Study protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study)

Study protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study) Paula Darm, Catharina Scholl, Elisabeth Paulus, Christine Reif-Leonhard, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9017719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antidepressant metabolism varies widely due to polymorphic cytochrome P450 (CYP) enzymeslike CYP2D6 and CYP2C19, of which various genetic variants exist. These variants influence serum concentrations, leading to side effects, low remission rates, and insufficient responses. Thus, physicians often try multiple antidepressants within a short period, causing increased psychological distress and negatively affecting disease progression. Pharmacogenetic information can be used for more targeted prescriptions. This monocentric observational study at the University Hospital Frankfurt (April 2025-June 2026) will recruit 200 patients with depression and aims to analyze the impact of different genotypes on treatment outcomes. Following genotyping, patients will be classified as normal or non-normal metabolizers and as having actionable or non-actionable genotypes. Clinical parameters, including the number and duration of depressive episodes, antidepressants used, sick leave days, and standardized clinical scores, will be recorded at admission. The study’s primary endpoint is antidepressant discontinuation. Reasons for discontinuation and other treatment interventions will be correlated with patients' genotypes for CYP2D6, CYP2C19, CYP2B6, and other genes. In the experimental part of the study, Oxford Nanopore long-read sequencing will be further investigated and validated. Changes in gut microbiome composition and metabolism will be analyzed during treatment in relation to CYP status and antidepressant use. Medical Genetics Genetics Pharmacogenomics Depression Study Protocol INTRODUCTION Background and rationale Depression is a globally prevalent mental disorder affecting approximately 5% of the adult population, which is equivalent to approximately 280 million people worldwide [1]. The treatment of depression often involves antidepressants, which are among the most commonly prescribed medications in Germany. However, after initial treatment, only 49% of patients with a first episode responded, and just 37% reached remission [2]. Many individuals suffer from side effects and treatment failure, and the repeated switching of medications often exacerbates their distress. The pharmacokinetics of antidepressants follow the ADME principle: absorption, distribution, metabolism, and excretion. These processes are primarily mediated by proteins that influence the pharmacokinetic properties of drugs. This can lead to interindividual differences in serum levels, even with the same dosage. This variability is influenced by factors such as age, sex, kidney and liver function, and drug interactions. A particularly important factor is genetic polymorphisms in the genes encoding transporters and enzymes, which alter the activity of these proteins. A central component of antidepressant metabolism is the CYP enzyme system, which plays a key role in drug metabolism. Genetic variants of these enzymes influence metabolic capacity, thereby affecting both therapeutic response and side effect profiles. A prominent example is the gene encoding the CYP enzyme 2D6, which is significantly involved in the metabolism of numerous antidepressants. This enzyme is highly polymorphic, which can lead to altered pharmacokinetics in individuals with different genetic variants. Genotyping enables the classification of patients into different metabolizer types. For example, four distinct phenotypes can be differentiated based on CYP2D6 enzyme activity: normal, intermediate, poor and ultrarapid-each with distinct pharmacokinetic profiles [3]. In empirical treatment, patients who are non-normal metabolizers may receive suboptimal treatment. Side effects can occur, or treatment may fail. Switching antidepressants is time-consuming because of the delayed onset of action and potential withdrawal symptoms. Depression can worsen, which, in the worst case, may lead to suicidal thoughts and attempts. The longer depression is inadequately treated, the greater the volume reduction in the hippocampus progresses, delaying recovery, particularly in terms of cognitive impairment. National treatment guidelines for unipolar depression therefore recommend antidepressants and/or psychotherapy even for moderate first-episode depression and for recurrent depression, even in mild cases [4]. For patients with a non-normal metabolizer status, pharmacogenetic guidelines recommend adjustments to standard treatment [5–7]. International guidelines for the approach to antidepressant treatment already exist but are still underutilized in practice owing to the lack of genotyping data. For example, the Dutch Pharmacogenetics Working Group (DPWG) guidelines have recommended preemptive testing (i.e., before starting antidepressants) for certain antidepressants for years. For some genotypes, switching to alternative antidepressants or different starting and target doses is advised [5–7]. Genetic variants in CYP2D6 and CYP2C19, which affect the main metabolic pathways of antidepressants, are detectable in 87% of inpatients in German psychiatric wards [8,9]. CYP2B6 also plays a role in the metabolism of certain antidepressants, such as the reserve drug esketamine (Spravato®), which is used for treatment-resistant depression but is associated with high costs (€3,300 per treatment week in the initial phase) [10]. The most commonly prescribed antidepressant in Germany, sertraline, is also metabolized by CYP2B6, and there are guideline recommendations for treatment adjustments on the basis of genotype [6]. International guidelines, such as those from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the DPWG, as well as German drug information, offer specific recommendations for clinical practice in the presence of certain genotypes 5-7 . Phenoconversion must also be considered to ensure accurate therapeutic recommendations on the basis of genotyping results [8,11]. It is crucial to consider comedications in combination with genotyping findings to ensure optimal therapy. Meta-analyses have shown that personalized selection of antidepressants significantly increases the likelihood of remission (from 41% to 71%) [12,13]. The average length of hospital stay for patients with depression is 20 to 40 days, and even after discharge, many patients remain only partially remitted and are unable to return to work immediately. Genotyping can significantly shorten the time to remission: 16.5% of patients who received stratified therapy achieved remission by week 12, whereas only 11.2% of patients who received empirical therapy achieved remission [14]. A hospital stay could be avoided with early genotyping, which would be highly important for patients, their environment, employers, health insurance companies, and the economy. Commercial pharmacogenetic tests currently use primarily polymerase chain reaction (PCR), microarray or mass-array technologies (e.g., the iPLEX® PGx 74 Panel by Agena Bioscience or Stratipharm). These tests focus on common genetic variants that significantly impact the clinical phenotype. Pharmacogenetics (PGx) testing is primarily implemented for the genes CYP2D6 and CYP2C19 [15]. For example, the iPLEX® PGx 74 Panel tests only 10 variants of the CYP2C9 gene, although 36 functional variants are known. The Stratipharm panel detects only 12 variants of CYP2D6, although more than 400 variants (177 star alleles) have been described. Another issue with microarray technologies is their limited ability to fully capture structural complexity, such as copy number variations of certain pharmacogenes, such as CYP2D6, which may lead to incorrect results. Exome and whole-genome sequencing (WGS) are gaining importance in pharmacogenetic research. Studies have shown that pharmacogenetic variants can also be derived from sequencing data. In particular, long-read sequencing offers promising possibilities, as it enables more comprehensive identification of functional and structural variants and is at the heart of the new field of pharmacoeepigenomics [16]. However, there are still few data on the analytical validity of this method for pharmacogenetic tests. In addition to individual genetics, the microbial gut flora could also play a role in the variation in antidepressant efficacy and side effects. The gut microbiota consists of trillions of microbes and contains 10 to 100 times more genes than the human genome does [17]. It is involved in the processing of substances ingested orally and interacts with the central nervous system via the gut-brain system [18]. Antidepressants such as SSRIs reduce microbial diversity in the gut and can alter the dynamics of the microbial community [19]. In vitro studies have shown that SSRIs influence serotonin metabolism in the gut, which could lead to side effects and a reduced treatment response [20]. However, clinical data on the interaction between antidepressants and the gut microbiota, as well as the influence of host genetics, remain limited [21]. Therefore, in this study, stool samples were collected at the beginning and upon discharge for sequencing and assessment of metabolic performance. In Germany, only a few clinical studies have investigated the impact of pharmacogenetic testing on the treatment course of depressive patients. The goal of our research is to demonstrate, via the use of a heterogeneous naturalistic sample, how genotyping in patients with depression can help reduce the need for clinical interventions. Explanation for the choice of comparators In this study, individuals are compared based on their metabolizer status and genetic actionability in psychotropic drug metabolism. Patients are categorized as normal metabolizers (NM) or non-normal metabolizers , including poor (PM), intermediate (IM), and ultrarapid metabolizers (UM) , following established pharmacogenetic principles. This comparison is essential for understanding the clinical impact of genetic variability on drug metabolism and treatment response in psychiatric patients. Additionally, we distinguish between actionable genotypes (those with recommended dose adjustments or alternative treatment suggestions) and non-actionable genotypes based on current pharmacogenomic guidelines (e.g., CPIC, DPWG). While actionable genotypes may have direct clinical implications , non-actionable genotypes typically do not require immediate intervention. A non-interventional, observational design is appropriate, as the study aims to evaluate naturally occurring genetic variations and their associations with psychiatric drug metabolism. Since genetic predisposition, rather than drug effects, is the primary variable of interest, neither a placebo nor an active comparator is necessary. Objectives We hypothesize that genetic differences significantly influence these two endpoints. The primary endpoint is the frequency of antidepressant discontinuation in relation to the genotypes of CYP2D6, CYP2C19, and CYP2B6. The secondary endpoint is a self-defined score based on the number of clinical events during hospitalization. DISCUSSION Genetic testing is rarely implemented in routine clinical practice in psychiatric clinics across Germany. One key reason for this is the lack of reimbursement by health insurance providers, as well as the absence of standardized testing protocols [ 22 ]. Additionally, there are insufficient training programs to enable medical professionals to reliably interpret pharmacogenetic findings [ 23 ]. To address this barrier, we aim to develop a training video on pharmacogenetics and interpretation of results. Previous studies have demonstrated that pharmacogenetically guided drug selection can reduce adverse drug reactions, improve treatment efficacy, and shorten hospitalization [ 24 , 25 ]. The objective of our study is to integrate genetic testing more comprehensively into the routine treatment of depression in Germany. Genotyping should not only assist in dose adjustments for antidepressants but also facilitate the targeted selection of pharmacological agents. Currently, only one-third of patients with depression achieve sustained remission with the first prescribed medication, whereas two-thirds require medication adjustments. This leads to persistent symptoms, which can reduce work productivity, increase healthcare utilization, and increase suicide risk [ 26 , 27 ]. Our study examines how genetic variants influence treatment trajectories, with the goal of developing algorithms to accelerate symptom remission. To achieve this goal, we systematically collect comprehensive data on treatment outcomes, clinical interventions, and prescribed medications while ensuring that the course of treatment remains unaffected. These data are then correlated with pharmacogenetic findings. For genetic analyses, we employ commercial pharmacogenetic tests alongside long-read sequencing, a novel method with the potential to enhance genetic