Personalized Medicine and Opioid Analgesic Prescribing for Chronic Pain: Opportunities and Challenges

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• 作者：Stephen Bruehl^&lowast ; ; ^{stephen.bruehl@vanderbilt.edu} ; A. Vania Apkarian^&dagger ; ; Jane C. Ballantyne^&Dagger ; ; Ann Berger^§ ; ; David Borsook^¡¬ ; Wen G. Chen^&para ; ; John T. Farrar^# ; Jennifer A. Haythornthwaite^&lowast ; &lowast ; ; Susan D. Horn^&dagger ; &dagger ; ; Michael J. Iadarola^&Dagger ; &Dagger ; ; Charles E. Inturrisi^§ ; § ; ; Lixing Lao^¡¬¡¬ ; Sean Mackey^&para ; &para ; ; Jianren Mao^## ; Andrea Sawczuk^&lowast ; &lowast ; &lowast ; ; George R. Uhl^&dagger ; &dagger ; &dagger ; ; James Witter^&Dagger ; &Dagger ; &Dagger ; ; Clifford J. Woolf^§ ; § ; § ; ; Jon-Kar Zubieta^¡¬¡¬¡¬ ; Yu Lin^&dagger ; &dagger ; &dagger ;
• 关键词：Opioid analgesics ; chronic pain ; personalized medicine ; side effects ; opioid abuse
• 刊名：The Journal of Pain
• 出版年：2013
• 出版时间：February, 2013
• 年：2013
• 卷：14
• 期：2
• 页码：103-113
• 全文大小：306 K

文摘

Use of opioid analgesics for pain management has increased dramatically over the past decade, with corresponding increases in negative sequelae including overdose and death. There is currently no well-validated objective means of accurately identifying patients likely to experience good analgesia with low side effects and abuse risk prior to initiating opioid therapy. This paper discusses the concept of data-based personalized prescribing of opioid analgesics as a means to achieve this goal. Strengths, weaknesses, and potential synergism of traditional randomized placebo-controlled trial (RCT) and practice-based evidence (PBE) methodologies as means to acquire the clinical data necessary to develop validated personalized analgesic-prescribing algorithms are overviewed. Several predictive factors that might be incorporated into such algorithms are briefly discussed, including genetic factors, differences in brain structure and function, differences in neurotransmitter pathways, and patient phenotypic variables such as negative affect, sex, and pain sensitivity. Currently available research is insufficient to inform development of quantitative analgesic-prescribing algorithms. However, responder subtype analyses made practical by the large numbers of chronic pain patients in proposed collaborative PBE pain registries, in conjunction with follow-up validation RCTs, may eventually permit development of clinically useful analgesic-prescribing algorithms.

Perspective

Current research is insufficient to base opioid analgesic prescribing on patient characteristics. Collaborative PBE studies in large, diverse pain patient samples in conjunction with follow-up RCTs may permit development of quantitative analgesic-prescribing algorithms that could optimize opioid analgesic effectiveness and mitigate risks of opioid-related abuse and mortality.