A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience
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Hot Topics in Drug Development [April 2016] 12 JOURNAL WATCH their model and compare its performance to those from other methods. They found that deep learning significantly outperforms linear regression with 15.33% relative improvement. Deep learning achieved lower error than linear regression in 99.97% of the target genes. They also tested the performance of their learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2,921 expression profiles. The authors found that deep learning still outperforms linear regression with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. * PMID: 26873929 NOVEL APPROACHES TO DRUG DISCOVERY AND ANALYSIS Addressing the challenges of low clearance in drug research. Di L, Obach RS. AAPS J. 2015 Mar;17(2):352–7. doi: 10.1208/s12248-014-9691-7. Epub 2015 Jan 8. Summary: The authors review the significant advances that have been made in recent years and the novel approaches that have been developed to address the challenges of low clearance in drug discovery, s uch as the hepatocyte relay method, use of qNMR-based standards of biosynthesized drug metabolites to permit monitoring metabolite formation, coculture hepatocyte systems, and the time depending modeling approach. * PMID: 25567366 The first structure-activity relationship studies for designer receptors exclusively activated by designer drugs. Chen X, Choo H, Huang XP, et al. ACS Chem Neurosci. 2015 Mar 18;6(3):476–84. doi: 10.1021/cn500325v. Epub 2015 Jan 27. Summary: Here researchers explored multiple regions of the scaffold represented by clozapine N- oxide (CNO), a pharmacologically inert ligand, identified interesting structure-activity relationships (SARs) trends, and discovered several compounds that are very potent hM3Dq agonists but do not activate the native human M3 receptor (hM3). The authors reveal that the approved drug perlapine is a novel hM3Dq agonist with >10,000- fold selectivity for hM3Dq over hM3. * PMID: 25587888 Polypharmacology in drug discovery: a review from systems pharmacology perspective. Zhang W, Bai Y, Wang Y1, Xiao W. Curr Pharm Des. 2016 Feb 24. [Epub ahead of print] Summary: In this perspective article, the authors provide a global view on polypharmacology—defined as the foundation of the next paradigm in drug discovery. The authors focus on the pharmacological properties of current polypharmacology, discuss potential novel drug indication arising from drug repurposing, and introduce approaches to the rational design of multi-target drugs. They also present s ome computational methods to predict the potential novel multi- target drugs with lower toxicity and higher efficacy. * PMID: 26907941 Network-based strategies can help mono- and poly- pharmacology drug discovery: a systems biology view. Engin HB, Gursoy A, Nussinov R, Keskin O. Curr Pharm Des. 2014;20(8):1201–7. Summary: In this mini-review, researchers provide an overview of the usefulness of network description and tools for mono- and poly-pharmacology, and the ways through which protein interactions can help single- and multi-target drug discovery efforts. The authors also describe how, when combined with experimental data, modeled structural networks (which can predict which proteins interact and provide the structures of their interfaces) can model the cellular pathways. The authors go on to suggest which specific pathways are likely to be affected. The authors propose that such structural networks may facilitate structure- based drug design, they forecast side effects of drugs, and they and suggest how the effects of drug binding can propagate in multi- molecular complexes and pathways. * PMID: 23713773 Quo vadis1 computational analysis of PPI data or why the future isn't here yet Theofilatos KA, Likothanassis S, Mavroudi S. Front Genet. 2015;6: 289. Published online 2015 Sep 15. doi:10.3389/fgene.2015.00289 Summary: In this paper, the authors summarize the developments on computational analysis of protein- protein interactions (PPI) data and present their predictions for the