Antidepressant Selection Research Articles

Machine learning for antidepressant treatment selection in depression

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.

Use of Serum Biomarkers to Aid Antidepressant Selection in Depressive Patients.

: This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in out-patients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. : This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3-12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. : Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1β and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. : Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.

Do sociodemographic and clinical factors affect the selection of initial antidepressant treatment for depression in older adults? Results from a nationwide descriptive study in Denmark

BackgroundThe choice of antidepressants for initial pharmacological treatment of depression in older adults and associated patients' characteristics are understudied. We aimed to describe the first selected antidepressant (first-choice) for depression in older adults (≥65 years) and whether patients' sociodemographic and clinical characteristics influence selecting an alternative first-choice (any other antidepressant than the nationally recommended first-choice sertraline) in Denmark. MethodsRegister-based cross-sectional study including all older adults who redeemed their first antidepressant prescription for depression at community pharmacies in Denmark in 2015–2019. We analyzed the effect of patients' characteristics on the first-choice antidepressant selection using multinomial logistic regression. ResultsAmong 34,337 older adults with a first antidepressant-prescription, over two-thirds filled alternative first-choice antidepressants than sertraline (28.9 %): escitalopram or citalopram (30.3 %) or mirtazapine (34.4 %). Socially disadvantaged older adults (e.g., with short educational attainment, being single, or of non-western ethnicity) and clinically vulnerable older adults (e.g., having somatic diagnoses and hospital contacts) were more likely to use alternative first-choice antidepressants. LimitationsInformation on prescribers and in-hospital medications was not included in this study. ConclusionsFurther investigation of the first antidepressant selection and its impact on depression treatment outcomes in older adults is necessary. Moreover, for older patients, national guidelines on depression treatment should be more specific. Article summaryAntidepressant selection for initial pharmacological treatment of depression in older adults can be difficult due to comorbidity, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics. Real-world evidence/knowledge on first-choice antidepressant selection and associated user characteristics are rare. This Danish register-based cross-sectional study found over two-thirds of older adults filled alternative antidepressants (primarily escitalopram/citalopram or mirtazapine) than nationally recommended first-choice sertraline for depression treatment and identified wide-ranging sociodemographic and clinical factors influencing the first antidepressant selection.

AI-assisted prediction of differential response to antidepressant classes using electronic health records

Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.

Evaluation of polysomnography changes in patients using antidepressants

Aim: In patients followed up with sleep disorders and receiving antidepressant treatment, the choice of antidepressant should be appropriate to the characteristics of the patient and the drug. The aim of this study is to show the effects of antidepressant drug selection on polysomnography results.
 Material and Method: This study was planned retrospectively and was conducted by scanning patient files. Between 01.06.2018 and 01.06.2022, the files of patients who underwent polysomnography in the sleep laboratory of our hospital and were using antidepressants were scanned. Differences between antidepressant groups and polysomnography results were analyzed with IBM SPSS v23. 
 Results: 103 patients (43 women/60 men) were included in the study. It was determined that 56 of these patients used selective serotonin reuptake inhibitors and 47 of them used serotonin and norepinephrine reuptake inhibitors. Yates correction was used in the comparison of gender according to the groups, and there was no statistically significant difference between the distributions of gender according to the groups (p=0.248). According to the drug groups, the results of the polysomnography examined in the study do not differ according to the groups.
 Conclusion: It was shown that the antidepressant groups examined in the study did not differ in terms of affecting the results of polysomnography. Other clinical characteristics of the patient can be taken into account in the selection of antidepressants in these groups.

