Dataset Information

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

ABSTRACT: The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R² of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.

SUBMITTER: Basant N

PROVIDER: S-EPMC6062388 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

Basant Nikita N Gupta Shikha S Singh Kunwar P KP

Toxicology research 20160426 4

The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidel ...[more]

PMID: 30090410

Similar Datasets

Project description:The experimental determination of the developmental toxicity potential (LEL) of chemicals is not only tedious, time and resource intensive, but it also involves unethical tests on animals. In this study, we have established quantitative structure activity relationship (QSAR) models for predicting the developmental toxicity potential of chemicals in rodents following the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) based local (L-QSAR), global (G-QSAR) and interspecies quantitative structure activity-activity relationship (ISC QSAAR) models were developed for estimating the LEL (lowest effective level) dose of chemicals for developmental toxicity in rats and rabbits. The structural features of chemicals responsible for developmental toxicity in rodents were extracted and used in QSAR/QSAAR analysis. The external predictive power of the developed models was evaluated through the internal and external validation procedures. In test data, the L-QSAR models (DTF, DTB) yielded R2 values of >0.846 (rat) and >0.906 (rabbit), whereas in G-QSAR, the correlation value was >0.870 between the measured and predicted endpoint values. In ISC QSAAR models, the R2 values in test data were 0.830 (DTF) and 0.927 (DTB), respectively. Values of various statistical validation coefficients derived from the test data (except rm2 in DTF based rat L-QSAR and ISC QSAAR models) were above their respective threshold limits, thus putting a high confidence in this analysis. The prediction quality of the developed QSAR/QSAAR models was also assessed using the mean absolute error (MAE) criteria and found good. The applicability domains of the constructed models were defined using the descriptor range, leverage, and standardization approaches. The results suggest that the developed QSAR/QSAAR models can reliably predict the developmental toxicity potential of structurally diverse chemicals in rodents, generating useful toxicity data for risk assessment in humans.

Project description:Spirulina (Spirulina platensis), has numerous health benefits including antioxidant, immunomodulatory, and anti-inflammatory activities, works against heavy metal toxicity, and is often used as a food supplement in human, animals, birds and fishes. This study aimed to evaluate the protective ability of the dietary spirulina against the toxic effects of inorganic arsenic (iAs) on male reproductive parameters in rats. Seventy-two mature Long-Evans male rats, dividing into six groups (T0, T1, T2, T3, T4 and T5) (12 rats/group) were included in this study. The T3, T4 and T5 group rats were treated with three consecutive doses (1.0 g, 1.5 g and 2.0 g/kg feed) of spirulina in feed along with 3.0 mg NaAsO2/kg body weight (BW) in drinking water (DW) daily for 90 days. Each rat of group T1 received NaAsO2 (3.0 mg/kg BW) in DW, and those of T2 group were fed with spirulina (2.0 g/kg feed) daily for 90 days. The rats of group T0 served as the control with normal feed and water. Total arsenic (tAs) contents, reproductive parameters (testicular weight, sperm motility and morphology), and histological changes in the testicles were evaluated in these rats. Arsenic dosing significantly (p=0.003, Kruskal-Wallis test) increased the tAs contents in the testicles, decreased testes weight, sperm morphology and motility compared to the controls. The effect of arsenic dosing was also evidenced by the histological changes like decreased germinal layers in the seminiferous tubules of the treated rats. Moreover, dietary spirulina (2.0 g/kg feed) supplementation significantly (p=0.011, Kruskal-Wallis test) lowered tAs contents in testicles and increases testes weights, sperm motility and morphology. Therefore, spirulina can be used as an effective dietary supplement to ameliorate the adverse effects of arsenic induced reproductive toxicities. However, further study is required to elucidate the underlying molecular mechanisms of reduction of arsenic induced reproductive toxicity by spirulina.

Dataset Information

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

Publications

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets