Pharmacokinetics of venlafaxine in treatment responders and non-responders: a retrospective analysis of a large naturalistic database
- 39 Downloads
To assess in a large naturalistic sample, whether clinical response to a treatment with venlafaxine is associated with different patterns of plasma concentrations of active moiety, AM (sum of venlafaxine (VEN) and its active metabolite O-desmethylvenlafaxine (ODVEN)).
Applying a regression model, plasma concentrations and plasma concentrations corrected-by-dosage (C/D) for AM were included as independent variable with Clinical Global Impressions-Improvement (CGI-I) scale ratings as dependent variable. Moreover, AM, VEN, and ODVEN were compared between treatment responders and non-responders, defining response as much or very much improved on the CGI-I scale based on the non-parametric Mann-Whitney U (M-W-U) test with a significance level of 0.05.
No correlations were found between AM and C/D AM plasma concentrations and CGI-I ratings (regression coefficient 0.0, CI 0.000, 0.001, p = 0.492 for AM and 0.047, CI − 0.065, 0.159, p = 0.408 for C/D AM). Venlafaxine daily dosage did not differ between responders and non-responders (217.7 ± 76.9 vs. 222.0 ± 72.7 mg/day, p = 0.45 for M-W-U). Responders displayed lower ODVEN (p = 0.033) and AM (p = 0.031) plasma concentrations than non-responders (p = 0.033 and 0.031, respectively for M-W-U). No other differences were detected. Using a cut-off level of 400 ng/mL for AM concentrations, a higher percentage of responders was reported in the group of patients with AM < 400 ng/mL (13.04%) compared to patients with AM > 400 ng/mL (8%) (p = 0.038).
Higher ODVEN and AM concentrations in non-responders than in responders indicate that treatment escalation above upper thresholds of therapeutic reference ranges of venlafaxine is not promising. Hence, the therapeutic reference range for venlafaxine can help in improving outcomes in a measurement-based care model that takes advantage of therapeutic drug monitoring.
KeywordsVenlafaxine Therapeutic drug monitoring Plasma concentration CGI Treatment response
The authors wish to express their gratitude to the number of people who contributed with excellent professional technical as well as pharmacological competence to build up the KONBEST database with 50.049 clinical pharmacological comments as of February 2, 2016 (ranked among the professional groups in historical order):
A. Köstlbacher, University of Regensburg, Germany created the KONBEST software in his Ph.D. thesis based on an idea of E. Haen, C. Greiner, and D. Melchner along the work flow in the clinical pharmacological laboratory at the Department of Psychiatry and Psychotherapy of the University of Regensburg, Germany. He ongoing maintains together with his colleague A. Haas the KONBEST software and its data mining platform (Haas & Köstlbacher GbR, Regensburg/Germany).
The lab technicians performed the quantitative analysis: D. Melchner, T. Jahner, S. Beck, A. Dörfelt, U. Holzinger, and F. Pfaff-Haimerl.
The clinical pharmacological comments to drug concentrations were composed by licensed pharmacists and medical doctors: Licensed pharmacists: C. Greiner, W. Bader, R. Köber, A. Hader, R. Brandl, M. Onuoha, N. Ben Omar, K. Schmid, A. Köppl, M. Silva, B. Fay, S. Unholzer, C. Rothammer, S. Böhr, F. Ridders, D. Braun, M. Schwarz; M. Dobmeier, M. Wittmann, M. Vogel, M. Böhme, K. Wenzel-Seifert, B. Plattner, P. Holter, R. Böhm, R. Knorr.
Participated in research design: GS, EH, GG, CH, KE, FR, CUC, MP.
Performed data analysis: GS, MP.
Wrote or contributed to the writing of the manuscript: GS, EH, GG, CH, KE, FR, CUC, MP.
