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Central Mechanisms II: Pharmacology of Brainstem Pathways

  • D. C. Bolser
Part of the Handbook of Experimental Pharmacology book series (HEP, volume 187)

Abstract

Following systemic administration, centrally acting antitussive drugs are generally assumed to act in the brainstem to inhibit cough. However, recent work in humans has raised the possibility of suprapontine sites of action for cough suppressants. For drugs that may act in the brainstem, the specific locations, types of neurones affected, and receptor specificities of the compounds represent important issues regarding their cough-suppressant actions. Two medullary areas that have received the most attention regarding the actions of antitussive drugs are the nucleus of the tractus solitarius (NTS) and the caudal ventrolateral respiratory column. Studies that have implicated these two medullary areas have employed both microinjection and in vitro recording methods to control the location of action of the antitussive drugs. Other brainstem regions contain neurones that participate in the production of cough and could represent potential sites of action of antitussive drugs. These regions include the raphe nuclei, pontine nuclei, and rostral ventrolateral medulla. Specific receptor subtypes have been associated with the suppression of cough at central sites, including 5-HT1A, opioid (μ, κ, and δ), GABA-B, tachykinin neurokinin-1 (NK-1) and neurokinin-2, non-opioid (NOP-1), cannabi-noid, dopaminergic, and sigma receptors. Aside from tachykinin NK-1 receptors in the NTS, relatively little is known regarding the receptor specificity of putative antitussive drugs in particular brainstem regions. Our understanding of the mechanisms of action of antitussive drugs would be significantly advanced by further work in this area.

Keywords

Nucleus Tractus Solitarii Cough Suppressant Antitussive Effect Brainstem Pathway Antitussive Drug 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • D. C. Bolser
    • 1
  1. 1.Department of Physiological Sciences, University of FloridaCollege of Veterinary MedicineGainesvilleUSA

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