What is a typical use case that Open Targets can help in answering?

Open Targets is most often used to identify and prioritise drug targets, speeding up the early stages of drug discovery. Below is a step-by-step walkthrough of how a pharma team might use the platform to uncover new therapeutic options for Type 2 Diabetes (T2D).

Getting into Open Targets

Suppose I'm part of a pharmaceutical company interested in developing a new therapeutic drug for T2D. My research team needs to identify and prioritize potential drug targets—genes or proteins that, when modulated, could have a therapeutic effect on the disease.

Platform Access

For the purpose of this use case, we will use The Hyve’s instance of Open Targets Platform hosted at https://platform.gtd.thehyve.nl/. Sign up to get access and get to the landing page.

Disease Association Exploration

• Query by Disease: I start by querying "Type 2 Diabetes" (T2D) in the Open Targets platform. I select the disease that matches my query, which is “type 2 diabetes mellitus”. The platform takes me to the associated genes page, which contains a list of genes linked to T2D. Each gene is annotated with evidence from various biological sources, including genome-wide association studies (GWAS), expression data, and literature.

• Examine genes: I look at the list of genes. I recognize some of them, such as INSR (the insulin receptor) on line 5. I click on the dots next to the gene name and select “Navigate to evidence”.

• Examine Evidence: I scroll down the page of evidence linking INSR to T2D.
  • I scroll down the page of evidence linking INSR to T2D. 
    I note that ClinVar documents a missense mutation in INSR that is associated with T2D - insulin, insulin receptor, that seems to make sense.
  • I see many drugs that are related to this gene documented in the ChEMBL section of the page - many of them are variants of insulin, which is to be expected.
• Pick a few favorites: I go back to the associations page. I want to use gene INSR as a reference, so I pin it to the top by clicking on it and selecting “pin target”. As an extra reference gene, I search for the gene insulin (select “Symbol” from drop-down and type “INS” in the search field) and pin it too so that I now have INS and INSR pinned to the top of the table.

Evaluate Evidence Strength

• Review Genetic Associations: I want to focus on the genetic associations between genes and T2D, so I click the bar under OT Genetics, Gene Burden, … The weights field opens and allows me to select specific data sources, or adjust the weights. In this case, I just click the bar again to select all genetic association data sources.
• Functional Data Integration: The platform integrates data on gene expression, protein interactions, and pathways. I can see how highly a gene is expressed in tissues relevant to T2D (like pancreatic tissue) or its role in insulin signaling pathways.

Prioritization of Targets

• Score and Rank Targets: Using Open Targets’ scoring system, I rank the genes based on the strength and breadth of the evidence supporting their role in T2D. Genes with high confidence scores and those involved in critical pathways related to insulin secretion or glucose metabolism are considered top candidates.
• I click the top gene KCNJ11 to see what its link is with T2D. I click the 3 dots and review the evidence. I look at the abstracts of the papers at the bottom of the evidence page and find out that this potassium channel is essential for insulin secretion. That explains why its association is so strong - while I would not (with my limited knowledge of T2D) have expected a priori that a potassium channel would be of crucial importance. Also the second hit, ABCC8,  is associated with potassium channel functionality.

Examine Druggability & Potential Side Effects 

• Existing Compounds and Druggability: The platform also provides information on existing drugs or compounds that interact with a target. I click on KCNJ11 and select ‘Profile’. I scroll down to the ‘Known Drugs’ section and see that there are already several drugs available for this target, and that T2D is listed as a target disease. This verifies that this target has been targeted for T2D.
• Safety: I check for side effects by scrolling down to Safety. There are obvious effects listed (‘decreased blood insulin’, that is the whole point), but also ‘abnormal pulse’ and a few other hits in the cardiovascular system, signaling potential safety concerns.

Decision-Making

• Selection of Lead Targets: Based on the integrated evidence, I select a shortlist of lead targets for further experimental validation. I go back to the main association page and from there, I can download a list of targets including evidence scores. These I can use to do my own special magic to filter out the target I want; I can of course go back to Open Targets to get a good look at my new target before I decide on what to validate. 

By using Open Targets, I efficiently identify and prioritize potential drug targets for Type 2 Diabetes. The platform's integrated data helps me make informed decisions, and prioritize fast without switching to other data environments, reducing the time and resources spent on target identification and increasing the likelihood of successful drug development.