Researchers from the University of Oxford together with colleagues across Europe as part of the ULTRA-DD Consortium (Unrestricted Leveraging of Targets for Research Advancement and Drug Discovery) have established a novel approach to maximise the informativeness of genetic evidence for drug target prioritisation. In a paper published today in the journal Nature Genetics, the authors describe a route-map of the drug target landscape for 30 different immune-mediated diseases.
The study addresses a major challenge faced by the pharmaceutical industry, namely how to successfully identify potential drug targets in early stage discovery as the majority of candidate drugs fail late-stage clinical trials. We know drugs with genetic evidence are more likely to be therapeutically valid but how to best use the astronomical amounts of genetic association data now available for different diseases through genome-wide association studies has been a major challenge.
“Moving from association to identifying the likely responsible specific gene or pathway is a key first step but the discovery that it is more often the interacting partners of genes implicated by association rather than the genes themselves that are current drug targets was a crucial insight” says Julian Knight, Professor of Genomic Medicine at the Wellcome Centre for Human Genetics at the University of Oxford who led the study.
The Priority Index or ‘Pi’ pipeline was developed by the study first-author Dr Hai Fang to be open-source and customisable, promoting collaborative efforts to accelerate early-stage drug development. This ethos was central to the ULTRA-DD Consortium, which included academic and pharma partners through an Innovative Medicines Initiative. A web interface allows users to visualise gene priority ratings, underlying data and analysis.
The paper describes findings for 30 different immune-mediated diseases, ranging from rheumatoid arthritis to multiple sclerosis, showing how the drug target prioritisation landscapes for these different diseases relate to each other and identifying novel under-explored targets.
Evidence validating the approach was based on a range of cellular screens. The authors found that disease-specific activity from a compound screen was correlated with the Pi ranking for the target of the compounds tested. They also found that Pi can predict activity for CRISPR and mutagenesis screens, and a panel of epigenetic inhibitors applied to patient-derived cells.
The work was done with researchers in the Structural Genomics Consortium (Oxford), Botnar Research Centre (Oxford), MRC Weatherall Institute for Molecular Medicine (Oxford), Target Discovery Institute (Oxford), Kennedy Institute of Rheumatology (Oxford), Janssen (Belgium), University of Tartu (Estonia) and the Karolinska Institutet (Sweden).