Biomarkers predicting antidepressant treatment response - how can we advance the field?

Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack of conceptually novel antidepressant compounds. Both, biomarker predicting a priori whether an individual patient will respond to the treatment of choice as well as an early distinction of responders and nonresponders during antidepressant therapy can have a significant impact on improving this situation. Biosignatures predicting antidepressant response a priori or early in treatment would enable an evidence-based decision making on available treatment options. However, research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the selection of specific antidepressant compound for an individual patient. In this review, we propose an optimized translational research strategy to overcome some of the major limitations in biomarker discovery. We are confident that early transfer and integration of data between both species, ideally leading to mutual supportive evidence from both preclinical and clinical studies, are most suitable to address some of the obstacles of current depression research

Ketamine's antidepressant effect is mediated by energy metabolism and antioxidant defense system.

Fewer than 50% of all patients with major depressive disorder (MDD) treated with currently available antidepressants (ADs) show full remission. Moreover, about one third of the patients suffering from MDD does not respond to conventional ADs and develop treatment-resistant depression (TRD). Ketamine, a non-competitive, voltage-dependent N-Methyl-D-aspartate receptor (NMDAR) antagonist, has been shown to have a rapid antidepressant effect, especially in patients suffering from TRD. Hippocampi of ketamine-treated mice were analysed by metabolome and proteome profiling to delineate ketamine treatment-affected molecular pathways and biosignatures. Our data implicate mitochondrial energy metabolism and the antioxidant defense system as downstream effectors of the ketamine response. Specifically, ketamine tended to downregulate the adenosine triphosphate (ATP)/adenosine diphosphate (ADP) metabolite ratio which strongly correlated with forced swim test (FST) floating time. Furthermore, we found increased levels of enzymes that are part of the 'oxidative phosphorylation' (OXPHOS) pathway. Our study also suggests that ketamine causes less protein damage by rapidly decreasing reactive oxygen species (ROS) production and lend further support to the hypothesis that mitochondria have a critical role for mediating antidepressant action including the rapid ketamine response

Biomarkers Predicting Antidepressant Treatment Response: How Can We Advance the Field?

Major depression, affecting an estimated 350 million people worldwide, poses a serious social and economic threat to modern societies. There are currently two major problems calling for innovative research approaches, namely, the absence of biomarkers predicting antidepressant response and the lack of conceptually novel antidepressant compounds. Both, biomarker predicting a priori whether an individual patient will respond to the treatment of choice as well as an early distinction of responders and nonresponders during antidepressant therapy can have a significant impact on improving this situation. Biosignatures predicting antidepressant response a priori or early in treatment would enable an evidence-based decision making on available treatment options. However, research to date does not identify any biologic or genetic predictors of sufficient clinical utility to inform the selection of specific antidepressant compound for an individual patient. In this review, we propose an optimized translational research strategy to overcome some of the major limitations in biomarker discovery. We are confident that early transfer and integration of data between both species, ideally leading to mutual supportive evidence from both preclinical and clinical studies, are most suitable to address some of the obstacles of current depression research

Training performance of Homer1KO mice.

<p>(<b>a</b>) In 5 habituation trials, all wild type (WT) mice show growing interest in the presented reward. Most of the Homer1KO mice, however, do not consume the sucrose pellets. (<b>b</b>) The learning curve in the fixed ratio/variable ratio (FR/VR) stage is slightly, but not significantly higher in Homer1KO mice compared to WT animals. This is due to the above-average performance of a subset of Homer1KO mice that already showed a response to the reward in the habituation phase, while the greater part of the Homer1KO animals show a below-average performance, thereby largely increasing the variance of the sample. (<b>c</b>) FR/VR results of training trial 15. A strong bimodal distribution of the Homer1KO group becomes apparent, consequently resulting in no significant difference when compared to WT animals.</p

Training performance of Homer1 OE mice.

<p>(<b>a</b>) During habituation, both groups quickly recognized and consumed the presented rewards. No differences between the treatments were detected. (<b>b</b>) During the training stage, both Empty and Homer1 OE animals learned to associate lever presses with the reception of a reward. Although Homer1 OE mice appear to show less motivational behavior, repeated measures ANOVA did not reveal a significant time × AAV interaction. (<b>c</b>) Fixed ratio/variable ratio results of training trial 10. No difference in lever press activity was evident between the experimental groups.</p

Classification features selected from differential gene expression in a mouse model for antidepressant treatment response are informative for treatment response in a human gene expression data set.

<p>Histogram shows the distribution of prediction accuracy over 1,000 simulated classification models that were computed with randomly chosen gene expression probe sets from human gene expression data (median prediction accuracy = 69.77%). The red dashed line marks the observed prediction accuracy (76.74%) when using our informed feature selection to build classifiers. Because only 25 of the randomly chosen feature sets yield equal or better classification results, the predictive ability of our features selected from the presented mouse model is significantly greater than expected by chance (<i>p</i> = 0.026). All raw data for Fig 5 are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002690#pbio.2002690.s001" target="_blank">S1 Data</a>.</p

Murine approach modeling heterogeneity of treatment outcome in the FST.

<p>This figure illustrates the underlying hypothesis: in a large number of animals that are treated with an antidepressant, animals are stratified into subgroups of extremes according to their time-floating behavior. To allow for the distinction of effects truly related to the phenomenon of response (and not treatment per se), a second group of animals is treated with a vehicle under identical conditions. FST, forced swim test.</p

Differential gene expression in animals stratified for behavioral treatment response to chronic paroxetine treatment.

<p>(A) Volcano plot showing results from blood samples. The biological effect size (difference in expression) is plotted against statistical significance (as negative log 10 transformed values of the FDR-based <i>q</i>-value). Regarded as significantly regulated, 259 probes with <i>q</i>-values < 0.1 are colored according to their difference in expression. (B) Heat map comparing patterns of differential gene expression in blood and prefrontal cortex. Each row in the plot represents the difference in expression of 1 microarray probe between poor and good responders in both blood and prefrontal cortex. The array probes are ordered according to agglomerative hierarchical clustering, but no large common gene regulation patterns are revealed between the 2 tissues. Scale for color coding difference in expression is identical for (A) and (B). All raw data for Fig 4 are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002690#pbio.2002690.s001" target="_blank">S1 Data</a>. FDR, false discovery rate.</p

Enrichment of dexamethasone-regulated genes.

<p>Histogram of 100,000 random samples indicates that paroxetine response genes show higher overlap with dex-regulated genes than expected by chance. The overlap between differential microarray probes from paroxetine-response and dex-regulated microarrays is 134 and this threshold is indicated as a vertical red dashed line. In this simulation, on average, 115 probes do overlap by chance and in 70 samples, the random overlap is higher than the tested one. All raw data for Fig 6 are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002690#pbio.2002690.s001" target="_blank">S1 Data</a>. dex, dexamethasone.</p