variant detection. This project aims to validate the clinical utility of long-read sequencing and improve the detection of highly polymorphic genes, such as CYP2D6, thereby facilitating its integration into clinical practice. METHODS Study design This open-label, non-randomized, observational study investigates the pharmacogenetic impact on treatment outcomes before, during and 4 weeks after hospitalization in patients with depression. Study setting This multicenter clinical trial is being conducted in Germany at the University Hospital Frankfurt, Department of Psychiatry, Psychosomatics, and Psychotherapy, and at the Federal Institute for Drugs and Medical Devices in Bonn. Participant timeline Run-in phase (days 0 - 14) After patients sign the informed consent form and are enrolled in the study, an oral mucosal swab is collected and sent to a accredited commercial laboratory. The aim of this study was to determine the pharmacogenetic findings and phenotype classifications. Additionally, patients provide further biomaterial samples, including a second mucosal swab and a blood sample for long-read sequencing validation at the BfArM, as well as four stool samples—two at admission and two at discharge—for microbiome analysis. During the run-in phase, various data are collected. These include demographic information such as age and sex, results from the Inventory of Depressive Symptomatology (IDS) and the Montgomery-Åsberg Depression Rating Scale (MADRS) (both part of routine ward assessments), details on pharmacological and psychotherapeutic treatments prior to admission, the number of medications taken and potential drug interactions, incapacity for work status at admission, the suicide risk level at admission, and the use of ‘actionable’ antidepressants, as recorded in the patient file. Additionally, the Global Assessment of Functioning (GAF), Clinical Global Impression (CGI), and CGI toxicity scores are documented. Three separate questionnaires are used to assess side effects (Bonn Survey Instrument for Direct Subjective Assessment of Side Effects), quality of life (WHO-5 Well-Being Index), and adherence. Observation phase (until discharge) New data, including TDM findings, glomerular filtration rate (GFR), liver function, and pharmacogenetic results, including actionable genotypes, comedication, medication discontinuation, changes in antidepressant treatment (switches, dose adjustments), total number of psychotropic drugs, number of ECT sessions, number of (es)ketamine treatments, number of TMS sessions, drug interactions and phenoconversion, transfers due to adverse drug reactions (ADRs), reasons for antidepressant discontinuation and corrected QT-time (QTc) interval measurements, were collected from the patient file. At discharge At discharge, the following parameters are reassessed: IDS and MADRS scores, length of hospital stay, sick leave status at discharge, drug interactions at discharge, total number of medications and psychotropic drugs, number of ‘actionable’ antidepressants, and GAF and CGI scores. Additionally, the questionnaire on side effects is administered again, and another two stool samples are collected. Follow-up (4 weeks after discharge) As part of the telephone follow-up, information is gathered on changes in medication, side effects, reasons for antidepressant discontinuation, comedication, number of ‘actionable’ antidepressants, total number of medications, drug interactions, sick leave status (incapacity to work) and quality of life. Eligibility criteria Patients hospitalized for affective disorders at the University Hospital Frankfurt, Department of Psychiatry, Psychosomatics, and Psychotherapy, will be included if they meet the following criteria: (1) age ≥18 years and (2) diagnosis of depressive episode or recurrent depressive disorder (ICD codes: F32.x, F33.x). Patients meeting any of the following criteria will be excluded from the study: (1) Diagnosis of specific personality disorders, combined or other personality disorders, schizophrenia, bipolar affective disorder, or posttraumatic stress disorder (ICD codes: F60.x, F61.x, F2x.x, F31.x, F43.1). (2) Prior genotyping before admission. Recruitment Participants will be screened for eligibility and informed about the study upon admission during the regular hospital admission interview. Informed consent During enrollment, patients receive an information leaflet and an informed consent form. These documents provide relevant information about the study, including its background, procedure, duration, objectives, data collection, risks, and potential benefits. Patients are informed that participation is voluntary and can be withdrawn at any time without providing a reason. Patients can also access an informational video on the ward via a QR code or link. The video provides a clear explanation of the pharmacogenetics in precise and comprehensible language. Watching the video is optional, and patients may view it as often as they like. Informed consent is obtained before study participation begins. The consent form is signed and dated by the principal investigator or an authorized representative. Sample size The sample size is set at 200 patients, who are categorized into normal metabolizers and non-normal metabolizers (other phenotypes) after genotyping. The calculation of the sample size is based on a power analysis with an effect size of 0.35, an α error of 0.05 and a power of 80%. The methodological derivation was carried out in consultation with a biostatistician at the BfArM. Primary and secondary outcomes The primary objective is to compare antidepressant discontinuation rates in relation to (1) non-normal vs. normal metabolizer status and (2) actionable vs. non-actionable genotypes. Actionable genotypes indicate that there are dosage recommendations in the CPIC and DPWG guidelines for the antidepressant and the genotype of the patient. Antidepressants prescribed before study enrollment are also considered by reviewing the medication history of the patient. Data will be collected from genotyping results, patient records, personal interviews, and three self-administered questionnaires. The following questionnaires will be used: Inventory of Depressive Symptomatology (IDS), Clinical Global Impression (CGI), CGI Toxicity, Montgomery-Åsberg Depression Rating Scale (MADRS), Bonn Survey Instrument for Subjective Side Effect Assessment (BESEN-direkt), WHO-5 Well-Being Index, and an adherence questionnaire. Data will be collected at four time points—admission, hospitalization, discharge, and four weeks post discharge—via telephone follow-up. Reasons for antidepressant discontinuation and the frequency of clinical events will be analyzed as secondary outcomes. Clinical events include therapeutic drug monitoring (TDM), medication changes, augmentation and combination therapies, and escalation procedures such as electroconvulsive therapy (ECT), (es)ketamine, and repetitive transcranial magnetic stimulation (rTMS). Secondary outcome data will be collected at the same four time points as the primary outcome data. The experimental part of the study investigates and validates the long-read sequencing method. The goal is to identify pharmacogenetically relevant genotypes and compare them with a commercial test. This study will also analyze changes in the gut microbiome composition and metabolic activity throughout treatment, considering correlations with medical history and side effects. Baseline microbiome composition will be compared among normal, intermediate, poor, rapid, and ultrarapid metabolizers. A follow-up analysis will validate and assess the robustness of these findings. Additionally, the study evaluates physicians’ knowledge gains through pre- and post-training comparisons in pharmacogenetics. Implementation Once commercial pharmacogenetic results are available, participants will be assigned to either the normal metabolizers or non-normal metabolizers group based on phenotype prediction. The allocation is carried out by a clinical pharmacist. Data collection and measures Data collected as outlined in the ‘Participant Timeline’ section are partly assessed via validated self-report questionnaires. These paper-based questionnaires were approved by the local ethics committee. The Global Assessment of Functioning (GAF) and Clinical Global Impression (CGI) scores are determined by a trained rater. All other data are directly retrieved from the electronic patient records. Plans to promote participant retention and complete follow-up The risk of participant dropout is considered negligible, as all assessments occur during the inpatient stay in the psychiatric hospital. However, a dropout rate of up to 10% is expected during the follow-up period (4 weeks after discharge). Patients who withdraw consent will be informed that all the data collected until the time of withdrawal will remain encrypted and used for analysis unless the patient explicitly requests data deletion. Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use The oral mucosa swabs analyzed by commercial laboratory will be destroyed after completion of the analysis. The pharmacogenetic findings will remain accessible to the patient's treating physicians and pharmacists for the patient's lifetime. Deletion of genetic findings at the laboratory can be initiated only by the patient. Blood and oral mucosa samples intended for long-read sequencing are identified by a unique patient identifier, collected in sterile containers, and regularly sent to the pharmacogenomics research group at the BfArM. The BfArM will store the DNA extracted from blood and oral mucosa samples for up to ten years for potential retesting. Stool samples will also be stored for a maximum of ten years in the BaseClear B.V. laboratory in Leiden, Netherlands. Laboratory personnel outside the Department of Psychiatry, Psychosomatics, and Psychotherapy at Frankfurt University Hospital do not have access to the patient identification list and therefore cannot trace samples back to individual patients. All the data are recorded, stored, and transmitted in a password-protected, pseudonymized Excel spreadsheet at Frankfurt University Hospital. Consents and questionnaires are stored for ten years in a locked room at the Department of Psychiatry, Psychosomatics, and Psychotherapy. Patients can withdraw their consent at any time. Upon withdrawal, the data are deleted or destroyed. Data management and quality control Data from biomaterials, questionnaires, personal interviews, and patient records will be regularly transferred to an electronic database by a clinical research coordinator. Each dataset will be coded via a unique patient identifier. All study data will be stored for 10 years after study completion. All institutional and funding information is provided in Supplementary Table 2. Trial monitoring This observational study will be monitored internally by the principal investigator and the research team. Recruitment protocols will be reviewed weekly to ensure that inclusion and exclusion criteria are applied consistently. Data entry will be checked monthly for completeness, internal consistency and plausibility. Any discrepancies or protocol deviations will be documented and discussed in monthly team meetings. Genotyping procedures Commercial test from Humatrix AG (Stratipharm) In the laboratory, extracted deoxyribonucleic acid (DNA) is quantified and normalized via an automated robotic platform and a spectrometer. The DNA is stored at 4°C until it is ready for PCR amplification as part of single-nucleotide polymorphism (SNP) or copy number variation (CNV) analysis. SNP determination is performed via real-time PCR. The Master Mix is combined with an intradiagnostic (IVD) PCR primer/probe panel. For each SNP, a PCR reaction is carried out using the diluted DNA sample, the Master Mix, and the specific assay. Each assay contains two PCR primers and two fluorescence-labeled probes, which are short DNA molecules that specifically bind to either the wild-type or mutant sequences. For sample sets of fewer than 12 or to confirm ambiguous SNP determination results obtained via real-time PCR with an array, a classical PCR approach is used. In this method, standard PCR is performed to ensure accuracy of the results. For the array-based approach, all 94 TaqMan® SNP genotyping assays are preloaded and dried within the through-holes of the array. Real-time PCR in OpenArray format follows the same principles as classical PCR but requires a specifically configured device with a modified thermal block and heated cover. Prior to real-time PCR, a multiplex short PCR reaction is performed for preamplification of the 94 loci of interest. Pharmacogenetic laboratory diagnostics (lab-developed test – LDT) at the MVZ Labor Dr. Limbach medical center The DNA is automatically isolated from oral mucosal swabs using QIAcube, purified, and quantified using a spectrophotometer. The DNA is then stored at 4°C until PCR amplification for genotyping. The MassARRAY® DX Analyzer 4 system from Agena Bioscience is used for genotyping (SNP and CNV analysis), using the VeriDose® Core Panel and the CYP2D6 