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Trial Lecture – time and place
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Adjudication committee
- First opponent: Professor Inger Gjertsson, Gothenburg University, Sweden
- Second opponent: Branch manager, PhD Boel Brynedal, Karolinska Institutet, Sweden
- Third member and chair of the evaluation committee: Professor Michael Rory Daws, University of Oslo
Chair of the Defence
Professor Heidi Kiil Blomhoff, University of Oslo
Principal Supervisor
Professor Bjørn Steen Skålhegg, University of Oslo
Summary
Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects 0.5-1% of the world's population, primarily causing joint inflammation and pain. Without sufficient therapy, it can result in significant disability and reduced quality of life. Early diagnosis and effective treatment will reduce symptoms and disease progression. Despite that methotrexate (MTX) have revolutionized RA treatment and is used as a first-line treatment, only half of the RA patients respond positively to this medicine. MTX efficacy has been shown to be associated with polymorphisms in genes encoding proteins involved in MTX metabolism. Li has investigated the relationship between MTX efficacy identified by various clinical outcomes, and specific gene polymorphisms associated with MTX metabolism. Li has also developed a model using machine-learning approaches to predict MTX efficacy with demographic, clinical and genetic polymorphism variables at baseline. The model could predict remission status with the clinical variable RA impact of disease (RAID) as the most robust predictor, while MTX metabolism associated genetic polymorphisms showed a general limited contribution. To explore potential novel therapeutic strategies and to establish a drug screening murine platform, Li examined the involvement of immune cellular components and pharmacological effects in a novel murine arthritic model, the Delayed-Type Hypersensitivity Arthritis model. Li documented the dynamic involvement of proinflammatory T cells and activated B cells in the inflammatory process and the effects of two widely prescribed anti-RA drugs. This animal model for RA holds promise for screening, identifying and optimizing novel drugs targeting RA. Overall, Li’s study identified associations between MTX efficacy and genetic variations, proposed a machine-learning prediction model for RA remission status, and deciphered an appropriate murine model for drug screening and RA therapeutic development.
Additional information
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