Personality as a basis for antidepressant selection for patients with depression: A two-point outcome study at 4 and 8 weeks

BackgroundThe treatment course for depression is multifactorial, and the gold standard method for antidepressant selection remains unclear. Therefore, we focused on patients' personality as a possible indicator of the treatment response to mirtazapine and selective serotonin reuptake inhibitors (SSRIs) and whether it can contribute to antidepressant selection. MethodsOne hundred one patients with major depressive disorder were randomized at baseline to receive either mirtazapine or SSRI treatment. Their personality was measured using the NEO Five-Factor Inventory at baseline, and depressive symptoms were evaluated using the Hamilton Rating Scale for Depression at baseline and 4 and 8 weeks. Stepwise multivariable logistic regression and receiver operating characteristic analyses were performed to determine the association of personality traits with remission and better antidepressant selection. ResultsNeuroticism had the substantial influence on remission at 4 and 8 weeks among the entire sample. The cutoff T-score of neuroticism for predicting remission at 4 weeks was 62.5. The patients with moderate neuroticism (scores below the cutoff) were more likely to experience remission after 4-week mirtazapine treatment (remission rate: 73.7 %) than after SSRI treatment (40.0 %); those with high neuroticism (scores above the cutoff) were more likely to experience remission after 8-week SSRI treatment (74.1 %) than after mirtazapine treatment (35.7 %). LimitationsThe small sample size increased the confidence intervals. ConclusionsThe treatment response of the patients with depression differed according to the type of antidepressants and degree of neuroticism. Measuring personality traits at treatment initiation may help in selecting better antidepressants and predicting the time to remission.

A Mixed Methods Comparison of Artificial Intelligence-Powered Clinical Decision Support System Interfaces for Multiple Criteria Decision Making in Antidepressant Selection

A Mixed Methods Comparison of Artificial Intelligence-Powered Clinical Decision Support System Interfaces for Multiple Criteria Decision Making in Antidepressant Selection

The potential for adverse effects in fish exposed to antidepressants in the aquatic environment.

Antidepressants are one of the most commonly prescribed pharmaceutical classes for the treatment of psychiatric conditions. They act via modulation of brain monoaminergic signaling systems (predominantly serotonergic, adrenergic, dopaminergic) that show a high degree of structural conservation across diverse animal phyla. A reasonable assumption, therefore, is that exposed fish and other aquatic wildlife may be affected by antidepressants released into the natural environment. Indeed, there are substantial data reported for exposure effects in fish, albeit most are reported for exposure concentrations exceeding those occurring in natural environments. From a critical analysis of the available evidence for effects in fish, risk quotients (RQs) were derived from laboratory-based studies for a selection of antidepressants most commonly detected in the aquatic environment. We conclude that the likelihood for effects in fish on standard measured end points used in risk assessment (i.e., excluding effects on behavior) is low for levels of exposure occurring in the natural environment. Nevertheless, some effects on behavior have been reported for environmentally relevant exposures, and antidepressants can bioaccumulate in fish tissues. Limitations in the datasets used to calculate RQs revealed important gaps in which future research should be directed to more accurately assess the risks posed by antidepressants to fish. Developing greater certainty surrounding risk of antidepressants to fish requires more attention directed toward effects on behaviors relating to individual fitness, the employment of environmentally realistic exposure levels, on chronic exposure scenarios, and on mixtures analyses, especially given the wide range of similarly acting compounds released into the environment.

Pharmacist-guided pre-emptive pharmacogenetic testing in antidepressant therapy (PrePGx): study protocol for an open-label, randomized controlled trial

BackgroundIt is known that only 50% of patients diagnosed with major depressive disorders (MDD) respond to the first-line antidepressant treatment. Accordingly, there is a need to improve response rates to reduce healthcare costs and patient suffering. One approach to increase rates of treatment response might be the integration of pharmacogenetic (PGx) testing to stratify antidepressant drug selection. The goal of PGx assessments is to identify patients who have an increased risk to experience adverse drug reactions or non-response to specific drugs. Especially for antidepressants, there is compiling evidence on PGx influencing drug exposure as well as response.MethodsThis study is an open-label, randomized controlled trial conducted in two study centers in Switzerland: (1) the Psychiatric Clinic of Solothurn and (2) the Private Clinic Wyss in Münchenbuchsee. Adult inpatients diagnosed with a unipolar moderate or severe depressive episode are recruited at clinic admission and are included in the study. If the adjustment to a new antidepressant pharmacotherapy is necessary, the participants are randomized to either Arm A (intervention group) or Arm B (control group). If no new antidepressant pharmacotherapy is introduced the participants will be followed up in an observational arm. The intervention is the service of pharmacist-guided pre-emptive PGx testing to support clinical decision making on antidepressant selection and dosing. As a comparison, in the control group, the antidepressant pharmacotherapy is selected by the treating physician according to current treatment guidelines (standard of care) without the knowledge of PGx test results and support of clinical pharmacists. The primary outcome of this study compares the response rates under antidepressant treatment after 4 weeks between intervention and control arm.DiscussionThe findings from this clinical trial are expected to have a direct impact on inter-professional collaborations for the handling and use of PGx data in psychiatric practice.Trial registrationClinicalTrials.govNCT04507555. Registered on August 11, 2020. Swiss National Clinical Trials Portal SNCTP000004015. Registered August 18, 2020.

