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Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests) focus on antidepressants Eap, C. B.; Gründer, G.; Baumann, P.; Ansermot, N.; Conca, A.; Corruble, E.; Crettol, S.; Dahl, M. L.; de Leon, J.; Greiner, C.; Howes, O.; Kim, E.; Lanzenberger, R.; Meyer, J. H.; Moessner, R.; Mulder, H.; Müller, D. J.; Reis, M.; Riederer, P.; Ruhe, H. G.; Spigset, O.; Spina, E.; Stegman, B.; Steimer, W.; Stingl, J.; Suzen, S.; Uchida, H.; Unterecker, S.; Vandenberghe, F.; Hiemke, C. Published in: World Journal of Biological Psychiatry
DOI: 10.1080/15622975.2021.1878427
Publication date: 2021
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Citation for pulished version (APA): Eap, C. B., Gründer, G., Baumann, P., Ansermot, N., Conca, A., Corruble, E., Crettol, S., Dahl, M. L., de Leon, J., Greiner, C., Howes, O., Kim, E., Lanzenberger, R., Meyer, J. H., Moessner, R., Mulder, H., Müller, D. J., Reis, M., Riederer, P., ... Hiemke, C. (2021). Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants. World Journal of Biological Psychiatry. https://doi.org/10.1080/15622975.2021.1878427
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Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants
C. B. Eap, G. Gründer, P. Baumann, N. Ansermot, A. Conca, E. Corruble, S. Crettol, M. L. Dahl, J. de Leon, C. Greiner, O. Howes, E. Kim, R. Lanzenberger, J. H. Meyer, R. Moessner, H. Mulder, D. J. Müller, M. Reis, P. Riederer, H. G. Ruhe, O. Spigset, E. Spina, B. Stegman, W. Steimer, J. Stingl, S. Suzen, H. Uchida, S. Unterecker, F. Vandenberghe & C. Hiemke
To cite this article: C. B. Eap, G. Gründer, P. Baumann, N. Ansermot, A. Conca, E. Corruble, S. Crettol, M. L. Dahl, J. de Leon, C. Greiner, O. Howes, E. Kim, R. Lanzenberger, J. H. Meyer, R. Moessner, H. Mulder, D. J. Müller, M. Reis, P. Riederer, H. G. Ruhe, O. Spigset, E. Spina, B. Stegman, W. Steimer, J. Stingl, S. Suzen, H. Uchida, S. Unterecker, F. Vandenberghe & C. Hiemke (2021): Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants, The World Journal of Biological Psychiatry, DOI: 10.1080/15622975.2021.1878427 To link to this article: https://doi.org/10.1080/15622975.2021.1878427
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REVIEW ARTICLE Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants
a,b,c,d,e f g a h,i j,k C. B. Eap ,G.Grunder€ , P. Baumann , N. Ansermot , A. Conca , E. Corruble , S. Crettola , M. L. Dahll, J. de Leonm , C. Greinern, O. Howeso, E. Kimp,q, R. Lanzenbergerr , J. H. Meyers, R. Moessnert, H. Mulderu,v,w,x,D.J.Muller€ y,z,aa, M. Reisab,ac, P. Riedererad,ae, H. G. Ruheaf,ag , O. Spigsetah,ai, E. Spinaaj , B. Stegmanak, W. Steimeral, J. Stinglam, S. Suzenan, H. Uchidaao, S. Untereckerap, F. Vandenberghea and C. Hiemkeaq aUnit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; bCenter for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; cSchool of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; dInstitute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland; eInstitute of Pharmaceutical Sciences of Western Switzerland, University of Lausanne, Switzerland, Geneva, Switzerland; fDepartment of Molecular Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; gDepartment of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; hDepartment of Psychiatry, Health Service District Bolzano, Bolzano, Italy; iDepartment of Child and Adolescent Psychiatry, South Tyrolean Regional Health Service, Bolzano, Italy; jINSERM CESP, Team MOODS , Service Hospitalo-Universitaire de Psychiatrie, Universite Paris Saclay, Le Kremlin Bicetre, France; kService Hospitalo-Universitaire de Psychiatrie, Hopital^ Bic^etre, Assistance Publique Hopitaux^ de Paris, Le Kremlin Bic^etre, France; lDivision of Clinical Pharmacology, Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden; mEastern State Hospital, University of Kentucky Mental Health Research Center, Lexington, KY, USA; nBundesinstitut fur€ Arzneimittel und Medizinprodukte, Bonn, Germany; oKing’s College London and MRC London Institute of Medical Sciences (LMS)-Imperial College, London, UK; pDepartment of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea; qDepartment of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea; rDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; sCampbell Family Mental Health Research Institute, CAMH and Department of Psychiatry, University of Toronto, Toronto, Canada; tDepartment of