abundant in the intestinal subtype gastric tumors. This critical finding should pave the path for the targeted therapeutics treatment in the subtype of gastric tumors.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48834217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards systematic read-across using Generalised Read-Across (GenRA)","authors":"Grace Patlewicz,&nbsp;Imran Shah","doi":"10.1016/j.comtox.2022.100258","DOIUrl":"10.1016/j.comtox.2022.100258","url":null,"abstract":"<div><p>Read-across continues to be a popular data gap filling technique within category and analogue approaches. One of the main issues hindering read-across acceptance is the notion of addressing and reducing uncertainties. Frameworks and formats have been created to help facilitate read-across development, evaluation, and residual uncertainties. However, read-across remains an expert-driven approach with each assessment decided on its own merits with no objective means of evaluating performance or quantifying uncertainties. Here, the underlying motivation of creating an algorithmic approach to read-across, namely the Generalised Read-Across (GenRA) approach, is described. The overall objectives of the approach were to quantify performance and uncertainty. Progress made in quantifying the impact of each similarity context commonly relied upon as part of read-across assessment are discussed. The framework underpinning the approach, the software tools developed to date and how GenRA can be used to make and interpret predictions as part of a screening level hazard assessment decision context are illustrated. Future directions and some of the overarching issues still needed in this field and the extent to which GenRA might facilitate those needs are discussed.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10605706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the utility of the Threshold of Toxicological Concern (TTC) and its exclusions in the biocompatibility assessment of extractable chemical substances from medical devices","authors":"Grace Patlewicz ,&nbsp;Mark Nelms ,&nbsp;Diego Rua","doi":"10.1016/j.comtox.2022.100246","DOIUrl":"10.1016/j.comtox.2022.100246","url":null,"abstract":"<div><p>The Threshold of Toxicological Concern (TTC) is a pragmatic approach used to establish safe thresholds below which there can be no appreciable risk to human health. Here, a large inventory of ∼45,000 substances (referred to as the LRI dataset) was profiled through the Kroes TTC decision module within Toxtree v3.1 to assign substances into their respective TTC categories. Four thousand and two substances were found to be not applicable for the TTC approach. However, closer examination of these substances uncovered several implementation issues: substances represented in their salt forms were automatically assigned as not appropriate for TTC when many of these contained essential metals as counter ions which would render them TTC applicable. High Potency Carcinogens and dioxin-like substances were not fully captured based on the rules currently implemented in the software. Phosphorus containing substances were considered exclusions when many of them would be appropriate for TTC. Refinements were proposed to address the limitations in the current software implementation. A second component of the study explored a set of substances representative of those released from medical devices and compared them to the LRI dataset as well as other toxicity datasets to investigate their structural similarity. A third component of the study sought to extend the exclusion rules to address application to substances released from medical devices that lack toxicity data. The refined rules were then applied to this dataset and the TTC assignments were compared. This case study demonstrated the importance of evaluating the software implementation of an established TTC workflow, identified certain limitations and explored potential refinements when applying these concepts to medical devices.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40486272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico prediction of dermal absorption from non-dietary exposure to plant protection products","authors":"Christian J. Kuster ,&nbsp;Jenny Baumann ,&nbsp;Sebastian M. Braun ,&nbsp;Philip Fisher ,&nbsp;Nicola J. Hewitt ,&nbsp;Michael Beck ,&nbsp;Fabian Weysser ,&nbsp;Linus Goerlitz ,&nbsp;Petrus Salminen ,&nbsp;Christian R. Dietrich ,&nbsp;Magnus Wang ,&nbsp;Matthias Ernst","doi":"10.1016/j.comtox.2022.100242","DOIUrl":"10.1016/j.comtox.2022.100242","url":null,"abstract":"<div><p>An <em>in silico</em> model for predicting skin penetration of active ingredients formulated in plant protection products (PPP) has been developed using random forests (machine learning technique) that were trained with data from <em>in vitro</em> human skin studies taken from the EFSA dermal absorption database and in-house data from Bayer. In addition to the applied dose, various physicochemical properties were considered as model parameters. The model has been linked to a novel percentile approach in order to make the results usable for regulatory purposes. Application to an external validation data set demonstrated that the tool is ready for use. Finally, we propose to follow a tiered decision tree approach for non-dietary risk assessments including the use of the <em>in silico</em> dermal absorption prediction model as part of a safety assessment of a PPP.