Approaches to Assessing the Safety of Medicines during the COVID-19 Pandemic Using the Example of Azithromycin

Abstract

Most of the medicines used to treat the novel coronavirus infection (COVID-19) are either approved under an accelerated procedure or not approved for the indication. Consequently, their safety requires special attention.The aim of the study was to review methodological approaches to collecting data on the safety of medicines, using COVID-19 treatment regimens involving azithromycin as a case study.Materials and methods: PubMed® (MEDLINE), Scopus, eLIBRARY, and Cyberleninka databases were searched for publications on azithromycin as part of combination therapy for COVID-19 in 2020–2021. Search queries included names of the medicinal product or its pharmacotherapeutic group and words describing adverse drug reactions (ADRs) during treatment.Results: the analysis included 7 publications presenting the results of studies covering the use of azithromycin as part of COVID-19 combination therapy in more than 4000 patients. Most commonly, the patients receiving COVID-19 therapy including azithromycin developed cardiovascular ADRs (up to 30% of azithromycin prescription cases). In 3 of the analysed publications, safety information was collected through spontaneous reporting and active identification based on the findings of laboratory and instrumental investigations performed during the clinical studies; in other 3, only spontaneous reports were used; and in the last one, ADR database information was studied.Conclusion: currently, information on ADRs associated with the use of medicines is mainly gathered via spontaneous reporting. Direct sourcing of information on personal experiences with a certain product from patients, among other means through social media analysis, opens a promising direction towards the improvement of existing approaches to collecting safety data.

Keywords

• pharmacovigilance
• adverse drug reactions
• spontaneous reports
• azithromycin
• novel coronavirus infection
• covid-19
• database
• social networks
• patient community