Psychiatry and Psychotherapy, University of Tubingen,€ Tubingen,€ Germany; uDepartment of Clinical Pharmacy, Wilhelmina Hospital Assen, Assen, The Netherlands; vGGZ Drenthe Mental Health Services Drenthe, Assen, The Netherlands; wDepartment of Pharmacotherapy, Epidemiology and Economics, Department of Pharmacy and Pharmaceutical Sciences, University of Groningen, Groningen, The Netherlands; xDepartment of Psychiatry, Interdisciplinary Centre for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands; yCampbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; zDepartment of Psychiatry, University of Toronto, Toronto, ON, Canada; aaDepartment of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; abDepartment of Biomedical and Clinical Sciences, Linkoping€ University, Linkoping,€ Sweden; acClinical Chemistry and Pharmacology, Skåne University Hospital, Lund, Sweden; adCenter of Mental Health, Clinic and Policlinic for Psychiatry, Psychosomatics and Psychotherapy, University Hospital Wurzburg,€ Wurzburg,€ Germany; aeDepartment of Psychiatry, University of Southern Denmark Odense, Odense, Denmark; afDepartment of Psychiatry, Radboudumc, Nijmegen, the Netherlands; agDonders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands; ahDepartment of Clinical Pharmacology, St. Olav University Hospital, Trondheim, Norway; aiDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; ajDepartment of Clinical and Experimental Medicine, University of Messina, Messina, Italy; akInstitut fur€ Pharmazie der Universit€at Regensburg, Regensburg, Germany; alInstitute for Clinical Chemistry and Pathobiochemistry, Technical University of Munich, Munich, Germany; amInstitute for Clinical Pharmacology, University Hospital of RWTH Aachen, Germany; anDepartment of Toxicology, Faculty of Pharmacy, Ankara University, Ankara, Turkey; aoDepartment of Neuropsychiatry, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan; apDepartment of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Wurzburg,€ Wurzburg,€ Germany; aqDepartment of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
CONTACT CB Eap [email protected] Unit of Pharmacogenetics and Clinical Psychopharmacology, Center for Psychiatric Neurosciences, Lausanne University Hospital and University of Lausanne, Hospital of Cery, Prilly-Lausanne, Switzerland; G Grunder€ [email protected] Department of Molecular Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; C Hiemke [email protected] Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany Joint first authorship ß 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by- nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. 2 C. B. EAP ET AL.
ABSTRACT ARTICLE HISTORY Objectives: More than 40 drugs are available to treat affective disorders. Individual selection of Received 16 July 2020 the optimal drug and dose is required to attain the highest possible efficacy and acceptable tol- Revised 20 October 2020 erability for every patient. Accepted 1 December 2020 Methods: This review, which includes more than 500 articles selected by 30 experts, combines KEYWORDS relevant knowledge on studies investigating the pharmacokinetics, pharmacodynamics and Antidepressants; brain pharmacogenetics of 33 antidepressant drugs and of 4 drugs approved for augmentation in imaging; precision cases of insufficient response to antidepressant monotherapy. Such studies typically measure medicine; pharmacogenet- drug concentrations in blood (i.e. therapeutic drug monitoring) and genotype relevant genetic ics; therapeutic polymorphisms of enzymes, transporters or receptors involved in drug metabolism or mechan- drug monitoring ism of action. Imaging studies, primarily positron emission tomography that relates drug con- centrations in blood and radioligand binding, are considered to quantify target structure occupancy by the antidepressant drugs in vivo. Results: Evidence is given that in vivo imaging, therapeutic drug monitoring and genotyping and/or phenotyping of drug metabolising enzymes should be an integral part in the develop- ment of any new antidepressant drug. Conclusions: To guide antidepressant drug therapy in everyday practice, there are multiple indi- cations such as uncertain adherence, polypharmacy, nonresponse and/or adverse reactions under therapeutically recommended doses, where therapeutic drug monitoring and cytochrome P450 genotyping and/or phenotyping should be applied as valid tools of precision medicine.