CNV Panel v2.0. First, the target DNA is amplified by PCR. This is followed by enzymatic purification (SAP digestion) and an allele-specific primer extension reaction (iPLEX® Pro) using mass-modified nucleotides. The resulting DNA fragments are transferred to a SpectroCHIP® matrix and separated in the MALDI-TOF mass spectrometer based on their mass, which enables precise differentiation of the alleles. Bioinformatic evaluation and determination of the diplotypes is performed using MassARRAY Typer® software. Long-Read-Sequencing with PromethION2 from Oxford Nanopore Technologies For this method, high-molecular-weight DNA is isolated from blood samples via the Monarch HMW DNA Extraction Kit from New England Biolabs GmbH. Alternatively, DNA can be extracted from oral mucosa samples, which can be purified via the Puregene Kit from Qiagen. For sequencing with PromethION2, library preparation is performed first. This involves preparing the ends of the previously isolated DNA by removing protruding bases. In addition, single-strand breaks are repaired to ensure seamless sequencing. After successful preparation, the DNA ends are ligated to sequencing adapters. The prepared library is then loaded into the flow cell of the PromethION2 device. Here, sequencing occurs via small pores embedded in a membrane. The DNA strands pass through the pores, resulting in a base-dependent change in the current flow through the pore. These electrical signals can then be translated into the DNA sequence, which is known as ‘basecalling’. A special feature of sequencing with the Oxford Nanopore system is so-called ‘adaptive sampling’. This technique enables the enrichment of predetermined target sequences within the genome by ensuring that only these strands are fully sequenced. Sequences outside the target region are selectively discarded, leading to an increased concentration of the desired sequences. Statistical methods The primary endpoint is antidepressant discontinuation. The analysis evaluates whether certain genotypes are associated with an increased risk of discontinuation. Additionally, the reasons for discontinuation are examined. The secondary endpoint is recorded as a self-defined score based on the number of clinical events, including the therapeutic drug monitoring (TDM) frequency, number of medication changes (switch), number of augmentation therapies, number of combination therapies and number of escalating procedures such as ECT, (es)ketamine treatment and repetitive transcranial magnetic stimulation (rTMS), as well as the number of treatments involving escalation procedures. Treatment discontinuation and adverse effects, quality of life, MADRS, CGI, GAF, and CGI toxicity scores. Primary endpoint analysis: The Z test for two proportions will be used to compare the frequency of clinical events between the two groups. H₀: No difference in clinical event proportions between groups H₁: Significant difference in clinical event proportions between groups Assumed odds ratio: 1.4 (effect size) Sample size: N₁ = N₂ = 100 Secondary endpoint analysis: A two-sample t- test will compare the mean event scores between groups. H₀: No difference in the mean number of clinical events between groups H₁: Significant difference in the mean number of clinical events between groups Statistical power (Cohen’s d): Small effect: 56% Medium effect: 94% Large effect: 99% The significance level is α = 0.05. Confounding factors will be adjusted for in subsequent statistical analyses. Any deviation from the original statistical plan will be documented and justified in the final study report. Methods for additional analyses (e.g., subgroup analyses) The results from long-read sequencing may be used to correlate individual genes with antidepressant response or side effects. Table 1 includes common pharmacogenes, with mainly level 3 and 4 evidence for antidepressants in accordance with pharmgkb.org (information from 12.03.2025). Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data If possible, missing data will be retrospectively retrieved from medical records, personal interviews, or telephone follow-up surveys. If missing data cannot be recovered, the last observed value will be used for analysis (the last observation carried forward method). Definition of who will be included in each analysis All participants who meet the inclusion criteria and provide informed consent will be included in the analysis. Analyses will be restricted to participants with complete baseline and follow-up data. Adverse event reporting and harms During the study, oral mucosa swabs, blood samples (during routine blood checks), and stool samples will be collected. These procedures are low-risk and are part of routine clinical practice. If serious adverse events (SAEs) related to antidepressant pharmacotherapy occur, they will be reported to the National Drug Commission in accordance with regulatory requirements. Trial status The trial is registered in a public trial registry for clinical trials, ensuring public access to the trial procedure and current status. (German clinical trial registration number DRKS00036105) Protocol version number and date: Version 1, 22.04.2025 Date recruitment started: 01.04.2025 Date recruitment approximately will be completed: 31.05.2026 Amendments to protocol Any significant changes to the study design, organization, protocol, or relevant study documents will be submitted to the local ethics committee for approval before implementation. In emergency situations, protocol deviations may be necessary to protect the rights, safety, or well-being of study participants. Any such deviations must be documented and communicated to the ethics committee as soon as possible. Patients who continue to participate will be asked to reconsent to any major changes in study procedures. Minor modifications that do not significantly impact the study will be reported to the ethics committee in an annual report. Provisions for post-trial care After genotyping, patients receive the genotyping results from the commercial test. The participants do not receive financial compensation. Ethics approval and consent to participate The study was approved by the Ethics Committee of the Faculty of Medicine at Goethe University Frankfurt on January 13, 2025 (reference number 5/25). Informed consent will be obtained from all trial participants. Confidentiality Study and participant data will be handled with strict confidentiality and will only be accessible to authorized personnel who require access for study-related tasks. The participants are identified solely by their unique number of participants. The participant identification list will be kept in a locked location at the study site under the supervision of the study director. Only encrypted data may be transferred outside the study site. Additionally, all collected data will be stored on password-protected drives within the study clinic, accessible only to authorized personnel. However, all the study data must remain traceable to the original source data at the study site. For quality assurance, the ethics committee or an independent study monitor may visit the research sites. They will be granted direct access to source data and all study-related documents. All parties involved are committed to treating participant data as strictly confidential. Ethics and dissemination The study results will be published in peer-reviewed open-access journals. Additionally, findings will be presented at relevant scientific and clinical conferences to ensure dissemination among researchers and healthcare professionals. The study was approved by the Ethics Committee of the Faculty of Medicine at Goethe University Frankfurt on January 13, 2025 (Reference No. 5/25). Declarations Author Contributions Statement P. Darm and M. Hahn contributed equally to the writing of this manuscript. A. Reif is the principal investigator. C. Scholl, A. Glässner, M. Steffens and M. Bloemendaal were involved in the study design and contributed to the additional content. E. Paulus, C. Reif-Leonhard, M. Qubad, and S. Oppermann were responsible for the critical revision. All authors reviewed the manuscript. Additional Information Competing interests The authors declare that they have no conflicts of interest related to this study. However, M. Hahn is a member of CPIC, and A. Reif is, among others, a board member of DGPPN and the president of the ECNP. These memberships did not influence the study in any way. Funding The Federal Institute for Drugs and Medical Devices supports the study by covering the costs of the study materials. M. Bloemendaal receives funding from the Brain and Behavior Research Foundation and an EU Marie S. Curie Postdoctoral Fellowship. The authors declare that the funding organizations had no influence on the study design, data interpretation, or writing of the report. Data Availability Anonymized data will be made available upon reasonable request to the corresponding author after publication of the main results, subject to ethics approval. Genotype and microbiome data will be deposited upon study completion. The investigators will have complete access to the final dataset of the trial. 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Pharmacogenomics 20:37–47 Oslin DW et al (2022) Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial. JAMA 328:151 Scherf-Clavel M, Weber H, Deckert J, Erhardt-Lehmann A (2021) The role of pharmacogenetics in the treatment of anxiety disorders and the future potential for targeted therapeutics. Expert Opin Drug Metab Toxicol 17:1249–1260 Kalinin AA et al (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19:629–650 Doestzada M et al (2018) Pharmacomicrobiomics: a novel route towards personalized medicine? Protein Cell 9:432–445 Cryan JF et al (2019) The Microbiota-Gut-Brain Axis. Physiol Rev 99:1877–2013 Brushett S et al (2023) Gut feelings: the relations between depression, anxiety, psychotropic drugs and the gut microbiome. Gut Microbes 15:2281360 Fung TC et al (2019) Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol 4:2064–2073 Wang Y et al (2023) Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder. Microbiome 11:195 Relling MV, Evans WE (2015) Pharmacogenomics in the clinic. Nature 526:343–350 Eckert A, Frantz A, Reif A, Hahn M, Akzeptanz (2023) Wissen und Einstellungen von Ärzten zu pharmakogenetischen Tests in der Psychiatrie. Nervenheilkunde 42:459–466 Bättig VAD, Roll SC, Hahn M (2020) Pharmacogenetic Testing in Depressed Patients and Interdisciplinary Exchange between a Pharmacist and Psychiatrists Results in Reduced Hospitalization Times. Pharmacopsychiatry 53:185–192 Winner J, Allen JD, Altar CA, Spahic-Mihajlovic A (2013) Psychiatric pharmacogenomics predicts health resource utilization of outpatients with anxiety and depression. Transl Psychiatry 3:e242 Rush AJ et al (2006) Acute and Longer-Term Outcomes in Depressed Outpatients Requiring One or Several Treatment Steps: A STAR*D Report. Am J Psychiatry 163:1905–1917 Greenberg PE, Fournier A-A, Sisitsky T, Pike CT, Kessler RC (2015) The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). J Clin Psychiatry 76:155–162 Table Table 1 . Genpanel PromethION adaptive sequencing Gen Protein ABCB1 P-Glykoprotein ABCC2 Multidrug Resistance Protein 2 ABCG2 Breast Cancer Resistance Protein ACE Angiotensin Converting Enzyme ACTB Beta-Actin ADRB1 Adrenozeptor Beta 1 ADRB2 Adrenozeptor Beta 2 APOE Apolipo-Protein E CACNA1S Calcium Voltage-Gated Channel Subunit Alpha1 S CFTR Cystic Fibrosis Transmembrane Conductance Regulator CHRNA5 Cholinergic Receptor Nicotinic Alpha 5 Subunit COMT Catechol-O-Methyltransferase COQ2 P-Hydroxybenzoat-Polyprenyltransferase CREB1 cAMP Response Element-Binding Protein CYP17A1 CYP17A1 CYP19A1 CYP19A1 CYP1A1 CYP1A1 CYP1A2 CYP1A2 CYP1B1 CYP1B1 CYP26A1 CYP26A1 CYP2A13 CYP2A13 CYP2A6 CYP2A6 CYP2A7 CYP2A7 CYP2B6 CYP2B6 CYP2B7 CYP2B7 CYP2C19 CYP2C19 CYP2C8 CYP2C8 CYP2C9 CYP2C9 CYP2D6-8 CYP2D6-8 CYP2E1 CYP2E1 CYP2F1 CYP2F1 CYP2J2 CYP2J2 CYP2R1 CYP2R1 CYP2S1 CYP2S1 CYP2W1 CYP2W1 CYP3A4-43-5-7 CYP3A4-43-5-7 CYP4A11 CYP4A11 CYP4A22 CYP4A22 CYP4B1 CYP4B1 CYP4F2 CYP4F2 DPYD Dihydropyrimidin-Dehydrogenase DRD1 Dopamin Rezeptor D1 DRD2 Dopamin Rezeptor D2 DRD3 Dopamin Rezeptor D3 EGFR Epidermal Growth Factor Receptor F2 Prothrombin G6PD Glukose-6-phosphat-Dehydrogenase GABRA6 Gamma-aminobutyric acid receptor subunit alpha-6 GABRP Gamma-aminobutyric acid receptor subunit pi GABRQ Gamma-aminobutyric acid receptor subunit theta GAPDH Glycerinaldehyd-3-Phosphat-Dehydrogenase GLDC Glycin Decarboxylase GLP1R Glucagon like Peptide 1 Rezeptor GNB3 Guanin nucleotide-binding protein subunit beta-3 GRIA3 Glutamat ionotropic receptor AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate) type subunit 3 GSK3B Glykogen Synthase Kinase-3 Beta GSTM1 Glutathion-S Transferase M1 GSTP1 Glutathion-S Transferase P1 HMGCR 3-Hydroxy-3-Methylglutaryl-CoA Reduktase HTR1A 5-Hydroxytryptamin Rezeptor 1A HTR1B 5-Hydroxytryptamin Rezeptor 1B HTR2A 5-Hydroxytryptamin Rezeptor 2A HTR2C 5-Hydroxytryptamin Rezeptor 2C HTR7 5-Hydroxytryptamin Rezeptor 7 IFNL3 Interferon Lambda 3 IL11 Interleukin 11 ITPA Inosin Triphosphatase MTHFR Methylen-Tetrahydrofolat-Reduktase NAT1 N-Acetyltransferase 1 NAT2 N-Acetyltransferase 2 NOS3 Stickstoffmonoxid-Synthase 3 NUDT15 Nudix Hydrolase 15 OPRM1 Opioid Rezeptur Mu 1 PNPLA5 Patatin-Like Phospholipase Domain-Containing 5 POR Cytochrom-P450-Oxidoreduktase PTGIS Prostaglandin I2 Synthase RYR1 Ryanodin Rezeptor 1 SLC15A2 Solute Carrier Family 15 Member 2 SLC19A1 Solute Carrier Family 19 Member 1 SLC22A1 Solute Carrier Family 22 Member 1 SLC22A2 Solute Carrier Family 22 Member 2 SLC22A6 Solute Carrier Family 22 Member 6 SLC6A4 Solute Carrier Family 6 Member 4 SLCO1A2 Solute Carrier Organic Anion Transporter Family Member 1A2 SLCO1B1 Solute Carrier Organic Anion Transporter Family Member 1B1 SLCO1B3 Solute Carrier Organic Anion Transporter Family Member 1B3 SLCO2B1 Solute Carrier Organic Anion Transporter Family Member 2B1 SULT1A1 Sulfotransferase Family 1A Member 1 SULT4A1 Sulfotransferase Family 4A Member 1 TBXAS1 Thromboxan A Synthase 1 TPH1 Tryptophan Hydroxylase 1 TPMT Thiopurin S-Methyltransferase UGT1A1 UDP-Glucuronosyltransferase Family 1 Member A1 UGT1A4 UDP-Glucuronosyltransferase Family 1 Member A4 UGT2B15 UDP-Glucuronosyltransferase Family 2 Member B15 UGT2B17 UDP-Glucuronosyltransferase Family 2 Member B17 UGT2B7 UDP-Glucuronosyltransferase Family 2 Member B7 VDR Vitamin-D-Rezeptor VKORC1 Vitamin-K Epoxid Reduktase Complex Subunit-1 XPC DNA Damage Recognition and Repair Factor Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformation21022026a.