Computational Toxicology最新文献_第10页

Towards a qAOP framework for predictive toxicology - Linking data to decisions 面向预测毒理学的qAOP框架——将数据与决策联系起来

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100195

Alicia Paini , Ivana Campia , Mark T.D. Cronin , David Asturiol , Lidia Ceriani , Thomas E. Exner , Wang Gao , Caroline Gomes , Johannes Kruisselbrink , Marvin Martens , M.E. Bette Meek , David Pamies , Julia Pletz , Stefan Scholz , Andreas Schüttler , Nicoleta Spînu , Daniel L. Villeneuve , Clemens Wittwehr , Andrew Worth , Mirjam Luijten

{"title":"Towards a qAOP framework for predictive toxicology - Linking data to decisions","authors":"Alicia Paini , Ivana Campia , Mark T.D. Cronin , David Asturiol , Lidia Ceriani , Thomas E. Exner , Wang Gao , Caroline Gomes , Johannes Kruisselbrink , Marvin Martens , M.E. Bette Meek , David Pamies , Julia Pletz , Stefan Scholz , Andreas Schüttler , Nicoleta Spînu , Daniel L. Villeneuve , Clemens Wittwehr , Andrew Worth , Mirjam Luijten","doi":"10.1016/j.comtox.2021.100195","DOIUrl":"10.1016/j.comtox.2021.100195","url":null,"abstract":"<div><p>The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including <em>in silico</em>, <em>in vitro</em> and <em>in vivo</em> assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959705","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}

引用次数: 12

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network 基于简化不良结果通路网络的发育性神经毒性概率模型

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100206

Nicoleta Spînu , Mark T.D. Cronin , Junpeng Lao , Anna Bal-Price , Ivana Campia , Steven J. Enoch , Judith C. Madden , Liadys Mora Lagares , Marjana Novič , David Pamies , Stefan Scholz , Daniel L. Villeneuve , Andrew P. Worth

{"title":"Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network","authors":"Nicoleta Spînu , Mark T.D. Cronin , Junpeng Lao , Anna Bal-Price , Ivana Campia , Steven J. Enoch , Judith C. Madden , Liadys Mora Lagares , Marjana Novič , David Pamies , Stefan Scholz , Daniel L. Villeneuve , Andrew P. Worth","doi":"10.1016/j.comtox.2021.100206","DOIUrl":"10.1016/j.comtox.2021.100206","url":null,"abstract":"<div><p>In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as <em>in silico</em> and <em>in vitro</em> information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959706","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}

引用次数: 9

Will qAOPs modernise toxicology? 社论：qAOPs会使毒理学现代化吗？

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100199

Mark T.D. Cronin , Nicoleta Spînu , Andrew P. Worth

引用次数: 0

ALOHA: Aggregated local extrema splines for high-throughput dose–response analysis ALOHA:聚合局部极值样条用于高通量剂量反应分析

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100196

Sarah E. Davidson , Matthew W. Wheeler , Scott S. Auerbach , Siva Sivaganesan , Mario Medvedovic

{"title":"ALOHA: Aggregated local extrema splines for high-throughput dose–response analysis","authors":"Sarah E. Davidson , Matthew W. Wheeler , Scott S. Auerbach , Siva Sivaganesan , Mario Medvedovic","doi":"10.1016/j.comtox.2021.100196","DOIUrl":"10.1016/j.comtox.2021.100196","url":null,"abstract":"<div><p><span>Computational methods for genomic dose–response integrate dose–response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene’s dose–response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose–response relationship, resulting in ‘co-regulated’ gene sets containing genes having different dose–response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose–response functions using a flexible class </span>Bayesian<span> shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose–response relationships, we better identify co-regulation clusters for genes that have co-expressed dose–response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.</span></p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10597137","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}

引用次数: 0

Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients 预测某些化妆品成分致粉刺潜力的QSAR模型的开发

