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

JOURNAL OF PAIN

卷:14

Article

Bruehl, Stephen¹  Apkarian, A. Vania²  Ballantyne, Jane C.³  Berger, Ann⁴  Borsook, David⁵  Chen, Wen G.⁶  Farrar, John T.⁷  Haythornthwaite, Jennifer A.⁸  Horn, Susan D.⁹  Iadarola, Michael J.¹⁰  Inturrisi, Charles E.¹¹  Lao, Lixing¹²  Mackey, Sean¹³  Mao, Jianren¹⁴  Sawczuk, Andrea¹⁵  Witter, James¹⁶  Woolf, Clifford J.¹⁷  Zubieta, Jon-Kar¹⁸ 

[1] Vanderbilt Univ, Sch Med, Dept Anesthesiol, Nashville, TN 37212 USA

[2] Northwestern Univ, Sch Med, Dept Physiol, Chicago, IL 60611 USA

[3] Univ Washington, Sch Med, Dept Anesthesiol & Pain Med, Seattle, WA USA

[4] NIH, Pain & Palliat Care Serv, Ctr Clin, Bethesda, MD 20892 USA

[5] Harvard Univ, Sch Med, Dept Psychiat, Boston, MA 02115 USA

[6] NIA, NIH, Bethesda, MD 20892 USA

[7] Univ Penn, Sch Med, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA

[8] Johns Hopkins Univ, Sch Med, Dept Psychiat & Behav Sci, Baltimore, MD 21205 USA

[9] Int Sever Informat Syst, Inst Clin Outcomes Res, Salt Lake City, UT USA

[10] Natl Inst Dent & Craniofacial Res, NIH, Bethesda, MD USA

[11] Cornell Univ, Weill Med Coll, Dept Pharmacol, New York, NY 10021 USA

[12] Univ Maryland, Sch Med, Dept Family & Community Med, Baltimore, MD 21201 USA

[13] Stanford Univ, Sch Med, Dept Anesthesia, Stanford, CA 94305 USA

[14] Harvard Univ, Sch Med, Dept Anesthesia, Boston, MA 02115 USA

[15] NIH, Natl Ctr Adv Translat Sci, Bethesda, MD 20892 USA

[16] NIAMSD, NIH, Bethesda, MD 20892 USA

[17] Harvard Univ, Sch Med, Dept Neurol & Neurobiol, Boston, MA USA

[18] Univ Michigan, Dept Psychiat, Mol & Behav Neurosci Inst, Ann Arbor, MI 48109 USA

关键词: Opioid analgesics;    chronic pain;    personalized medicine;    side effects;    opioid abuse;   

DOI  :  10.1016/j.jpain.2012.10.016

来源: Elsevier

【 摘 要 】

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. (C) 2013 by the American Pain Society. Published by Elsevier Inc. All rights reserved

【 授权许可】

Free