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9017719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599819359,"identity":"833088f2-3dd5-4eab-9b4d-3adbb0c2d38e","order_by":0,"name":"Paula Darm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDCCAxBKhoG9AUgZWBCvhYeBB8QykCBFi0QCiCZCC9/xs083F9Qc5uGf+fzqhh8FEgz87d0JeLVInkk3uz3j2GEeids5ZTd7gA6TOHN2A14tBgfS2G7zsB3mYbidk3aDB6jFQCKXgJbzz4Ba/h3mkb95Ju3mH6K03ADawtt2mMfgBvux20TZInkDaMvMvnQewzM5bLdlDCR4CPqF7zzQloJv1nJyx48/u/nmj40cf3svfi0gwAyheAzAJEHlSFrYHxClehSMglEwCkYeAAAwXUo/SlPGkQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-0245-9221","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":true,"prefix":"","firstName":"Paula","middleName":"","lastName":"Darm","suffix":""},{"id":599819780,"identity":"a6e2c6c7-23f6-4993-a38f-9567bbd9053b","order_by":1,"name":"Catharina Scholl","email":"","orcid":"","institution":"Federal Institute for Drugs and Medical Devices, Research Division, Bonn, Germany","correspondingAuthor":false,"prefix":"","firstName":"Catharina","middleName":"","lastName":"Scholl","suffix":""},{"id":599827784,"identity":"0a666838-ec0a-4959-a077-cfb735ca7002","order_by":2,"name":"Elisabeth Paulus","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"","lastName":"Paulus","suffix":""},{"id":599827785,"identity":"e21d4fb2-8144-4ccc-83e6-c02f778a200a","order_by":3,"name":"Christine Reif-Leonhard","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Reif-Leonhard","suffix":""},{"id":599827786,"identity":"0c917f8e-9136-44b7-9403-7b25bb5362a3","order_by":4,"name":"Andreas Glaessner","email":"","orcid":"","institution":"Federal Institute for Drugs and Medical Devices, Research Division, Bonn, Germany","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Glaessner","suffix":""},{"id":599827787,"identity":"ffcc942d-4e2c-4032-ab11-59587957ae21","order_by":5,"name":"Mishal Qubad","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Mishal","middleName":"","lastName":"Qubad","suffix":""},{"id":599827788,"identity":"aded189f-6639-45ed-b106-b0f89628388d","order_by":6,"name":"Mirjam Bloemendaal","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Mirjam","middleName":"","lastName":"Bloemendaal","suffix":""},{"id":599827789,"identity":"f8749d70-3c2b-4d8d-855a-fe10e5494348","order_by":7,"name":"Michael Steffens","email":"","orcid":"","institution":"Federal Institute for Drugs and Medical Devices, Research Division, Bonn, Germany","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Steffens","suffix":""},{"id":599827790,"identity":"c74a7bfc-3073-4bc0-86f7-5534ec707e43","order_by":8,"name":"Sina Oppermann","email":"","orcid":"","institution":"Goethe University Frankfurt, Institute of Pharmacology and Clinical Pharmacy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Sina","middleName":"","lastName":"Oppermann","suffix":""},{"id":599827791,"identity":"17d94936-f578-4df6-928d-cdb3f9acbace","order_by":9,"name":"Andreas Reif","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Reif","suffix":""},{"id":599827792,"identity":"cdace756-ce69-4077-9c78-6f15eddd9091","order_by":10,"name":"Martina Hahn","email":"","orcid":"","institution":"Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatics and Psychotherapy, Frankfurt am Main, Germany","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Hahn","suffix":""}],"badges":[],"createdAt":"2026-03-03 08:15:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9017719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9017719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103889057,"identity":"f18d5e49-0134-4f4b-b355-893458cfb8cf","added_by":"auto","created_at":"2026-03-04 07:42:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1288962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9017719/v1/e310e97e-90e6-4558-8f54-7aa24d6ee1c4.pdf"},{"id":103888993,"identity":"ae1cf370-f800-4ca9-955c-245adf23b55e","added_by":"auto","created_at":"2026-03-04 07:42:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19847,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"SupplementaryInformation21022026a.docx","url":"https://assets-eu.researchsquare.com/files/rs-9017719/v1/87d43a03494729d60b7a5472.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eStudy protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e\u003cstrong\u003eBackground and rationale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepression is a globally prevalent mental disorder affecting approximately 5% of the adult population, which is equivalent to approximately 280 million people worldwide [1]. The treatment of depression often involves antidepressants, which are among the most commonly prescribed medications in Germany. However, after initial treatment, only 49% of patients with a first episode responded, and just 37% reached remission\u0026nbsp;[2]. Many individuals suffer from side effects and treatment failure, and the repeated switching of medications often exacerbates their distress.\u003c/p\u003e\n\u003cp\u003eThe pharmacokinetics of antidepressants follow the ADME principle: absorption, distribution, metabolism, and excretion.\u0026nbsp;These processes are primarily mediated by proteins that influence the pharmacokinetic properties of drugs. This can lead to interindividual differences in serum levels, even with the same dosage. This variability is influenced by factors such as age, sex, kidney and liver function, and drug interactions. A particularly important factor is genetic polymorphisms in the genes encoding transporters and enzymes, which alter the activity of these proteins.\u003c/p\u003e\n\u003cp\u003eA central component of antidepressant metabolism is the CYP enzyme system, which plays a key role in drug metabolism. Genetic variants of these enzymes influence metabolic capacity, thereby affecting both therapeutic response and side effect profiles. A prominent example is the gene encoding the CYP enzyme 2D6, which is significantly involved in the metabolism of numerous antidepressants. This enzyme is highly polymorphic, which can lead to altered pharmacokinetics in individuals with different genetic variants. Genotyping enables the classification of patients into different metabolizer types. For example, four distinct phenotypes can be differentiated based on CYP2D6 enzyme activity: normal, intermediate, poor and ultrarapid-each with distinct pharmacokinetic profiles [3].\u003c/p\u003e\n\u003cp\u003eIn empirical treatment, patients who are non-normal metabolizers may receive suboptimal treatment. Side effects can occur, or treatment may fail. Switching antidepressants is time-consuming because of the delayed onset of action and potential withdrawal symptoms. Depression can worsen, which, in the worst case, may lead to suicidal thoughts and attempts. The longer depression is inadequately treated, the greater the volume reduction in the hippocampus progresses, delaying recovery, particularly in terms of cognitive impairment. National treatment guidelines for unipolar depression therefore recommend antidepressants and/or psychotherapy even for moderate first-episode depression and for recurrent depression, even in mild cases [4]. For patients with a non-normal metabolizer status, pharmacogenetic guidelines recommend adjustments to standard treatment [5–7].\u003c/p\u003e\n\u003cp\u003eInternational guidelines for the approach to antidepressant treatment already exist but are still underutilized in practice owing to the lack of genotyping data. For example, the\u0026nbsp;Dutch Pharmacogenetics Working Group (DPWG)\u0026nbsp;guidelines have\u0026nbsp;recommended preemptive testing (i.e., before starting\u0026nbsp;antidepressants) for certain antidepressants for years. For some genotypes, switching to alternative antidepressants or different starting and target doses is advised\u0026nbsp;[5–7].\u003c/p\u003e\n\u003cp\u003eGenetic variants in CYP2D6 and CYP2C19, which affect the main metabolic pathways of antidepressants, are detectable in 87% of inpatients in German psychiatric wards [8,9]. CYP2B6 also plays a role in the metabolism of certain antidepressants, such as the reserve drug esketamine (Spravato®), which is used for treatment-resistant depression but is associated with high costs (€3,300 per treatment week in the initial phase) [10]. The most commonly prescribed antidepressant in Germany, sertraline, is also metabolized by CYP2B6, and there are guideline recommendations for treatment adjustments on the basis of genotype [6]. International guidelines, such as those from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the DPWG, as well as German drug information, offer specific recommendations for clinical practice in the presence of certain genotypes\u0026nbsp;\u003csup\u003e5-7\u003c/sup\u003e\u003csub\u003e.\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ePhenoconversion must also be considered to ensure accurate therapeutic recommendations on the basis of genotyping results [8,11]. It is crucial to consider comedications in combination with genotyping findings to ensure optimal therapy.\u003c/p\u003e\n\u003cp\u003eMeta-analyses have shown that personalized selection of antidepressants significantly increases the likelihood of remission (from 41% to 71%) [12,13]. The average length of hospital stay for patients with depression is 20 to 40 days, and even after discharge, many patients remain only partially remitted and are unable to return to work immediately. Genotyping can significantly shorten the time to remission: 16.5% of patients who received stratified therapy achieved remission by week 12, whereas only 11.2% of patients who received empirical therapy achieved remission [14]. A hospital stay could be avoided with early genotyping, which would be highly important for patients, their environment, employers, health insurance companies, and the economy.\u003c/p\u003e\n\u003cp\u003eCommercial pharmacogenetic tests currently use primarily polymerase chain reaction (PCR), microarray or mass-array technologies (e.g., the iPLEX® PGx 74 Panel by Agena Bioscience or Stratipharm). These tests focus on common genetic variants that significantly impact the clinical phenotype. Pharmacogenetics (PGx) testing is primarily implemented for the genes CYP2D6 and CYP2C19 [15]. For example, the iPLEX® PGx 74 Panel tests only 10 variants of the CYP2C9 gene, although 36 functional variants are known. The Stratipharm panel detects only 12 variants of CYP2D6, although more than 400 variants (177 star alleles) have been described. Another issue with microarray technologies is their limited ability to fully capture structural complexity, such as copy number variations of certain pharmacogenes, such as CYP2D6, which may lead to incorrect results.\u003c/p\u003e\n\u003cp\u003eExome and whole-genome sequencing (WGS) are gaining importance in pharmacogenetic research. Studies have shown that pharmacogenetic variants can also be derived from sequencing data. In particular, long-read sequencing offers promising possibilities, as it enables more comprehensive identification of functional and structural variants and is at the heart of the new field of pharmacoeepigenomics [16]. However, there are still few data on the analytical validity of this method for pharmacogenetic tests.\u003c/p\u003e\n\u003cp\u003eIn addition to individual genetics, the microbial gut flora could also play a role in the variation in antidepressant efficacy and side effects. The gut microbiota consists of trillions of microbes and contains 10 to 100 times more genes than the human genome does [17].