Postmarket surveillance: a review on key aspects and measures on the effective functioning in the context of the United Kingdom and Canada

Abstract

Regulatory approvals for the marketing of medicinal products authorize medical practitioners to prescribe drugs to a group of patients that are defined within the license of the medicinal product. However, such prescriptions are carried out in a controlled manner. Prior to being approved, the medicinal product will have been evaluated in a population pool containing fewer than 5,000 patients and in a predesigned environment where several factors may be lacking, such as the absence of women of childbearing potential, geriatric patients and paediatric patients. Therefore, it is not surprising that several major adverse drug reactions are detected only when the product has been prescribed to the general population. National and international regulatory bodies have devised systems for monitoring medicinal products after marketing, commonly known as postmarketing surveillance systems. Postmarketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. The primary purpose for conducting postmarketing surveillance is to identify previously unrecognized adverse effects as well as positive effects. The Yellow Card scheme, practiced in the United Kingdom and the Canada Vigilance Program adopted in the Canadian jurisdiction, are two of the most successful postmarketing surveillance systems implemented across the world. Therefore, this article intends to discuss postmarketing surveillance and its role in the context of the United Kingdom and Canadian jurisdictions with a view on presenting key aspects and measures that are employed for operating an efficient postmarketing surveillance system in regulated markets.

A systematic review of effectiveness of BNT162b2 mRNA and ChAdOx1 adenoviral vector COVID-19 vaccines in the general population

“…Hence, pharmacovigilance of the rolled out vaccines is a critical need for evaluating its effectiveness across all groups including older persons. This post marketing surveillance of new drugs is a critical aspect of evaluating medicine safety and effectiveness, particularly in groups that are usually considered ineligible for inclusion in Phase 2 and 3 trials (Raj et al 2019 ). Real world effectiveness of available vaccines against a range of SARS-CoV-2 outcomes is crucial for the determination of health impacts of the vaccines.…”

Relevance of physicochemical properties and functional pharmacology data to predict the clinical safety profile of direct oral anticoagulants

“…The Yellow Card Scheme run by the Medicines and Healthcare Products Regulatory Agency (MHRA), administers the collection of data relating to suspected ADRs in the United Kingdom. It was devised to improve patient safety and identification of adverse patient events in 1964 in the wake of the thalidomide disaster 5 . Adverse patient event data were collected from the Yellow Card Scheme Interactive Drug Analysis Profiles web portal (https://yellowcard.mhra.gov.uk/iDAP/) up to November 2018 36 .…”

“…It was devised to improve patient safety and identification of adverse patient events in 1964 in the wake of the thalidomide disaster. 5 Adverse patient event data were collected from the Yellow Card Scheme Interactive Drug Analysis Profiles web portal (https://yello wcard.mhra.gov.uk/iDAP/) up to November 2018. 36 The spontaneous reporting system had processed information from 2008 up to October 2018.…”

“…Furthermore, the occurrence of off‐target ADRs are not predictable based on the drug's specific therapeutic effect 1 . Therefore, postmarketing surveillance (PMR) schemes have been introduced in many countries and internationally to improve patient safety by identifying previously unrecognized ADRs, drug interactions, and patient subgroup vulnerability 3‐5 . However, these schemes are reactive rather than proactive.…”

Predicting adverse drug reactions of two‐drug combinations using structural and transcriptomic drug representations to train an artificial neural network

“…If each of the drugs contributes to the cumulative probability of the observed ADR, the overall risk due to multiple drugs increases. To compound this problem, it is believed that 90% of ADRs go unreported (Raj et al, 2019) .…”