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000305/pdfft?md5=c90bb4ed0173c371f577aae223b9254d&pid=1-s2.0-S2468111322000305-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42816408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing","authors":"Grace Patlewicz,&nbsp;Ann M. Richard,&nbsp;Antony J. Williams,&nbsp;Richard S. Judson,&nbsp;Russell S. Thomas","doi":"10.1016/j.comtox.2022.100250","DOIUrl":"10.1016/j.comtox.2022.100250","url":null,"abstract":"<div><p>Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ∼150 PFAS through an array of different <em>in vitro</em> high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9197645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble super learner based genotoxicity prediction of multi-walled carbon nanotubes","authors":"B. Latha ,&nbsp;Sheena Christabel Pravin ,&nbsp;J. Saranya ,&nbsp;E. Manikandan","doi":"10.1016/j.comtox.2022.100244","DOIUrl":"10.1016/j.comtox.2022.100244","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43841069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting toxicity of endocrine disruptors and blood–brain barrier permeability using chirality-sensitive descriptors and machine learning","authors":"Anish Gomatam ,&nbsp;Blessy Joseph ,&nbsp;Ulka Gawde ,&nbsp;Kavita Raikuvar ,&nbsp;Evans Coutinho","doi":"10.1016/j.comtox.2022.100240","DOIUrl":"10.1016/j.comtox.2022.100240","url":null,"abstract":"<div><p>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 <span><math><mrow><msubsup><mi>r</mi><mrow><mi>train</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.84</mn></mrow></math></span> and <span><math><mrow><msubsup><mi>r</mi><mrow><mi>test</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.70</mn></mrow></math></span>. 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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42931524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reverse molecular docking and deep-learning to make predictions of receptor activity for neurotoxicology","authors":"M.J. McCarthy,&nbsp;Y. Chushak,&nbsp;J.M. Gearhart","doi":"10.1016/j.comtox.2022.100238","DOIUrl":"10.1016/j.comtox.2022.100238","url":null,"abstract":"<div><p>To address the need for rapid assessment of neurotoxicity from potential exposure to molecules of unknown toxicity, we developed an <em>in silico</em> 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 <span>https://github.com/mmccarthy1/Autodock_deeplearning_toxicology_GUI</span><svg><path></path></svg>.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000263/pdfft?md5=6da6be3566229a5abb2abf58758302da&pid=1-s2.0-S2468111322000263-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42410864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Asbestos exposure, lung fiber burden, and mesothelioma rates: Mechanistic modelling for risk assessment","authors":"Andrey A. Korchevskiy ,&nbsp;Ann G. Wylie","doi":"10.1016/j.comtox.2022.100249","DOIUrl":"10.1016/j.comtox.2022.100249","url":null,"abstract":"<div><h3>Context</h3><p>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.</p></div><div><h3>Objective</h3><p>(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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p><span><span>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 </span>chrysotile as 6.45 vs average published value of 6.36 (years</span>⁻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 &lt; 0.05) and only weakly correlates with the most remote event (R = 0.032, p &lt; 0.05).</p></div><div><h3>Conclusion</h3><p>In spite of potential limitations, mechanistical modelling of asbestos exposure can serve as an effective tool for risk assessment purposes.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43597476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of bioinformatics and its impact on modern bio-science in the twenty-first century: Special attention to pharmacology, plant science and drug discovery","authors":"Debasis Mitra ,&nbsp;Debanjan Mitra ,&nbsp;Mohamed Sabri Bensaad ,&nbsp;Somya Sinha ,&nbsp;Kumud Pant ,&nbsp;Manu Pant ,&nbsp;Ankita Priyadarshini ,&nbsp;Pallavi Singh ,&nbsp;Saliha Dassamiour ,&nbsp;Leila Hambaba ,&nbsp;Periyasamy Panneerselvam ,&nbsp;Pradeep K. Das Mohapatra","doi":"10.1016/j.comtox.2022.100248","DOIUrl":"10.1016/j.comtox.2022.100248","url":null,"abstract":"<div><p>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; <em>in silico</em> 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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42969608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}