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

Abstract

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 which could optimize opioid analgesic effectiveness, and mitigate risks of opioid-related abuse and mortality.

Self-reported cumulative medical opioid exposure and subjective responses on first use of opioids predict analgesic and subjective responses to placebo-controlled opioid administration

“…It seems unlikely that this moderate level of prediction using the HOME, by itself, would be sufficient to accurately guide opioid therapy in clinical settings. It is more likely that a predictive algorithm incorporating multiple measures predictive of opioid responses (eg, the HOME, negative affect measures, evoked pain responsiveness, genetic markers) may be necessary to predict opioid responses to a more clinically meaningful extent 4. This predictive approach using a phenotypic/genotypic algorithm avoids the undesirable alternative of directly evaluating analgesic and subjective responsiveness to a test dose of opioids in clinical settings that might otherwise be required to guide treatment decisions.…”

Analgesics Use in Dentistry

“…Data-based personalized prescribing of opioids for optimization of analgesic effectiveness and mitigate risks of opioid-related mortality and abuse is highly desirable with the potential to benefit patients by raising world clinical care and optimizing cost effectiveness of opioid analgesic therapy [90].…”

PASTOR/PROMIS ® Pain Outcomes System: What does it Mean to Pain Specialists?

“…As has been observed, there are not well-validated algorithms for identifying individuals who are the best candidates for opioid therapy [36]. The same can be said for identifying the patients who are most likely to benefit from complementary and nontraditional pain interventions.…”

“…The same can be said for identifying the patients who are most likely to benefit from complementary and nontraditional pain interventions. A potential approach is the application of 'personalized medicine', defined as "optimizing medication types and dosages for individual patients based upon genetic, biomarker, and other patient-related factors" [36]. PROs may be among the predictors that could help identify the treatments that are best suited for individualized patient care.…”