Oxford Drug Discovery Institute Harnesses Target Discovery to Accelerate Alzheimer’s Breakthroughs

Ontotext’s Target Discovery solution helped the team of scientists accelerate target analysis and decision-making

  • Quick insight discovery across 30+ structured datasets and literature data – all in one place
  • High confidence of target prioritization based on custom criteria
  • Improved efficiency in finding sources of information thanks to Ontotext’s highly specialized expertise 
  • Quick and high-quality assessment of targets at scale, accelerating lab validation process
  • Easy interdisciplinary collaboration between scientists and computational biologists

The Goal

The Alzheimer’s Research UK Oxford Drug Discovery Institute wanted to speed up their assessment of novel Alzheimer’s disease targets in the context of the immune system. 

The team had a set of 54 genes with high genetic association to Alzheimer’s Disease generated from the valuable GWAS (Genome Wide Association Study) data from Alzheimer’s disease patients. Their goal was to set up a rank methodology based on validated genes, which to be used on new potential targets to narrow the set down for easier experimental validation

The Challenge

The main challenge for the team was the vast amount of sources with publicly available data that they had to screen. The available data about the 54 pre-selected genes was very sparse and spread across various databases. Alzheimer’s Disease, which is the most common form of dementia, has very high heritability and its high genetic component provides a considerable area for exploration in the screening of potential therapeutic targets. Gathering all relevant information about each gene manually and analyzing the necessary gene properties in bulk took a lot of time and resources and created a significant backlog.

As a result, the laborious process of shortlisting candidates among thousands of genes for lab testing, based on tailored criteria and previous disease knowledge, slowed down scientific breakthroughs. 

“It’s quite hard to go to all EMBL (European Molecular Biology Laboratory) databases and pick out the relevant experimental data. The Target Discovery platform is quite helpful for getting meaningful insights from any information we are interested in.”

Ayesha Khan, Research informatician

The Solution: Ontotext’s AI-powered Target Discovery

Ontotext’s Target Discovery contains a rich collection of relevant data about diseases and related targets, pathways, processes, chemical components, and even literature and clinical trials.

For the purposes of Alzheimer’s Disease target identification and selection, very little customization was necessary to the analytical layers and the UI. The institute chose specific expression data from the Target Discovery Collection to use for target ranking that contained relevant baseline and differential expression levels. Additionally, the Ontotext team integrated MarkerDB through its LinkedLifeData Inventory in order to enrich the target selection criteria.

Ontotext also offered advanced analytics, including gene-disease association scores, delivering valuable insights into the biological relevance and supporting evidence for each target. Highly ranked targets exhibited high association and risk factor scores, enhancing their overall evaluation. Additionally, AI-derived literature data was utilized to assess novelty and confirm target-disease associations, further refining the prioritization process.

Ontotext’s Target Discovery system, designed with an adaptable, indication-agnostic framework, has been specifically customized for prioritizing targets in neurological diseases for this project. The innovative approach was initially validated using a range of known and confirmed targets, spanning from low to high confidence, ensuring robust and reliable results. The easy to use UI allowed for fast iterations and easy fine-tuning of the selection criteria.

Target Discovery is a system designed to help scientists and medical experts expedite their R&D processes at least 6 times. Experts can utilize diverse analytical tools with ease without any technical knowledge. Our team aims at closely supporting R&D divisions through latest technology and expert consultancy in order to deliver safe and efficient drugs to patients in need faster and for a fraction of the usual cost.

Martina Markova, Product Manager Target Discovery

Business Benefits

  • Streamlined screening process by consolidating critical data and insights from multiple sources into a user-friendly system with visual dashboards and analytical tools.
  • More efficient prioritization of drug target candidates with higher therapeutic potential and high confidence with  fast, easy, and case-specific ranking of genes.
  • Removed barriers of not knowing how to approach certain sources as a result of specialized subject matter expertise.
  • Optimized and expedited wet lab validation by simplifying the shortlisting of potential successful candidates. 
  • Accelerated target analysis and decision-making.

Why Choose Ontotext?

Ontotext’s Target Discovery enabled the team to consolidate critical data and insights from multiple sources into a single, cohesive space. The platform made it easy to retrieve all the publicly available information about any gene they were interested in, including Ontotext analytics such as target-disease association scores and novelty scores. The ranking algorithm also helped them prioritize genes with the highest therapeutic potential for subsequent wet lab validation. 

As a result, deriving meaningful insights about the disease biology and assessing potential target candidates took a lot less time and resources. Once the configuration was set up to the specific research requirements, it became a lot faster to narrow down a gene set, which would otherwise take months. The platform also reduced the need to delegate between many different people in order to achieve results at a faster speed and required only 1-2 people to do the job.

Ontotext’s expertise was of great help in the process of tailoring the custom criteria to fit the needs of the team and their specific research questions. The platform made it easy to further fine-tune the UI. Thanks to the expert consultancy and easy-to-customize system, relevant and useful results were obtained for a fraction of the time required for alternative approaches, such as manual evaluation.

It’s been helpful to get an idea of how to rank and stratify genes, so we would definitely recommend your platform to our colleagues and consider it for use in other projects.

Dr. Emma Mead, Chief Scientific Officer, Oxford Drug Discovery Institute

Do you think this case resembles your particular business needs?

New call-to-action

Contact Us Now