\u0026nbsp;It is involved in\u0026nbsp;the\u0026nbsp;processing\u0026nbsp;of\u0026nbsp;substances ingested orally and interacts with the central nervous system via the gut-brain\u0026nbsp;system\u0026nbsp;[18]. Antidepressants such as SSRIs reduce microbial diversity in the gut and can alter the dynamics of the microbial community\u0026nbsp;[19].\u0026nbsp;In vitro studies have shown that SSRIs influence serotonin metabolism in the gut, which could lead to side effects and\u0026nbsp;a\u0026nbsp;reduced treatment response\u0026nbsp;[20]. However, clinical data on the interaction between antidepressants and\u0026nbsp;the\u0026nbsp;gut microbiota, as well as the influence of host genetics, remain limited\u0026nbsp;[21]. Therefore, in this study, stool samples\u0026nbsp;were\u0026nbsp;collected at the\u0026nbsp;beginning\u0026nbsp;and upon discharge for sequencing and assessment of metabolic performance.\u003c/p\u003e\n\u003cp\u003eIn Germany, only a few clinical studies have investigated the impact of pharmacogenetic testing on the treatment course of depressive patients. The goal of our research is to demonstrate, via the use of a heterogeneous naturalistic sample, how genotyping in patients with depression can help reduce the need for clinical interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplanation for the choice of comparators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, individuals are compared based on their \u003cstrong\u003emetabolizer status\u003c/strong\u003e and \u003cstrong\u003egenetic actionability\u003c/strong\u003e in psychotropic drug metabolism. Patients are categorized as \u003cstrong\u003enormal metabolizers (NM)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eor\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003enon-normal metabolizers\u003c/strong\u003e\u003c/strong\u003e, including \u003cstrong\u003epoor (PM), intermediate (IM), and ultrarapid metabolizers (UM)\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003efollowing established pharmacogenetic principles.\u003c/p\u003e\n\u003cp\u003eThis comparison is essential for understanding the\u0026nbsp;\u003cstrong\u003eclinical impact of genetic variability\u003c/strong\u003e on drug metabolism and treatment response in psychiatric patients. Additionally, we distinguish between\u0026nbsp;\u003cstrong\u003eactionable\u003c/strong\u003e genotypes (those with recommended dose adjustments or alternative treatment suggestions) and\u0026nbsp;\u003cstrong\u003enon-actionable\u003c/strong\u003e genotypes based on current pharmacogenomic guidelines (e.g., CPIC, DPWG). While actionable genotypes may have\u0026nbsp;\u003cstrong\u003edirect clinical implications\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e non-actionable genotypes typically do not require immediate intervention.\u003c/p\u003e\n\u003cp\u003eA non-interventional, observational design is appropriate, as the study aims to evaluate naturally occurring genetic variations and their associations with psychiatric drug metabolism. Since genetic predisposition, rather than drug effects, is the primary variable of interest, neither a placebo nor an active comparator is necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hypothesize that genetic differences significantly influence these two endpoints.\u003c/p\u003e\n\u003cp\u003eThe primary endpoint is the frequency of antidepressant discontinuation in relation to the genotypes of CYP2D6, CYP2C19, and CYP2B6. The secondary endpoint is a self-defined score based on the number of clinical events during hospitalization.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGenetic testing is rarely implemented in routine clinical practice in psychiatric clinics across Germany. One key reason for this is the lack of reimbursement by health insurance providers, as well as the absence of standardized testing protocols [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, there are insufficient training programs to enable medical professionals to reliably interpret pharmacogenetic findings [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To address this barrier, we aim to develop a training video on pharmacogenetics and interpretation of results.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that pharmacogenetically guided drug selection can reduce adverse drug reactions, improve treatment efficacy, and shorten hospitalization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The objective of our study is to integrate genetic testing more comprehensively into the routine treatment of depression in Germany. Genotyping should not only assist in dose adjustments for antidepressants but also facilitate the targeted selection of pharmacological agents. Currently, only one-third of patients with depression achieve sustained remission with the first prescribed medication, whereas two-thirds require medication adjustments. This leads to persistent symptoms, which can reduce work productivity, increase healthcare utilization, and increase suicide risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study examines how genetic variants influence treatment trajectories, with the goal of developing algorithms to accelerate symptom remission. To achieve this goal, we systematically collect comprehensive data on treatment outcomes, clinical interventions, and prescribed medications while ensuring that the course of treatment remains unaffected. These data are then correlated with pharmacogenetic findings.\u003c/p\u003e \u003cp\u003eFor genetic analyses, we employ commercial pharmacogenetic tests alongside long-read sequencing, a novel method with the potential to enhance genetic variant detection. This project aims to validate the clinical utility of long-read sequencing and improve the detection of highly polymorphic genes, such as CYP2D6, thereby facilitating its integration into clinical practice.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis open-label, non-randomized, observational study investigates the pharmacogenetic impact on treatment outcomes before, during and 4 weeks after hospitalization in patients with depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter clinical trial is being conducted in Germany at the University Hospital Frankfurt, Department of Psychiatry, Psychosomatics, and Psychotherapy, and at the Federal Institute for Drugs and Medical Devices in Bonn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant timeline\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRun-in phase (days 0 - 14)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter patients sign the informed consent form and are enrolled in the study, an oral mucosal swab is collected and sent to a accredited commercial laboratory. The aim of this study was to determine the pharmacogenetic findings and phenotype classifications. Additionally, patients provide further biomaterial samples, including a second mucosal swab and a blood sample for long-read sequencing validation at the BfArM, as well as four stool samples—two at admission and two at discharge—for microbiome analysis.\u003c/p\u003e\n\u003cp\u003eDuring the run-in phase, various data are collected. These include demographic information such as age and sex, results from the Inventory of Depressive Symptomatology (IDS) and the Montgomery-Åsberg Depression Rating Scale (MADRS) (both part of routine ward assessments), details on pharmacological and psychotherapeutic treatments prior to admission, the number of medications taken and potential drug interactions, incapacity for work status at admission, the suicide risk level at admission, and the use of ‘actionable’ antidepressants, as recorded in the patient file.\u003c/p\u003e\n\u003cp\u003eAdditionally, the Global Assessment of Functioning (GAF), Clinical Global Impression (CGI), and CGI toxicity scores are documented. Three separate questionnaires are used to assess side effects (Bonn Survey Instrument for Direct Subjective Assessment of Side Effects), quality of life (WHO-5 Well-Being Index), and adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObservation phase (until discharge)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNew data, including TDM findings, glomerular filtration rate (GFR), liver function, and pharmacogenetic results, including actionable genotypes, comedication, medication discontinuation, changes in antidepressant treatment (switches, dose adjustments), total number of psychotropic drugs, number of ECT sessions, number of (es)ketamine treatments, number of TMS sessions, drug interactions and phenoconversion, transfers due to adverse drug reactions (ADRs), reasons for antidepressant discontinuation and corrected QT-time (QTc) interval measurements, were collected from the patient file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAt discharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAt discharge, the following parameters are reassessed: IDS and MADRS scores, length of hospital stay, sick leave status at discharge, drug interactions at discharge, total number of medications and psychotropic drugs, number of ‘actionable’ antidepressants, and GAF and CGI scores. Additionally, the questionnaire on side effects is administered again, and another two stool samples are collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up (4 weeks after discharge)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of the telephone follow-up, information is gathered on changes in medication, side effects, reasons for antidepressant discontinuation, comedication, number of ‘actionable’ antidepressants, total number of medications, drug interactions, sick leave status (incapacity to work) and quality of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility criteria\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients hospitalized for affective disorders at the University Hospital Frankfurt, Department of Psychiatry, Psychosomatics, and Psychotherapy, will be included if they meet the following criteria: (1) age ≥18 years and (2) diagnosis of depressive episode or recurrent depressive disorder (ICD codes: F32.x, F33.x).\u003c/p\u003e\n\u003cp\u003ePatients meeting any of the following criteria will be excluded from the study: (1) Diagnosis of specific personality disorders, combined or other personality disorders, schizophrenia, bipolar affective disorder, or posttraumatic stress disorder (ICD codes: F60.x, F61.x, F2x.x, F31.x, F43.1). (2) Prior genotyping before admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants will be screened for eligibility and informed about the study upon admission during the regular hospital admission interview.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring enrollment, patients receive an information leaflet and an informed consent form.\u0026nbsp;These documents provide relevant information about the study, including its background, procedure, duration, objectives, data collection, risks, and potential benefits. Patients are informed that participation is voluntary and can be withdrawn at any time without providing a reason.\u003c/p\u003e\n\u003cp\u003ePatients can also access an informational video on the ward via a QR code or link. The video provides a clear explanation of the pharmacogenetics in precise and comprehensible language.\u0026nbsp;Watching the video is optional, and patients may view it as often as they like.\u003c/p\u003e\n\u003cp\u003eInformed consent is obtained before study participation begins. The consent form is signed and dated by the principal investigator or an authorized representative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size is set at 200 patients, who are categorized into normal metabolizers and non-normal metabolizers (other phenotypes) after genotyping. The calculation of the sample size is based on a power analysis with an effect size of 0.35, an α error of 0.05 and a power of 80%. The methodological derivation was carried out in consultation with a biostatistician at the BfArM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary and secondary outcomes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary objective is to compare antidepressant discontinuation rates in relation to (1) non-normal vs. normal metabolizer status and (2) actionable vs. non-actionable genotypes. Actionable genotypes indicate that there are dosage recommendations in the CPIC and DPWG guidelines for the antidepressant and the genotype of the patient.\u0026nbsp;Antidepressants prescribed before study enrollment\u0026nbsp;are\u0026nbsp;also considered by reviewing the medication history of the patient. Data will be collected from genotyping results, patient records, personal\u0026nbsp;interviews, and three self-administered questionnaires. The following questionnaires will be used: Inventory of Depressive Symptomatology (IDS), Clinical Global Impression (CGI), CGI Toxicity, Montgomery-Åsberg Depression Rating Scale (MADRS), Bonn Survey Instrument for Subjective Side Effect Assessment (BESEN-direkt), WHO-5 Well-Being Index, and an adherence questionnaire. Data will be collected at four time\u0026nbsp;points—admission,\u0026nbsp;hospitalization, discharge, and four weeks\u0026nbsp;post discharge—via\u0026nbsp;telephone follow-up.