1. Introduction therefore be used in the early phase of drug develop- ment, while TDM and selected genotyping should be More than 40 compounds are available to treat affective used in clinical practice to guide antidepressant disorders. For selection of the optimal drug and dose, drug therapy. rational decision making must consider the psycho- In the first part of this review the use of TDM, pathological status of the patient and the pharmacoki- pharmacogenetics and brain imaging to optimise and/ netic and pharmacodynamic profiles of the drugs. or personalise pharmacotherapy with antidepressants Clinical decision making is therefore a complex process will be introduced. In the second part, pharmaco- (Serretti 2018). Measurement of drug concentrations in logical and pharmacokinetic profiles, TDM, pharmaco- blood [i.e. the use of therapeutic drug monitoring genetics and brain imaging studies will be presented (TDM)] is most helpful in detecting individual pharma- for individual antidepressant drugs, including the tri- cokinetic characteristics (Hiemke et al. 2018). Major cyclic antidepressants (TCAs), the SSRIs, the serotonin determinants of drug concentrations in blood are noradrenaline reuptake inhibitors (SNRIs), other antide- enzymes involved in the metabolism of drugs, primarily pressants, and non-antidepressants (e.g. quetiapine). enzymes of the cytochrome P450 (CYP) family. Multiple Specific topics such as augmentation with non-antide- variants of CYP genes lead to inactive, active or highly pressants in major depression or bipolar depression, active enzymes and give rise to abnormal drug concen- as well as chirality will also be presented. Finally, per- trations (Eap 2016). Identifying CYP genetic polymor- spectives of TDM, pharmacogenetics and brain imag- phisms can therefore also be helpful in understanding ing will be discussed. This review did not consider all and predicting interindividual pharmacokinetic varia- drugs that are available to treat depression. We tions. Positron emission tomography (PET) with specific excluded drugs like buspirone with almost no data on radioligands provides insight into brain pharmacokinet- TDM, pharmacogenetics or molecular imaging. This € ics (Grunder et al. 2011). PET coupled with measurement also holds true for a fixed combination of drugs (e.g. of drug concentrations in blood enables the calculation fluoxetine and olanzapine for the treatment of resist- of target molecule occupancy. When such studies have ant unipolar depression). been conducted, one can extrapolate from the concen- PART 1 Introduction to therapeutic drug monitor- tration in blood how much receptor is occupied. ing, pharmacogenetics and brain imaging of Binding studies on serotonin transporters and blood antidepressants level measurement of selective serotonin reuptake Multiple publications including review papers have inhibitors (SSRIs) revealed that 80% occupancy should been published on the use of TDM, pharmacogenetics/ be attained to obtain full antidepressant efficacy (Meyer pharmacogenomics and brain imaging to optimise the et al. 2004). Brain imaging (especially PET) should pharmacological treatment of patients with psychiatric THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 3 disorders. Concerning TDM, of special note is the con- the TCAs imipramine and amitriptyline and their N- sensus paper published by TDM group of the demethylated metabolites desipramine and nortripty- Arbeitsgemeinschaft fur€ Neuropsychopharmakologie line, respectively (Hammer and Sjoqvist 1967). Similar und Pharmakopsychiatrie (AGNP or Association for observations were made later for all antidepressant Neuropsychopharmacology and Pharmacopsychiatry, drugs. This gave rise to the suggestion to use TDM as a hereafter referred to as AGNP) which published in-depth tool to control and correct the dose of antidepressant reviews (Baumann et al. 2004; Hiemke et al. 2011; medications individually and identify non-adherence. Hiemke et al. 2018), with recommendations and refer- TDM was introduced for nortriptyline almost 50 years ence value ranges for plasma concentrations of psycho- ago by Åsberg and co-workers (Åsberg et al. 1971) and tropic drugs (Hiemke et al. 2018). These consensus soon extended to TCAs in general (Preskorn 1989; Perry guidelines were introduced in numerous clinics and lab- et al. 1994) and to new antidepressant