Nortriptyline

BackgroundIncreased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms.MethodsHere we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins.ResultsWe identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration.ConclusionWe believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs.

Transcriptional differences in interleukin-11 (IL11) after antidepressant treatment have been found to correspond to clinical response in major depressive disorder (MDD) patients. Expression differences were partly mediated by a single-nucleotide polymorphism (rs1126757), identified as a predictor of antidepressant response as part of a genome-wide association study. Here we attempt to identify whether DNA methylation, another baseline factor known to affect transcription factor binding, might also predict antidepressant response, using samples collected from the Genome-based Therapeutic Drugs for Depression project (GENDEP). DNA samples from 113 MDD individuals from the GENDEP project, who were treated with either escitalopram (n=80) or nortriptyline (n=33) for 12 weeks, were randomly selected. Percentage change in Montgomery-Åsberg Depression Rating Scale scores between baseline and week 12 were utilized as our measure of antidepressant response. The Sequenom EpiTYPER platform was used to assess DNA methylation across the only CpG island located in the IL11 gene. Regression analyses were then used to explore the relationship between CpG unit methylation and antidepressant response. We identified a CpG unit predictor of general antidepressant response, a drug by CpG unit interaction predictor of response, and a CpG unit by rs1126757 interaction predictor of antidepressant response. The current study is the first to investigate the potential utility of pharmaco-epigenetic biomarkers for the prediction of antidepressant response. Our results suggest that DNA methylation in IL11 might be useful in identifying those patients likely to respond to antidepressants, and if so, the best drug suited to each individual.

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.

Pilot randomised-controlled trial of nortriptyline and escitalopram compared to placebo for depressive symptoms in people with Parkinson's disease (PD). Participants with PD and depressive symptoms received either nortriptyline, escitalopram or placebo with assessments at baseline and 8 weeks. Feasibility of the full trial was assessed using recruitment rate, loss to follow up before the 8-week primary endpoint, adherence to trial medication and rate of clinically significant adverse reactions. Intended efficacy outcomes included the BDI-II, Patient Health Questionnaire (PHQ-9), Parkinson Anxiety Scale (PAS), MDS-UPDRS and adverse effects. The aim was to recruit 46 participants to determine feasibility of the full trial. 52 participants were recruited from 24 NHS sites and randomised to nortriptyline (n = 16), escitalopram (n = 17) or placebo (n = 19). However, despite multiple strategies, recruitment took two years, and the study therefore did not reach its feasibility aim. Exploratory analyses showed that at 8 weeks depressive symptoms decreased significantly in all three arms. There was no difference in BDI-II score changes between the nortriptyline or the escitalopram when compared to the placebo arm, but PHQ-9 scores showed a greater reduction compared to placebo with nortriptyline (-3.4, 95% CI -5.75 to -0.95) but not with escitalopram (-1.2, 95% CI -3.54 to -1.17). Persistent Anxiety Scale scores showed a similar pattern (nortriptyline: -2.8, 95% CI -5.5 to -0.01; escitalopram: -2.2, 95% CI -4.9 to 0.05). As the study did not reach its primary aim of feasibility due to recruitment difficulties it did not continue to the full trial. The exploratory results are in keeping with previous studies suggesting a beneficial effect of nortriptyline on symptoms of depression and persistent anxiety. The small sample size however limits conclusions on efficacy and a beneficial effect of escitalopram is not excluded. STUDY DATASET IS AVAILABLE ON CLINICALTRIALS.GOV ID: NCT03652870, registered 29 August 2018.