Abstract

Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.

Details

Title
Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis
Author
Kozlowska, Justyna 1 ; Humphryes-Kirilov, Neil 1 ; Pavlovets, Anastasia 1 ; Connolly, Martin 1 ; Kuncheva, Zhana 1 ; Horner, Jonathan 1 ; Manso, Ana Sousa 1 ; Murray, Clare 1 ; Fox, J. Craig 1 ; McCarthy, Alun 1 

 C4X Discovery Ltd, Manchester, UK (GRID:grid.498229.c) 
Pages
14153
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3069700002
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.