Katie Paul Friedman , Miran J. Foster , Ly Ly Pham , Madison Feshuk , Sean M. Watford , John F. Wambaugh , Richard S. Judson , R. Woodrow Setzer , Russell S. Thomas
{"title":"Reproducibility of organ-level effects in repeat dose animal studies","authors":"Katie Paul Friedman , Miran J. Foster , Ly Ly Pham , Madison Feshuk , Sean M. Watford , John F. Wambaugh , Richard S. Judson , R. Woodrow Setzer , Russell S. Thomas","doi":"10.1016/j.comtox.2023.100287","DOIUrl":null,"url":null,"abstract":"<div><p>This work estimates benchmarks for new approach method (NAM)<!--> <!-->performance in predicting<!--> <!-->organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the<!--> <!-->Toxicity<!--> <!-->Reference database (v2.1)<!--> <!-->for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, defined<!--> <!-->by<!--> <!-->repeated chemical only, chemical and species, or chemical and study type, were calculated. Concordance<!--> <!-->was 39–88%, depending on organ, and was highest within species.<!--> <!-->Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) values<!--> <!-->when available, was calculated by organ. Multilinear regression modeling,<!--> <!-->using<!--> <!-->study descriptors<!--> <span>of organ-level effect values as covariates<span>, was used to estimate total variance, mean square error</span></span> <!-->(MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggest<!--> <!-->study<!--> <!-->descriptors<!--> <!-->accounted<!--> <!-->for<!--> <!-->52–69% of total<!--> <!-->variance in<!--> <!-->organ-level<!--> <!-->LELs.<!--> <!-->RMSE ranged from<!--> <!-->0.41 to 0.68 log<sub>10</sub>-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from − 0.38 to − 0.19 log<sub>10</sub> <!-->mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, <em>in vitro</em> to <em>in vivo</em> extrapolation (IVIVE) was employed to compare bioactive concentrations from <em>in vitro</em> NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log<sub>10</sub>-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log<sub>10</sub>-mg/kg/day, with qualitative accuracy not exceeding 70%.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111323000282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 1
Abstract
This work estimates benchmarks for new approach method (NAM) performance in predicting organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the Toxicity Reference database (v2.1) for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, defined by repeated chemical only, chemical and species, or chemical and study type, were calculated. Concordance was 39–88%, depending on organ, and was highest within species. Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) values when available, was calculated by organ. Multilinear regression modeling, using study descriptors of organ-level effect values as covariates, was used to estimate total variance, mean square error (MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggest study descriptors accounted for 52–69% of total variance in organ-level LELs. RMSE ranged from 0.41 to 0.68 log10-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from − 0.38 to − 0.19 log10 mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, in vitro to in vivo extrapolation (IVIVE) was employed to compare bioactive concentrations from in vitro NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log10-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log10-mg/kg/day, with qualitative accuracy not exceeding 70%.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs