The current treatment paradigm for rheumatoid arthritis (RA) involves the sequential use of disease modifying anti-rheumatic drugs (DMARDS). The efficacy and patient tolerability of each therapy is currently determined using a trial and error approach. Many patients therefore derive no clinical benefit, yet are exposed to a spectrum of side effects and increased disability risk .
There are several single nucleotide polymorphisms (SNPs) which are associated with treatment response to DMARDs. These include SNPs within the folate pathway for methotrexate, within cytokines for hydroxychloroquine, effecting acetylators for sulpsalazine, and influencing cytochrome enzyme activity for leflunomide.
The aim of this research therefore is to design and test the ability of a pharmacogenomic model to stratify RA patients into treatment groups which maximise drug efficacy and minimise toxicity.
Hypothesis: A pharmacogenomic machine learning model can accurately predict DMARD therapy response for RA patients.
1. To develop a comprehensive database of treatment and outcome data features.
2. To define a discrete multiplex panel of SNPs and methylated genes which influence DMARD metabolism.
3. To develop and test the accuracy of a DMARD treatment predictive model.
1. Detailed prescribing, drug efficacy and adverse event information will be extracted from patient medical notes for RA patients currently recruited (n=450).
2. A panel of candidate SNPs for the drugs of interest will be selected using PharmGKB, along with regulatory regions known to be subject to hyo/hypermethylation.
3. Data Analysis
a. Whole genome sequence data generated by third party provider (n=152*) will be screened for the selected panel of SNPs and methylation sites.
b. Data analysis will examine patient group stratification for each drug and outcomes relative to their pharmacogenetic profile. This data will be used to develop a predictive pharmacogenomic model for DMARD selection by machine learning methods.
c. An independent treatment and response blinded cohort of rheumatoid arthritis patients, (n=152*) also genotyped by targeted sequencing/methylation assay will be used to validate the predictive model sensitivity and specificity.
Ethical approvals to fulfil this project is already in place.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Sound understanding of subject area as evidenced by a comprehensive research proposal
- A comprehensive and articulate personal statement
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Completion of Masters at a level equivalent to commendation or distinction at Ulster
- Research project completion within taught Masters degree or MRES
- Experience using research methods or other approaches relevant to the subject domain
- Work experience relevant to the proposed project
- Publications - peer-reviewed
- Experience of presentation of research findings
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
The Doctoral College at Ulster University
My experience has been great and the people that I have worked with have been amazing
Kieran O'Donnell - 3D printing of biological cells for tissue engineering applicationsWatch Video
Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.
Michelle Clements Clements - MRes - Life and Health SciencesWatch Video
Throughout my PhD I’ve been provided with continuous support and guidance by my supervisors and the staff at the University.I’ve also received many opportunities to further enhance my professional development in the form of teaching experience and presenting my work at conferences which will aid in my pursuit of a career in academia or industry.