Computational Toxicology最新文献

{"title":"Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science","authors":"Joseph D. Romano ,&nbsp;Liang Mei ,&nbsp;Jonathan Senn ,&nbsp;Jason H. Moore ,&nbsp;Holly M. Mortensen","doi":"10.1016/j.comtox.2023.100261","DOIUrl":"10.1016/j.comtox.2023.100261","url":null,"abstract":"<div><p>Adverse outcome pathways provide a powerful tool for understanding the biological signaling cascades that lead to disease outcomes following toxicity. The framework outlines downstream responses known as key events, culminating in a clinically significant adverse outcome as a final result of the toxic exposure. Here we use the AOP framework combined with artificial intelligence methods to gain novel insights into genetic mechanisms that underlie toxicity-mediated adverse health outcomes. Specifically, we focus on liver cancer as a case study with diverse underlying mechanisms that are clinically significant. Our approach uses two complementary AI techniques: Generative modeling via automated machine learning and genetic algorithms, and graph machine learning. We used data from the US Environmental Protection Agency’s Adverse Outcome Pathway Database (AOP-DB; <span>aopdb.epa.gov</span><svg><path></path></svg>) and the UK Biobank’s genetic data repository. We use the AOP-DB to extract disease-specific AOPs and build graph neural networks used in our final analyses. We use the UK Biobank to retrieve real-world genotype and phenotype data, where genotypes are based on single nucleotide polymorphism data extracted from the AOP-DB, and phenotypes are case/control cohorts for the disease of interest (liver cancer) corresponding to those adverse outcome pathways. We also use propensity score matching to appropriately sample based on important covariates (demographics, comorbidities, and social deprivation indices) and to balance the case and control populations in our machine language training/testing datasets. Finally, we describe a novel putative risk factor for LC that depends on genetic variation in both the aryl-hydrocarbon receptor (<em>AHR</em>) and ATP binding cassette subfamily B member 11 (<em>ABCB11</em>) genes.</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/PMC10569310/pdf/nihms-1933008.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215139","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":"ToxicR: A computational platform in R for computational toxicology and dose–response analyses","authors":"Matthew W. Wheeler ,&nbsp;Sooyeong Lim ,&nbsp;John S. House ,&nbsp;Keith R. Shockley ,&nbsp;A. John Bailer ,&nbsp;Jennifer Fostel ,&nbsp;Longlong Yang ,&nbsp;Dawan Talley ,&nbsp;Ashwin Raghuraman ,&nbsp;Jeffery S. Gift ,&nbsp;J. Allen Davis ,&nbsp;Scott S. Auerbach ,&nbsp;Alison A. Motsinger-Reif","doi":"10.1016/j.comtox.2022.100259","DOIUrl":"10.1016/j.comtox.2022.100259","url":null,"abstract":"<div><p>The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose–response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA’s Benchmark Dose software and NTP’s BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.</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/PMC9997717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9113776","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":"Benzoxazole based thiazole hybrid analogs: Synthesis, in vitro cholinesterase inhibition, and molecular docking studies","authors":"Rafaqat Hussain ,&nbsp;Fazal Rahim ,&nbsp;Wajid Rehman ,&nbsp;Syed Adnan Ali Shah ,&nbsp;Shoaib Khan ,&nbsp;Imran Khan ,&nbsp;Liaqat Rasheed ,&nbsp;Syahrul Imran ,&nbsp;Abdul Wadood ,&nbsp;Magda H. Abdellatif","doi":"10.1016/j.comtox.2022.100253","DOIUrl":"10.1016/j.comtox.2022.100253","url":null,"abstract":"<div><p>Acetylcholinesterase and butyrylcholinesterase enzymes are therapeutic target for Alzheimer disease and their inhibitors play a vital role for the treatment of this disease. <strong>A</strong> new series of benzoxazole based 1,3-thiazole hybrid scaffolds (<strong>1</strong>–<strong>20</strong>) were synthesized and assessed for acetylcholinesterase and butyrylcholinesterase inhibition profile and then characterized by using different spectroscopic tools such as ¹H NMR, ¹³C NMR and HREI-MS spectroscopy. Four scaffolds such as <strong>1</strong>, <strong>4</strong>, <strong>12</strong> and <strong>19</strong> showed AChE potency almost comparable to standard drug having IC₅₀ values 0.692 ± 0.087, 0.947 ± 0.089, 0.38 ± 0.016 and 0.742 ± 0.042 µM, while nine scaffolds such as <strong>1</strong>, <strong>4</strong>, <strong>6</strong>, <strong>8</strong>, <strong>9</strong>, <strong>12</strong>, <strong>13</strong>, <strong>14</strong> and <strong>19</strong> showed superior BuChE potency than standard drug having IC₅₀ values 2.54 ± 0.10, 1.79 ± 0.20, 3.25 ± 0.18, 2.48 ± 0.05, 1.33 ± 0.05, 2.19 ± 0.08, 2.81 ± 0.20, 2.23 ± 0.10 and 2.10 ± 0.05 µM respectively. Nonetheless, remaining analogs were found to have moderate activity. Among the synthesized series, analogs <strong>12</strong> (IC₅₀ <strong>=</strong> 0.38 ± 0.016 µM) and <strong>9</strong> (IC₅₀ <strong>=</strong> 1.33 ± 0.05 µM) were identified as the most potent inhibitors of acetylcholinesterase and butyrylcholinesterase enzymes. In addition, the molecular docking studies were carried out to find out the possible binding mode of interactions of most active analogs with enzymes active site and results supported the experimental data.