Abstract
Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.
Highlights
Enormous quantities of “omics” data characterizing both normal and diseased tissues continue to accumulate, leading to the development of increasingly complex molecular biomarkers for diseases
This result suggests that only the docking scores achieved with the highest quality Pocketome pockets should be included in our “omics” set of compound-receptor scores, which are used to predict mechanistic signatures solely from chemotype
For our “omics” set we docked over 600,000 unique non-overlapping chemical structures from PubChem Bioassay, ChEMBL, and DrugBank against these 6857 pockets for a total of 4.2 billion pairwise docking scores between compounds and targets. These “omics” in silico docking scores together with the compound–receptor affinities obtained experimentally, constitute the bioactivity scores data set (Figure 2), which comprise a significant fraction of the druggable targets encoded in the human genome, by one estimate to be around 4000 targets (Reardon, 2013)
Summary
Enormous quantities of “omics” data characterizing both normal and diseased tissues continue to accumulate, leading to the development of increasingly complex molecular biomarkers for diseases. Many if not most of these drugs likely act polypharmacologically (on multiple receptors simultaneously). These two trends result in a growing knowledge gap between the efforts to mechanistically and genomically characterize diseases on the molecular level and the chemicals used for their treatment (Figure 1). Polypharmacology partly addresses this gap and has gained increasing attention in the field of drug discovery (Peters, 2011). Improved polypharmacological profiles of a drug can be identified only by a more comprehensive analysis of drug-target interactions on a proteome-wide scale (Xie et al, 2012)
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