Antidepressant Response Research Articles

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

A comparative analysis of antidepressant and anti-suicidal effects of repeated ketamine infusions in elderly and younger adults with depression

ObjectivesThis study aims to investigate the differences in safety and antidepressant effects of multi-infusion ketamine treatment between elderly and young adults with depression. MethodsThe safety, antidepressant, and anti-suicidal effects of multi-infusion ketamine were compared between 19 elderly (≥50 years) and 116 younger (<50 years) adults with depression; all were treated with six ketamine infusions (0.5 mg/kg). Montgomery-Åsberg Depression Rating Scale (MADRS) was used to measure the depressive symptoms, and suicidal ideation was measured with Beck Scale for Suicide Ideation (SSI)-part 1, Hamilton Rating Scale for Depression (HAMD) item 3, and (MADRS) item 10. Dissociative and psychotomimetic symptoms were evaluated based on the Clinician-Administered Dissociative States Scale (CADSS) and the Brief Psychiatric Rating Scale (BPRS)-four items. ResultsMulti-Ketamine infusions resulted in a lower (trend) antidepressant response (37.1 % versus 57.8 %) and antidepressant remission (15.8 % versus 47.4 %) in elderly patients with depression compared with younger patients with depression (all ps > 0.05). Interestingly, elderly patients with depression had a higher MADRS score after six ketamine infusions compared with younger patients (p = 0.04). No significant differences in SSI-part 1 scores, HAMD item 3 scores, MADRS item 10 scores, CADSS scores, and BPRS-four items scores were found between the two groups at any assessment point (all ps > 0.05). ConclusionOur study shows that repeated-dose infusions of ketamine may be a feasible treatment strategy in elderly Chinese patients with depression; however, elderly patients with depression may be less responsive to ketamine compared with younger adults with depression.

Ketamine supresses REM sleep and markedly increases EEG gamma oscillations in the Wistar Kyoto rat model of treatment-resistant depression

Wistar–Kyoto (WKY) rats exhibit depression-like characteristics and decreased sensitivity to monoamine-based antidepressants, making them a suitable model of treatment-resistant depression (TRD). Ketamine has emerged recently as a rapidly acting antidepressant with high efficacy in TRD. Our aim was to determine whether subanaesthetic doses of ketamine can correct sleep and electroencephalogram (EEG) alterations in WKY rats and whether any ketamine-induced changes differentially affect WKY rats compared to Sprague-Dawley (SD) rats. Thus, we surgically implanted 8 SD and 8 WKY adult male rats with telemetry transmitters and recorded their EEG, electromyogram, and locomotor activity after vehicle or ketamine (3, 5 or 10 mg/kg, s.c.) treatment. We also monitored the plasma concentration of ketamine and its metabolites, norketamine and hydroxynorketamine in satellite animals. We found that WKY rats have an increased amount of rapid eye movement (REM) sleep, fragmented sleep-wake pattern, and increased EEG delta power during non-REM sleep compared to SD rats. Ketamine suppressed REM sleep and increased EEG gamma power during wakefulness in both strains, but the gamma increase was almost twice as large in WKY rats than in SD rats. Ketamine also increased beta oscillations, but only in WKY rats. These differences in sleep and EEG are unlikely to be caused by dissimilarities in ketamine metabolism as the plasma concentrations of ketamine and its metabolites were similar in both strains. Our data suggest an enhanced antidepressant-like response to ketamine in WKY rats, and further support the predictive validity of acute REM sleep suppression as a measure of antidepressant responsiveness.

Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

Serum cytokines-based biomarkers in the diagnosis and monitoring of therapeutic response in patients with major depressive disorder

Several lines of evidence have suggested that cytokines are implicated in the pathophysiology of depression and antidepressant treatment outcome. However, the results are not always congruent and partly contradictory. We therefore examined the serum levels of multiple cytokines in patients with major depressive disorder (MDD), with the aim to identify serum cytokines-based biomarkers for MDD diagnosis and antidepressant response. Fifty-nine patients with MDD and 61 healthy controls were included. The baseline levels of serum cytokines between MDD group and control group were compared, and the discriminative ability of different cytokines in predicting MDD patients from healthy controls was investigated using the receiver operating characteristic (ROC) curve method. The baseline levels of serum cytokines between antidepressant nonresponders and responders were compared, and the discriminative ability of different cytokines in predicting nonresponders from responders was evaluated using the ROC curve method. Compared to controls, 15 of the 37 serum cytokines were increased, while 8 cytokines were decreased in MDD patients (all P<0.05). The results of ROC curve showed that the Area Under Curve (AUC) values of 15 cytokines including IL-2, IL-5, IL-6, IL-8, IL-12, IL-13, IL-16, CCL3, CCL4, CCL17, CXCL10, TNF-α, TNF-β, VEGF-C, and FGF basic were greater than 0.7 in discriminating MDD patients from healthy control. Moreover, after 4-week treatment, levels of the 2 cytokines (IL-12 and TSLP) elevated at baseline significantly down-regulated, and levels of the 6 cytokines (IL-5, IL-16, CCL17, CXCL10, TNF-β, and PIGF) decreased at baseline significantly up-regulated (all P<0.05). Furthermore, a positive relationship was found between TNF-α levels and Hamilton Depression Rating Scale-24 (HAMD-24) scores in patients with MDD at baseline (r=0.302, P=0.019). Additionally, compared to responders, nonresponders exhibited decreased levels of IL-1α, IL-5, IL-13, IL-15, VEGF, and ICAM-1 (all P<0.05). The ROC curve analysis demonstrated that a combined panel of IL-1α, IL-5, and ICAM-1 achieved a high accuracy in discriminating antidepressant nonresponders from responders (AUC=0.850, sensitivity=83.3%, specificity=81.8%). These results suggested that alterations in peripheral cytokines levels hold significant promise as biomarkers for MDD diagnosis and antidepressant response.

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches

ObjectiveTo identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.MethodHan Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. Variable importance was evaluated by random forest algorithm.ResultRF with RFE outperformed the other models in our samples based on the demographic information and clinical features (AUC = 61.2%, 95%CI: 60.1-62.4%) / TPH2 CpGs features (AUC = 66.6%, 95%CI: 65.4-67.8%) / both clinical and TPH2 CpGs features (AUC = 72.9%, 95%CI: 71.8-74.0%).ConclusionThe effects of TPH2 on the early-stage antidepressant response were explored by machine learning algorithms. On the basis of the baseline depression severity and TPH2 CpG sites, machine learning approaches can enhance our ability to predict the early-stage antidepressant response. Some potentially important predictors (e.g., TPH2-10-60 (rs2129575), TPH2-2-163 (rs11178998), age of first onset, age) in early-stage treatment response could be utilized in future fundamental research, drug development and clinical practice.

Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response

Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.

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.

Association of plasma cytokines and antidepressant response following mild-intensity whole-body hyperthermia in major depressive disorder

Whole-body hyperthermia (WBH) shows promise for the treatment of major depressive disorder (MDD). Because MDD is associated with increased inflammation, and anti-inflammatory agents show some promise as antidepressants, the current study sought to identify the acute and longer-term immune effects of WBH in participants with MDD and to explore whether these effects associate with the procedure’s antidepressant properties. Thirty participants who met DSM-IV-TR criteria for MDD were randomized to receive a single session of WBH (n = 16) or sham treatment (n = 14). Hamilton Depression Rating Scale (HDRS) scores were assessed at baseline and 1, 2, 4, and 6 weeks post-treatment (WBH vs. sham), and plasma cytokine concentrations were assessed at baseline, immediately post-treatment, and 1 and 4 weeks post-treatment. As previously reported, WBH produced a rapid and sustained antidepressant effect. When compared to sham, WBH increased plasma interleukin (IL)-6 immediately post-treatment (time by treatment: χ2(3, N=108) = 47.33, p < 0.001), while having no effect on other cytokines acutely and no impact on IL-6, or any other cytokine, at 1 or 4 weeks post treatment. In the study sample as a whole, increased IL-6 post-treatment was associated with reduced HDRS depression scores over the 6 weeks of follow-up (F(1, 102.3) = 6.74, p = 0.01). These results suggest a hitherto unrecognized relationship between hyperthermia, the immune system, and depression, and may point to WBH as a novel modality for exploring behavioral effects of IL-6 when the cytokine is activated in isolation from the inflammatory mediators with which it frequently travels.

