Computational Toxicology最新文献

{"title":"The predictivity of QSARs for toxicity: Recommendations for improving model performance","authors":"Mark T.D. Cronin,&nbsp;Homa Basiri,&nbsp;Georgios Chrysochoou,&nbsp;Steven J. Enoch,&nbsp;James W. Firman,&nbsp;Nicoleta Spînu,&nbsp;Judith C. Madden","doi":"10.1016/j.comtox.2024.100338","DOIUrl":"10.1016/j.comtox.2024.100338","url":null,"abstract":"<div><div>Quantitative structure–activity relationships (QSARs) are invaluable computational tools for the prediction of the biological effects and physico-chemical properties of molecules. For chemical safety assessment they are used frequently to make predictions of toxic or adverse effects, as well as other activities related to toxicokinetics. QSARs and their predictions can be assessed against a number of criteria for their potential use as surrogates for animal, or other, tests. A recent exercise by the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan, assessed QSARs to predict the outcome of the Ames test. The predictive performance of models was scrutinised with full disclosure of results. The authors of this publication developed one such model, which had disappointing performance in this predictive exercise. In order to understand why the QSAR had poor performance metrics, this paper reflects on factors that affect a QSAR model. There is no one reason for poor performance of a QSAR model, rather it is likely to be a combination of factors. Reasons for poor performance included inadequate consideration of the underlying data quality, consistency and relevance; lack of appropriate descriptors relating to the endpoint and mechanism of action; not selecting a model correctly in terms of its structure (i.e., complexity) and number of descriptors; not addressing metabolism adequately in the modelling process; ill-defined assessment of the uncertainties within a model; and not ensuring predictions are within the applicability domain of the model. Whilst this paper draws on examples for the prediction of mutagenicity, the findings are applicable to all toxicological activities and physico-chemical properties.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100338"},"PeriodicalIF":3.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147586","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":"Reconstruction of exposure to volatile organic compounds from venous blood concentration and an uncertain physiologically-based pharmacokinetic model","authors":"L. Simon,&nbsp;M.K. Prakasha","doi":"10.1016/j.comtox.2024.100336","DOIUrl":"10.1016/j.comtox.2024.100336","url":null,"abstract":"<div><div>Physiologically-based pharmacokinetic modeling was applied to determine exposures to volatile organic compounds, specifically focusing on m-xylene. Passive diffusion was used to describe permeation through the skin. The proposed model agreed with the experimental data and allowed researchers to monitor the concentration profiles in different compartments. The study also focused on the impact of parameter uncertainty on the model predictions. Local and global sensitivity analyses evaluated the influence of partition parameters, diffusion coefficients in the skin, and metabolic parameters on the blood concentration. Both methods show that the Michaelis-Menten kinetics and the lean tissue:blood partition coefficients contributed the most to the total variability. A reverse dosimetry approach used the measured biomarker level to estimate the exposure dose in four hours. The results aligned with experimental data when simulations were conducted using random parameters selected within twenty-five percent of the mean.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100336"},"PeriodicalIF":3.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652584","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":"Developing quantitative Adverse Outcome Pathways: An ordinary differential equation-based computational framework","authors":"Filippo Di Tillio,&nbsp;Joost B. Beltman","doi":"10.1016/j.comtox.2024.100330","DOIUrl":"10.1016/j.comtox.2024.100330","url":null,"abstract":"<div><div>The Adverse Outcome Pathway (AOP) biological framework was introduced in 2012, yet defining a mathematical/computational framework for quantitative AOP (qAOP) development remains an open problem. In order to properly unravel the intricate biological mechanisms described by AOPs and provide quantitative predictions to support risk assessment, a computational model should provide a clear time-course prediction of key events (KEs), as well as describe the key event relationships (KERs) linking a molecular initiating event (MIE) to an adverse outcome (AO). Ultimately, the mathematical description of those links entails the possibility of quantitatively predicting adverse effects based on early events.