\u003c/p\u003e\n\u003cp\u003eReasons for antidepressant discontinuation and the frequency of clinical events will be analyzed as secondary outcomes. Clinical events include therapeutic drug monitoring (TDM), medication changes, augmentation and combination therapies, and escalation procedures such as electroconvulsive therapy (ECT), (es)ketamine, and repetitive transcranial magnetic stimulation (rTMS).\u0026nbsp;Secondary outcome data will be collected at the same four time points as\u0026nbsp;the\u0026nbsp;primary outcome data.\u003c/p\u003e\n\u003cp\u003eThe experimental part of the study investigates and validates the long-read sequencing method. The goal is to identify pharmacogenetically relevant genotypes and compare them with a commercial test.\u003c/p\u003e\n\u003cp\u003eThis study will also analyze changes in the gut microbiome composition and metabolic activity throughout treatment, considering correlations with medical history and side effects.\u0026nbsp;Baseline microbiome composition will be compared among normal, intermediate, poor, rapid, and\u0026nbsp;ultrarapid\u0026nbsp;metabolizers.\u0026nbsp;A follow-up analysis will validate and assess the robustness of these findings. Additionally, the study evaluates physicians’ knowledge gains through pre- and\u0026nbsp;post-training\u0026nbsp;comparisons in pharmacogenetics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplementation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce commercial pharmacogenetic results are available, participants will be assigned to either the normal metabolizers or non-normal metabolizers group based on phenotype prediction. The allocation is carried out by a clinical pharmacist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collected as outlined in the ‘Participant Timeline’ section are partly assessed via validated self-report questionnaires. These paper-based questionnaires were approved by the local ethics committee.\u003c/p\u003e\n\u003cp\u003eThe Global Assessment of Functioning (GAF) and Clinical Global Impression (CGI) scores are determined by a trained rater. All other data are directly retrieved from the electronic patient records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlans to promote participant retention and complete follow-up\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe risk of participant dropout is considered negligible, as all assessments occur during the inpatient stay in the psychiatric hospital.\u003c/p\u003e\n\u003cp\u003eHowever, a dropout rate of up to 10% is expected during the follow-up period (4 weeks after discharge). Patients who withdraw consent will be informed that all the data collected until the time of withdrawal will remain encrypted and used for analysis unless the patient explicitly requests data deletion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe oral mucosa swabs analyzed by commercial laboratory will be destroyed after completion of the analysis. The pharmacogenetic findings will remain accessible to the patient's treating physicians and pharmacists for the patient's lifetime. Deletion of genetic findings at the laboratory can be initiated only by the patient.\u003c/p\u003e\n\u003cp\u003eBlood and oral mucosa samples intended for long-read sequencing are identified by a unique patient identifier, collected in sterile containers, and regularly sent to the pharmacogenomics research group at the BfArM. The BfArM will store the DNA extracted from blood and oral mucosa samples for up to ten years for potential retesting. Stool samples will also be stored for a maximum of ten years in the BaseClear B.V. laboratory in Leiden, Netherlands.\u003c/p\u003e\n\u003cp\u003eLaboratory personnel outside the Department of Psychiatry, Psychosomatics, and Psychotherapy at Frankfurt University Hospital do not have access to the patient identification list and therefore cannot trace samples back to individual patients.\u003c/p\u003e\n\u003cp\u003eAll the data are recorded, stored, and transmitted in a password-protected, pseudonymized Excel spreadsheet at Frankfurt University Hospital. Consents and questionnaires are stored for ten years in a locked room at the Department of Psychiatry, Psychosomatics, and Psychotherapy.\u003c/p\u003e\n\u003cp\u003ePatients can withdraw their consent at any time. Upon withdrawal, the data are deleted or destroyed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from biomaterials, questionnaires, personal interviews, and patient records will be regularly transferred to an electronic database by a clinical research coordinator. Each dataset will be coded via a unique patient identifier.\u003c/p\u003e\n\u003cp\u003eAll study data will be stored for 10 years after study completion.\u003c/p\u003e\n\u003cp\u003eAll institutional and funding information is provided in Supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial monitoring\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational study will be monitored internally by the principal investigator and the research team. Recruitment protocols will be reviewed weekly to ensure that inclusion and exclusion criteria are applied consistently. Data entry will be checked monthly for completeness, internal consistency and plausibility. Any discrepancies or protocol deviations will be documented and discussed in monthly team meetings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommercial test from Humatrix AG (Stratipharm)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the laboratory, extracted\u0026nbsp;deoxyribonucleic acid (DNA) is quantified and normalized via an automated robotic platform and a spectrometer. The DNA is stored at 4°C until it is ready for PCR amplification as part of single-nucleotide polymorphism (SNP) or copy number variation (CNV) analysis.\u003c/p\u003e\n\u003cp\u003eSNP determination is performed via real-time PCR. The Master Mix is combined with an intradiagnostic (IVD) PCR primer/probe panel. For each SNP, a PCR reaction is carried out using the diluted DNA sample, the Master Mix, and the specific assay. Each assay contains two PCR primers and two fluorescence-labeled probes, which are short DNA molecules that specifically bind to either the wild-type or mutant sequences.\u003c/p\u003e\n\u003cp\u003eFor sample sets of fewer than 12 or to confirm ambiguous SNP determination results obtained via real-time PCR with an array, a classical PCR approach is used. In this method, standard PCR is performed to ensure accuracy of the results.\u003c/p\u003e\n\u003cp\u003eFor the array-based approach, all 94 TaqMan® SNP genotyping assays are preloaded and dried within the through-holes of the array. Real-time PCR in OpenArray format follows the same principles as classical PCR but requires a specifically configured device with a modified thermal block and heated cover.\u003c/p\u003e\n\u003cp\u003ePrior to real-time PCR, a multiplex short PCR reaction is performed for preamplification of the 94 loci of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePharmacogenetic laboratory diagnostics (lab-developed test – LDT) at the MVZ Labor Dr. Limbach medical center\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DNA is automatically isolated from oral mucosal swabs using QIAcube, purified, and quantified using a spectrophotometer. The DNA is then stored at 4°C until PCR amplification for genotyping.\u003c/p\u003e\n\u003cp\u003eThe MassARRAY® DX Analyzer 4 system from Agena Bioscience is used for genotyping (SNP and CNV analysis), using the VeriDose® Core Panel and the CYP2D6 CNV Panel v2.0. First, the target DNA is amplified by PCR. This is followed by enzymatic purification (SAP digestion) and an allele-specific primer extension reaction (iPLEX® Pro) using mass-modified nucleotides. The resulting DNA fragments are transferred to a SpectroCHIP® matrix and separated in the MALDI-TOF mass spectrometer based on their mass, which enables precise differentiation of the alleles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBioinformatic evaluation and determination of the diplotypes is performed using MassARRAY Typer® software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong-Read-Sequencing with PromethION2 from Oxford Nanopore Technologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this method, high-molecular-weight DNA is isolated from blood samples via the Monarch HMW DNA Extraction Kit from New England Biolabs GmbH. Alternatively, DNA can be extracted from oral mucosa samples, which can be purified via the Puregene Kit from Qiagen.\u003c/p\u003e\n\u003cp\u003eFor sequencing with PromethION2, library preparation is performed first. This involves preparing the ends of the previously isolated DNA by removing protruding bases. In addition, single-strand breaks are repaired to ensure seamless sequencing. After successful preparation, the DNA ends are ligated to sequencing adapters.\u003c/p\u003e\n\u003cp\u003eThe prepared library is then loaded into the flow cell of the PromethION2 device. Here, sequencing occurs via small pores embedded in a membrane. The DNA strands pass through the pores, resulting in a base-dependent change in the current flow through the pore. These electrical signals can then be translated into the DNA sequence, which is known as ‘basecalling’.\u003c/p\u003e\n\u003cp\u003eA special feature of sequencing with the Oxford Nanopore system is so-called ‘adaptive sampling’. This technique enables the enrichment of predetermined target sequences within the genome by ensuring that only these strands are fully sequenced. Sequences outside the target region are selectively discarded, leading to an increased concentration of the desired sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint is antidepressant discontinuation. The analysis evaluates whether certain genotypes are associated with an increased risk of discontinuation. Additionally, the reasons for discontinuation are examined.\u003c/p\u003e\n\u003cp\u003eThe secondary endpoint is recorded as a self-defined score based on the number of clinical events, including the therapeutic drug monitoring (TDM) frequency, number of medication changes (switch), number of augmentation therapies, number of combination therapies and number of escalating procedures such as ECT, (es)ketamine treatment and repetitive transcranial magnetic stimulation (rTMS), as well as the number of treatments involving escalation procedures. Treatment discontinuation and adverse effects, quality of life, MADRS, CGI, GAF, and CGI toxicity scores.\u003c/p\u003e\n\u003cp\u003ePrimary endpoint analysis: The Z test for two proportions will be used to compare the frequency of clinical events between the two groups.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eH₀: No difference in clinical event proportions between groups\u003c/li\u003e\n \u003cli\u003eH₁: Significant difference in clinical event proportions between groups\u003c/li\u003e\n \u003cli\u003eAssumed odds ratio: 1.4 (effect size)\u003c/li\u003e\n \u003cli\u003eSample size: N₁\u0026nbsp;= N₂\u0026nbsp;= 100\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSecondary endpoint analysis: A two-sample t- test will compare the mean event scores between groups.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eH₀: No difference in the mean number of clinical events between groups\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eH₁: Significant difference in the mean number of clinical events between groups\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStatistical power (Cohen’s d):\u003cul\u003e\n \u003cli\u003eSmall effect: 56%\u003c/li\u003e\n \u003cli\u003eMedium effect: 94%\u003c/li\u003e\n \u003cli\u003eLarge effect: 99%\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe significance level is α = 0.05. Confounding factors will be adjusted for in subsequent statistical analyses. Any deviation from the original statistical plan will be documented and justified in the final study report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods for additional analyses (e.g., subgroup analyses)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from long-read sequencing may be used to correlate individual genes with antidepressant response or side effects. Table 1 includes common pharmacogenes, with mainly level 3 and 4 evidence for antidepressants in accordance with pharmgkb.org (information from 12.03.2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods in analysis to handle protocol non-adherence and any statistical methods to handle missing data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf possible, missing data will be retrospectively retrieved from medical records, personal interviews, or telephone follow-up surveys. If missing data cannot be recovered, the last observed value will be used for analysis (the last observation carried forward method).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of who will be included in each analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants who meet the inclusion criteria and provide informed consent will be included in the analysis. Analyses will be restricted to participants with complete baseline and follow-up data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdverse event reporting and harms\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study, oral mucosa swabs, blood samples (during routine blood checks), and stool samples will be collected. These procedures are low-risk and are part of routine clinical practice.