Structured physical exercise for bipolar depression: an open-label, proof-of concept study

BackgroundPhysical exercise (PE) is a recommended lifestyle intervention for different mental disorders and has shown specific positive therapeutic effects in unipolar depressive disorder. Considering the similar symptomatology of the depressive phase in patients with bipolar disorder (BD) and unipolar depressive disorder, it is reasonable to suggest that PE may also be beneficial for bipolar depression. However, there is an absence of studies evaluating the antidepressant effect of a structured PE intervention in BD.MethodsThis is an open-label, single-arm study trial. Fifteen patients with a diagnosis of BD Type I or Type II, presenting a depressive episode were included in the study. After physical and functional evaluation, patients participated in supervised training sessions with aerobics followed by strength exercises, three times per week, for 12 weeks (36 training sessions). Depressive and manic symptoms were assessed at baseline and 2, 4, 8, and 12 weeks. Additionally, quality of Life and functioning were assessed at baseline and 4, 8, and 12 weeks). Finally, we tested cardiorespiratory fitness, muscle strength and body composition at baseline and week-12.ResultsThe mean (± SD) Montgomery Asberg Depression Rating Scale (MADRS) score at baseline was 23.6 ± 8.3 points and after 12 weeks of PE the mean score was 10.2 ± 4.8 points. Nine patients (82%) presented an antidepressant response defined as a reduction of more than 50% of depressive symptoms at week 12 with five of those patients (45%) presenting criteria for full remission. A large and significant Cohen’s D Effect Size (pre-post) was verified for MADRS reduction [1.98 (95% Confidence interval = 0.88 to 3.08)]. We did not detect a significant change in manic symptoms, functioning, and quality of life during the 12-week follow-up. At week-12, all patients increased their muscular strength (one repetition maximal test − 1RM) and reduced the percentage of body fat (spectral bioelectrical impedance analysis).ConclusionsThis study, using rigorous criteria and a structured intervention, provides valid pilot data, showing the feasibility of a structured PE intervention for the treatment of depressive symptoms in BD, and suggesting a potential adjunctive antidepressant effect. Moreover, PE showed a positive impact on muscle strength and body composition. This should be further verified by randomized controlled studies.

Neurocognitive effects of subanesthetic serial ketamine infusions in treatment resistant depression

IntroductionKetamine treatment prompts a rapid antidepressant response in treatment-resistant depression (TRD). We performed an exploratory investigation of how ketamine treatment in TRD affects different cognitive domains and relates to antidepressant response. MethodsPatients with TRD (N = 66; 30 M/35F; age = 39.5 ± 11.1 years) received four ketamine infusions (0.5 mg/kg). Neurocognitive function and depressive symptoms were assessed at baseline, 24 h after the first and fourth ketamine infusion, and 5 weeks following end of treatment. Mixed effect models tested for changes in seven neurocognitive domains and antidepressant response, with post-hoc pairwise comparisons between timepoints, including follow-up. Relationships between change in neurocognitive function and antidepressant response over the course of treatment were tested with Pearson's correlation and mediation analyses. Associations between baseline neurocognitive performance and antidepressant response were tested with Pearson's correlation. ResultsSignificant improvements in inhibition, working memory, processing speed, and overall fluid cognition were observed after the first and fourth ketamine infusion. Improvements in processing speed and overall fluid cognition persisted through follow-up. Significant improvements in depressive symptoms reverted towards baseline at follow-up. Baseline working memory and change in inhibition were moderately correlated with antidepressant response, however, improvements in neurocognitive performance were statistically independent from antidepressant response. ConclusionAntidepressant ketamine leads to improved neurocognitive function, which persist for at least 5 weeks. Neurocognitive improvements observed appear independent of antidepressant response, suggesting ketamine may target overlapping but distinct functional brain systems.LimitationsResearch investigating repeated serial ketamine treatments is important to determine cognitive safety. This study is a naturalistic design and does not include placebo.