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100207

Sebla Oztan Akturk, Gulcin Tugcu, Hande Sipahi

{"title":"Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients","authors":"Sebla Oztan Akturk, Gulcin Tugcu, Hande Sipahi","doi":"10.1016/j.comtox.2021.100207","DOIUrl":"10.1016/j.comtox.2021.100207","url":null,"abstract":"<div><p>Comedogenicity is a common adverse reaction to cosmetic ingredients that cause blackheads or pimples by blocking the pores, especially for acne-prone skin. Before animal testing was banned by European Commission in 2013, comedogenic potential of cosmetics were tested on rabbits. However, full replacement of animal tests by alternatives has not been possible yet. Therefore, there is a need for applying new approach methodologies. In this study, we aimed to develop a QSAR model to predict comedogenic potential of cosmetic ingredients by using different machine learning algorithms and types of molecular descriptors.</p><p>The dataset consists of 121 cosmetic ingredients including such as fatty acids, fatty alcohols and their derivatives and pigments tested on rabbit ears was obtained from the literature. 4837 molecular descriptors were calculated via various software. Different machine learning classification algorithms were used in the modelling studies with WEKA software. The model performance was evaluated by using 10-fold cross validation. All models were compared by the means of classification accuracy, area under the ROC curve, area under the precision-recall curve, MCC, F score, kappa statistic, sensitivity, specificity and the best model was chosen accordingly. The QSAR modelling results for two models are promising for comedogenicity prediction. The random forest models by the means of Mold2 and alvaDesc descriptors gave the successful results with 85.87% and 84.87% accuracy for the cross-validated models and 75.86% and 79.31% accuracy for the test sets. In conclusion, this study is the first step in terms of comedogenicity prediction. In the near future, advances in <em>in silico</em> modelling studies will provide us non-animal based alternative models by regarding animal rights and ethical issues for the safety evaluation of cosmetics.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42179593","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}

引用次数: 2

Synthesis and characterization of novel thiazole derivatives as potential anticancer agents: Molecular docking and DFT studies 新型噻唑类抗癌药物的合成与表征:分子对接与DFT研究

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100202

R. Raveesha , A.M. Anusuya , A.V. Raghu , K. Yogesh Kumar , M.G. Dileep Kumar , S.B. Benaka Prasad , M.K. Prashanth

{"title":"Synthesis and characterization of novel thiazole derivatives as potential anticancer agents: Molecular docking and DFT studies","authors":"R. Raveesha , A.M. Anusuya , A.V. Raghu , K. Yogesh Kumar , M.G. Dileep Kumar , S.B. Benaka Prasad , M.K. Prashanth","doi":"10.1016/j.comtox.2021.100202","DOIUrl":"10.1016/j.comtox.2021.100202","url":null,"abstract":"<div><p>New thiazole derivatives (2a-l) were synthesized via the reaction of 2-(3-cyano-4-isobutoxyphenyl)-4-methylthiazole-5-carboxylic acid with substituted phenyl amines. The anticancer activity of the synthesized thiazole derivatives was examined against MCF-7 (human breast), MDA-MB-231 (mammary carcinomas), HeLa (Cervical cancer), HT-29, HCT 116 (Colon cancer), and normal chang liver cancer cell lines, whereas cisplatin was employed as a positive control. The anticancer mechanisms were studied via apoptosis assessments, as well as molecular docking. The molecular docking study of potent compounds was carried out against the human epidermal growth factor receptor (HER2, PDB ID: 3RCD) as a possible target for anticancer activity using Auto Dock vina. ADMET results indicated that tested compounds have significant results within the close agreement of Lipinski’s rule of five. In addition, computational work employing density functional theory (DFT) was also carried out at the B3LYP/6-31G (d,p) level to investigate the electronic properties of the potent compounds. The frontier molecular orbital energy and atomic net charges were discussed.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43976194","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}

引用次数: 38

A review of in silico toxicology approaches to support the safety assessment of cosmetics-related materials 支持化妆品相关材料安全性评估的硅内毒理学方法综述

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2022.100213

Mark T.D. Cronin , Steven J. Enoch , Judith C. Madden , James F. Rathman , Andrea-Nicole Richarz , Chihae Yang

{"title":"A review of in silico toxicology approaches to support the safety assessment of cosmetics-related materials","authors":"Mark T.D. Cronin , Steven J. Enoch , Judith C. Madden , James F. Rathman , Andrea-Nicole Richarz , Chihae Yang","doi":"10.1016/j.comtox.2022.100213","DOIUrl":"10.1016/j.comtox.2022.100213","url":null,"abstract":"<div><p><em>In silico</em> tools and resources are now used commonly in toxicology and to support the “Next Generation Risk Assessment” (NGRA) of cosmetics ingredients or materials. This review provides an overview of the approaches that are applied to assess the exposure and hazard of a cosmetic ingredient. For both hazard and exposure, databases of existing information are used routinely. In addition, for exposure, <em>in silico</em> approaches include the use of rules of thumb for systemic bioavailability as well as physiologically-based kinetics (PBK) and multi-scale models for estimating internal exposure at the organ or tissue level. (Internal) Thresholds of Toxicological Concern are applicable for the safety assessment of ingredients at low concentrations. The use of structural rules, (Quantitative) Structure-Activity Relationships ((Q)SARs) and read-across are the most typically applied modelling approaches to predict hazard. Data from exposure and hazard assessment are increasingly being brought together in NGRA to provide an overall assessment of the safety of a cosmetic ingredient. All <em>in silico</em> approaches are reviewed in terms of their maturity and robustness for use.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000019/pdfft?md5=4400391120a66de64203858cd49f172c&pid=1-s2.0-S2468111322000019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46760793","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}

引用次数: 15

Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta 定量构效关系(QSAR)模型预测环境化学物质在胎盘中的转移

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100211

Laura Lévêque , Nadia Tahiri , Michael-Rock Goldsmith , Marc-André Verner

{"title":"Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta","authors":"Laura Lévêque , Nadia Tahiri , Michael-Rock Goldsmith , Marc-André Verner","doi":"10.1016/j.comtox.2021.100211","DOIUrl":"https://doi.org/10.1016/j.comtox.2021.100211","url":null,"abstract":"<div><p>The increasing diversity of environmental chemicals in the environment, some of which may be developmental toxicants, is a public health concern. The aim of this work was to contribute to the development of rapid and effective methods to assess prenatal exposure. Quantitative structure–activity relationships (QSAR) modeling has emerged as a promising method in the development of a predictive model for the placental transfer of contaminants. Cord to maternal plasma or serum concentration ratios for 105 chemicals were extracted from the literature, and 214 molecular descriptors were generated for each of these chemicals. Ten predictive models were built using Molecular Operating Environment (MOE) software, and the Python and R programming languages. Training and test datasets were used, respectively, to build and validate the models. The Applicability Domain Tool v1.0 was used to determine the applicability domain. Models developed with the partial least squares regression method in MOE and SuperLearner in R showed the best precision and predictivity, with internal coefficients of determination (R<sup>2</sup>) of 0.88 and 0.82, cross-validated R<sup>2</sup>s of 0.72 and 0.57, and external R<sup>2</sup>s of 0.73 and 0.74, respectively. All test chemicals were within the domain of applicability. The results obtained in this study suggest that QSAR modeling can help estimate the placental transfer of environmental chemicals.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111321000578/pdfft?md5=4c8c23c2a121692de4fd42f72e6c133d&pid=1-s2.0-S2468111321000578-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135682","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}

引用次数: 0

Evaluating confidence in toxicity assessments based on experimental data and in silico predictions 评估基于实验数据和计算机预测的毒性评估的可信度

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100204

Candice Johnson , Lennart T. Anger , Romualdo Benigni , David Bower , Frank Bringezu , Kevin M. Crofton , Mark T.D. Cronin , Kevin P. Cross , Magdalena Dettwiler , Markus Frericks , Fjodor Melnikov , Scott Miller , David W. Roberts , Diana Suarez-Rodrigez , Alessandra Roncaglioni , Elena Lo Piparo , Raymond R. Tice , Craig Zwickl , Glenn J. Myatt

{"title":"Evaluating confidence in toxicity assessments based on experimental data and in silico predictions","authors":"Candice Johnson , Lennart T. Anger , Romualdo Benigni , David Bower , Frank Bringezu , Kevin M. Crofton , Mark T.D. Cronin , Kevin P. Cross , Magdalena Dettwiler , Markus Frericks , Fjodor Melnikov , Scott Miller , David W. Roberts , Diana Suarez-Rodrigez , Alessandra Roncaglioni , Elena Lo Piparo , Raymond R. Tice , Craig Zwickl , Glenn J. Myatt","doi":"10.1016/j.comtox.2021.100204","DOIUrl":"10.1016/j.comtox.2021.100204","url":null,"abstract":"<div><p>Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for <em>in silico</em> predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, <em>in silico</em> assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to <em>in silico</em> data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on <em>in silico</em> methods, showing that reliability, relevance, and confidence of <em>in silico</em> assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10598755","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}

引用次数: 8

Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform 在可视化和交互式危害评估平台内实施计算机毒理学协议

Computational Toxicology Pub Date : 2022-02-01 DOI: 10.1016/j.comtox.2021.100201

Glenn J. Myatt , Arianna Bassan , Dave Bower , Candice Johnson , Scott Miller , Manuela Pavan , Kevin P. Cross

引用次数: 2