</div><div>Here, we propose an ordinary differential equation (ODE) - based qAOP framework, as ODEs provide a time-course description of KEs and KERs. We illustrate how the application of computational techniques, such as Bayesian inference and Leave-one-out cross-validation (LOO-CV), can assist AOP development, introducing concepts of qAOP model selection and qAOP updating. Furthermore, we compare ODE and response–response based qAOP models, showing that ODE-based qAOPs can avoid erroneous predictions potentially resulting from response–response qAOPs. Finally, we show how ODE parameter variability can be linked to AO variability across a population. Overall, this framework serves as a valuable mathematical and computational tool for the development of qAOP models, enhancing our comprehension of intricate biological pathways associated with adverse outcomes.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100330"},"PeriodicalIF":3.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586391","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":"Quantitative prediction of hemolytic activity of peptides","authors":"Dmitry A. Karasev ,&nbsp;Georgii S. Malakhov ,&nbsp;Boris N. Sobolev","doi":"10.1016/j.comtox.2024.100335","DOIUrl":"10.1016/j.comtox.2024.100335","url":null,"abstract":"<div><div>Peptides are currently considered promising therapeutic agents, ranging from antimicrobial to anticancer drugs. Damage to the cell membrane is the most studied mechanism of action of antibacterial peptides. The membrane toxicity of peptides towards human cells is assessed using hemolysis estimation. Several in silico methods have been developed to predict the hemolytic activity of potential antibacterial drugs. Most of the programs use classification models whose results are difficult to interpret. Usually, a researcher does not have the opportunity to understand under what conditions the prediction results can be realized. Furthermore, the authors often use the same external data as training ones not considering the principles of dividing the active and non-active subjects despite that underlying results were obtained under differed conditions. To overcome the gap between the prognosis and real study, we developed the regression models involving the details of differed experimental protocols. We reviewed the literature and supplemented the training data for 951 peptides with quantitative descriptors of the experimental conditions. The resulting regression models predicted the peptide concentration that would cause a certain level of hemolysis at a certain incubation time. Under different validation schemes, our models achieved acceptable performance estimates of 0.69 for R² and 58 µM for RMSE. Having evaluated the impact of descriptors on model performance, we confirmed the importance of accounting for the experimental conditions for reliable prediction of the peptide membrane toxicity.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100335"},"PeriodicalIF":3.1,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652583","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":"Species specific kinetics of imidacloprid and carbendazim in mouse and rat and consequences for biomonitoring","authors":"Bohan Hu,&nbsp;Ivonne M.C.M. Rietjens,&nbsp;Bert Spenkelink,&nbsp;Nico W. van den Brink","doi":"10.1016/j.comtox.2024.100334","DOIUrl":"10.1016/j.comtox.2024.100334","url":null,"abstract":"<div><div>This study aimed to develop physiologically based kinetic (PBK) models to predict the blood concentrations of imidacloprid and carbendazim and their primary metabolites 5-hydroxy-imidacloprid and 2-aminobenzimidazole after single or repeated oral exposure in mouse (<em>Mus musculus</em>), and compare this to corresponding kinetic data in rat (<em>Rattus norvegicus</em>). PBK model constants for conversion of imidacloprid and carbendazim and formation and clearance of their selected primary metabolites were quantified by <em>in vitro</em> mouse liver microsomal and S9 incubations. The performance of the newly developed PBK models was evaluated, based on a comparison to available literature data, showing that the models performed well. Predictions made were also compared to results from PBK model simulations for rats reported previously to obtain insight in species dependent differences in kinetics of these pesticides. The results thus obtained revealed substantial species differences in kinetics for these two pesticides between mouse and rat, especially for imidacloprid and to a lesser extent for carbendazim. Repeated dose PBK model simulations revealed that the models can facilitate estimation of external exposure levels under wildlife conditions based on internal blood concentrations of the parent compound. The rate of conversion and liver volume fraction were shown to influence the accuracy of these predictions with lower values providing less variable outcomes. It is concluded that PBK modeling provides a new approach methodology of use for wildlife biomonitoring studies and that results of the present study facilitate benchmarking of the species and compounds for which kinetics enable this with sufficient accuracy.