St. Mary Mercy Hospital, Saint Joseph Mercy Health System, Livonia, Michigan.
Technische Universität München, Institute for Computational Mechanics, Garching, Germany.
Pharmacol Res Perspect. 2017 Oct;5(5). doi: 10.1002/prp2.355.
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.
研究证明新型抗抑郁药的应用通常仅仅依靠证明新化合物的疗效，将其与安慰剂和单一化合物进行比较。大型在线数据库的出现使得患者自己可以对药物进行评分，从而为比较疗效和患者满意度提供了一种新的大数据驱动方法，其样本量超过了以往的研究。我们以抗抑郁药为例，展示了不同的在线用户驱动数据库中，药物的患者满意度与药物的发布日期呈高度负相关。这一发现表明，与它们的老一代药物相比，对当前经常受专利保护的药物进行系统的重新评估可能是有帮助的，尤其是考虑到新型药物相对于单胺氧化酶抑制剂 (MAOIs) 和三环类抗抑郁药 (TCAs) 等老一代抗抑郁药的疗效在数量上还尚未得到探索。