\u003c/p\u003e\n\u003cp\u003eIf serious adverse events (SAEs) related to antidepressant pharmacotherapy occur, they will be reported to the National Drug Commission in accordance with regulatory requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trial is registered in a public trial registry for clinical trials, ensuring public access to the trial procedure and current status. (German clinical trial registration number DRKS00036105)\u003c/p\u003e\n\u003cp\u003eProtocol version number and date: Version 1, 22.04.2025\u003c/p\u003e\n\u003cp\u003eDate recruitment started: 01.04.2025\u003c/p\u003e\n\u003cp\u003eDate recruitment approximately will be completed: 31.05.2026\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmendments to protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny significant changes to the study design, organization, protocol, or relevant study documents will be submitted to the local ethics committee for approval before implementation.\u003c/p\u003e\n\u003cp\u003eIn emergency situations, protocol deviations may be necessary to protect the rights, safety, or well-being of study participants. Any such deviations must be documented and communicated to the ethics committee as soon as possible.\u003c/p\u003e\n\u003cp\u003ePatients who continue to participate will be asked to reconsent to any major changes in study procedures. Minor modifications that do not significantly impact the study will be reported to the ethics committee in an annual report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvisions for post-trial care\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter genotyping, patients receive the genotyping results from the commercial test.\u0026nbsp;The participants do not receive financial compensation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Faculty of Medicine at Goethe University Frankfurt on January 13, 2025 (reference number 5/25). Informed consent will be obtained from all trial participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfidentiality\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy and participant data will be handled with strict confidentiality and will only be accessible to authorized personnel who require access for study-related tasks.\u003c/p\u003e\n\u003cp\u003eThe participants are identified solely by their unique number of participants. The participant identification list will be kept in a locked location at the study site under the supervision of the study director. Only encrypted data may be transferred outside the study site.\u003c/p\u003e\n\u003cp\u003eAdditionally, all collected data will be stored on password-protected drives within the study clinic, accessible only to authorized personnel. However, all the study data must remain traceable to the original source data at the study site.\u003c/p\u003e\n\u003cp\u003eFor quality assurance, the ethics committee or an independent study monitor may visit the research sites. They will be granted direct access to source data and all study-related documents.\u003c/p\u003e\n\u003cp\u003eAll parties involved are committed to treating participant data as strictly confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and dissemination\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study results will be published in peer-reviewed open-access journals.\u003c/p\u003e\n\u003cp\u003eAdditionally, findings will be presented at relevant scientific and clinical conferences to ensure dissemination among researchers and healthcare professionals.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Faculty of Medicine at Goethe University Frankfurt on January 13, 2025 (Reference No. 5/25).\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eP. Darm and M. Hahn contributed equally to the writing of this manuscript. A. Reif is the principal investigator. C. Scholl, A. Gl\u0026auml;ssner, M. Steffens and M. Bloemendaal were involved in the study design and contributed to the additional content. E. Paulus, C. Reif-Leonhard, M. Qubad, and S. Oppermann were responsible for the critical revision. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\u003ch3\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this study. However, M. Hahn is a member of CPIC, and A. Reif is, among others, a board member of DGPPN and the president of the ECNP. These memberships did not influence the study in any way.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Federal Institute for Drugs and Medical Devices supports the study by covering the costs of the study materials.\u003cbr\u003e\u0026nbsp;M. Bloemendaal receives funding from the Brain and Behavior Research Foundation and an EU Marie S. Curie Postdoctoral Fellowship.\u003cbr\u003e\u0026nbsp;The authors declare that the funding organizations had no influence on the study design, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnonymized data will be made available upon reasonable request to the corresponding author after publication of the main results, subject to ethics approval. Genotype and microbiome data will be deposited upon study completion. The investigators will have complete access to the final dataset of the trial.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation, Seattle (2021) \u003cem\u003eGlobal Burden of Disease (GBD) [Online Database]\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreden JF et al (2019) Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study. 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Clin Pharmacol Ther 102:37\u0026ndash;44\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBousman CA et al (2023) Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for \u003cem\u003eCYP2D6\u003c/em\u003e, \u003cem\u003eCYP2C19\u003c/em\u003e, \u003cem\u003eCYP2B6\u003c/em\u003e, \u003cem\u003eSLC6A4\u003c/em\u003e, and \u003cem\u003eHTR2A\u003c/em\u003e Genotypes and Serotonin Reuptake Inhibitor Antidepressants. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e 114, 51\u0026ndash;68\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrouwer JMJL et al (2022) Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2C19 and CYP2D6 and SSRIs. Eur J Hum Genet EJHG 30:1114\u0026ndash;1120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckert A et al (2024) Divergent Phenotypes, Actionable Genotypes, and Phenoconversion in a German Psychiatric Inpatient Population: Results from the FACT-PGx Study. J Explor Res Pharmacol 9:79\u0026ndash;85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHahn M, M\u0026uuml;ller DJ, Roll SC (2021) Frequencies of Genetic Polymorphisms of Clinically Relevant Gene-Drug Pairs in a German Psychiatric Inpatient Population. Pharmacopsychiatry 54:81\u0026ndash;89\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmia IM, Just KS, Yamoune S, M\u0026uuml;ller JP, Stingl JC (2022) Pharmacogenetic and drug interaction aspects on ketamine safety in its use as antidepressant - implications for precision dosing in a global perspective. Br J Clin Pharmacol 88:5149\u0026ndash;5165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherf-Clavel M et al (2024) The Relevance of Integrating CYP2C19 Phenoconversion Effects into Clinical Pharmacogenetics. Pharmacopsychiatry 57:69\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown LC et al (2022) Pharmacogenomic Testing and Depressive Symptom Remission: A Systematic Review and Meta-Analysis of Prospective, Controlled Clinical Trials. Clin Pharmacol Ther 112:1303\u0026ndash;1317\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBousman CA, Arandjelovic K, Mancuso SG, Eyre HA, Dunlop BW (2019) Pharmacogenetic Tests and Depressive Symptom Remission: A Meta-Analysis of Randomized Controlled Trials. Pharmacogenomics 20:37\u0026ndash;47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOslin DW et al (2022) Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial. JAMA 328:151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherf-Clavel M, Weber H, Deckert J, Erhardt-Lehmann A (2021) The role of pharmacogenetics in the treatment of anxiety disorders and the future potential for targeted therapeutics. Expert Opin Drug Metab Toxicol 17:1249\u0026ndash;1260\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalinin AA et al (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19:629\u0026ndash;650\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoestzada M et al (2018) Pharmacomicrobiomics: a novel route towards personalized medicine? Protein Cell 9:432\u0026ndash;445\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCryan JF et al (2019) The Microbiota-Gut-Brain Axis. Physiol Rev 99:1877\u0026ndash;2013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrushett S et al (2023) Gut feelings: the relations between depression, anxiety, psychotropic drugs and the gut microbiome. Gut Microbes 15:2281360\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung TC et al (2019) Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut. Nat Microbiol 4:2064\u0026ndash;2073\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y et al (2023) Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder. Microbiome 11:195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRelling MV, Evans WE (2015) Pharmacogenomics in the clinic. Nature 526:343\u0026ndash;350\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckert A, Frantz A, Reif A, Hahn M, Akzeptanz (2023) Wissen und Einstellungen von \u0026Auml;rzten zu pharmakogenetischen Tests in der Psychiatrie. Nervenheilkunde 42:459\u0026ndash;466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026auml;ttig VAD, Roll SC, Hahn M (2020) Pharmacogenetic Testing in Depressed Patients and Interdisciplinary Exchange between a Pharmacist and Psychiatrists Results in Reduced Hospitalization Times. Pharmacopsychiatry 53:185\u0026ndash;192\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinner J, Allen JD, Altar CA, Spahic-Mihajlovic A (2013) Psychiatric pharmacogenomics predicts health resource utilization of outpatients with anxiety and depression. Transl Psychiatry 3:e242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRush AJ et al (2006) Acute and Longer-Term Outcomes in Depressed Outpatients Requiring One or Several Treatment Steps: A STAR*D Report. Am J Psychiatry 163:1905\u0026ndash;1917\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenberg PE, Fournier A-A, Sisitsky T, Pike CT, Kessler RC (2015) The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). J Clin Psychiatry 76:155\u0026ndash;162\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Genpanel PromethION adaptive sequencing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eP-Glykoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eABCC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eMultidrug Resistance Protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eABCG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eBreast Cancer Resistance Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eACE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eAngiotensin Converting Enzyme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eACTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eBeta-Actin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eADRB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eAdrenozeptor Beta 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eADRB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eAdrenozeptor Beta 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eApolipo-Protein E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCACNA1S\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCalcium Voltage-Gated Channel Subunit Alpha1 S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCFTR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCystic Fibrosis Transmembrane Conductance Regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCHRNA5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCholinergic Receptor Nicotinic Alpha 5 Subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCOMT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCatechol-O-Methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCOQ2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eP-Hydroxybenzoat-Polyprenyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCREB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003ecAMP Response