Evaluating the Safety Profiles of Withdrawn Medications: A Data-Driven Approach to Adverse Drug Reactions Using Statistical models

Background Adverse drug reactions (ADRs) are critical in evaluating a medicine's safety and effectiveness both during the drug development process and post-marketing surveillance. This research focuses on drugs that had serious adverse drug reactions (ADRs) and were either taken off the market or withdrawn. We try to find to identify trends in ADR reporting following the discontinuation of these medications by applying data visualization techniques to ADR reports obtained from VigiAccess and the FDA's Adverse Event Reporting System (FAERS). We find a variety of trends and contrary to the expected sharp decline in ADR reports post-withdrawal, we observe various trends—sigmoidal, exponential, and linear—which may offer new insights for comparing medication safety. Objectives This study aimed to investigate the patterns of ADR reporting associated with medications that have been withdrawn or banned due to severe adverse reactions. We plan to analyze these patterns and investigate their possible usefulness in evaluating and comparing the safety profiles of various drugs by using data visualization methods. Methods We used publicly available datasets from VigiAccess, the WHO’s global database of reported potential side effects of medicinal products, and the FDA’s Adverse Event Reporting System (FAERS) Public Dashboard. A comprehensive analysis was performed on cumulative count of ADR reports for selected medications that have been withdrawn from the market. The analysis focused on identifying various trends in ADR reporting counts of withdrawal of these medications. For visualization of these trends, we applied various curve-fitting techniques, including linear and non-linear statistical models such as sigmoidal, exponential, and linear forms. Additionally, for comparing the safety profiles of cancer drugs, a ranking was conducted using an exponential growth rate model to assess and contrast their safety dynamics. Results The withdrawn or banned drugs are expected to conclude usage, resulting in a zero cumulative count of adverse drug reactions (ADRs), a pattern expected for all banned drugs. Total 39 drugs analyzed showed various linear and nonlinear patterns, including 17 following a saturation pattern, 10 showing a linear pattern, 7 showing an exponential pattern, and 5 showing a sigmoidal pattern. Examples shown in this study found that drugs Benoxaprofen, Rosiglatazone, Temazepam, and Rofecoxib presented a strong fit with various models, with Benoxaprofen showing a saturating hyperbola model with R² value of 0.98, Rosiglatazone showing a linear model R² value of 0.96, and Temazepam indicating an excellent fit with the exponential model with R² value of 0.99. Rofecoxib followed a sigmoidal pattern with an R² value of 0.92, reflecting a strong fit with the sigmoidal model. For safety comparison of 15 drugs used in cancer treatment the drug Tamoxifen is a safer drug due to its slower ADR accumulation rate and growth rate (i.e., 0.0972), while drug Pembrolizumab has a higher exponential growth rate (i.e., 0.8277), indicating higher associated risks. Conclusion The study demonstrates the value of data visualization in uncovering diverse ADR reporting patterns for withdrawn medications. These patterns offer a novel perspective on post-market drug safety and could serve as a comparative tool for evaluating the safety profiles of various medications. Tamoxifen was found to be safer due to slower ADR accumulation, while Pembrolizumab presented greater hazards. These findings provide valuable insights into drug safety.