</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":"42637305","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":"Safety assessment of the use of recycled high-density polyethylene in cosmetics packaging based on in silico modeling migration of representative chemical contaminants for dermal sensitization and systemic endpoints","authors":"Ramez Labib ,&nbsp;Ripal Amin ,&nbsp;Christal Lewis ,&nbsp;Valer Toşa ,&nbsp;Peter Mercea","doi":"10.1016/j.comtox.2023.100260","DOIUrl":"10.1016/j.comtox.2023.100260","url":null,"abstract":"<div><p>A safety assessment of recycled high-density polyethylene (rHDPE) in cosmetic packaging was performed based on guidelines published by the European Food Safety Authority (EFSA) on the use of recycled plastics for food packaging. EFSA guidelines require demonstration that the concentration of selected representative chemical contaminants in recycled plastic resulting from exposure from food is lower than the threshold of toxicological concern (TTC) for genotoxic substances of 0.0025 µg/kg bw/day. To investigate the highest concentration (Cmod) of representative chemical contaminants, that would not exceed the genotoxic TTC, when migrating from rHDPE packaging to foodstuffs, used as cosmetic formulation surrogates, we used mathematical modeling software (MIGRATEST®EXP). The Cmod values of representative chemical contaminants were then compared with the EFSA-reported residual concentration (Cres) of each contaminant in the rHDPE. For each of the cosmetic product/packaging combinations evaluated, we found that the modeled values were clearly lower for Cmod than Cres, i.e., the recycling process could effectively reduce potential contaminants of rHDPE to levels that would not result in daily consumer exposure from cosmetic use exceeding the genotoxic TTC. For skin sensitization, we modeled a worst-case scenario and assumed 100 % of each representative chemical contaminant migrates into the cosmetic formulation from rHDPE. We then calculated the consumer exposure level for each contaminant based on the dose per unit area and compared it with the dermal sensitization threshold (DST) for reactive materials, which is 64 µg/cm². In each case, we demonstrated that the migration of each representative chemical contaminant from rHDPE into each cosmetic formulation was far below the DST, confirming that there is no appreciable risk of sensitization for protein-reactive chemicals. In conclusion, these data support the safe use of rHDPE in the packaging of cosmetic products for leave-on and rinse-off applications.</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":"45566249","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 persistent, mobile, and toxic pharmaceuticals (PMT): A case study in São Paulo Metropolitan Region, Brazil","authors":"Vinicius Roveri ,&nbsp;Luciana Lopes Guimarães","doi":"10.1016/j.comtox.2022.100254","DOIUrl":"10.1016/j.comtox.2022.100254","url":null,"abstract":"<div><p>Computational modelling (in silico) methods based on quantitative structure-activity relationship ((Q)SAR) models, are powerful tools for the assessment of the potential “persistency, mobility, and toxicity” (PMT) of pharmaceuticals compounds. Moreover, the use of (Q)SAR models, is recommended by European Union’s REACH Regulation. In this context, the aims of this research were estimating, for the first time and based by REACH guidelines, the PMT potentials of 115 most sold pharmaceuticals in São Paulo Metropolitan Region (a megacity with 21 million of Brazilian), through five (Q)SAR updated models, namely: the OPERA QSAR; the VEGA QSAR (Version 1.1.5); the <em>EPI</em> Suite (Version 4.11); the ECOSAR (Version, 2.0); and the QSAR Toolbox (Version 4.5). This study prioritized the in-silico predictions from the OPERA and the VEGA, because both QSARs can generate reliable predictions, i.e., they have detailed information about the applicability domains. In silico predictions were performed considering ten endpoints: (i) Molecular weight (MW); (ii) “STP total removal”: Sewage Treatment Plant; (iii) Octanol-water partition coefficient (KOW); (iv) Predicted ready biodegradability; (v) Soil organic adsorption coefficient (KOC); (vi) “Short-term and long-term ecological assessments”; (vii) “Carcinogenicity”; (viii) “Mutagenicity”; (ix) “Estrogen receptor binding”; (x) “Cramer decision tree”. The main results showed that: (a) These 115 pharmaceuticals cover a wide range of so-called small molecules (range from 100 to 600 MW); (b) In STP, a predicted removal lower than 10 % was found for 76 pharmaceuticals; (c) Additionally, 101 chemicals has low (Log KOW &lt;2.5), or medium sorption potential (2.5&lt; log KOW &lt;4.0); (d) Ultimately, 36 PPCPs were considered “persistent” after a weight-of-evidence assessment. In addiction, 17 among these 36 persistent chemicals, were classified as “very mobile” in water (log KOC &lt;3). Finally, only three among 17 PPCPs, namely ciprofibrate, fluconazole and metoclopramide, exhibited one or more toxic characteristics (described in items vi – x). These results it will be possible to alert about the potential risks arising from the indiscriminate disposal of these PPCPs along the water sources of this Brazilian mega metropolis.</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":"42771289","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":"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}