Pharmacovigilance of Herbal Medicines

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Typically, ethnobotanical/ethnopharmacological (EB/EP) surveys are used to describe uses, doses/dosages, sources and methods of preparation of traditional herbal medicines; their application to date in examining the adverse effects, contraindications and other safety aspects of these preparations is limited. From a pharmacovigilance perspective, numerous challenges exist in applying its existing methods to studying the safety profile of herbal medicines, particularly where used by indigenous cultures. This paper aims to contribute to the methodological aspects of EB/EP field work, and to extend the reach of pharmacovigilance, by proposing a tool comprising a list of questions that could be applied during interview and observational studies. The questions focus on the collection of information on the safety profile of traditional herbal medicines as it is embedded in traditional knowledge, as well as on identifying personal experiences (spontaneous reports) of adverse or undesirable effects associated with the use of traditional herbal medicines. Questions on the precise composition of traditional prescriptions or ‘recipes’, their preparation, storage, administration and dosing are also included. Strengths and limitations of the tool are discussed. From this interweaving of EB/EP and pharmacovigilance arises a concept of ethnopharmacovigilance for traditional herbal medicines: the scope of EB/EP is extended to include exploration of the potential harmful effects of medicinal plants, and the incorporation of pharmacovigilance questions into EB/EP studies provides a new opportunity for collection of ‘general’ traditional knowledge on the safety of traditional herbal medicines and, importantly, a conduit for collection of spontaneous reports of suspected adverse effects. Whether the proposed tool can yield data sufficiently rich and of an appropriate quality for application of EB/EP (e.g. data verification and quantitative analysis tools) and pharmacovigilance techniques (e.g. causality assessment and data mining) requires field testing.

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