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Finan, G.M.,Realubit, R.,Chung, S.,Lutjohann, D.,Wang, N.,Cirrito, J.R.,Karan, C.,Kim, T.W. Elsevier 2016 Cell chemical biology Vol.23 No.12
<P>Pharmacological screening in physiologically relevant brain cells is crucial for identifying neuroactive compounds that better translate into in vivo biology and efficacious therapeutics. Pharmacological enhancement of apolipoprotein E (apoE), a cholesterol-transporting apolipoprotein, has been proposed as a promising therapeutic approach for Alzheimer's disease. Several nuclear receptor agonists were initially shown to increase brain apoE levels together with ATP-binding cassette transporter 1 (ABCA1), but their underlying mechanisms remain unclear. To gain an insight on brain apoE regulation, we performed an unbiased high-throughput screening of known drugs and bioactive compounds in cultured human primary astrocytes, the major apoE-producing cell type in the brain. We have identified several small molecules that increase apoE secretion via previously unknown mechanisms, including those not co-inducing ABCA1. These newly identified compounds are active preferentially in human astrocytes but not in an astrocytoma cell line, furnishing new tools for investigating biological pathways underlying brain apoE production.</P>
A community computational challenge to predict the activity of pairs of compounds
Bansal, Mukesh,Yang, Jichen,Karan, Charles,Menden, Michael P,Costello, James C,Tang, Hao,Xiao, Guanghua,Li, Yajuan,Allen, Jeffrey,Zhong, Rui,Chen, Beibei,Kim, Minsoo,Wang, Tao,Heiser, Laura M,Realubit Nature Publishing Group, a division of Macmillan P 2014 Nature biotechnology Vol.32 No.12
Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.