What Are the Barriers and Enablers to the Implementation of Pharmacogenetic Testing in Mental Health Care Settings?

In psychiatry, the selection of antipsychotics and antidepressants is generally led by a trial-and-error approach. The prescribing of these medications is complicated by sub-optimal efficacy and high rates of adverse drug reactions (ADRs). These both contribute to poor levels of adherence. Pharmacogenetics (PGx) considers how genetic variation can influence an individual’s response to a drug. Pharmacogenetic testing is a tool that could aid clinicians when selecting psychotropic medications, as part of a more personalized approach to prescribing. This may improve the use of and adherence to these medications. Yet to date, the implementation of PGx in mental health environments in the United Kingdom has been slow. This review aims to identify the current barriers and enablers to the implementation of PGx in psychiatry and determine how this can be applied to the uptake of PGx by NHS mental health providers. A systematic searching strategy was developed, and searches were carried out on the PsychInfo, EmBase, and PubMed databases, yielding 11 appropriate papers. Common barriers to the implementation of PGx included cost, concerns over incorporation into current workflow and a lack of knowledge about PGx; whilst frequent enablers included optimism that PGx could lead to precision medicine, reduce ADRs and become a more routine part of psychiatric clinical care. The uptake of PGx in psychiatric care settings in the NHS should consider and overcome these barriers, while looking to capitalize on the enablers identified in this review.

Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial

BackgroundThe lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics. MethodsParticipants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models. ResultsThe predictive model for sertraline achieved R2 of 48% (95% CI, 33%–61%; p < 10−3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R2 of 28% (95% CI, 15%–42%; p < 10−3) and NNT of 2.95 in predicting response. The bupropion model achieved R2 of 34% (95% CI, 10%–59%, p < 10−3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion. ConclusionsThese findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome.

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Selection of Anti-Depressants in Geriatric Dermatoses- A Questionnaire Study of Dermatologists in Western Rajasthan, India

Background: The Global Wealth Migration Review 2019 report, published by New World Wealth, estimated that 48% of India's total wealth was held by high-net worth individuals. Distinct factors dominate the prescription of anti-depressants in the geriatric population. Accordingly, the intention of pharmacotherapy in geriatric psycho dermatoses is deciphered in the study. . Objective: To survey and analyze the selection of anti-depressants by the dermatologists in the practice of geriatric pharmacotherapy. Methods: A questionnaire was designed and circulated among ninety-three dermatologists in three sessions and the data collected was analyzed through the cross-sectional study statistics. Results: The typical senior people attending the specialist were 25-50%. A sizeable familiarity of psycho cutaneous disturbance was attributed to dermatophyte infections, lichen simplex chronic, and Prurigo in descending order. The largely established primary disorder was neurotic excoriation. The prescription pattern was classified and 51.9% dermatologists are customary with psychotropic agent Doxepin over other agents. The discipline of the multidisciplinary approach was also studied. Limitation: Small sample size, uniregional study, prevalence study. Conclusions: The survey aids in evaluating mental health in cutaneous dermatoses of the elderly and assists the dermatologists to offer understated economical options and amend existing guidelines.

Efficacy and safety of Morinda officinalis oligosaccharide capsules for depressive disorder: a systematic review and meta-analysis.

