Oxford Drug Discovery Institute leveraged Target Discovery to support the team of scientists accelerating target analysis and decision-making by consolidating diverse data sources and enabling high-confidence target prioritization.
The Client
Alzheimer’s Research UK Oxford Drug Discovery Institute focused on accelerating novel Alzheimer's disease target assessment in the context of the immune system
The Challenge
The team needed to rapidly identify and prioritize highly promising Alzheimer's disease gene targets from vast, fragmented data to accelerate lab validation
The Solution
Target Discovery was used to integrate expression data and MarkerDB and offer advanced analytics like gene-disease association and AI-derived novelty scores for enhanced target prioritization
Technical capabilities
Integrated 30+ structured databases into user-friendly system
Utilized AI-derived insights and custom criteria for target ranking
Business outcomes
Accelerated drug discovery process by streamlining screening & target analysis
The team needed to rapidly assess 54 genes with high genetic association to Alzheimer’s Disease pre-selected from the valuable GWAS (Genome Wide Association Study) data from Alzheimer’s disease patients.
The main challenge was the vast, fragmented, and sparse amount of publicly available data 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.
Manually gathering and analyzing this information for bulk gene properties was time-consuming, resource-intensive, and created significant backlogs, slowing down the process of shortlisting candidates for lab testing.
“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 AT Oxford Drug Discovery Institute
The Solution
The Oxford Drug Discovery Institute implemented AI-powered Target Discovery. This solution provided a rich collection of integrated data on diseases, targets, pathways, chemicals, and literature, effectively consolidating critical data and insights from multiple sources into a single, cohesive space.
For the purposes of Alzheimer’s Disease target identification and selection, minimal customization was needed for the analytical layers and UI. The Institute chose specific expression data from the Target Discovery Collection for target ranking and integrated MarkerDB to enrich criteria.
The platform offered advanced analytics, including gene-disease association scores, allowing easy retrieval of public information about any gene with analytics like target-disease association and novelty scores. This data, along with a ranking algorithm, helped prioritize genes with the highest therapeutic potential for subsequent wet lab validation.
The adaptable, indication-agnostic framework was customized for neurological diseases and validated with known targets, ensuring robust and reliable results, and the easy-to-use UI allowed for fast iterations and fine-tuning.
“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 At Graphwise
The Impact
The Institute significantly accelerated its drug discovery process by streamlining the screening of potential Alzheimer’s targets, reducing the time from months to weeks or days.
This led to a more efficient and confident prioritization of drug candidates for lab validation with higher therapeutic potential and high confidence with fast, easy, and case-specific ranking of genes,
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.
Overall, this new approach enabled cutting down on R&D time and resources and fostering faster scientific breakthroughs in Alzheimer’s research.
“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 At Oxford Drug Discovery Institute
