Utilization of Pharmacogenomics and Therapeutic Drug Monitoring for Opioid Pain Management

Paul J Jannetto; Nancy C Bratanow

Pharmacogenomics. 2009;10(7):1157-1167.

Conclusion

It is estimated that almost half of all Americans have at least one family member who suffers from chronic pain due to a specific illness or medical condition.^[41] An example is back pain,^[42] which has a lifetime prevalence of 60-80% and results in significant physical, psychological and financial consequences.^[43] In general, pain costs Americans more than US$100 billion each year in healthcare costs and lost productivity.^[106]

Pharmacogenomics has the potential to allow clinicians and researchers to prescribe and design less toxic and more efficacious medications based on an individual's genotype. This study showed that the majority of individuals that experienced ADRs had impaired CYP2D6 metabolism. Furthermore, PMs and IMs generally had higher Css and TDM could potentially guide oxycodone therapy. As a result, personalized medical therapy for chronic pain patients based on their CYP2D6 genotype may predict which drug a patient may respond to without suffering any needless side effects and help them return to a normal, productive life. Personalized medicine for pain management offers the possibility of optimized pain control, with proper drug selection and dose determination, with increased patient safety and decreased risk of serious, potentially life-threatening side effects. Overall, this study illustrates that additional pain management studies investigating the role of pharmacogenomics and TDM need to be conducted using larger sample sizes to prove statistical significance.

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Acknowledgements
The authors are deeply indebted to Dr Steven Wong; Dr Robert Kettler and his colleagues, nurses and staff at the Froedtert Hospital Pain Management Center; Dr Constantine Sarantopolous at the Zablocki VA Pain Management Center and the nurses and staff at Midwest Comprehensive Pain Care; the Van Slyke Society for financial support of the clinical research study; Dr Elvan Laleli-Sahin, Dr Agnieszka Rogalska and B Charles Schur for their assistance in the data collection and analysis; and Abbott Diagnostics, Abbott Molecular, Dade-Behring, Roche Diagnostics, Roche Molecular System and Promega for providing educational grants for Dr Paul J Jannetto-Postdoctoral fellow in Toxicology, Therapeutic Drug Management and Pharmacogenetics.

Financial & competing interests disclosure
This study was funded by a Van Slyke Foundation Grant from the American Association for Clinical Chemistry (AACC). Educational grants were received by Dr Paul J Jannetto from Abbott Diagnostics, Abbott Molecular, Dade-Behring, Roche Diagnostics, Roche Molecular System and Promega.
The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research
The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Cite this: Utilization of Pharmacogenomics and Therapeutic Drug Monitoring for Opioid Pain Management - Medscape - Jul 01, 2009.

Table 1. Summary of pain management patient demographics, diagnosis, therapy and *CYP2D6* genotype.
Patient information		Pain cohort (n = 61)	CYP2D6 genotype
Patient information		Pain cohort (n = 61)	Wt/Wt (n = 33)	Wt/Vt (n = 25)	Vt/Vt (n = 3)
Average age (years)		51	49	53	43
Sex	Male	31	14	15	2
	Female	30	19	10	1
Race	Caucasian	54	27	24	3
	African-American	5	4	1	0
	Hispanic	1	1	0	0
	American-Indian	1	1	0	0
Diagnosis	Chronic lower back pain	23	12	11	0
	Migraine/headache	5	4	1	0
	Neuropathic pain	4	4	0	0
	Post-laminectomy syndrome	3	0	2	1
	Myofascial pain	3	2	1	0
	Other (e.g., fibromyalgia)	23	11	10	2
Opioid medications	Oxycodone only	23	10	11	2
	Tramadol only	11	9	2	0
	Methadone only	8	4	3	1
	Hydrocodone only	4	3	1	0
	Oxycodone and methadone	6	3	3	0
	Oxycodone and tramadol	3	2	1	0
	Methadone and tramadol	3	1	2	0
	Hydrocodone and tramadol	2	1	1	0
	Hydrocodone and oxycodone	1	0	1	0

Vt: Variant; Wt: Wild-type.

Table 2. Summary of opioid Css according to *CYP2D6* genotype.
Opioid	Css units	Extensive metabolizers	Intermediate metabolizers	Poor metabolizers	p-value
Hydrocodone	Css (ng/ml)	16 ± 16	19 ± 24	0	0.83
	Css (ng/ml per mg)	0.6 ± 0.5	0.7 ± 1.0	0
	Css (ng/ml per mg/kg)	39 ± 27	55 ± 77	0
	n	4	3	0
Methadone	Css (ng/ml)	114 ± 153	138 ± 163	342 ± 0	0.56
	Css (ng/ml per mg)	2.7 ± 3.1	3.1 ± 1.9	3.8 ± 0
	Css (ng/ml per mg/kg)	240 ± 242	269 ± 165	296 ± 0
	n	8	8	1
Tramadol	Css (ng/ml)	31 ± 38	51 ± 70	0	0.89
	Css (ng/ml per mg)	0.2 ± 0.4	0.4 ± 0.5	0
	Css (ng/ml per mg/kg)	10 ± 19	28 ± 44	0
	n	12	5	0
Oxycodone	Css (ng/ml)	22 ± 21	28 ± 22	10 ± 0.4	0.91
	Css (ng/ml per mg)	0.5 ± 0.6	0.6 ± 0.5	0.2 ± 0.2
	Css (ng/ml per mg/kg)	39 ± 36	60 ± 72	20 ± 18
	n	13	14	2

Concentrations expressed as the mean ± the standard deviation.
Css: Steady-state concentration.

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