Pub Date : 2024-12-18DOI: 10.1016/j.comtox.2024.100337
Henk J. van Lingen , Edoardo Saccenti , Maria Suarez-Diez , Marta Baccaro , Nico W. van den Brink
Toxicokinetic models for predicting contents of nanomaterials and other toxic chemicals are often fitted without evaluation of the residual variance structure. The aim of the present study was to evaluate various residual variance structures, assuming either homoscedasticity or heteroscedasticity, when fitting non-linear toxicokinetic one-compartment models for predicting uptake, bioaccumulation and elimination of chemicals in invertebrate organisms. Data describing the exposure of several aquatic and terrestrial invertebrates to specific metal nanomaterials and other chemicals were available from real experiments for evaluating the residual variance functions for toxicokinetic models. As proof of concept, datasets of truly homoscedastic and heteroscedastic nature were simulated. Depending the dataset, applying models with different residuals variance assumption largely affected the residual plots and the error margins of parameters or the predicted content of a chemical. Consequently, selecting the most accurate residual variance functions for toxicokinetic modeling, either homoscedastic or heteroscedastic, improves the prediction of chemical contents in invertebrate organisms and the estimation of the associated uptake and elimination rates.
{"title":"Predicting uptake and elimination kinetics of chemicals in invertebrates: A technical note on residual variance modeling","authors":"Henk J. van Lingen , Edoardo Saccenti , Maria Suarez-Diez , Marta Baccaro , Nico W. van den Brink","doi":"10.1016/j.comtox.2024.100337","DOIUrl":"10.1016/j.comtox.2024.100337","url":null,"abstract":"<div><div>Toxicokinetic models for predicting contents of nanomaterials and other toxic chemicals are often fitted without evaluation of the residual variance structure. The aim of the present study was to evaluate various residual variance structures, assuming either homoscedasticity or heteroscedasticity, when fitting non-linear toxicokinetic one-compartment models for predicting uptake, bioaccumulation and elimination of chemicals in invertebrate organisms. Data describing the exposure of several aquatic and terrestrial invertebrates to specific metal nanomaterials and other chemicals were available from real experiments for evaluating the residual variance functions for toxicokinetic models. As proof of concept, datasets of truly homoscedastic and heteroscedastic nature were simulated. Depending the dataset, applying models with different residuals variance assumption largely affected the residual plots and the error margins of parameters or the predicted content of a chemical. Consequently, selecting the most accurate residual variance functions for toxicokinetic modeling, either homoscedastic or heteroscedastic, improves the prediction of chemical contents in invertebrate organisms and the estimation of the associated uptake and elimination rates.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100337"},"PeriodicalIF":3.1,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147585","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}
Pub Date : 2024-12-17DOI: 10.1016/j.comtox.2024.100340
Upendra K. Devisetty , Emma De Neef , Eric R.L. Gordon , Valeria Velásquez-Zapata , Kenneth Narva , Laurent Mézin , Peter Mc Cahon , Kenneth W. Witwer , Krishnakumar Sridharan
Current plant protection methods rely predominantly on conventional chemical pesticides that can have negative human health and environmental impacts. Consequently, there is a pressing need to develop sustainable crop protection solutions that have improved safety profiles for humans and other non-target organisms (NTOs). RNA interference (RNAi) is a natural defense mechanism against viruses found in eukaryotes that silences viral genes in a sequence-specific manner. Recently, RNAi has been utilized to specifically target essential genes of pests with a novel class of topical, sprayable biopesticides based on dsRNA (double-stranded RNA). A critical step in the regulatory approval of such externally applied dsRNA-based biopesticides is a robust bioinformatics analysis of potential off-target effects to humans and other organisms. However, no generally applicable guidelines are available for risk assessment of dsRNA-based biopesticides for humans. Here, we address this gap by describing a bioinformatics framework for risk assessment in humans, informed by peer-reviewed literature, that quantifies potential off-targets with a primary focus on externally applied dsRNA-based biopesticides. The framework comprises three main components: bioinformatics tools for predicting off-target effects in humans, a mismatch tolerance for sequence divergence between dsRNA and unintended targets to delineate potential human off-target effects, and siRNA criteria for quantifying the possibility of theoretical gene silencing in the presence of mismatches in humans. This bioinformatics framework represents the most comprehensive approach described to date and has been used successfully for evaluating the potential risks of the externally applied dsRNA-based biopesticide CalanthaTM to humans.