Element-Binding Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP17A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP17A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP19A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP19A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP1A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP1A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP1B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP1B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP26A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP26A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2A13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2A13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2A6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2A6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2A7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2A7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2B6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2B6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2B7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2B7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2C19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2C19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2C8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2C9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2C9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2D6-8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2D6-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2E1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2E1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2F1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2F1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2J2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2J2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2R1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2R1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2S1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2S1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2W1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP2W1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP3A4-43-5-7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP3A4-43-5-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP4A11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP4A11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP4A22\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP4A22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP4B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP4B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP4F2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCYP4F2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eDPYD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eDihydropyrimidin-Dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eDRD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eDopamin Rezeptor D1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eDRD2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eDopamin Rezeptor D2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eDRD3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eDopamin Rezeptor D3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eEpidermal Growth Factor Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eProthrombin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eG6PD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlukose-6-phosphat-Dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGABRA6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGamma-aminobutyric acid receptor subunit alpha-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGABRP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGamma-aminobutyric acid receptor subunit pi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGABRQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGamma-aminobutyric acid receptor subunit theta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlycerinaldehyd-3-Phosphat-Dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGLDC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlycin Decarboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGLP1R\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlucagon like Peptide 1 Rezeptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGNB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGuanin nucleotide-binding protein subunit beta-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGRIA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlutamat ionotropic receptor AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate) type subunit 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGSK3B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlykogen Synthase Kinase-3 Beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGSTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlutathion-S Transferase M1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eGSTP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eGlutathion-S Transferase P1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHMGCR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e3-Hydroxy-3-Methylglutaryl-CoA Reduktase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHTR1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e5-Hydroxytryptamin Rezeptor 1A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHTR1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e5-Hydroxytryptamin Rezeptor 1B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHTR2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e5-Hydroxytryptamin Rezeptor 2A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHTR2C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e5-Hydroxytryptamin Rezeptor 2C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eHTR7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003e5-Hydroxytryptamin Rezeptor 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eIFNL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eInterferon Lambda 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eIL11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eInterleukin 11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eITPA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eInosin Triphosphatase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eMTHFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eMethylen-Tetrahydrofolat-Reduktase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eNAT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eN-Acetyltransferase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eNAT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eN-Acetyltransferase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eNOS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eStickstoffmonoxid-Synthase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eNUDT15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eNudix Hydrolase 15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eOPRM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eOpioid Rezeptur Mu 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003ePNPLA5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003ePatatin-Like Phospholipase Domain-Containing 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003ePOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eCytochrom-P450-Oxidoreduktase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003ePTGIS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eProstaglandin I2 Synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eRYR1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eRyanodin Rezeptor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC15A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 15 Member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC19A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 19 Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 22 Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 22 Member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 22 Member 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC6A4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Family 6 Member 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLCO1A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Organic Anion Transporter Family Member 1A2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLCO1B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Organic Anion Transporter Family Member 1B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLCO1B3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Organic Anion Transporter Family Member 1B3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLCO2B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSolute Carrier Organic Anion Transporter Family Member 2B1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSULT1A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSulfotransferase Family 1A Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eSULT4A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eSulfotransferase Family 4A Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eTBXAS1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eThromboxan A Synthase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eTPH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eTryptophan Hydroxylase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eTPMT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eThiopurin S-Methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eUGT1A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eUDP-Glucuronosyltransferase Family 1 Member A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eUGT1A4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eUDP-Glucuronosyltransferase Family 1 Member A4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eUGT2B15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eUDP-Glucuronosyltransferase Family 2 Member B15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eUGT2B17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eUDP-Glucuronosyltransferase Family 2 Member B17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eUGT2B7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eUDP-Glucuronosyltransferase Family 2 Member B7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eVDR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eVitamin-D-Rezeptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eVKORC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eVitamin-K Epoxid Reduktase Complex Subunit-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.6161%;\"\u003e\n \u003cp\u003e\u003cem\u003eXPC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.3839%;\"\u003e\n \u003cp\u003eDNA Damage Recognition and Repair Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Federal Institute for Drugs and Medical Devices, Research Division, Bonn, Germany","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetics, Pharmacogenomics, Depression, Study Protocol","lastPublishedDoi":"10.21203/rs.3.rs-9017719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9017719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntidepressant metabolism varies widely due to polymorphic cytochrome P450 (CYP) enzymeslike CYP2D6 and CYP2C19, of which various genetic variants exist. These variants influence serum concentrations, leading to side effects, low remission rates, and insufficient responses. Thus, physicians often try multiple antidepressants within a short period, causing increased psychological distress and negatively affecting disease progression. Pharmacogenetic information can be used for more targeted prescriptions. This monocentric observational study at the University Hospital Frankfurt (April 2025-June 2026) will recruit 200 patients with depression and aims to analyze the impact of different genotypes on treatment outcomes. Following genotyping, patients will be classified as normal or non-normal metabolizers and as having actionable or non-actionable genotypes. Clinical parameters, including the number and duration of depressive episodes, antidepressants used, sick leave days, and standardized clinical scores, will be recorded at admission. The study’s primary endpoint is antidepressant discontinuation. Reasons for discontinuation and other treatment interventions will be correlated with patients' genotypes for CYP2D6, CYP2C19, CYP2B6, and other genes.\u003c/p\u003e\n\u003cp\u003eIn the experimental part of the study, Oxford Nanopore long-read sequencing will be further investigated and validated. Changes in gut microbiome composition and metabolism will be analyzed during treatment in relation to CYP status and antidepressant use.\u003c/p\u003e","manuscriptTitle":"Study protocol for an observational trial: Investigating pharmacogenetic impact on depression treatment using various sequencing and array methods (PharmGen-TRD Study)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 07:40:34","doi":"10.21203/rs.3.rs-9017719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dadc0748-6533-4306-a5ca-c9c03431311a","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63893402,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2026-03-04T07:40:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 07:40:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9017719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9017719","identity":"rs-9017719","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}