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Population Genetic-Based Pharmacokinetic Modeling of Methadone and its Relationship with the QTc Interval in Opioid-Dependent Patients

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Abstract

Background and Objectives

Methadone is a μ-opioid agonist widely used for the treatment of pain, and for detoxification or maintenance treatment in opioid addiction. It has been shown to exhibit large pharmacokinetic variability and concentration–QTc relationships. In this study we investigated the relative influence of genetic polymorphism and other variables on the dose concentration–QTc relationship.

Patients and Methods

A population model for methadone enantiomers in 251 opioid-dependent patients was developed using non-linear mixed effect modeling (NONMEM®). Various models were tested to characterize the pharmacokinetics of (R)- and (S)-methadone and the pharmacokinetic–pharmacodynamic relationship, while including demographics, physiological conditions, co-medications, and genetic variants as covariates. Model-based simulations were performed to assess the relative increase in QTc with dose upon stratification according to genetic polymorphisms involved in methadone disposition.

Results

A two-compartment model with first-order absorption and lag time provided the best model fit for (R)- and (S)-methadone pharmacokinetics. (S)-methadone clearance was influenced by cytochrome P450 (CYP) 2B6 activity, ABCB1 3435C>T, and α-1 acid glycoprotein level, while (R)-methadone clearance was influenced by CYP2B6 activity, POR*28, and CYP3A4*22. A linear model described the methadone concentration–QTc relationship, with a mean QTc increase of 9.9 ms and 19.2 ms per 1000 ng/ml of (R)- and (S)-methadone, respectively. Simulations with different methadone doses up to 240 mg/day showed that <8 % of patients presented with a QTc interval above 450 ms; however, this might reach 12 to 18 % for (R)- and (S)-methadone, respectively, in patients with a genetic status associated with a decreased methadone elimination at doses exceeding 240 mg/day.

Conclusion

Risk factor assessment, electrocardiogram monitoring, and therapeutic drug monitoring are beneficial to optimize treatment in methadone patients, especially for those who have low levels despite high methadone doses, or who are at risk of overdosing.

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Acknowledgments

The authors thank all patients for their participation, as well as Dr. J.J. Déglon, Prof. J. Besson, Dr. M. Croquette-Krokkar, Dr. I. Gothuey, Dr. R. Hämmig, Dr. M. Monnat, Dr. H. Hüttemann, Dr. A. Al Amine, Dr. M. Bourquin, Dr. R. Rajeswaran, Mr J. Bergeron, Mr D. Uk, Mrs I. Girod, Mrs S. Meynet, and Mrs I. Soulignac for their collaboration in the recruitment of patients, and Mrs M. Brocard, N. Cochard, A.C. Aubert, L. Koeb, and M. Brawand for analyzing the samples. The authors also thank the Vital-IT Platform of the Swiss Institute of Bioinformatics for providing the computational resources for the population analyses, J.F. Wavre for data management, and E. Retamales for help with the bibliography.

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    Authors and Affiliations

    1. School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland

      Chantal Csajka, Monia Guidi & Chin B. Eap

    2. Division of Clinical Pharmacology and Toxicology, Service of Biomedicine, University Hospital Center and University of Lausanne, Lausanne, Switzerland

      Chantal Csajka & Monia Guidi

    3. Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland

      Séverine Crettol & Chin B. Eap

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    1. Chantal Csajka
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    Correspondence to Chantal Csajka.

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    Funding

    This work was supported in part by Grants from the Swiss National Science Foundation (3200-065427.01, 320030-120686, and 324730-144064). No external funding was used in the preparation of this manuscript.

    Conflict of interest

    In the past 3 years, Chin B. Eap has received research support from Takeda and the Roche Organ Transplantation Research Foundation, and has also received honoraria for conferences or teaching Continuing Medical Education (CME) courses from Astra Zeneca, Janssen-Cilag, Lundbeck, Merck Sharp & Dohme, Mepha, Otsuka, Sandoz, Servier, and Vifor-Pharma in the past 3 years.

    Chantal Csajka, Séverine Crettol, and Monia Guidi declare that they have no conflicts of interest to declare that might be relevant to the content of this manuscript.

    Additional information

    C. Csajka and S. Crettol contributed equally to this work.

    Electronic supplementary material

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    40262_2016_415_MOESM1_ESM.docx

    Supplementary material 1 (DOCX 1461 kb) Supplementary Figure 1: Goodness-of-fit plots for A: (R)-methadone and B : (S)-methadone population and individual pharmacokinetic and QTc predictions vs observations and conditional weighted residuals (CWRES) vs. time after last dose

    40262_2016_415_MOESM2_ESM.docx

    Supplementary material 2 (DOCX 164 kb) Supplementary Figure 2: Receiver Operating Characteristic (ROC) curve indicating the best (R,S)-methadone plasma concentration threshold to predict a QTc prolongation above 450 ms. *(R,S)-methadone plasma concentration value; sensitivity and specificity enclosed in parentheses

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    Csajka, C., Crettol, S., Guidi, M. et al. Population Genetic-Based Pharmacokinetic Modeling of Methadone and its Relationship with the QTc Interval in Opioid-Dependent Patients. Clin Pharmacokinet 55, 1521–1533 (2016). https://doi.org/10.1007/s40262-016-0415-2

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