Computational Toxicology最新文献

{"title":"Can the Predictive Analytics Toolkit (PAT) handle a genomic data set?","authors":"Ted W. Simon ,&nbsp;Louis A. (Tony) Cox ,&nbsp;Richard A. Becker","doi":"10.1016/j.comtox.2022.100241","DOIUrl":"10.1016/j.comtox.2022.100241","url":null,"abstract":"<div><p>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 (<span>https://doi.org/10.1016/j.comtox.2021.100166)</span><svg><path></path></svg>, who used random forest models to predict <em>in vivo</em> transcriptomic responses in rat liver from <em>in vitro</em> Tox21 AC50 values for a set of 221 chemicals. Gene ontologies helped identify 735 biological pathways based on differential <em>in vivo</em> expression of specific gene sets. Ring et al. used 12 models that varied in use of toxicokinetics to predict <em>in vivo</em> 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 <em>in vivo</em> doses converted to circulating plasma concentrations; the lowest ranking model (Model 2) used nominal <em>in vitro</em> concentrations and administered <em>in vivo</em> 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.</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/S2468111322000299/pdfft?md5=57556db7f1c9f97e6dd8e33e956d67d5&pid=1-s2.0-S2468111322000299-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42536154","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":"Potential inhibitory activity of phytoconstituents against black fungus: In silico ADMET, molecular docking and MD simulation studies","authors":"Narmin Hamaamin Hussen ,&nbsp;Aso Hameed Hasan ,&nbsp;Joazaizulfazli Jamalis ,&nbsp;Sonam Shakya ,&nbsp;Subhash Chander ,&nbsp;Harsha Kharkwal ,&nbsp;Sankaranaryanan Murugesan ,&nbsp;Virupaksha Ajit Bastikar ,&nbsp;Pramodkumar Pyarelal Gupta","doi":"10.1016/j.comtox.2022.100247","DOIUrl":"10.1016/j.comtox.2022.100247","url":null,"abstract":"<div><p>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. <em>In-silico</em> 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.</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.ncbi.nlm.nih.gov/pmc/articles/PMC9508704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10471269","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 structure-based activity in a high-throughput assay for steroid biosynthesis","authors":"Miran J Foster ,&nbsp;Grace Patlewicz ,&nbsp;Imran Shah ,&nbsp;Derik E. Haggard ,&nbsp;Richard S. Judson ,&nbsp;Katie Paul Friedman","doi":"10.1016/j.comtox.2022.100245","DOIUrl":"10.1016/j.comtox.2022.100245","url":null,"abstract":"<div><p>Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for &gt; 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 <em>in silico</em> approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.</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":"41241694","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":"Dehydroacetic acid hydrazones as potent enzyme inhibitors: design, synthesis and computational studies","authors":"Raman Lakhia ,&nbsp;Neera Raghav ,&nbsp;Rashmi Pundeer","doi":"10.1016/j.comtox.2022.100239","DOIUrl":"10.1016/j.comtox.2022.100239","url":null,"abstract":"<div><p>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)-2<em>H</em>-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.</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":"45124952","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":"Principles and procedures for assessment of acute toxicity incorporating in silico methods","authors":"Craig M. Zwickl ,&nbsp;Jessica C. Graham ,&nbsp;Robert A. Jolly ,&nbsp;Arianna Bassan ,&nbsp;Ernst Ahlberg ,&nbsp;Alexander Amberg ,&nbsp;Lennart T. Anger ,&nbsp;Lisa Beilke ,&nbsp;Phillip Bellion ,&nbsp;Alessandro Brigo ,&nbsp;Heather Burleigh-Flayer ,&nbsp;Mark T.D. Cronin ,&nbsp;Amy A. Devlin ,&nbsp;Trevor Fish ,&nbsp;Susanne Glowienke ,&nbsp;Kamila Gromek ,&nbsp;Agnes L. Karmaus ,&nbsp;Ray Kemper ,&nbsp;Sunil Kulkarni ,&nbsp;Elena Lo Piparo ,&nbsp;Glenn J. Myatt","doi":"10.1016/j.comtox.2022.100237","DOIUrl":"10.1016/j.comtox.2022.100237","url":null,"abstract":"<div><p>Acute <em>toxicity in silico</em> 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 <em>in silico</em> analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including <em>in silico</em> methods and <em>in vitro</em> or <em>in vivo</em> experiments. <em>In silico</em> methods that can assist the prediction of <em>in vivo</em> outcomes (<em>i.e.</em>, LD₅₀) are analyzed concluding that predictions obtained using <em>in silico</em> 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 <em>in vitro</em> studies commonly evaluated for predicting acute toxicity (<em>e.g.</em>, 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 <em>in vitro</em> data allow for a shift away from assessments solely based on endpoints such as LD₅₀, to mechanism-based endpoints that can be accurately assessed <em>in vitro</em> or by using <em>in silico</em> 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 <em>in silico</em> approaches support the assessment of acute toxicity.