drugs more or oratories and have been accepted by treating physi- less gradually (Ostad Haji et al. 2012). With the introduc- cians. Thorough reviews have also been published on tion of new antidepressant drugs, namely the SSRIs, in brain imaging in psychiatry and depression (e.g. the 1990s the value of TDM was rated as low. The main Grunder€ et al. 2011; Ruhe et al. 2014) in addition to arguments against routine TDM of SSRIs or other new many recommendations on pharmacogenetic tests (e.g. antidepressants were low toxicity, large therapeutic Eap 2016; Genetic testing statement 2020). The aim of windows and uncertain therapeutic reference ranges the present task force is to provide a comprehensive (Rasmussen and Brøsen 2000). TDM was regarded as jus- review of the current evidence on methods allowing tified for new antidepressants in special cases such as one to assess or understand patient variability in ther- suspected nonadherence or drug-drug interactions. apy outcome. Such evidence can explain why and how Meanwhile, SSRIs and other new antidepressants brain imaging studies should be an essential part of the have become first-line antidepressants. Meta-analyses early clinical phase of psychoactive drug development of clinical trials, however, revealed similar efficacy for and why and how TDM and genotyping can be useful older and newer antidepressants (Cipriani et al. 2018). tools in optimising the pharmacotherapy of psychiatric Problems of treatment failure and non-adherence still patients. The present review focuses on patient variabil- remained with the new drugs. Evidence and convic- ity in drug metabolism of single antidepressants, tion have grown gradually that TDM should be used reviewing the major pharmacokinetic, TDM, pharmaco- to optimise the treatment of affective disorders genetic and brain imaging data for each drug. Essential (Bengtsson 2004; Jaquenoud Sirot et al. 2006; Ostad data are summarized in Table 1. This task force is com- Haji et al. 2012). With adequate use of TDM it has mitted to providing a second review focussing on anti- even been shown that direct and indirect costs of psychotics in the near future. health care may be reduced (Simmons et al. 1985; Preskorn and Fast 1991; Lundmark et al. 2000; von Knorring et al. 2006; Ostad Haji et al. 2013). 1.1. TDM for optimising antidepressant TDM aims to improve efficacy and safety of drug pharmacotherapy therapies. Not only for TDM of antidepressant drugs, In spite of the availability of almost 40 drugs with anti- but for TDM in general, there is a lack of valid studies depressant efficacy, outcomes of antidepressant phar- that determined optimal plasma concentrations and macotherapies are so far not optimal. Most depressed showed clear-cut beneficial effects of this tool. patients fail to achieve or maintain response or remis- Molecular imaging, as considered in this review for anti- sion under the first-line antidepressant medication depressant drugs, is an essential add on tool to find (Rush et al. 2006). Other problems are related to adverse therapeutic ranges when based on mostly preliminary reactions. Among many possible determinants for non- clinical trials. Optimal studies require fixed dose treat- response or intolerability are interpatient variability in ment of a sufficiently high number of real-world pharmacokinetics, in particular extreme phenotypes/ patients that would positively respond to a given drug. genotypes. A high prevalence of non-adherence to Patients must be separated according to ascending medication ranging between 10 and 60% of patients plasma drug levels into large enough subgroups with (Lingam and Scott 2002) is another obstacle in the treat- subtherapeutic, therapeutic and supratherapeutic drug ment of affective disorders. As a consequence, drug lev- concentrations. Such design allows precise definition of els in plasma are highly variable among individual optimal plasma levels. Such studies, however, are logis- patients. Variability in antidepressant drug metabolism tic challenges. Nevertheless, they are urgently needed. was detected and reported for the first time in 1967 for This is particularly true in times when pharmaceutical Table 1. Drugs available to treat depression, their major metabolites, therapeutic target sites, major enzymes of degradation, elimination half-lives and recommended therapeutic 4 reference ranges.