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100334"},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554625","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":"In silico analysis of the melamine structural analogues interaction with calcium-sensing receptor: A potential for nephrotoxicity","authors":"Mandisi Sithole ,&nbsp;Gary Gabriels ,&nbsp;Thankhoe A. Rants’o","doi":"10.1016/j.comtox.2024.100333","DOIUrl":"10.1016/j.comtox.2024.100333","url":null,"abstract":"<div><div>In recent years, melamine, and its structural analogues, as adulterants in various food products including protein supplements,<!--> <!-->have been widely studied for their nephrotoxic effects. Previous research has presented evidence that certain small molecules can alter the calcium-sensing receptor (CaSR) function, contributing to nephrotoxicity. Melamine, for example, has been observed in <em>in vitro</em> settings to interact with the allosteric binding site of CaSR, resulting in uncontrolled CaSR activation. This activation results in the production of reactive oxygen species, which eventually causes kidney cell apoptosis and/or necrosis. The present research used the <em>in silico</em> molecular modelling to evaluate the CaSR binding profiles<!--> <!-->of four common adulterants in protein supplements: melamine, cyanuric acid, uric acid, and melamine cyanurate. Using Schrödinger’s Maestro docking software (version 13.2.128), the docking studies coupled a noncovalent extra precision mode with the molecular mechanics-generalized born surface area (MM-GBSA) simulation for enhanced binding affinity prediction accuracy. This study identified that cyanuric acid, uric acid, and melamine cyanurate have greater CaSR binding affinities than melamine. Interestingly, melamine cyanurate had the highest binding potential to CaSR. Previous animal studies have reported high concentrations of melamine cyanurate complex in rat kidneys following melamine administration. These findings demonstrate a molecular explanation melamine cyanurate complex-induced nephrotoxicity. This research offers new insight regarding the probable mechanism through which melamine, its analogues, and complexes may cause nephrotoxicity.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100333"},"PeriodicalIF":3.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441531","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":"Modeling chemical bioaccumulation in snakes, part 1: Model development","authors":"Xiaoyu Zhang,&nbsp;Zijian Li","doi":"10.1016/j.comtox.2024.100332","DOIUrl":"10.1016/j.comtox.2024.100332","url":null,"abstract":"<div><div>Environmental chemical emission influences ecological health to some extent. Predators (e.g., snakes) could bioaccumulate chemicals along the food chain, which also leaves potential health implications on their reproduction. For the difficulty of collecting related biomatrices for exposure assessment, part 1 of this study proposed a modeling method relying on physiologically based kinetic (PBK) theory to estimate snake chronic exposure to environmental chemicals. In the steady state, the biotransfer factors of chemicals produced by the PBK model can indicate a snake’s chronic internal exposure to environmental chemicals and their potential for bioaccumulation at this level of the food web. Specifically, 3074 organic chemicals were compelled into the dataset for PBK modeling (part 2 of the study). The modeling framework covered the physiological process of the skin to consider shed snakeskin as a potential biomarker for future study. The proposed modeling approach was integrated into a spreadsheet, enabling the modification of input values to simulate outcomes for a wide range of chemical and snake species. The proposed model can help assess the ecological risks of environmental chemicals and quantify their behavior in the food web.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100332"},"PeriodicalIF":3.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432363","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":"Modeling chemical bioaccumulation in snakes, part 2: Model testing and high-throughput simulation","authors":"Xiaoyu Zhang,&nbsp;Zijian Li","doi":"10.1016/j.comtox.2024.100331","DOIUrl":"10.1016/j.comtox.2024.100331","url":null,"abstract":"<div><div>In part 2 of the physiologically based kinetic (PBK) model for snakes, using default and generic input values, the simulation outcomes showed that chemicals with moderate lipophilicity, low volatility, and low biotransformability exhibited a high potential for biotransfer in the snake’s skin. Furthermore, the inclusion or exclusion of the skin compartment in the PBK model had a substantial impact on the fate, transport, and distribution of these chemicals within the snake’s body. In comparison to the elimination routes via blood transport and volatilization, the shedding of skin and growth processes did not contribute substantially to the overall kinetics of