459 Clinical Utility of Pharmacogenetic Tests When Prescribing Antidepressant Therapy for Depression, A Systematic Review and Meta-analysis of Randomised Controlled Trials

Abstract Introduction Pharmacogenetic testing when prescribing medicines has the potential to reduce harmful side effects, optimise treatment, and improve clinical outcomes. Depression is a major global disease burden with high economic costs and it’s becoming more prevalent (1). There is a growing number of clinical trials and reviews that support the use of pharmacogenetic testing for depression and more results are available from randomised controlled trials (RCTs) that were completed recently to warrant a new systematic review (2). Aim The aim was to evaluate the clinical utility of pharmacogenetic tests when prescribing antidepressant therapy for depression (including mild, moderate, and major depression) by comparing the remission rate, antidepressant response and adverse events from RCTs for the pharmacogenetic guided therapy group and the treatment as usual group (TAU) through a systematic review and meta-analysis. Methods The PICO inclusion criteria are adults with depression requiring antidepressant therapy for depression. The intervention is a pharmacogenetic gene test(s) and the comparator is usual treatment. The outcomes are depression remission, antidepressant response and adverse events. This research was undertaken between April and September 2022 by the main author (SJ). The second author (HA) peer reviewed the title/abstract screening, full text review, and the third author (PD) provided supervisory support. A systematic literature search was completed using Embase, Medline, Cochrane, and PubMed databases according to a published PROSPERO protocol. Articles published between 2000 - 2022 were eligible. Only RCTs that had TAU as the comparator were included. Bias and quality assessments were carried out using the RoB 2 tool. Studies with a high risk of bias were excluded. Heterogeneity was calculated using I2. A random effects model meta-analysis was conducted using RevMan. Results In total, 520 articles were retrieved at first search (excluding duplicates) and seven double-blinded (rater and patient blinded) RCTs were identified that met the full inclusion and exclusion criteria. The RCTs were based in North America (5), Australia (1) and Europe (1). The meta-analysis revealed the remission rate was greater for pharmacogenetic guided groups compared to the TAU groups, pooled risk ratio (RR) = 1.54 (95% CI 1.07-2.21, p = 0.02). For the response rate, results from 6 double blinded RCTs which measured this had a pooled RR= 1.23 (95% CI 0.95-1.59, p = 0.11) which indicates a greater (but not statistically significant) response rate in the pharmacogenetic guided group. Conclusion A limitation of this study is that it showed improvement in remission but not response rate, which is potentially due to response data missing from one of the seven pooled studies. Pharmacogenetic tests might have the potential to improve remission and response rates for antidepressant therapy compared to treatment as usual. However, further studies in the UK are warranted to help determine whether pharmacogenetic tests should be used in an NHS setting.

224. Effect of Borderline Personality Disorder on Antidepressant Response to TMS: A Replication

224. Effect of Borderline Personality Disorder on Antidepressant Response to TMS: A Replication

The Impact of BDNF, NTRK2, NGFR, CREB1, GSK3B, AKT, MAPK1, MTOR, PTEN, ARC, and SYN1 Genetic Polymorphisms in Antidepressant Treatment Response Phenotypes

This study aimed to investigate the influence of genetic variants in neuroplasticity-related genes on antidepressant treatment phenotypes. The BDNF-TrkB signaling pathway, as well as the downstream kinases Akt and ERK and the mTOR pathway, have been implicated in depression and neuroplasticity. However, clinicians still struggle with the unpredictability of antidepressant responses in depressed patients. We genotyped 26 polymorphisms in BDNF, NTRK2, NGFR, CREB1, GSK3B, AKT, MAPK1, MTOR, PTEN, ARC, and SYN1 in 80 patients with major depressive disorder treated according to the Texas Medical Algorithm for 27 months at Hospital Magalhães Lemos, Porto, Portugal. Our results showed that BDNF rs6265, PTEN rs12569998, and SYN1 rs1142636 SNP were associated with treatment-resistant depression (TRD). Additionally, MAPK1 rs6928 and GSK3B rs6438552 gene polymorphisms were associated with relapse. Moreover, we found a link between the rs6928 MAPK1 polymorphism and time to relapse. These findings suggest that the BDNF, PTEN, and SYN1 genes may play a role in the development of TRD, while MAPK1 and GSK3B may be associated with relapse. GO analysis revealed enrichment in synaptic and trans-synaptic transmission pathways and glutamate receptor activity with TRD-associated genes. Genetic variants in these genes could potentially be incorporated into predictive models of antidepressant response.