In vivo occupancy of Recommended AL. ET EAP B. C. major target site at Major Genetic polymorphisms Mean therapeutic reference Target sites for therapeutic metabolising of metabolising t1/2 ranges Conversion Parent drugs Active metabolites therapeutic action concentrations enzymes enzymes [h] [ng/mL] factors
Agomelatine – MT1 n.d. CYP1A2 Genetic polymorphism 1.5 7–300 (Cmax) 4.11 MT2 n.d. CYP2C19 are not relevant 5-HT2C n.d. CYP2C9 or unknown Amitriptyline Nortriptyline SERT n.d. CYP2C19 CYP2D6 16 80–200 (AM) 3.60 NET 50–70% CYP2C9 CYP2C19 36 3.80 CYP1A2 CYP2D6 CYP3A4 Amoxapine 8-Hydroxyamoxapine NET 50–70% CYP1A2 CYP2D6 10 200–500 (AM) 3.19 5-HT2A n.d. CYP2D6 30 3.03 Bupropion Hydroxybupropion DAT 20–33% CYP2B6 CYP2B6 20 (SR) 850–1500 4.17 NET n.d. 20 (SR) (Hydroxybupropion) 3.91 Citalopram Desmethylcitalopram SERT 80% CYP2C19 CYP2C19 33 50–110 3.08 Didesmethylcitalopram CYP2D6 51/108 CYP3A4 Clomipramine Desmethylclomipramine SERT >90% CYP1A2 CYP2D6 21 230–450 (AM) 3.18 NET CYP2C19 CYP2C19 36 3.32 CYP2D6 CYP3A4 Desipramine – NET n.d. CYP2D6 CYP2D6 17 100–300 3.75 Desvenlafaxine – SERT n.d. CYP2C19 Genetic polymorphism 11 100–400 3.80 NET n.d. CYP3A4 are not relevant or unknown Dothiepin Dothiepine S-oxide n.d. CYP2C19 CYP2C19 22 45–100 3.39 Northiaden CYP2D6 19/33 Doxepin Nordoxepin SERT n.d. CYP2C19 CYP2C19 15 50–150 (AM) 3.58 NET n.d. CYP2D6 CYP2D6 31 3.77 CYP2C9 Duloxetine – SERT 68–78% CYP1A2 Genetic polymorphism 12 30–120 3.36 NET 30–40% CYP2D6 are not relevant COMT or unknown SULT Escitalopram S-Desmethylcitalopram SERT 75–80% CYP2C19 CYP2C19 33 15–80 3.08 S-Didesmethylcitalopram CYP3A4 53/n.d. CYP2D6 MAO Fluoxetine Norfluoxetine SERT 92% CYP2C9 CYP2D6 1–4 days 120–500 (AM) 3.23 CYP2D6 7–15 days 3.39 CYP2C19 CYP3A4 Fluvoxamine – SERT 80% CYP2D6 CYP2D6 15 60–230 3.14 CYP1A2 Imipramine Desipramine SERT n.d. CYP2C19 CYP2C19 12 175–300 (AM) 3.57 NET n.d. CYP2D6 CYP2D6 17 3.75 CYP1A2 CYP3A4 (continued) Table 1. Continued. In vivo occupancy of Recommended major target site at Major Genetic polymorphisms Mean therapeutic reference Target sites for therapeutic metabolising of metabolising t1/2 ranges Conversion Parent drugs Active metabolites therapeutic action concentrations enzymes enzymes [h] [ng/mL] factors Isocarboxazid – MAO n.d. unknown MAO unknown unknown – Levomilnacipran p-OH-Milnacipran SERT n.d. CYP3A4 Genetic polymorphism 12 (ER) 80–120 2.24 NET n.d. CYP2C8 not relevant CYP2C19 or unknown CYP2D6 CYP2J2 Maprotiline Desmethylmaprotiline NET n.d. CYP2D6 CYP2D6 48 75–130 3.60 3-Hydroxymaprotiline CYP1A2 Mianserin Desmethylmianserin Alpha2 n.d. CYP1A2 CYP2D6 30 15–70 3.78 5-HT2 CYP2D6 5-HT3 CYP3A4 Milnacipran p-OH-Milnacipran SERT < 50 % CYP3A4 Genetic polymorphism 8 100–150 2.24 NET < 50 % not relevant or unknown Mirtazapine Desmethylmirtazapine Alpha2 CYP2D6 Genetic polymorphism 25 30–80 3.77 5-HT2 CYP3A4 not relevant 5-HT3 CYP1A2 or unknown Moclobemide – MAO-A 74–84% CYP2C19 CYP2C19 1.5 300–1000 3.72 Nefazodone Hydroxy-nefazodone, Triazoledione 5-HT2a 40–50% CYP3A4 Genetic polymorphism 3 unknown – m-Chlorophenylpiperazine SERT CYP2D6 not relevant or unknown Paroxetine – SERT 80% CYP2D6 CYP2D6 24 20–65 3.04 CYP3A4 Reboxetine NET n.d. CYP3A4 Genetic polymorphism 12.5 60–350 3.19 not relevant or unknown Sertraline SERT 80% CYP2B6 CYP2B6 35 10–150 3.27
DAT n.d. CYP2C19 CYP2C19 PSYCHIATRY BIOLOGICAL OF JOURNAL WORLD THE CYP2D6 CYP3A4 CYP2C9 Tranylcypromine – MAO >58% MAO Genetic polymorphism 250–70 (Cmax) 7.51 not relevant or unknown Trazodone m-Chlorophenylpiperazine SERT n.d. CYP3A4 Genetic polymorphism 6.6 (IR) 700–1000 2.69 5-HT2A CYP2D6 not relevant 12 (ER) 5HT2C or unknown Trimipramine Desmethyltrimipramine SERT n.d. CYP2C19 CYP2C19 24 150–300 3.40 NET n.d. CYP2D6 CYP2D6 CYP2C9 CYP1A2 CYP3A4 Venlafaxine O-Desmethyl-venlafaxine SERT 80% CYP2D6 CYP2C19 5 (IR) 100–400 (AM) 3.61 NET n.d. CYP2C19 CYP2D6 11 (IR) 3.80 CYP3A4 15 (ER) 15 (ER) Vilazodone – 25 35–67 2.26 (continued) 5 Table 1. Continued. 6 In vivo occupancy of Recommended
