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UK funding (202 554 £) : Utilisation des gènes et des voies de base pour stratifier la polyarthrite rhumatoïde et prédire les résultats de la polyarthrite rhumatoïde Ukri01/09/2024 UK Research and Innovation, Royaume Uni
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Utilisation des gènes et des voies de base pour stratifier la polyarthrite rhumatoïde et prédire les résultats de la polyarthrite rhumatoïde
| Abstract | Rheumatoid arthritis is a long-term condition in which the immune system attacks the joints. It affects 1 in 100 people in the United Kingdom. This inflammation causes pain and, if uncontrolled, can lead to joint damage and disability. The earlier we can control the inflammation, the better the long-term outcomes. The development of biologic therapies means that we now have more options than ever before to treat rheumatoid arthritis. There are a number of types of these drugs, all affecting different immune pathways to try and reduce inflammation. We have no reliable way of predicting which type of biologic treatment will be most effective for an individual. Currently, selection is done by a process of trial and error with each new drug trialled for a period of time. During this time, if the drug does not work, a person may develop irreversible joint damage which could lead to permanent disability. The aim of this study is to see whether genetic and protein markers, identified using a blood sample, can be used to predict whether a person with rheumatoid arthritis is likely to respond to a certain treatment and inform us about the likely severity of their disease. These are known as biomarkers. This project will analyse data collected in MATURA (Maximising Therapeutic Utility in Rheumatoid Arthritis), a nationwide consortium of academics, doctors and industry groups developing personalised approaches to treatment of rheumatoid arthritis. When researching biomarkers of treatment response, it is really important to first establish what we consider a good response to be. One of the methods health professionals use in clinical practice is a scoring system known as the DAS-28 score. This score is made up of four components: number of tender joints, number of swollen joints, the patient global visual analogue score (a self-reported score from 0-100 of a patient's overall health) and CRP (C-reactive protein, a blood test which measures inflammation). Lots of factors can affect the tender joint count and patient global visual analogue score, such as presence of other joint conditions. This can result in a high DAS-28 score even though inflammation is well-controlled. As biologic drugs work by targeting inflammation, a switch in therapy would not provide additional benefit if there is no inflammation present. Research has shown that only "swollen joint count" and CRP are linked to levels of inflammation in the joints. For these reasons, a 2-component DAS-28 score would provide a better indication if these drugs are working. Adherence to therapy refers to whether patients take their medication as prescribed. For lots of reasons, including side effects, forgetting and having to stop therapy due to infections or planned operations, a patient may not take their biologic treatment as prescribed. We know that this impacts on the likelihood of treatment response. However, none of the studies to date looking for genetic and protein predictors has taken adherence to therapy into account. Taking these factors into account, I will identify the key genes and proteins involved in causing rheumatoid arthritis. I will then determine whether these factors can also tell us how likely one is to respond to medications, as well as how severe their disease is likely to be. This research will use new, cutting edge statistical techniques to narrow down the most important genes and proteins in both causing rheumatoid arthritis and those which influence the course of the disease. This research could enable a doctor to decide at disease onset which biologic drug is most likely to be effective for that individual. Ultimately, the goal is to use this work to develop a "biologics calculator", where we can use a blood sample to personalise therapy for each patient. It could also help us find new drug targets to help develop future medications. |
| Category | Fellowship |
| Reference | MR/Z505080/1 |
| Status | Active |
| Funded period start | 01/09/2024 |
| Funded period end | 31/08/2026 |
| Funded value | £202 554,00 |
| Source | https://gtr.ukri.org/projects?ref=MR%2FZ505080%2F1 |
Participating Organisations
| University of Manchester |
Cette annonce se réfère à une date antérieure et ne reflète pas nécessairement l’état actuel. L’état actuel est présenté à la page suivante : The University of Manchester, Manchester, Royaume Uni.
