Quantification of a Methadone Metabolite (EDDP) in Urine: Assessment of Compliance

Strict adherence to pharmacological dosage regimens is a prerequisite to the success of most treatments, particularly for patients in drug abuse programs or in patients with chronic pain who are on opioids for pain control. Although urine drug screening collection procedures attempt to ensure specimen integrity, some patients demonstrate considerable ingenuity in their efforts to defeat the testing process.1–4 Possible methods of avoiding detection of drug misuse include diversion, excessive water consumption, ingesting diuretics such as herbal teas, and substitution of someone else’s urine. Individuals who divert pain medication for illegitimate resale often reserve a few pills to be taken before a physician visit so the medication will appear in their urine ensuring that the physician will extend the prescription, and therefore will allow continued diversion of the medication. Individuals also overuse medications, often gaining it from multiple sources. These individuals pass basic presence/ absence screening tests and continue to receive the medication. A recent retrospective study of a chronic pain population using urine screen monitoring suggested that 75% of patients were likely not taking their pain medication as prescribed.5 While laboratory assays are quantitative, current screening tests can only assure presence or absence of drug metabolites in the urine. The major problem facing urine testing is the large amount of variance in urine drug concentrations, mostly due to variations in hydration and urine output volume. In a recent study by the Clinical Pharmacology and Therapeutics Research Branch of the National Institute on Drug Abuse, this variability proved to be too great to permit estimation of methadone dose or missed doses; however, another group of researchers reported a clear benefit from the use of urine creatinine to adjust for concentration of urine methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenyl-pyrrolidine (EDDP) using a semiquantitative immunoassay and found that these values were lower on days after missed doses.6 Currently, no quantitative screening method is available that accurately identifies individuals who are non-compliant with the prescribed dosage regimen through over- or under-use. Such a method would be of tremendous clinical benefit if it could be developed to allow statistical analysis of a single sample, thereby allowing a prescribing provider the ability to understand use of the medication as prescribed versus over- or under-use. Oral creatine supplementation is widely used in athletes attempting to enhance their performance. This should not interfere with the proposed monitoring method. Several studies have demonstrated that neither short-, medium-, nor long-term oral creatine supplements have any significant effects on urine excretion rates for creatinine, urea or albumin in healthy individuals.7,8 Of more concern is the fact that intraindividual variability in renal excretion of creatinine can be temporarily increased by meat consumption.9–11 This variability may account for 10% to 29% of between-day variation in calculated creatinine clearance for a given individual.12 The purpose of this study was to explore the possibility of using methadone metabolite/urine creatinine ratios to develop a regression model for predicting methadone adherence over a clinically significant range of prescribed dosages in patients on methadone therapy for chronic pain or opioid addiction. Methadone is prescribed under strict dosage regimens to patients in substance abuse programs, is closely monitored in chronic pain patients, and has a readily quantifiable urinary metabolite (EDDP). The direct methadone metabolite (methadone) was not assessed during this study, as it was not reported in these samples by our system’s laboratory; Marshfield Laboratories only began reporting methadone levels quantitatively, along with EDDP levels, in February 2009. We sought to develop a model with predictive properties that could be used to compare predicted urine metabolite levels for a prescribed regimen against actual metabolite levels to determine non-adherence to a given regimen. We report our findings here.