Abstract
Studies establishing the use of new antidepressants often rely simply on proving efficacy of a new compound, comparing against placebo and single compound. The advent of large online databases in which patients themselves rate drugs allows for a new Big Data–driven approach to compare the efficacy and patient satisfaction with sample sizes exceeding previous studies. Exemplifying this approach with antidepressants, we show that patient satisfaction with a drug anticorrelates with its release date with high significance, across different online user‐driven databases. This finding suggests that a systematic reevaluation of current, often patent‐protected drugs compared to their older predecessors may be helpful, especially given that the efficacy of newer agents relative to older classes of antidepressants such as monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs) is as yet quantitatively unexplored.
Highlights
Direct comparisons between medications are commonly expensive and time-consuming endeavors, even though they are of significant economic importance (Zentner et al 2005; Clement et al 2009)
Pharmacology Research & Perspectives published by John Wiley & Sons Ltd, British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics
We demonstrate that large online databases can be used to perform low-cost comparisons between a large number of different drugs
Summary
Direct comparisons between medications are commonly expensive and time-consuming endeavors, even though they are of significant economic importance (Zentner et al 2005; Clement et al 2009). The FDA approval process for new medication focuses on efficacy over placebo, or at best compared to a selected previous treatment (Pande et al 1996), rather than noninferiority to the whole set of previously established treatments (Hanrahan and New 2014). Even using such an uncomplicated criterion, the data gathering of the phase II and phase III trials that establish safety and efficacy remains the most expensive bottleneck in the drug development pipeline (Sertkaya et al 2016).
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