Establishing the Clinical Potential of Brain Aging in Depression: Implications for Suicidality and Antidepressant Response

Establishing the Clinical Potential of Brain Aging in Depression: Implications for Suicidality and Antidepressant Response

368 Development of Interactome Network to Understand Adverse Drug Reactions for Antidepressant Medications

OBJECTIVES/GOALS: Patient adherence with antidepressant therapy is a critical aspect of clinical management. Drug molecules often interact with other off-target proteins resulting in adverse events. We utilized an interactome model for predicting the likelihood of adverse drug reactions in order to accurately prescribe antidepressant medications to a patient. METHODS/STUDY POPULATION: We utilized an interactome model to study physical interactions between proteins and biological functions disrupted by major depression to understand how changes in genes alter patient-specific drug efficacy and cause adverse drug-reactions. To study how drugs and diseases propagate through the proteins and biological functions, we harnessed diffusion profiles to represent nodes in the graphical model and used a biased random walks model to illustrate how signals propagate in a heterogeneous biological network. The edge weights were defined for the drug, disease, protein, and biological function node types. The interactions studied included drug-protein, disease-protein, protein-protein, protein-biological function, biological function-biological function. RESULTS/ANTICIPATED RESULTS: Based on previous studies, we anticipate that the genetic variants modulating antidepressant response include 5-HTT, STin2, HTR1A, HTR2A, TPH1 and BDNF. Additionally, genetic variants such as SLC6A4 as well as the HPA pathway may play an important role in antidepressant therapeutic response. With regards to medication drug response, the genes, SLC6A4 and HTR2A, have been known for encoding proteins that affect the synaptic cleft of serotonergic neurons. Also, the gene SLC6A4 has been shown to encode for the serotonin reuptake transporter, which is the main pharmacological target of SSRIs. In female patients with major depression, the polymorphism associated with MAOA gene may be involved in the pharmacological response. DISCUSSION/SIGNIFICANCE: The key to successful depression treatment is early adherence but nearly 60% of patients discontinue antidepressants within three months. By developing a strong understanding of the genes that alter treatment efficacy, we can provide patients with more awareness regarding the most effective treatment plan to minimize adverse events.

Adjunctive Cariprazine in Patients With Major Depressive Disorder: Post Hoc Analysis of Efficacy by Baseline Antidepressant Response

