Innovations In Clinical Neuroscience

Hot Topics in Drug Development Apr 2016

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Hot Topics in Drug Development [April 2016] 11 type of data should be shared, m ethods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. * PMID: 25590113 APPLICATION OF BIOMARKERS Autoantibodies in CNS trauma and neuropsychiatric disorders: a new generation of biomarkers. Kobeissy F, Moshourab RA. In: Kobeissy FH (ed). Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton FL: CRC Press/Taylor & Francis; 2015. Chapter 29. Summary: In this book chapter, the authors discuss the genesis and implications of autoantibodies in neurotrauma—focusing on the area of spinal cord injury and shedding light on recent application in traumatic brain injury. In addition, the authors evaluate the potential pathogenic mechanistic role of autoantibodies in the areas of autism spectrum disorder and neurotoxicity, as this may reflect on the neural injury observed in brain trauma. * PMID: 2626990 Novel agents for the treatment of childhood acute leukemia. Annesley CE, Brown P. Ther Adv Hematol. 2015;6(2):61–79. doi: 10.1177/2040620714565963. Summary: Here the authors detail the rationale and implementation of recent and specifically targeted therapies in acute pediatric leukemia. They discuss inhibition of critical cell signaling pathways [BCR-ABL, FMS- like tyrosine kinase 3 (FLT3), mammalian target of rapamycin (mTOR), and Janus-associated kinase (JAK)], proteasome inhibition, inhibition of epigenetic regulators of gene expression [DNA methyltransferase (DNMT) inhibitors, histone deacetylase ( HDAC) inhibitors, and disruptor of telomeric signaling-1 (DOT1L) inhibitors], monoclonal antibodies and immunoconjugated toxins, bispecific T-cell engaging (BiTE) antibodies, and chimeric antigen receptor-modified (CAR) T cells. * PMID: 25830014 Novel methodologies for biomarker discovery in atherosclerosis. Hoefer IE, Steffens S, Ala-Korpela M, et al. Eur Heart J. 2015 Oct 14;36(39):2635–42. doi: 10.1093/eurheartj/ehv236. Epub 2015 Jun 5. Summary: In this position paper, the authors provide a summary of current vascular biomarkers other than the traditional risk factors and specifically focus on the emerging -omics technologies. The authors introduce the definition of biomarkers and the identification and use of classical biomarkers, as well as discuss the limitations of current biomarkers such as high sensitivity C-reactive protein (hsCRP) or N-terminal pro-brain natriuretic peptide (NT-proBNP). * PMID: 26049157 "DEEP LEARNING" AND DRUG DEVELOPMENT Impact of the digital revolution on the future of pharmaceutical formulation science. Leuenberger H, Leuenberger MN. Eur J Pharm Sci. 2016 Feb 11. pii: S0928–0987(16)30035-5. doi: 10.1016/j.ejps.2016.02.005. [Epub ahead of print]. Summary: Researchers describe a new software platform called F- CAD (Formulation-Computer Aided Design) of CINCAP, which can be used to develop and test in silico capsule and tablet formulations. The software is based on a cellular automaton process that mimics the d issolution profile of the capsule or tablet formulation. The authors go into great detail describing the software and its applications in drug development. The authors suggest that in the future, it may be possible to rent at low costs F-CAD as an IT (Information Technology) platform based on a cloud computing solution. * PMID: 26876764 Applications of deep learning in biomedicine. Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Mol Pharm. 2016 Mar 29 [Epub ahead of print]. Summary: The authors describe deep neural networks (DNNs) as efficient algorithms based on the use of compositional layers of neurons. The authors discuss key features of deep learning that may give this approach an edge over other machine learning methods. They then discuss limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility. * PMID: 27007977 Gene expression inference with deep learning. Chen Y, Li Y, Narayan R, et al. Bioinformatics. 2016 Feb 11. pii: btw074. [Epub ahead of print]. Summary: Investigators present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. The authors used the microarray-based GEO dataset, consisting of 111K expression profiles, to train JOURNAL WATCH

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