</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":"10856922","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":"Discriminant analysis of asbestiform and non-asbestiform amphibole particles and its implications for toxicological studies","authors":"Ann G. Wylie ,&nbsp;Andrey A. Korchevskiy ,&nbsp;Drew R. Van Orden ,&nbsp;Eric J. Chatfield","doi":"10.1016/j.comtox.2022.100233","DOIUrl":"10.1016/j.comtox.2022.100233","url":null,"abstract":"<div><h3>Context</h3><p>Rock dusts often contain minerals called amphiboles. Elongate mineral particles produced by excavation, crushing, or grinding amphibole-containing rock can belong to different morphological groups, or habits: asbestiform or non-asbestiform. Some asbestiform particles are highly potent for causing mesothelioma, but non-asbestiform elongate structures have not been implicated in elevated cancer risk. Computational analysis and modelling of the dimensional characteristics of the elongate mineral particles is needed to develop efficient criteria for their differentiation, and also for determining the parameters driving their carcinogenic potential.</p></div><div><h3>Objectives</h3><p>To develop conceptual and quantitative models allowing reliable distinctions between asbestiform and non-asbestiform amphibole particles that are based on particle dimensions and are consistent with observed disease outcome following human exposure.</p></div><div><h3>Methods</h3><p>For modelling, the unique database including 56 datasets designated as dominantly asbestiform (67,876 amphibole particles), 37 designated as dominantly non-asbestiform (235,247 amphibole particles), and 12 as inhomogeneous or anomalous (35,277 amphibole particles) was utilized. The discriminant analysis was used to determine functions that separate elongate mineral particles by their habit based on length and width. Linear regression and cluster analysis were applied to determine the relationship between values of the selected discriminant function and relevant toxicological parameters.</p></div><div><h3>Results</h3><p>For particles longer than 5 µm, the function <span><math><mrow><mi>Y</mi><mo>=</mo><mn>2.99</mn><msub><mi>log</mi><mn>10</mn></msub><mi>L</mi><mi>e</mi><mi>n</mi><mi>g</mi><mi>t</mi><mi>h</mi><mo>-</mo><mn>5.82</mn><msub><mi>log</mi><mn>10</mn></msub><mi>W</mi><mi>i</mi><mi>d</mi><mi>t</mi><mi>h</mi><mo>-</mo><mn>3.80</mn></mrow></math></span> was selected as the best discriminator of particles for their asbestiform and non-asbestiform habits, with a misclassification rate of about 15% total. The value of the discriminant function derived for each particle correlates with the particle’s calculated aerodynamic diameter (R = −0.859, p &lt; 0.00001) and with its specific surface area (R = 0.857, p &lt; 0.00001). The cluster analysis demonstrated that subdivision of particles by two groups according to their length and width closely reconstructs the pre-defined habits.</p></div><div><h3>Conclusion</h3><p>The proposed methodology of differentiating between asbestiform and non-asbestiform particles can be used for analytical, toxicological, and regulatory purposes.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42325671","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":"Baicalin protected mice against radiation-induced lethality: A mechanistic study employing in silico and wet lab techniques","authors":"Dharmendra Kumar Maurya ,&nbsp;Rutuja Lomte","doi":"10.1016/j.comtox.2022.100229","DOIUrl":"10.1016/j.comtox.2022.100229","url":null,"abstract":"<div><p>Baicalin is a main active ingredient of the dried root of Scutellaria and has been extensively employed in Traditional Chinese Medicine for the treatment of asthma, fever, and psoriasis. Based on the reports of antioxidant, anti-inflammatory, anti-infection, and anti-tumor activities of baicalin, we have explored its radioprotective efficacy using in vitro and in vivo experimental model systems. In the present study, we have investigated the radioprotective, immunomodulatory, and anti-inflammatory properties of baicalin using wet lab and in silico approaches. It was observed that pre-treatment of murine splenic lymphocytes with baicalin protected cells against radiation-induced cell death possibly by decreasing the cellular reactive oxygen species levels. Prophylactic oral administration of baicalin offered significant increase in endogenous spleen colony counts and an enhancement in the survival of mice. We have also observed that baicalin suppressed mitogen-induced splenic lymphocyte proliferation and IL-2 production. It also inhibited the production of nitric oxide in RAW 264.7 cells in response to elicitation of lipopolysaccharide. Further, in silico study was performed to evaluate the possible mechanism of radioprotection and immunomodulation by selecting different pro-inflammatory mediators such as COX2, Lck, NIK, and IKK-β which have a significant role in radioprotection, lymphocyte activation, and inflammation. Our molecular docking and molecular dynamics study show that baicalin has a significant predicted binding affinity with COX2, Lck, NIK, and IKK-β. These in silico results can explain the experimentally observed radioprotective, immunosuppressive, and anti-inflammatory properties of baicalin. Thus, radioprotection offered by baicalin may be because of its antioxidant, anti-inflammatory, and immunomodulatory properties.