he fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) – European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on “Physiologically-Based Kinetic modelling in risk assessment – reaching a whole new level in regulatory decision-making” held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.
Paini, A., Leonard, J. A., Joossens, E., Bessems, J. G. M., Desalegn, A., Dorne, J. L., Gosling, J. P., Heringa, M. B., Klaric, M., Kliment, T., Kramer, N. I., Loizou, G., Louisse, J., Lumen, A., Madden, J. C., Patterson, E. A., Proença, S., Punt, A., Setzer, R. W., Suciu, N., Troutman, J., Yoon, M., Worth, A., Tan, Y. M., Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making, <<COMPUTATIONAL TOXICOLOGY>>, n; 9 (n): 61-72. [doi:10.1016/j.comtox.2018.11.002] [http://hdl.handle.net/10807/127488]
Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making
Suciu, Nicoleta;
2019
Abstract
he fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) – European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on “Physiologically-Based Kinetic modelling in risk assessment – reaching a whole new level in regulatory decision-making” held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.
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