Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands

Abstract

Background: The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands.

Results: Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program.

Conclusions: The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities.

Keywords: Data integration; Drug discovery; G-protein coupled receptors; Target identification.

Fig. 1

Distribution of the overall Open Targets score and relationship to individual data types. a Empirical density and cumulative distribution of the Open Targets score. Density and distribution functions were estimated using the R functions density() and ecdf(), respectively, with default parameters and using all pairs and 10,000 randomly selected pairs, respectively. b Number of gene-disease pairs with positive scores by type of score (overall, genetic association, somatic mutation, known drugs, RNA expression, affected pathways, animal models, and literature mining). c Comparison of the overall score of a disease-gene association and the number of data sources where the individual data type score is > 0. The top panel shows the counts of target-disease pairs corresponding to the scores below

Fig. 2

Characterizing GPCRs and endogenous ligands. a Number of endogenous ligands per GPCR and (b) number of GPCRs per endogenous ligand. c Average number of gene-disease pairs by GPCR, endogenous ligand, and all other target types using all pairs (left) and pairs with overall score > 0.5 (right). d Distribution of overall scores by target type (GPCR, endogenous ligand, and all other)

Fig. 3

Comparing the overall Open Targets score for disease-GPCR pairs and the corresponding disease-endogenous ligand pairs showing a two dimensional histogram (a), the distribution of the increase in overall score comparing the disease-GPCR pairs to the corresponding disease-GPCR/ligand pairs (b), the cumulative density function (CDF) for this change in score (c), and % change of the number of pairs in the indicated brackets when comparing disease-GPCR/ligand pairs to the corresponding disease-GPCR pair alone (d)

Fig. 4

GPCRs (a) and endogenous peptidergic ligands (b) and the highest stage in global pharma pipeline. In each panel, the leftmost chart shows the distribution of highest stage (post-clinical, clinical trial, pre-clinical, none) by target type while the other two charts show such distribution among the gene-disease pairs within the Open Targets platform stratified by corresponding overall score (< 0.5, middle; ≥0.5 right)