Placebo effects in osteoarthritis: implications for treatment and drug development

医学 安慰剂 临床试验 骨关节炎 背景(考古学) 疾病 安慰剂反应 重症监护医学 物理疗法 替代医学 内科学 病理 古生物学 生物
作者
Tuhina Neogi,Luana Colloca
出处
期刊:Nature Reviews Rheumatology [Nature Portfolio]
卷期号:19 (10): 613-626 被引量:45
标识
DOI:10.1038/s41584-023-01021-4
摘要

Osteoarthritis (OA) is the most common form of arthritis worldwide, affecting ~500 million people, yet there are no effective treatments to halt its progression. Without any structure-modifying agents, management of OA focuses on ameliorating pain and improving function. Treatment approaches typically have modest efficacy, and many patients have contraindications to recommended pharmacological treatments. Drug development for OA is hindered by the gradual and progressive nature of the disease and the targeting of established disease in clinical trials. Additionally, new medications for OA cannot receive regulatory approval without demonstrating improvements in both structure (pathological features of OA) and symptoms (reduced pain and/or improved function). In clinical trials, people with OA show high ‘placebo responses’, which hamper the ability to identify new effective treatments. Placebo responses refer to the individual variability in response to placebos given in the context of clinical trials and other settings. Placebo effects refer specifically to short-lasting improvements in symptoms that occur because of physiological changes. To mitigate the effects of the placebo phenomenon, we must first understand what it is, how it manifests, how to identify placebo responders in OA trials and how these insights can be used to improve clinical trials in OA. Leveraging placebo responses and effects in clinical practice might provide additional avenues to augment symptom management of OA. Placebo responses are prevalent in clinical trials for osteoarthritis, and they hinder the identification of effective new treatments. In this Review, Colloca and Neogi describe various aspects of the placebo phenomenon, and demonstrate how this knowledge can help to improve the design of clinical trials.
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