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American Society for Pharmacology and Experimental Therapeutics (ASPET), Drug Metabolism and Disposition, 12(39), p. 2275-2282, 2011

DOI: 10.1124/dmd.111.040782

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Systemic and Direct Nose-to-Brain Transport Pharmacokinetic Model for Remoxipride after Intravenous and Intranasal Administration

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Intranasal (IN) administration could be an attractive mode of delivery for drugs targeting the central nervous system, potentially providing a high bioavailability because of avoidance of a hepatic first-pass effect and rapid onset of action. However, controversy remains whether a direct transport route from the nasal cavity into the brain exists. Pharmacokinetic modeling is proposed to identify the existence of direct nose-to-brain transport in a quantitative manner. The selective dopamine-D2 receptor antagonist remoxipride was administered at different dosages, in freely moving rats, by the IN and intravenous (IV) route. Plasma and brain extracellular fluid (ECF) concentration-time profiles were obtained and simultaneously analyzed using nonlinear mixed-effects modeling. Brain ECF/plasma area under the curve ratios were 0.28 and 0.19 after IN and IV administration, respectively. A multicompartment pharmacokinetic model with two absorption compartments (nose-to-systemic and nose-to-brain) was found to best describe the observed pharmacokinetic data. Absorption was described in terms of bioavailability and rate. Total bioavailability after IN administration was 89%, of which 75% was attributed to direct nose-to brain transport. Direct nose-to-brain absorption rate was slow, explaining prolonged brain ECF exposure after IN compared with IV administration. These studies explicitly provide separation and quantitation of systemic and direct nose-to-brain transport after IN administration of remoxipride in the rat. Describing remoxipride pharmacokinetics at the target site (brain ECF) in a semiphysiology-based manner would allow for better prediction of pharmacodynamic effects.