Compliance with ethical standards
Conflict of interest
Ekkehard Haen received speaker’s or consultancy fees from the following pharmaceutical companies: Servier, Novartis, and Janssen-Cilag. He is managing director of AGATE, a non-profit working group to improve drug safety and efficacy in the treatment of psychiatric diseases. He reports no conflict of interest with this publication. Gerhard Gründer has served as a consultant for Boehringer Ingelheim (Ingelheim, Germany), Cheplapharm (Greifswald, Germany), Eli Lilly (Indianapolis, Ind, USA), Lundbeck (Copenhagen, Denmark), Ono Pharmaceuticals (Osaka, Japan), Roche (Basel, Switzerland), Servier (Paris, France), and Takeda (Osaka, Japan). He has served on the speakers’ bureau of Eli Lilly, Gedeon Richter (Budapest, Ungarn), Janssen Cilag (Neuss, Germany), Lundbeck, Roche, Servier, and Trommsdorf (Aachen, Germany). He has received grant support from Boehringer Ingelheim and Roche. He is co-founder of Pharma Image GmbH (Düsseldorf, Germany) and Brainfoods UG (Selfkant, Germany). He reports no conflict of interest with this publication. Christoph Hiemke has received speaker’s or consultancy fees from the following pharmaceutical companies: Astra Zeneca, Janssen-Cilag, Pfizer, Lilly, and Servier. He is managing director of the psiac GmbH which provides an Internet-based drug–drug interaction program for psychopharmacotherapy. He reports no conflict of interest with this publication. Christoph Correll has been a consultant and/or advisor to or has received honoraria from: Alkermes, Allergan, Angelini, Boehringer-Ingelheim, Gerson Lehrman Group, Indivior, IntraCellular Therapies, Janssen/J&J, LB Pharma, Lundbeck, Medavante, Medscape, Merck, Neurocrine, Noven, Otsuka, Pfizer, ROVI, Servier, Sunovion, Supernus, Takeda, and Teva. He has provided expert testimony for Bristol-Myers Squibb, Janssen, and Otsuka. He served on a Data Safety Monitoring Board for Boehringer-Ingelheim, Lundbeck, Rovi, Supernus, and Teva. He received royalties from UpToDate and grant support from Janssen and Takeda. He is also a shareholder of LB Pharma. All other authors declare no conflicts of interest as well. The research study did not receive funds or support from any source.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. No informed consent was necessary for this type of research.
- 1.Paulzen M, Haen E, Stegmann B, Unterecker S, Hiemke C, Grunder G, Schoretsanitis G (2017) Clinical response in a risperidone-medicated naturalistic sample: patients' characteristics and dose-dependent pharmacokinetic patterns. Eur Arch Psychiatry Clin Neurosci 267(4):325–333. https://doi.org/10.1007/s00406-016-0736-z CrossRefGoogle Scholar
- 2.Paulzen M, Haen E, Stegmann B, Hiemke C, Gründer G, Lammertz SE, Schoretsanitis G (2016) Body mass index (BMI) but not body weight is associated with changes in the metabolism of risperidone; a pharmacokinetics-based hypothesis. Psychoneuroendocrinology 73:9–15. https://doi.org/10.1016/j.psyneuen.2016.07.009 CrossRefGoogle Scholar
- 4.Paulzen M, Haen E, Gründer G, Stegmann B, Schruers KJR, Walther S, Lammertz SE, Schoretsanitis G (2016) Pharmacokinetic considerations in the treatment of hypertension in risperidone-medicated patients - thinking of clinically relevant CYP2D6 interactions. J Psychopharmacol 30(8):803–809Google Scholar
- 6.Cipriani A, Furukawa TA, Salanti G, Geddes JR, Higgins JP, Churchill R, Watanabe N, Nakagawa A, Omori IM, McGuire H, Tansella M, Barbui C (2009) Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet 373(9665):746–758. https://doi.org/10.1016/S0140-6736(09)60046-5 CrossRefGoogle Scholar
- 7.Fogelman SM, Schmider J, Venkatakrishnan K, von Moltke LL, Harmatz JS, Shader RI, Greenblatt DJ (1999) O- and N-demethylation of venlafaxine in vitro by human liver microsomes and by microsomes from cDNA-transfected cells: effect of metabolic inhibitors and SSRI antidepressants. Neuropsychopharmacology 20(5):480–490. https://doi.org/10.1016/S0893-133X(98)00113-4 CrossRefGoogle Scholar
- 9.Kuzin M, Schoretsanitis G, Haen E, Stegmann B, Hiemke C, Gründer G, Paulzen M (2017) Effects of the proton pump inhibitors omeprazole and pantoprazole on the cytochrome P450 mediated metabolism of venlafaxine. Clin Pharmacokinet in pressGoogle Scholar
