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Approved Research

Multimodal analysis of factors influencing LRRK2 related Parkinson Disease Phenotypes

Principal Investigator: Dr Luc Desnoyers
Approved Research ID: 75525
Approval date: September 22nd 2021

Lay summary

Affecting approximately 1-2% of the population over the age of 60, Parkinson's disease (PD) is a progressively disabling and ultimately fatal disease characterized by motor symptoms of tremor, rigidity, bradykinesia, and postural instability, as well as non-motor features including cognitive deficits, depression, constipation, pain, olfactory deficits, and sleep disorders.

Currently, there are no approved disease-modifying treatments for PD; front-line treatments aim to provide relief from motor symptoms using medication or deep brain stimulation. These treatments often cause side effects or complications and become less effective as the disease progresses. Thus, safer and more broadly effective disease-modifying treatments are desperately needed for patients and families affected by PD.

Most PD cases are of unknown origin, with approximately 5-10% carriers of genetic mutations. Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are among the most common mutations linked PD, accounting for approximately 5-13% of familial and 1-5% of sporadic PD cases. Extensive independent research has shown that PD-linked mutations in LRRK2 confer a toxic overactivation of LRRK2. These findings have spurred a focus on the development of potent LRRK2 inhibitors for the treatment of LRRK2-associated PD. Unfortunately, the clinical presentation of LRRK2 PD is indistinguishable from that of idiopathic PD in terms of signs, symptoms, and response to levodopa. Therefore, the identification of PD patients who could benefit from a LRRK2 inhibitor treatment is difficult.

In the current project, we will evaluate the potential of biomarkers for predicting the contribution of LRRK2 dysregulation to the susceptibility and progression of idiopathic PD. We will use machine learning methods to evaluate the association between genetic variants and the clinical features of LRRK2-driven PD.

Our goal is to develop models to predict disease progression and response to LRRK2-targeted therapies for PD patients. These models will be used to develop diagnostic strategies to select PD patients who are most likely to benefit from LRRK2-targeted therapies. The development of a clear diagnostic strategy will guide the design of clinical trials for new drugs. We hope that treating the right patients, at the right time, with the right intervention before irreparable damage is done will address unmet medical needs in patient with PD.