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48533708","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":"Editorial: In silico toxicology protocols initiative","authors":"Kevin P. Cross,&nbsp;Candice Johnson,&nbsp;Glenn J. Myatt","doi":"10.1016/j.comtox.2022.100236","DOIUrl":"10.1016/j.comtox.2022.100236","url":null,"abstract":"","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44832962","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 quantifying the uncertainty in in silico predictions using Bayesian learning","authors":"Timothy E.H. Allen ,&nbsp;Alistair M. Middleton ,&nbsp;Jonathan M. Goodman ,&nbsp;Paul J. Russell ,&nbsp;Predrag Kukic ,&nbsp;Steve Gutsell","doi":"10.1016/j.comtox.2022.100228","DOIUrl":"10.1016/j.comtox.2022.100228","url":null,"abstract":"<div><p>Next-generation risk assessment (NGRA) involves the combination of <em>in vitro</em> and <em>in silico</em> models for more human-relevant, ethical, and sustainable human chemical safety assessment. NGRA requires a quantitative mechanistic understanding of the effects of chemicals across human biology (be they molecular, cellular, organ-level or higher) coupled with a quantitative understanding of the uncertainty in any experimentally measured or predicted values. These values with their uncertainties can then be considered as a probability distribution, which can then be compared to exposure estimates to establish the presence or absence of a margin of safety. We have constructed Bayesian learning neural networks to provide such quantitative predictions and uncertainties for 20 pharmacologically important human molecular initiating events. These models produce high quality quantitative estimates (p(IC50), p(EC50), p(Ki), p(Kd)) of biochemical activity at a molecular initiating event (MIE) with average mean absolute errors (in Log units) of 0.625 ± 0.048 in test data and 0.941 ± 0.215 in external validation data. The key advantage of these models is their ability to also produce standard deviations and credible intervals (CIs) to quantify the uncertainty in these predictions, which we show to be able to distinguish between molecules close to the training data in chemical structure, those less similar to the training data, and decoy compounds drawn from the wider ChEMBL database. These uncertainty values mean that when a prediction is made a user can understand the certainty of the prediction, similar to a quantitative applicability domain, aiding prediction usefulness in NGRA. The ability for <em>in silico</em> methods to produce quantitative predictions with these kinds of probability distributions will be vital to their further use in NGRA, and here clear first steps have been taken.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45259197","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":"The role of ‘big data’ and ‘in silico’ New Approach Methodologies (NAMs) in ending animal use – A commentary on progress","authors":"Rebecca N. Ram ,&nbsp;Domenico Gadaleta ,&nbsp;Timothy E.H. Allen","doi":"10.1016/j.comtox.2022.100232","DOIUrl":"10.1016/j.comtox.2022.100232","url":null,"abstract":"<div><p><em>In silico</em><span> (computational) methods continue to evolve as part of a robust 21st century public health strategy in risk assessment, relevant to all sectors of chemical safety including preclinical drug discovery, industrial chemicals testing, food and cosmetics. Alongside </span><em>in vitro</em> methods as components of intelligent testing and pathway driven strategies, <em>in silico</em> models provide the potential for more human relevant solutions to the use of animals in safety testing and biomedical research. These are often termed ‘New Approach Methodologies’ (NAMs). Some NAMs incorporate the use of ‘big data’, for example the information provided from high throughput or high content <em>in vitro</em> screening assays or ‘omics’ technologies. Big data has increasing relevance to predictive toxicology but must be appropriately defined, particularly with regard to ‘quality vs quantity’. The purpose of this article is to provide a commentary on the progress of <em>in silico</em> human-based research methods within the context of NAMs, as well as discussion of the emerging use of big data with relevance to safety assessment. The current status of <em>in silico</em> methods is discussed, with input from researchers in the field. Scientific and legislative drivers for change are also considered, along with next steps to address challenges in funding and recognition, to achieve regulatory acceptance and uptake within the research community. To provide some wider context, the use of <em>in silico</em> methods alongside other relevant approaches (e.g., human-based <em>in vitro</em>) is also discussed.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45038466","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}