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Application of in vitro data in physiologically-based kinetic models for quantitative in vitro-in vivo extrapolation: A case-study for baclofen

Abstract : Physiologically-based kinetic (PBK) models can simulate concentrations of chemicals in tissues over time without animal experiments. Nevertheless, in vivo data are often used to parameterise PBK models. This study aims to illustrate that a combination of kinetic and dynamic readouts from in vitro assays can be used to parameterise PBK models simulating neurologically-active concentrations of xenobiotics. Baclofen, an intrathecally administered drug to treat spasticity, was used as a proof-of-principle xenobiotic. An in vitro blood-brain barrier (BBB) model was used to determine the BBB permeability of baclofen needed to simulate plasma and cerebrospinal concentrations. Simulated baclofen concentrations in individuals and populations of adults and children generally fall within 2-fold of measured clinical study concentrations. Further, in vitro micro-electrode array recordings were used to determine the effect of baclofen on neuronal activity (cell signalling). Using quantitative in vitro-in vivo extrapolations (QIVIVE) corresponding doses of baclofen were estimated. QIVIVE showed that up to 4600 times lower intrathecal doses than oral and intravenous doses induce comparable neurological effects. Most simulated doses were in the range of administered doses. This show that PBK models predict concentrations in the central nervous system for various routes of administration accurately without the need for additional in vivo data.
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https://hal-anses.archives-ouvertes.fr/anses-03317990
Contributor : Christine Druesne <>
Submitted on : Monday, August 9, 2021 - 9:34:50 AM
Last modification on : Tuesday, August 10, 2021 - 3:31:03 AM

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Emma E.J. Kasteel, Leonie Lautz, Maxime Culot, Nynke Kramer, Anne Zwartsen. Application of in vitro data in physiologically-based kinetic models for quantitative in vitro-in vivo extrapolation: A case-study for baclofen. Toxicology in Vitro, Elsevier, 2021, 76, pp.105223. ⟨10.1016/j.tiv.2021.105223⟩. ⟨anses-03317990⟩

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