Objective:To evaluate the efficacy and safety of Morinda officinalis oligosaccharide (MOO) capsules for depressive disorder.Methods:Eight electronic databases were searched for relevant studies from inception to April 19, 2020. Randomized controlled trials comparing MOO capsules with antidepressants were included. Data analysis was conducted using Review Manager 5.3 software. The risk of bias was assessed using the Cochrane Risk of Bias Tool, and the quality of the studies was evaluated by two researchers using the Grading of Recommendation, Assessment, Development and Evaluations (GRADE) software.Results:Seven studies involving 1,384 participants were included in this study. The effect of MOO capsules for moderate depressive disorder was not different from that of antidepressants (risk ratio [RR] = 0.99, 95%CI 0.92-1.06). Regarding adverse events, no significant difference was found between MOO capsules and antidepressants (RR = 0.84, 95%CI 0.65-1.07). In addition, the quality of evidence related to these adverse events was rated as low.Conclusion:This systematic review suggests that the efficacy of MOO capsules in the treatment of mild to moderate depression is not inferior to that of conventional antidepressants, which may provide a new direction for clinical alternative selection of antidepressants. However, more high-quality research and detailed assessments are needed.

An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial.

There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method. Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects. Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1-30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining. An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.

A Practical Utility and Benefit of Pharmacogenetic-based Antidepressant Treatment Strategy for Major Depressive Disorder Patients with Difficult-to-treat.

ObjectiveWe report the results of pharmacogenomics-based antidepressant treatment (PGXt) results in treating treatment-resistant major depressive disorder (TRD) patients in real practice.MethodsNine patients were prescribed NeuropharmagenⓇ for selection of antidepressants for individual patient and their clinical outcomes were followed.ResultsAfter treatment by PGXt results from current antidepressants, substantial reduction of depressive symptoms was observed at some point and maintained during observation period in six patients.ConclusionOur case series potentially shows the clinical utility and benefit of PGXt for treatment of TRD patients.

Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression.

Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy. To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment. Prospective. A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site. A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence. Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site. Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run. Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%). These findings support future research on clinical antidepressant selection for MDD. 1 TECHNICAL EFFICACY: Stage 2.

How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection

Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians’ treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians’ treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.

Selection of Anti-Depressants in Geriatric Dermatoses- A Questionnaire Study of Dermatologists in Western Rajasthan, India

Background: The Global Wealth Migration Review 2019 report, published by New World Wealth, estimated that 48% of India's total wealth was held by high-net worth individuals. Distinct factors dominate the prescription of anti-depressants in the geriatric population. Accordingly, the intention of pharmacotherapy in geriatric psycho dermatoses is deciphered in the study. . Objective: To survey and analyze the selection of anti-depressants by the dermatologists in the practice of geriatric pharmacotherapy. Methods: A questionnaire was designed and circulated among ninety-three dermatologists in three sessions and the data collected was analyzed through the cross-sectional study statistics. Results: The typical senior people attending the specialist were 25-50%. A sizeable familiarity of psycho cutaneous disturbance was attributed to dermatophyte infections, lichen simplex chronic, and Prurigo in descending order. The largely established primary disorder was neurotic excoriation. The prescription pattern was classified and 51.9% dermatologists are customary with psychotropic agent Doxepin over other agents. The discipline of the multidisciplinary approach was also studied. Limitation: Small sample size, uniregional study, prevalence study. Conclusions: The survey aids in evaluating mental health in cutaneous dermatoses of the elderly and assists the dermatologists to offer understated economical options and amend existing guidelines.

Nonresponse to High-Dose Bupropion for Depression in a Patient Carrying CYP2B6 *6 and CYP2C19 *17 variants: a case report

We report the case of a patient with major depression treated with high-dose bupropion due to prior detected subtherapeutic blood concentrations at standard dosing. Pharmacogenetic panel testing identified the patient as a carrier of the CYP2B6*6 allele, which has been associated with reduced bupropion metabolism and decreased concentrations of the pharmacologically active metabolite hydroxybupropion. Interestingly, we also found the patient to be homozygous for the CYP2C19*17 allele, predicting an ultra rapid metabolizer phenotype. We propose a combined effect of the detected CYP2C19 and CYP2B6 genetic variants on bupropion metabolism. This case underlines the potential benefit of pre-emptive pharmacogenotyping but also the yet still fragmentary evidence making precise pharmacogenotype guided antidepressant selection and dosing challenging.