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Article: Personalized medicine and opioid analgesic prescribing for chronic pain: Opportunities and challenges

TitlePersonalized medicine and opioid analgesic prescribing for chronic pain: Opportunities and challenges
Authors
Issue Date2013
PublisherChurchill Livingstone. The Journal's web site is located at http://www.elsevier.com/locate/jpain
Citation
Journal Of Pain, 2013, v. 14 n. 2, p. 103-113 How to Cite?
AbstractUse 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. © 2013 by the American Pain Society.
Persistent Identifierhttp://hdl.handle.net/10722/188662
ISSN
2015 Impact Factor: 4.463
2015 SCImago Journal Rankings: 2.124
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBruehl, Sen_US
dc.contributor.authorApkarian, AVen_US
dc.contributor.authorBallantyne, JCen_US
dc.contributor.authorBerger, Aen_US
dc.contributor.authorBorsook, Den_US
dc.contributor.authorChen, WGen_US
dc.contributor.authorFarrar, JTen_US
dc.contributor.authorHaythornthwaite, JAen_US
dc.contributor.authorHorn, SDen_US
dc.contributor.authorIadarola, MJen_US
dc.contributor.authorInturrisi, CEen_US
dc.contributor.authorLao, Len_US
dc.contributor.authorMackey, Sen_US
dc.contributor.authorMao, Jen_US
dc.contributor.authorSawczuk, Aen_US
dc.contributor.authorUhl, GRen_US
dc.contributor.authorWitter, Jen_US
dc.contributor.authorWoolf, CJen_US
dc.contributor.authorZubieta, JKen_US
dc.contributor.authorLin, Yen_US
dc.date.accessioned2013-09-03T04:10:55Z-
dc.date.available2013-09-03T04:10:55Z-
dc.date.issued2013en_US
dc.identifier.citationJournal Of Pain, 2013, v. 14 n. 2, p. 103-113en_US
dc.identifier.issn1526-5900en_US
dc.identifier.urihttp://hdl.handle.net/10722/188662-
dc.description.abstractUse 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. © 2013 by the American Pain Society.en_US
dc.languageengen_US
dc.publisherChurchill Livingstone. The Journal's web site is located at http://www.elsevier.com/locate/jpainen_US
dc.relation.ispartofJournal of Painen_US
dc.subject.meshAnalgesics, Opioid - Therapeutic Useen_US
dc.subject.meshBiological Markersen_US
dc.subject.meshBiomedical Researchen_US
dc.subject.meshChronic Pain - Drug Therapy - Genetics - Psychologyen_US
dc.subject.meshDrug Prescriptionsen_US
dc.subject.meshDrug Synergismen_US
dc.subject.meshGenetic Variationen_US
dc.subject.meshHumansen_US
dc.subject.meshIndividualized Medicine - Methodsen_US
dc.subject.meshNeurotransmitter Agents - Metabolism - Physiologyen_US
dc.subject.meshRandomized Controlled Trials As Topicen_US
dc.titlePersonalized medicine and opioid analgesic prescribing for chronic pain: Opportunities and challengesen_US
dc.typeArticleen_US
dc.identifier.emailLao, L: lxlao1@hku.hken_US
dc.identifier.authorityLao, L=rp01784en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.jpain.2012.10.016en_US
dc.identifier.pmid23374939-
dc.identifier.scopuseid_2-s2.0-84873304881en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84873304881&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume14en_US
dc.identifier.issue2en_US
dc.identifier.spage103en_US
dc.identifier.epage113en_US
dc.identifier.isiWOS:000314856600001-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridBruehl, S=7003821589en_US
dc.identifier.scopusauthoridApkarian, AV=7003265031en_US
dc.identifier.scopusauthoridBallantyne, JC=7103216757en_US
dc.identifier.scopusauthoridBerger, A=55506294800en_US
dc.identifier.scopusauthoridBorsook, D=7004519125en_US
dc.identifier.scopusauthoridChen, WG=35078238000en_US
dc.identifier.scopusauthoridFarrar, JT=55357606400en_US
dc.identifier.scopusauthoridHaythornthwaite, JA=7003968230en_US
dc.identifier.scopusauthoridHorn, SD=55578769700en_US
dc.identifier.scopusauthoridIadarola, MJ=7006175753en_US
dc.identifier.scopusauthoridInturrisi, CE=7006080961en_US
dc.identifier.scopusauthoridLao, L=7005681883en_US
dc.identifier.scopusauthoridMacKey, S=7006089174en_US
dc.identifier.scopusauthoridMao, J=55578253400en_US
dc.identifier.scopusauthoridSawczuk, A=55578161600en_US
dc.identifier.scopusauthoridUhl, GR=7102216410en_US
dc.identifier.scopusauthoridWitter, J=7003711812en_US
dc.identifier.scopusauthoridWoolf, CJ=7102854712en_US
dc.identifier.scopusauthoridZubieta, JK=36038261500en_US
dc.identifier.scopusauthoridLin, Y=52463907000en_US

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