major target site at Major Genetic polymorphisms Mean therapeutic reference AL. ET EAP B. C. Target sites for therapeutic metabolising of metabolising t1/2 ranges Conversion Parent drugs Active metabolites therapeutic action concentrations enzymes enzymes [h] [ng/mL] factors SERT n.d. CYP3A4 Genetic polymorphism 5-HT1A 47–58% CYP2C19 not relevant CYP2D6 or unknown Vortioxetine – SERT 35–95% CYP2D6 CYP2D6 57 10–40 3.35 5-HT3 CYP3A4 5-HT7 CYP2C9 5-HT1D 5-HT1B 5-HT1A Augmenting drugs and bipolar depression Aripiprazole Dehydroaripiprazole D 80% CYP2D6 CYP2D6 75 40–200 (AM) 2.23 2 5-HT1A CYP3A4 94 Esketamine Noresketamine NMDAR unclear CYP3A4 Genetic polymorphism 2 – D2 CYP2B6 not relevant or unknown Ketamine Norketamine NMDAR 15–20% CYP3A4 CYP2B6 2 unclear – D2 CYP2B6 Lamotrigine – voltage-sensitive UGT1A4 Genetic polymorphism 25 3000–14000 3.90 sodium channels UGT2B7 not relevant or unknown Lithium – Second Genetic polymorphism 24 0.5–0.8 mmol/L – messenger system not relevant or unknown b Lurasidone – 5-HT2A Not available for CYP3A4 Genetic polymorphism 18 2–20 2.03 5-HT1A antidepressant activity not relevant D2 or unknown a Quetiapine Norquetiapine 5-HT2A , Not avaible for CYP3A4 Genetic polymorphism 7 50–100 2.61 b D1 antidepressant activity CYP2D6 not relevant 11 50–200 3.39 D2 or unknown Half-life or conversion factor of the metabolite. Genetic polymorphisms affecting drug concentrations and possibly clinical outcomes; source: www.pharmgkb.org/pathways (and others). Metabolising enzymes are listed from top to bottom according to their contribution, for details see text. Drug concentrations given in mass units can be converted to molar units by multiplication with the conversion factor (CF) nmol/L ¼ ng/mL CF. aReference range for augmentation in unipolar depression. bReference range for bipolar depression Abbreviations: AM: active moiety (parent drug + active metabolite); Cmax: maximal concentration of the drug in blood; COMT: cathechol-O-methyl transferase; CYP: cytochrome P450; D2: dopamine D2 receptors; DAT: dopamine transporter; ER: extended release; 5-HT: 5-hydroxytryptamine receptors; IR: immediate release; MAO: monoamine oxidase; MT: melatonin receptors; n.d.: no data; NMDAR: N-methyl-D-aspartate receptor; NET: norepinephrine transporter; SERT: serotonin transporter. THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 7 industry has withdrawn from antidepressant drug dis- mean high drug concentrations. Since the type of covery and the clinicians need tools to make the best response is not predictable in the whole patient group out of what is currently available. that is treated and studied, only pooled data of non-responders, placebo responders plus verum res- 1.1.1. Antidepressant drug concentrations in plasma ponders are obtained at the endpoint, and a negative and clinical effects correlation between drug concentration and improve- A relation between drug concentrations in plasma and ment is the outcome of these studies (Hiemke 2019). clinical outcomes is a prerequisite for TDM (Bengtsson These findings insinuate that low concentrations of 2004). Such relations enable determination of thera- the drug are more effective than high ones - a wrong peutic reference ranges (i.e. drug concentrations conclusion based on artificial findings. required to attain good clinical efficacy associated Flexible dose studies are thus not suitable for with acceptable tolerability). For nortriptyline an identifying an antidepressant concentration-response inverse U-shaped relationship between plasma con- relationship, especially when high numbers of non-res- centrations and clinical amelioration, with less ponders