Drug Safety of Antihypertensive Medicines Using Pharmacovigilance Data: Signal Management Through Traditional Methods Versus Process Automation

Abstract

Hypertension remains a leading etiological agent of cardiovascular morbidity and mortality worldwide, thus requiring vigorous marketing pharmacovigilance of anti-hypertensive drugs. In the current study, the conventional approaches of signal detection used manually were compared with the processing automation procedure using extensive actual data accumulated using the FDA Adverse Event Reporting System (FAERS) during the past years, especially selected representative therapeutics, amlodipine, ramipril, and minoxidil. The old paradigms were based on manual case assessments together with disproportion measures (mainly the Reporting Odds Ratio (ROR)) and supplemented with expert clinical adjudication. Conversely, the automated model used a scripted pipeline, which included data cleaning, natural-language processing, automated codification and derivation of statistical signals. A mixed system was employed, combining a pre-screening with an expert validation as a requirement.

The automated approach increased the range of detections and the efficiency of operations and, at the same time, increased sensitivity, but at the cost of a higher rate of possible false-positive indications. The hybrid scheme was the most balanced, which offered the maximum credible signal capture, better sensitivity on genuine pharmacological matters, high specificity persistence, and equal accompanying noise decrease.

These results support the idea that hybrid signal management systems, which consist of automated routine and high-volume processes with domain knowledge to make conclusive judgments, is optimal, scalable, and regulatory compliant to the current condition of pharmacovigilance, especially in resource-limited environments.

Conclusion: This comparative evaluation of the signal detection methodologies of anti-hypertensive drugs gives a clear outline of the supplementary benefits of both the traditional and automated application techniques. Hybrid models are therefore an evidence based and pragmatic step towards modern signal management especially relevant in the changing pharmacovigilance set ups of India and other regions that face similar data and resource bottlenecks.