Precision therapy for multiple sclerosis
- Research Opportunity
- Masters by Research
- Project Status
- Medicine and Radiology
- Royal Melbourne Hospital
|A/Prof Tomas Kalincikemail@example.com||+61 3 93424404||Personal web page|
Multiple sclerosis (MS) is the second most common cause of disability in young adults. At the present time, no neuroregenerative or remyelinating therapies are available for clinical use and so the core of multiple sclerosis management lies in preventing episodic inflammation and relapse-related disability accrual.
Prevention of disability in patients with multiple sclerosis has been suboptimal. The most effective of the available immunotherapies mitigate the short-term risk of disability progression by 30-42%. This imperfect result is mainly attributed to the large inter-individual variability in the clinical MS phenotype and the treatment response. From the patients’ perspective, the time while exposed to MS disease modifying therapies with a suboptimal individual effect translates into ongoing loss of capacity. We have recently shown that demographic, clinical and paraclinical information helps predict individual response to disease modifying therapies at the time of their commencement (Kalincik et al., Brain in press). We have designed a prototype of predictive algorithm to help inform selection of therapies for individual patients in clinical practice.
The algorithm currently being implemented at 115 MS centres in 33 countries as part of the MSBase collaboration. This project will further our understanding of individual response to MS therapies. It aims at implementing biological predictors of MS outcomes at the Royal Melbourne Hostpial, including neurofilament light chain, chitinase 3-like 1, volumetric MRI and others. The project will implement these prognostic markers in the recently published prototype of the prognostic models. Finally, it will validate the prognostic value of the enhanced model in independent MS cohorts. This project will suit students with interest in statistics and health outcomes research. During the project, you will improve your statistical skills, learning some of the more complex statistical techniques. Knowledge of elementary statistics is a requisite. You will contribute to the evidence-based clinical management of MS.
Faculty Research Themes
School Research Themes
Masters by Research
Graduate Research Students who are interested in joining this project will need to consider their elegibility as well as other Graduate Research requirements before contacting the supervisor of this research
For further information about this research, please contact a supervisor.