and placebo responders have been included. response at both extremely low and extremely high However, using fixed-dose studies with appropriate drug levels, was found (Åsberg et al. 1971). Moreover, design and analysis enables identification of a concen- high concentrations of nortriptyline were associated tration-effect relationship also for newer antidepres- with severe adverse effects (Åsberg et al. 1970). sant drugs, as shown for paroxetine (Eggart et al. Significant correlations were also found between 2011). On the other hand, a randomised clinical trial plasma concentrations and clinical effects for amitrip- failed to find a relationship between a concentration tyline, clomipramine, imipramine and desipramine of paroxetine and clinical improvement (Tasker et al. (Perry et al. 1994; Ulrich and L€auter 2002). As men- 1989), but the methodology used here was inappropri- tioned above, however, most studies of SSRIs and ate. The authors had not corrected for numbers of other new antidepressant drugs failed to find signifi- patients in the various concentration ranges. Doing cant relationships. These studies, most of them retro- this, a clear-cut concentration-effect relationship spective analyses of flexible dose treatments, have a emerged that was fully superimposable with serotonin design that is inappropriate for establishing a concen- transporter (SERT) occupancy data obtained in humans tration-response relationship (Preskorn SH 2014). In by positron emission tomography (Eggart et al. 2011). patients with major depression in need of medical In sum, it appears difficult to establish a concentra- treatment, the placebo response amounts to one-third tion-effect relationship for antidepressants based on of patients, while another one-third each is allotted to the totality of study results. However, that many stud- verum responders and non-responders, respectively ies have failed to find a significant relationship does (Preskorn 2014; Meister et al. 2017). not prove that the relationship does not exist. Non-response is related to many factors including Absence of evidence is not evidence of absence heterogeneity of depression, trauma history, genetics (Altman and Bland 1995). or other factors. A significant correlation between anti- depressant drug concentration in plasma and clinical 1.1.2. Indications for TDM of antidepressants improvement can be expected for the verum respond- As specified in the AGNP consensus guidelines for ers only (i.e. for one-third of the patients). Placebo res- TDM (Hiemke et al. 2018), plasma level monitoring ponders will respond to any drug concentration and may be useful for many antidepressant drugs and non-responders neither to low nor to high drug con- many indications. Its value, however, varies by drug, centrations. These two groups give rise to marked depending upon the drug’s pharmacological proper- noise and make it difficult or even impossible to iden- ties and on patients’ characteristics. With regard to tify a relationship between drug concentration and antidepressants, typical indications for TDM and prob- antidepressant response in the whole patient group. lems that can be solved by measuring antidepressant Even worse, in case of flexible dose studies, a negative drug levels in plasma are as follows: correlation may result (Hiemke 2019). Antidepressant drug treatment usually starts with a low dose. Under Dose optimisation after initial prescription of the such conditions, placebo responders will stay on low drug or after dose change for drugs with well- doses and exhibit mean low drug concentrations asso- documented reference ranges ciated with clinical improvement. The group of later Avoidance of toxic effects for drugs like TCAs with non-responders will receive high doses and exhibit a narrow therapeutic index 8 C. B. EAP ET AL.