Network and pathway expansion of genetic disease associations identifies successful drug targets.

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization.

Preclinical evaluation of EPHX2 inhibition as a novel treatment for inflammatory bowel disease.

Epoxyeicosatrienoic acids (EETs) are signaling lipids produced by cytochrome P450 epoxygenation of arachidonic acid, which are metabolized by EPHX2 (epoxide hydrolase 2, alias soluble epoxide hydrolase or sEH). EETs have pleiotropic effects, including anti-inflammatory activity. Using a Connectivity Map (CMAP) approach, we identified an inverse-correlation between an exemplar EPHX2 inhibitor (EPHX2i) compound response and an inflammatory bowel disease patient-derived signature.

Pathway analysis of GWAS loci identifies novel drug targets and repurposing opportunities.

Genome-wide association studies (GWAS) have made considerable progress and there is emerging evidence that genetics-based targets can lead to 28% more launched drugs. We analyzed 1589 GWAS across 1456 pathways to translate these often imprecise genetic loci into therapeutic hypotheses for 182 diseases. These pathway-based genetic targets were validated by testing whether current drug targets were enriched in the pathway space for the same indication.

Predicting clinically promising therapeutic hypotheses using tensor factorization.

Background: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success.

Impact of genetically supported target selection on R&D productivity.

This corrects the article DOI: 10.1038/nrd.2016.

Systematic Analysis of Drug Targets Confirms Expression in Disease-Relevant Tissues.

It is commonly assumed that drug targets are expressed in tissues relevant to their indicated diseases, even under normal conditions. While multiple anecdotal cases support this hypothesis, a comprehensive study has not been performed to verify it. We conducted a systematic analysis to assess gene and protein expression for all targets of marketed and phase III drugs across a diverse collection of normal human tissues.

Combined Analysis of Phenotypic and Target-Based Screening in Assay Networks.

Small-molecule screens are an integral part of drug discovery. Public domain data in PubChem alone represent more than 158 million measurements, 1.2 million molecules, and 4300 assays.

Inhibition of p38 mitogen-activated protein kinase reduces inflammation after coronary vascular injury in humans.

Objective: To evaluate whether a p38α/β mitogen-activated protein kinase inhibitor, SB-681323, would limit the elevation of an inflammatory marker, high-sensitivity C-reactive protein (hsCRP), after a percutaneous coronary intervention (PCI).

Methods And Results: Coronary artery stents provide benefit by maintaining lumen patency but may incur vascular trauma and inflammation, leading to myocardial damage. A key mediator for such stress signaling is p38 mitogen-activated protein kinase.

Chfr regulates a mitotic stress pathway through its RING-finger domain with ubiquitin ligase activity.

Resistance to chemotherapy targeting microtubules could be partially because of the delay in chromosome condensation and segregation during mitosis. The Chfr pathway has been defined recently, and its activation causes a delay in chromosome condensation in response to mitotic stress. Because Chfr contains a RING-finger domain, we tested whether Chfr inhibits chromosome condensation through an ubiquitin (ubiquitin)-dependent pathway.