- 10.Hiemke C, Bergemann N, Clement HW, Conca A, Deckert J, Domschke K, Eckermann G, Egberts K, Gerlach M, Greiner C, Grunder G, Haen E, Havemann-Reinecke U, Hefner G, Helmer R, Janssen G, Jaquenoud E, Laux G, Messer T, Mossner R, Muller MJ, Paulzen M, Pfuhlmann B, Riederer P, Saria A, Schoppek B, Schoretsanitis G, Schwarz M, Gracia MS, Stegmann B, Steimer W, Stingl JC, Uhr M, Ulrich S, Unterecker S, Waschgler R, Zernig G, Zurek G, Baumann P (2018) Consensus guidelines for therapeutic drug monitoring in Neuropsychopharmacology: update 2017. Pharmacopsychiatry 51(1–02):9–62. https://doi.org/10.1055/s-0043-116492 Google Scholar
- 11.Stamm TJ, Becker D, Sondergeld LM, Wiethoff K, Hiemke C, O'Malley G, Ricken R, Bauer M, Adli M (2014) Prediction of antidepressant response to venlafaxine by a combination of early response assessment and therapeutic drug monitoring. Pharmacopsychiatry 47(4–5):174–179. https://doi.org/10.1055/s-0034-1383565 Google Scholar
- 12.Steen NE, Aas M, Simonsen C, Dieset I, Tesli M, Nerhus M, Gardsjord E, Morch R, Agartz I, Melle I, Vaskinn A, Spigset O, Andreassen OA (2015) Serum level of venlafaxine is associated with better memory in psychotic disorders. Schizophr Res 169(1–3):386–392. https://doi.org/10.1016/j.schres.2015.10.021 CrossRefGoogle Scholar
- 13.Unterecker S, Hiemke C, Greiner C, Haen E, Jabs B, Deckert J, Pfuhlmann B (2012) The effect of age, sex, smoking and co-medication on serum levels of venlafaxine and O-desmethylvenlafaxine under naturalistic conditions. Pharmacopsychiatry 45(6):229–235. https://doi.org/10.1055/s-0031-1301366 CrossRefGoogle Scholar
- 17.Jiang F, Kim HD, Na HS, Lee SY, Seo DW, Choi JY, Ha JH, Shin HJ, Kim YH, Chung MW (2015) The influences of CYP2D6 genotypes and drug interactions on the pharmacokinetics of venlafaxine: exploring predictive biomarkers for treatment outcomes. Psychopharmacology 232(11):1899–1909. https://doi.org/10.1007/s00213-014-3825-6 CrossRefGoogle Scholar
- 18.Lobello KW, Preskorn SH, Guico-Pabia CJ, Jiang Q, Paul J, Nichols AI, Patroneva A, Ninan PT (2010) Cytochrome P450 2D6 phenotype predicts antidepressant efficacy of venlafaxine: a secondary analysis of 4 studies in major depressive disorder. J Clin Psychiatry 71(11):1482–1487. https://doi.org/10.4088/JCP.08m04773blu CrossRefGoogle Scholar
- 19.Preskorn SH (2014) Therapeutic drug monitoring (TDM) in psychiatry (part I): why studies attempting to correlate drug concentration and antidepressant response don't work. J Psychiatr Pract 20(2):133–137. https://doi.org/10.1097/01.pra.0000445247.54048.68 CrossRefGoogle Scholar
- 21.Köstlbacher A, Haen E (2008) Konbest–a web-based laboratory information management system (LIMS) for TDM-laboratories. Pharmacopsychiatry 41(05):A23Google Scholar
- 22.Guy W (1976) ECDEU Assessment Manual for Psychopharmacology, revised. DHEW Publ No ADM 76-338 National Institute of Mental Health, RockvilleGoogle Scholar
- 24.Balestri M, Calati R, Souery D, Kautzky A, Kasper S, Montgomery S, Zohar J, Mendlewicz J, Serretti A (2016) Socio-demographic and clinical predictors of treatment resistant depression: a prospective European multicenter study. J Affect Disord 189:224–232. https://doi.org/10.1016/j.jad.2015.09.033 CrossRefGoogle Scholar
- 27.Uhr M, Tontsch A, Namendorf C, Ripke S, Lucae S, Ising M, Dose T, Ebinger M, Rosenhagen M, Kohli M, Kloiber S, Salyakina D, Bettecken T, Specht M, Putz B, Binder EB, Muller-Myhsok B, Holsboer F (2008) Polymorphisms in the drug transporter gene ABCB1 predict antidepressant treatment response in depression. Neuron 57(2):203–209. https://doi.org/10.1016/j.neuron.2007.11.017 CrossRefGoogle Scholar
- 28.Ising M, Lucae S, Binder EB, Bettecken T, Uhr M, Ripke S, Kohli MA, Hennings JM, Horstmann S, Kloiber S, Menke A, Bondy B, Rupprecht R, Domschke K, Baune BT, Arolt V, Rush AJ, Holsboer F, Muller-Myhsok B (2009) A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry 66(9):966–975. https://doi.org/10.1001/archgenpsychiatry.2009.95 CrossRefGoogle Scholar
- 29.Arakawa R, Stenkrona P, Takano A, Svensson J, Andersson M, Nag S, Asami Y, Hirano Y, Halldin C, Lundberg J (2019) Venlafaxine ER blocks the norepinephrine transporter in the brain of patients with major depressive disorder: a PET study using [18F]FMeNER-D2. Int J Neuropsychopharmacol. https://doi.org/10.1093/ijnp/pyz003
- 32.Karlsson L, Hiemke C, Carlsson B, Josefsson M, Ahlner J, Bengtsson F, Schmitt U, Kugelberg FC (2011) Effects on enantiomeric drug disposition and open-field behavior after chronic treatment with venlafaxine in the P-glycoprotein knockout mice model. Psychopharmacology 215(2):367–377. https://doi.org/10.1007/s00213-010-2148-5 CrossRefGoogle Scholar