AbstractIntroductionPatients with major depressive disorder (MDD) often have inadequate response to antidepressant treatment (ADT) requiring augmentation with other treatments. Cariprazine is a D3-preferring D3/D2 and serotonin 5-HT1A receptor partial agonist approved to treat schizophrenia and manic, mixed, and depressive episodes of bipolar I disorder. The efficacy of cariprazine as an adjunctive treatment for patients with MDD and inadequate response to ADT alone has been evaluated in phase 2/3 randomized, double-blind, placebo-controlled trials. Post hoc analyses of one phase 3 trial (NCT03738215) evaluated cariprazine + ADT for improving depressive symptoms in subgroups of patients categorized by 1) the level of response to ongoing ADT at baseline and 2) the number of ADTs associated with inadequate response during the current episode.MethodsPatients were randomized to placebo + ADT (n=254), cariprazine 1.5 mg/d + ADT (n=252), or cariprazine 3 mg/d + ADT (n=253) for 6 weeks of double-blind treatment. Post hoc analyses evaluated change from baseline to week 6 in MADRS total score in subgroups of patients who had ≥25%–&lt;50% or &lt;25% response to ongoing ADT at baseline, and in subgroups of patients who had inadequate response to 1 or ≥2 ADTs in the current episode. Analyses used a mixed-effects model for repeated measures; least squares mean differences (LSMD) versus placebo with 95% confidence interval (95% CI) were calculated.ResultsAt baseline, 65.1% (n=486) of patients had an ADT response level between 25%–&lt;50% and 34.9% (n=261) of patients had an ADT response level &lt;25%. Mean MADRS total score reductions were greater for cariprazine 1.5 mg/d + ADT versus placebo + ADT in both ADT response subgroups (25%–&lt;50% ADT response: -14.8 vs -11.9, LSMD [95% CI]=-2.3 [-4.2, -0.3]; &lt;25% response to ADT: (-14.7 vs -11.7, LSMD [95% CI]=-2.6 [-5.5, 0.3]). For cariprazine 3 mg/d + ADT, mean change in MADRS total score was numerically greater versus placebo in both response subgroups (25%–&lt;50% response=-14.2, LSMD [95% CI]=-1.5 [-3.5, 0.4]; &lt;25% response= -12.3, LSMD [95% CI]=-0.74 [-3.6, 2.1]). Approximately 86% (n=644) and 14% (n=105) of patients in this study had inadequate response to 1 ADT or ≥2 ADTs, respectively, during the current episode. The LSMD (95% CI) in MADRS total score change for cariprazine 1.5 mg/d + ADT versus placebo + ADT was -2.3 (-4.1, -0.6) in the subgroup of patients with 1 previous ADT and -3.2 [-7.1, 0.8]) in the subgroup of patients with ≥2 previous ADTs. For cariprazine 3 mg/d + ADT, the LSMD (95% CI) in MADRS total score change versus placebo was -0.7 (-2.5, 1.0) in the 1 previous ADT subgroup and -4.7 (-8.8, -0.6) in the ≥2 previous ADTs subgroup.ConclusionsIn these post hoc analyses, cariprazine + ADT was associated with greater reductions in MADRS total score versus placebo regardless of the level of response to ongoing ADT at baseline or number of prior ADT failures in the current episode.FundingAbbVie

Antidepressant Activities of Synthesized Benzodiazepine Analogues in Mice

Depression is a serious psychological disorder which negatively affects human feelings and actions. The use of antidepressants is the therapy of choice while treating depression. However, such drugs are associated with severe side effects. There is a need for efficient and harmless drugs. In this connection, the present study was designed to synthesize several substituted benzodiazepine derivatives and explore their antidepressant potentials in an animal model. The chalcone backbone was initially synthesized, which was then converted into several substituted benzodiazepine derivatives designated as 1-6. The synthesized compounds were identified using spectroscopic techniques. The experimental animals (mice) after acclimatation were subjected to forced swim test (FST) and tail suspension test (TST) after oral administration of the synthesized compounds to evaluate their antidepressant potentials. At the completion of the mentioned test, the animals were sacrificed to determine GABA level in their brain hippocampus. The chloro-substituent compound (2) significantly reduced the immobility time (80.81 ± 1.14 s; p < 0.001 at 1.25 mg/kg body weight and 75.68 ± 3.73 s with p < 0.001 at 2.5 mg/kg body weight dose), whereas nitro-substituent compound (5) reduced the immobility time to 118.95 ± 1.31 and 106.69 ± 3.62 s (p < 0.001), respectively, at the tested doses (FST). For control groups, the recorded immobility time recorded was 177.24 ± 1.82 s. The standard drug diazepam significantly reduced immobility time to 70.13 ± 4.12 s while imipramine reduced it to 65.45 ± 2.81 s (p < 0.001). Similarly, in the TST, the compound 2 reduced immobility time to 74.93 ± 1.14 s (p < 0.001) and 70.38 ± 1.43 s (p < 0.001), while compound 5 reduced it to 88.23 ± 1.89 s (p < 0.001) and 91.31 ± 1.73 s (p < 0.001) at the tested doses, respectively, as compared to the control group immobility time (166.13 ± 2.18 s). The compounds 1, 3, 4, and 6 showed weak antidepressant responses as compared to compounds 2 and 5. The compounds 2 and 5 also significantly enhanced the GABA level in the brain's hippocampus of experimental animals, indicating the possible involvement of GABAergic mechanism in alleviating the depression which is evident from the significant increase in mRNA levels for the α subunit of the GABAA receptors in the prefrontal cortex of mice as well. From the results, it can be concluded that compound 2 and 5 could be used as alternative drugs of depression. However, further exploration in this connection is needed in other animal models in order to confirm the observed results in this study.

ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study.

The P-glycoprotein efflux pump, encoded by the ABCB1 gene, has been shown to alter concentrations of various antidepressants in the brain. In this study, we conducted a systematic review and meta-analysis to investigate the association between six ABCB1 single-nucleotide polymorphisms (SNPs; rs1045642, rs2032582, rs1128503, rs2032583, rs2235015, and rs2235040) and antidepressant treatment outcomes in individuals with major depressive disorder (MDD), including new data from the Canadian Biomarker and Integration Network for Depression (CAN-BIND-1) cohort. For the CAN-BIND-1 sample, we applied regression models to investigate the association between ABCB1 SNPs and antidepressant treatment response, remission, tolerability, and antidepressant serum levels. For the meta-analysis, we systematically summarized pharmacogenetic evidence of the association between ABCB1 SNPs and antidepressant treatment outcomes. Studies were included in the meta-analysis if they investigated at least one ABCB1 SNP in individuals with MDD treated with at least one antidepressant. We did not find a significant association between ABCB1 SNPs and antidepressant treatment outcomes in the CAN-BIND-1 sample. A total of 39 studies were included in the systematic review. In the meta-analysis, we observed a significant association between rs1128503 and treatment response (T vs. C-allele, odds ratio=1.30, 95% confidence interval=1.15-1.48, P value (adjusted)=0.024, n=2,526). We did not find associations among the six SNPs and treatment remission nor tolerability. Our findings provide limited evidence for an association between common ABCB1 SNPs and antidepressant outcomes, which do not support the implementation of ABCB1 genotyping to inform antidepressant treatment at this time. Future research, especially on rs1128503, is recommended.

Dynamic Feedback Between Antidepressant Placebo Expectancies and Mood

Despite high antidepressant placebo response rates, the mechanisms underlying the persistence of antidepressant placebo effects are still poorly understood. To investigate the neurobehavioral mechanisms underlying the evolution of antidepressant placebo effects using a reinforcement learning (RL) framework. In this acute within-patient cross-sectional study of antidepressant placebos, patients aged 18 to 55 years not receiving medication for major depressive disorder (MDD) were recruited at the University of Pittsburgh between February 21, 2017, to March 1, 2021. The antidepressant placebo functional magnetic resonance imaging task manipulates placebo-associated expectancies using visually cued fast-acting antidepressant infusions and controls their reinforcement with sham visual neurofeedback while assessing expected and experienced mood improvement. The trial-by-trial evolution of expectancies and mood was examined using multilevel modeling and RL, relating model-predicted signals to spatiotemporal dynamics of blood oxygenation level-dependent (BOLD) response. A bayesian RL model comparison in 60 individuals (mean [SE] age, 24.5 [0.8] years; 51 females [85%]) with MDD revealed that antidepressant placebo trial-wise expectancies were updated by composite learning signals multiplexing sensory evidence (neurofeedback) and trial-wise mood (bayesian omnibus risk <0.001; exceedance probability = 97%). Placebo expectancy, neurofeedback manipulations, and composite learning signals modulated the visual cortex and dorsal attention network (threshold-free cluster enhancement [TFCE] = 1 - P >.95). As participants anticipated antidepressant infusions, learned placebo expectancies modulated the salience network (SN, TFCE = 1 - P >.95), positively scaling with depression severity. Results of this cross-sectional study suggest that on a timescale of minutes, antidepressant placebo effects were maintained by positive feedback loops between expectancies and mood improvement. During learning, representations of placebos and their perceived effects were enhanced in primary and secondary sensory cortices. Latent learned placebo expectancies were encoded in the SN.