Using VigiBase to Identify Substandard Medicines: Detection Capacity and Key Prerequisites

BACKGROUND: Substandard medicines, whether the result of intentional manipulation or lack of compliance with good manufacturing practice (GMP) or good distribution practice (GDP), pose a significant potential threat to patient safety. Spontaneous adverse drug reaction reporting systems can contribute to identification of quality problems that cause unwanted and/or harmful effects, and to identification of clusters of lack of efficacy. In 2011, the Uppsala Monitoring Centre (UMC) constructed a novel algorithm to identify reporting patterns suggestive of substandard medicines in spontaneous reporting, and applied it to VigiBase(®), the World Health Organization’s global individual case safety report database. The algorithm identified some historical clusters related to substandard products, which were later able to be confirmed in the literature or by contact with national centres (NCs). As relevant and detailed information is often lacking in the VigiBase reports but might be available at the reporting NC, further evaluation of the algorithm was undertaken with involvement from NCs. OBJECTIVE: To evaluate the effectiveness of an algorithm that identifies clusters of potentially substandard medicines, when these are assessed directly at the NC concerned. METHODS: The algorithm identifies countries and time periods with disproportionately high reporting of product inadequacy. NCs with at least 20 clusters were eligible to participate in the study, and six NCs—those in the Republic of Korea, Malaysia, Singapore, South Africa, the UK and the USA—were selected, taking into account the geographical spread and prevalence of recent clusters. The clusters were systematically assessed at the NCs, following a standardized protocol, and then compiled centrally at the UMC. The clusters were classified as ‘confirmed’, ‘potential’ or ‘unlikely’ substandard products; or as ‘confirmed not substandard’ when confirmed by an investigation; or as ‘indecisive’ when the information available did not allow a sound assessment even at the NC. RESULTS: The assessment of a total of 147 clusters resulted in 8 confirmed, 12 potential and 51 unlikely substandard products, and a further 19 clusters were confirmed as not substandard. Reflecting the difficulty of evaluating suspected substandard products retrospectively when additional information from the primary reporter, as well as samples, are no longer available, 57 clusters were classified as indecisive. CONCLUSION: While application of the algorithm to VigiBase allowed identification of some substandard medicines, some key prerequisites have been identified that need to be fulfilled at the national level for the algorithm to be useful in practice. Such key factors are fast handling and transfer of incoming reports into VigiBase, detailed information on the product and its distribution channels, the possibility of contacting primary reporters for further information, availability of samples of suspected products and laboratory capacity to analyse suspected products. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40264-015-0271-2) contains supplementary material, which is available to authorized users.