{"title":"A bioinformatics framework for human health risk assessment of externally applied dsRNA-based biopesticides","authors":"Upendra K. Devisetty , Emma De Neef , Eric R.L. Gordon , Valeria Velásquez-Zapata , Kenneth Narva , Laurent Mézin , Peter Mc Cahon , Kenneth W. Witwer , Krishnakumar Sridharan","doi":"10.1016/j.comtox.2024.100340","DOIUrl":"10.1016/j.comtox.2024.100340","url":null,"abstract":"<div><div>Current plant protection methods rely predominantly on conventional chemical pesticides that can have negative human health and environmental impacts. Consequently, there is a pressing need to develop sustainable crop protection solutions that have improved safety profiles for humans and other non-target organisms (NTOs). RNA interference (RNAi) is a natural defense mechanism against viruses found in eukaryotes that silences viral genes in a sequence-specific manner. Recently, RNAi has been utilized to specifically target essential genes of pests with a novel class of topical, sprayable biopesticides based on dsRNA (double-stranded RNA). A critical step in the regulatory approval of such externally applied dsRNA-based biopesticides is a robust bioinformatics analysis of potential off-target effects to humans and other organisms. However, no generally applicable guidelines are available for risk assessment of dsRNA-based biopesticides for humans. Here, we address this gap by describing a bioinformatics framework for risk assessment in humans, informed by peer-reviewed literature, that quantifies potential off-targets with a primary focus on externally applied dsRNA-based biopesticides. The framework comprises three main components: bioinformatics tools for predicting off-target effects in humans, a mismatch tolerance for sequence divergence between dsRNA and unintended targets to delineate potential human off-target effects, and siRNA criteria for quantifying the possibility of theoretical gene silencing in the presence of mismatches in humans. This bioinformatics framework represents the most comprehensive approach described to date and has been used successfully for evaluating the potential risks of the externally applied dsRNA-based biopesticide Calantha<sup>TM</sup> to humans.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100340"},"PeriodicalIF":3.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147584","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}
Pub Date : 2024-12-09DOI: 10.1016/j.comtox.2024.100338
Mark T.D. Cronin, Homa Basiri, Georgios Chrysochoou, Steven J. Enoch, James W. Firman, Nicoleta Spînu, Judith C. Madden
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.
{"title":"The predictivity of QSARs for toxicity: Recommendations for improving model performance","authors":"Mark T.D. Cronin, Homa Basiri, Georgios Chrysochoou, Steven J. Enoch, James W. Firman, Nicoleta Spînu, 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}
Pub Date : 2024-11-06DOI: 10.1016/j.comtox.2024.100336
L. Simon, M.K. Prakasha
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.
{"title":"Reconstruction of exposure to volatile organic compounds from venous blood concentration and an uncertain physiologically-based pharmacokinetic model","authors":"L. Simon, 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}
Pub Date : 2024-11-02DOI: 10.1016/j.comtox.2024.100330
Filippo Di Tillio, Joost B. Beltman
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.
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.
{"title":"Developing quantitative Adverse Outcome Pathways: An ordinary differential equation-based computational framework","authors":"Filippo Di Tillio, 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}
Pub Date : 2024-10-29DOI: 10.1016/j.comtox.2024.100335
Dmitry A. Karasev , Georgii S. Malakhov , Boris N. Sobolev
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.
{"title":"Quantitative prediction of hemolytic activity of peptides","authors":"Dmitry A. Karasev , Georgii S. Malakhov , 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 R<sup>2</sup> 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}
Pub Date : 2024-10-18DOI: 10.1016/j.comtox.2024.100334
Bohan Hu, Ivonne M.C.M. Rietjens, Bert Spenkelink, Nico W. van den Brink
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 (Mus musculus), and compare this to corresponding kinetic data in rat (Rattus norvegicus). PBK model constants for conversion of imidacloprid and carbendazim and formation and clearance of their selected primary metabolites were quantified by in vitro 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.
{"title":"Species specific kinetics of imidacloprid and carbendazim in mouse and rat and consequences for biomonitoring","authors":"Bohan Hu, Ivonne M.C.M. Rietjens, Bert Spenkelink, 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}
Pub Date : 2024-10-15DOI: 10.1016/j.comtox.2024.100333
Mandisi Sithole , Gary Gabriels , Thankhoe A. Rants’o
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 in vitro 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 in silico 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.
{"title":"In silico analysis of the melamine structural analogues interaction with calcium-sensing receptor: A potential for nephrotoxicity","authors":"Mandisi Sithole , Gary Gabriels , 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}
Pub Date : 2024-10-14DOI: 10.1016/j.comtox.2024.100332
Xiaoyu Zhang, Zijian Li
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.
{"title":"Modeling chemical bioaccumulation in snakes, part 1: Model development","authors":"Xiaoyu Zhang, 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}
Pub Date : 2024-10-13DOI: 10.1016/j.comtox.2024.100331
Xiaoyu Zhang, Zijian Li
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.
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