Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid
| Autor: | Morihiko Hirota, Maki Aiba née Kaneko, Tomoka Hisaki, Masato Matsuoka, Hirokazu Kouzuki |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Quantitative structure–activity relationship In silico Developmental toxicity Quantitative Structure-Activity Relationship 010501 environmental sciences Pharmacology Biology Toxicology 030226 pharmacology & pharmacy 01 natural sciences 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine In vivo medicine Humans 0105 earth and related environmental sciences No-Observed-Adverse-Effect Level Valproic Acid Systemic toxicity chemistry Toxicity Female Forecasting Toxicant medicine.drug |
| Zdroj: | The Journal of Toxicological Sciences. 45:95-108 |
| ISSN: | 1880-3989 0388-1350 |
| DOI: | 10.2131/jts.45.95 |
| Popis: | We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals. |
| Databáze: | OpenAIRE |
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