Literature DB >> 24096967

Semi-mechanistic modelling of the analgesic effect of gabapentin in the formalin-induced rat model of experimental pain.

A Taneja1, I F Troconiz, M Danhof, O Della Pasqua.   

Abstract

PURPOSE: The formalin-induced rat model of nociception involves moderate continuous pain. Formalin-induced pain results in a typical repetitive flinching behaviour, which displays a biphasic pattern characterised by peaks of pain. Here we described the time course of pain response and the analgesic effect of gabapentin using a semi-mechanistic modelling approach.
METHODS: Male Sprague-Dawley rats received gabapentin (10-100 mg/kg) or placebo 1 h prior to the formalin injection, as per standard protocol. A reduction in the frequency of the second peak of flinching was used as a behavioural measure of gabapentin-mediated anti-nociception. The flinching response was modelled using a mono-exponential function to characterise the first peak and an indirect response model with a time variant synthesis rate for the second. PKPD modelling was performed using a population approach in NONMEM v.7.1.2.
RESULTS: The time course of the biphasic response was adequately described by the proposed model, which included separate expressions for each phase. Gabapentin was found to reversibly decrease, but not suppress the flinching frequency of the second response peak only. The mean IC50 estimate was 7,510 ng/ml, with relative standard error (RSE%) of 40%.
CONCLUSIONS: A compartmental, semi-mechanistic model provides the basis for further understanding of the formalin-induced flinching response and consequently to better characterisation of the properties of gabapentin, such as the potency in individual animals. Moreover, despite high exposure levels, model predictions show that gabapentin does not completely suppress behavioural response in the formalin-induced pain model.

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Year:  2013        PMID: 24096967     DOI: 10.1007/s11095-013-1183-4

Source DB:  PubMed          Journal:  Pharm Res        ISSN: 0724-8741            Impact factor:   4.200


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