Lurasidone for the treatment of bipolar depression: an evidence-based review

Bipolar disorder (BD) is a debilitating and difficult-to-treat psychiatric disease that presents a serious burden to patients’ lives as well as health care systems around the world. The essential diagnostic criterion for BD is episodes of mania or hypomania; however, the patients report that the majority of their time is spent in a depressive phase. Current treatment options for this component of BD have yet to achieve satisfactory remission rates. Lurasidone is a drug in the benzisothiazole class approved by the US Food and Drug Administration in June 2013 for the acute treatment of bipolar depression. Its pharmacological profile features high-affinity antagonism at D[subscript 2], 5-HT[subscript 2A], and 5-HT[subscript 7] receptors; moderate-affinity antagonism at α[subscript 2C]-adrenergic receptors; low- to very low-affinity antagonism at α[subscript 1A]-adrenergic, α[subscript 2A]-adrenergic, H[subscript 1], M[subscript 1], and 5-HT[subscript 2C] receptors; and high-affinity partial agonism at 5-HT1A. Preliminary findings from two recent double-blinded clinical trials suggest that lurasidone is efficacious in treating bipolar I depression, with clinical effects manifesting as early as the first 2–3 weeks of treatment (as measured by the Montgomery–Åsberg Depression Rating Scale and Clinical Global Impressions Scale for use in bipolar illness). Its therapeutic benefit appears to be comparable to the current US Food and Drug Administration-indicated treatments: quetiapine and olanzapine–fluoxetine, according to a measure of effect size known as number needed to treat. These studies reported relatively limited extrapyramidal and metabolic side effects as a result of treatment with lurasidone, with the most common side effect being nausea. Safety data drawn from these studies, as well as a more extensive body of schizophrenia research, indicate that in comparison with other atypical antipsychotics, treatment with lurasidone is less likely to result in metabolic side effects such as weight gain or disturbances of serum glucose or lipid levels. Lurasidone holds clinical potential as a novel, efficacious pharmacological treatment for bipolar depression. However, current data on its use for the treatment of BD are limited, and more extensive research, both longer in duration as well as independently conducted, is needed

The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the Jigsaw algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). Jigsaw consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Jigsaw\u27s deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, Jigsaw not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of alpha-helical and 60 percent of beta-sheet structure. Jigsaw is extremely fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. Jigsaw could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome

Deep brain stimulation for substance use disorders?:An exploratory qualitative study of perspectives of people currently in treatment

Objective: Although previous studies have discussed the promise of deep brain stimulation (DBS) as a possible treatment for substance use disorders (SUDs) and collected researcher perspectives on possible ethical issues surrounding it, none have consulted people with SUDs themselves. We addressed this gap by interviewing people with SUDs.Methods: Participants viewed a short video introducing DBS, followed by a 1.5-hour semistructured interview on their experiences with SUDs and their perspective on DBS as a possible treatment option. Interviews were analyzed by multiple coders who iteratively identified salient themes.Results: We interviewed 20 people in 12-step–based, inpatient treatment programs (10 [50%] White/Caucasian, 7 Black/African American [35%], 2 Asian [10%], 1 Hispanic/Latino [5%], and 1 [5%] Alaska Native/American Indian; 9 women [45%], 11 men [55%]). Interviewees described a variety of barriers they currently faced through the course of their disease that mirrored barriers often associated with DBS (stigma, invasiveness, maintenance burdens, privacy risks) and thus made them more open to the possibility of DBS as a future treatment option.Conclusions: Individuals with SUDs gave relatively less weight to surgical risks and clinical burdens associated with DBS than previous surveys of provider attitudes anticipated. These differences derived largely from their experiences living with an often-fatal disease and encountering limitations of current treatment options. These findings support the study of DBS as a treatment option for SUDs, with extensive input from people with SUDs and advocates.<br/

Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD)

Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region

Electronic health records and stratified psychiatry: bridge to precision treatment?

The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR datas power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed