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<p>Next generation risk assessment (NGRA) has substantially contributed to development of animal-free chemical risk assessment by integrating new approach methodologies. The present study aimed to further expand the applicability of NGRA to compounds with bioactive metabolites, using dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP) as case studies. Physiologically based kinetic (PBK) models were developed for DBP, DEHP and their primary metabolites mono-butyl phthalate and mono-(2-ethylhexyl) phthalate by using in silico and in vitro-derived parameters, including measured metabolic clearance obtained by performing in vitro liver microsomal incubations. Models were evaluated against observed kinetic data and used to predict plasma maximum concentrations (C<sub>max</sub>) from external exposure scenarios, which were further compared with ToxCast-derived points of departure (PoDs) to calculate bioactivity-exposure ratios (BERs) for risk classification. Results showed that the in <i>silico</i>-in vitro based PBK models accurately predicted plasma kinetics for the metabolites within 2-fold difference. Predicted C<sub>max</sub> values for parent compounds across all exposure scenarios were much lower than minimal PoDs, resulting in BERs higher than 20,000. When metabolites were considered, BERs decreased markedly with the lowest value of 8.1, highlighting the importance of accounting for metabolites for phthalates. Our study provides a proof-of-principle for incorporating metabolites into PBK modelling and corresponding NGRA, enhancing its applicability to chemicals with bioactive metabolites.</p>

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PBK modelling of phthalates and their metabolites and the application in next generation risk assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Miaoying Shi,
  • Ans Punt,
  • Gopal Pawar,
  • Shuo Yang,
  • Hui Yang,
  • Xudong Jia,
  • Dawei Tang

摘要

Next generation risk assessment (NGRA) has substantially contributed to development of animal-free chemical risk assessment by integrating new approach methodologies. The present study aimed to further expand the applicability of NGRA to compounds with bioactive metabolites, using dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP) as case studies. Physiologically based kinetic (PBK) models were developed for DBP, DEHP and their primary metabolites mono-butyl phthalate and mono-(2-ethylhexyl) phthalate by using in silico and in vitro-derived parameters, including measured metabolic clearance obtained by performing in vitro liver microsomal incubations. Models were evaluated against observed kinetic data and used to predict plasma maximum concentrations (Cmax) from external exposure scenarios, which were further compared with ToxCast-derived points of departure (PoDs) to calculate bioactivity-exposure ratios (BERs) for risk classification. Results showed that the in silico-in vitro based PBK models accurately predicted plasma kinetics for the metabolites within 2-fold difference. Predicted Cmax values for parent compounds across all exposure scenarios were much lower than minimal PoDs, resulting in BERs higher than 20,000. When metabolites were considered, BERs decreased markedly with the lowest value of 8.1, highlighting the importance of accounting for metabolites for phthalates. Our study provides a proof-of-principle for incorporating metabolites into PBK modelling and corresponding NGRA, enhancing its applicability to chemicals with bioactive metabolites.