chemical elimination from the skin for most chemicals. The proposed model has demonstrated a consistent alignment with the observed patterns of chemical distribution, as supported by certain experimental data. Furthermore, it has the potential to provide an explanation for and enhance the understanding of the discrepancies found in other field observations. The modeling exercise clearly illustrated the significant role of the skin compartment in the biotransfer of chemicals within the snake’s body and highlighted the importance of including the snake’s physiological features into the PBK model. To further enhance the model’s performance and accuracy, additional research focused on obtaining specific physiological data pertaining to snakes would be beneficial.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100331"},"PeriodicalIF":3.1,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432364","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":"Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review","authors":"Jennifer L. Fisher ,&nbsp;Kelly T. Williams ,&nbsp;Leah J. Schneider ,&nbsp;Andrew J. Keebaugh ,&nbsp;Carrie L. German ,&nbsp;Adam M. Hott ,&nbsp;Narender Singh ,&nbsp;Rebecca A. Clewell","doi":"10.1016/j.comtox.2024.100329","DOIUrl":"10.1016/j.comtox.2024.100329","url":null,"abstract":"<div><p>The use of in silico modeling tools for predictive toxicology has potential to improve force health protection in the military by helping to efficiently evaluate the risk of adverse health effects from operational exposures. Thus, a systematic review was performed to understand if existing quantitative structure–activity relationship (QSAR) models for tissue-specific toxicity were potentially adaptable for use in risk assessments of military-relevant exposures. Within this systematic review, we assessed 563 peer-reviewed publications in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses<!--> <!-->(PRISMA) 2020 guidelines and Organization for Economic Co-operation and Development (OECD) 2023 quantitative structural-activity relationship Assessment Framework. From these publications, we further evaluated 129 existing models that utilize QSAR and tissue-specific data for predicting toxicity in the following tissues: liver (i.e., hepatotoxicity), heart (i.e., cardiotoxicity), lung (i.e., respiratory toxicity), the central nervous system (neurotoxicity), and kidney (i.e., nephrotoxicity). The methodology, performance, and accessibility of these models and analysis code were thoroughly documented and then assessed to determine advancements and inadequacies for occupational and military application. While ∼ 58 % of the 129 tissue-specific QSAR approaches followed at least 3 OECD guidelines, there were only 8 tissue-specific models that satisfied all screening criteria. The most common criteria not satisfied was mechanistic interpretation of the model (i.e., OECD criteria number five). Furthermore, while the greatest number of publications and models were available for the liver, many of them were for pharmaceutical applications. Moreover, there were limited available models for heart and kidney for any application. In conclusion, our findings underscore the necessity for additional and updated tissue-specific QSAR models to predict various organ-specific targets while addressing military specific needs. Furthermore, increased publication of model workflows or user-friendly applications are crucial to enhancing model accessibility. In this systematic review, we provide an overview of the databases, resources, and future strategies to advance tissue-specific QSAR model development.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100329"},"PeriodicalIF":3.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271593","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":"From model performance to decision support – The rise of computational toxicology in chemical safety assessments","authors":"","doi":"10.1016/j.comtox.2024.100303","DOIUrl":"10.1016/j.comtox.2024.100303","url":null,"abstract":"<div><p>In silico systems can reduce the need for (animal) testing, increase human safety and support critical decisions. They are increasingly being cited in regulatory guidance documents and are forming a key element of New Approach Methodologies (NAMs). Performance is being improved through a combination of new methodologies, increased understanding of mechanistic toxicology and access to experimental data from new assays. Trust and acceptance of in silico methodologies requires them to be accurate and transparent while also providing an explanation and confidence-assessment for each prediction. This paper summarises the state-of-art of in silico models and provides an action plan for further advances in this field.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"31 ","pages":"Article 100303"},"PeriodicalIF":3.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469981","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}