Does mismatch negativity have utility for NMDA receptor drug development in depression?

Rapid antidepressant effects associated with ketamine have shifted the landscape for the development of therapeutics to treat major depressive disorder (MDD) from a monoaminergic to glutamatergic model. Treatment with ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, may be effective, but has many non-glutamatergic targets, and clinical and logistical problems are potential challenges. These factors underscore the importance of manipulations of binding mechanics to produce antidepressant effects without concomitant clinical side effects. This will require identification of efficient biomarkers to monitor target engagement. The mismatch negativity (MMN) is a widely used electrophysiological signature linked to the activity of NMDA receptors (NMDAR) in humans and animals and validated in pre-clinical and clinical studies of ketamine. In this review, we explore the flexibility of the MMN and its capabilities for reliable use in drug development for NMDAR antagonists in MDD. We supplement this with findings from our own research with three distinct NMDAR antagonists. The research described illustrates that there are important distinctions between the mechanisms of NMDAR antagonism, which are further crystallized when considering the paradigm used to study the MMN. We conclude that the lack of standardized methodology currently prevents MMN from being ready for common use in drug discovery. Clinical trial registration: This manuscript describes data collected from the following National Institutes of Health (NIH) and Veterans Affairs (VA) studies: AV-101, NCT03583554; lanicemine, NCT03166501; ketamine, NCT02556606.

Effect of Vagus Nerve Stimulation on Attention and Working Memory in Neuropsychiatric Disorders: A Systematic Review.

BACKGROUND: It has been suggested that vagus nerve stimulation (VNS) may enhance attention and working memory. The neuromodulator effects of VNS are thought to activate the release of neurotransmitters involving cognition and to promote neuronal plasticity. Therefore, VNS has been studied for its effects on attention and working memory impairment in neuropsychiatric disorders. OBJECTIVES: This study aimed to assess the effects of VNS on attention and working memory among patients with neuropsychiatric disorders, examine stimulation parameters, provide mechanistic hypotheses, and propose future studies using VNS. MATERIALS AND METHODS: We conducted a systematic review using electronic databases MEDLINE (Ovid), Embase (Ovid), Cochrane library, and PsycINFO (Ovid). Narrative analysis was used to describe the therapeutic effects of VNS on attention and working memory, describe stimulation parameters, and propose explanatory mechanisms. RESULTS: We identified 20 studies reporting VNS effects on attention and working memory in patients with epilepsy or mood disorders. For epilepsy, there was one randomized controlled trial from all 18 studies. It demonstrated no statistically significant differences in the cognitive tasks between active and control VNS. From a within-subject experimental design, significant improvement of working memory after VNS was demonstrated. One of three nonrandomized controlled trials found significantly improved attentional performance after VNS. The cohort studies compared VNS and surgery and found attentional improvement in both groups. Nine of 12 pretest-posttest studies showed improvement of attention or working memory after VNS. For mood disorders, although one study showed significant improvement of attention following VNS, the other did not. CONCLUSIONS: This review suggests that, although we identified some positive results from eligible studies, there is insufficient good-quality evidence to establish VNS as an effective intervention to enhance attention and working memory in persons with neuropsychiatric disorders. Further studies assessing the efficacy of such intervention are needed.

Analysis of healthy and tumour DNA methylation distributions in kidney-renal-clear-cell-carcinoma using Kullback-Leibler and Jensen-Shannon distance measures.

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper-methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique 'average healthy methylation distribution' based on the methylation values of several healthy samples. They then obtain the Kullback-Leibler and Jensen-Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.

Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.

DNA Methylation is an epigenetic phenomenon in which methyl groups are added to the cytosines, thereby altering the physio-chemical properties of the DNA region and influencing gene expression. Aberrant DNA methylation in a set of genes or across the genome results in many epigenetic diseases including cancer. In this paper, we use entropy to analyze the extent and distribution of DNA methylation in Tumor Suppressor Genes (TSG's) and Oncogenes related to a specific type of cancer (viz.) KIRC (Kidney-renal-clear-cell-carcinoma). We apply various mathematical transformations to enhance the different regions in DNA methylation distribution and compare the resultant entropies for healthy and tumor samples. We also obtain the sensitivity and specificity of classification for the different mathematical transformations. Our findings show that it is not just the measure of methylation, but the distribution of the methylation levels in the genes that are significant in cancer.