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Funded Project听Summaries

Discovery of Analgesic Drugs Targeting the Acid-Sensing Ion Channel Family

Chronic pain is an invalidating neurological disease and a major public health issue affecting at least 20% of the population. Chronic pain treatment is often ineffective and the current opioid crisis tragically highlights the need for non-opioid pain killers.
We describe here an innovative strategy to discover novel analgesics by targeting an untapped family of pain transducers called the acid-sensing ion channels (ASICs).
Using state-of-the-art virtual screening of vast digital libraries of compounds, we propose to identify small-molecule blockers of human ASICs in silico, to validate these hits in mammalian cells in vitro before testing their pain-relieving effects in rodent models.
This multidisciplinary collaboration between the S茅gu茅la lab at the MNI, the startup Neurasic and major Quebec players in VC (AmorChem, adMare) and biopharma (NuChem, Paraza), has the potential to provide key first steps towards the development of first-in-class analgesics to fight refractory pain without the side effects of opiates.

HBHL Research Theme: 2

Principal Investigator: Philippe S茅gu茅la

Funding Received from HBHL:听$200,000

Promoting BEST (BEtter, Faster, Longer, STronger) Walking for People with Parkinsons

This project aims to improve how people with Parkinsons walk. Typically, people with Parkinsons develop a shuffling gait and limits walking for health and fitness and can also lead to falls and fractures. PhysioBiometrics Inc. is a company dedicated to developing technologies to improve mobility and making these innovations available to people in need. This project is based on a SMART sensor that attaches to the outside of the shoe and gives a 鈥渂eep鈥� when the wearer makes a good step, one in which the heel is the first point of contact with the surface. Starting a step with a strong heel strike, changes a stooped shuffling gait to one that is upright and striding. The aim here is to make this technology as useful as possible for people with Parkinsons by identifying their needs and learning about their experiences to make the products of PhysioBiometrics as best they can be. This project is possible because Parkinsons Quebec has made an investment in PhysioBiometrics Inc. underlying their support for providing products and services to improve the quality of life of people with Parkinsons.

HBHL Research Theme: 3

Principal Investigator: Etienne de Villers Sidani

Funding Received from HBHL: $66,276

Revealing the neural circuit mechanisms of psilocybin in a depression-relevant brain circuit

Clinical trials suggest that psilocybin, a psychedelic compound derived from Psilocybe fungus, rapidly alleviates depression. However, questions remain as to how and why psilocybin works in the brain to treat depression. Using preclinical mouse models, we will examine how psilocybin modulates plasticity in key brain circuits that are known to be disrupted by chronic stress and depression, and how genes are turned on and off in these brain circuits. This work contributes to Theme 3 goals in characterizing the neural changes associated with maladaptive plasticity and the potential mechanisms by which psilocybin intervenes to promote resilience. Findings will provide essential evidence to support efforts to increase acceptance of psilocybin as a novel treatment for depression and the development of the rapidly emerging psychedelic biopharma sector. This in turn will help accelerate bringing a novel effective treatment to the many people in Quebec and worldwide whose lives are impacted by depression.

HBHL Research Theme: 3

Principal Investigator: Rosemary Bagot

Funding Received from HBHL: $200,000

Closed loop AI-controlled neural stimulation for treating chronic conditions

This project is focused on the development of closed-loop neural digital therapy (NDT) platform for chronic conditions. Stimulation of the peripheral nervous system (PNS) has been successfully used to treat epilepsy, depression, and to assist in stroke recovery, among many other indications. Historically, these stimulating therapies have been open-loop, meaning the stimulation takes place in the absence of listening to the signals in the PNS; the 鈥渃ommunication鈥� is essentially a one-way street. This project is developing closed-loop NDT software that listens and talks with the PNS, recording the bioelectrical impulses that pass between the brain and body and using this information to write signals to the PNS. We rely on implanted hardware that is in constant communication with the PNS, which means that changes in the body鈥檚 neural signals can be immediately identified, and the therapies adjusted in real time. Closed-loop neural digital therapies offer the potential for significant improvement over existing pharma-based and open-loop stimulation therapies, resulting in better health outcomes.

HBHL Research Theme: 1

Principal Investigator: Blake Richards

Funding Received from HBHL: $129,500

Kinesthetic interaction device and multimodal authoring tool to support audio-haptic rendering of graphical content for blind and low-vision users

For the 206,000 Quebecers and 1.5 million Canadians who identify as having vision loss, lack of access to graphics on the internet poses a social and economic barrier. McGill鈥檚 existing IMAGE project uses AI to transform graphics on the web into audio-haptic experiences that provide richer experiences of photos, maps, and charts to people who are blind or low-vision. Of these haptic devices, Haply Robotics makes a range of haptic robotic hardware targeted at hobbyist and teaching environments at the low-end, and applications including haptic surgical simulation at the high end. This proposal involves building on IMAGE as a base to a) Integrate and deploy new AI modules for generating useful representations; b) refining with Haply the 2diy hardware design for better haptic fidelity and robust deployment at scale; c) developing novel multimodal content authoring tools suitable for supporting the needs of non-expert content creators, and d) implementing interactive audio-haptic experiences and adding French language support.

HBHL Research Theme: 3

Principal Investigator: Jeremy Cooperstock

Funding Received from HBHL: $56,438

A drug-screening platform to increase protein expression levels for the treatment of neurological disorders

We have developed a high throughput drug screening platform to identify therapeutic drugs to treat neurodegenerative diseases. By monitoring the production of endogenous disease proteins, before and after drug application in living human cells, the Geneboost platform enables simultaneous tracking of drug efficacy, specificity and toxicity over time, reducing the time and cost of current drug candidate discovery by over ten-fold. Using this platform, we have identified several novel and FDA-approved drug leads for the treatment of Parkinson鈥檚 disease and major depressive disorder. Compounds that demonstrated efficacy and safety in the pilot screen were validated in a second screen and are currently being further tested in the lab in cells and animal models using molecular and biochemical approaches.

HBHL Research Theme: 2

Principal Investigator: Brian Chen

Funding Received from HBHL: $580,000

Exploitation of a new pharmacological target for the development and validation of new anti-inflammatory drugs

Neuroinflammation has been implicated in a diverse array of degenerative conditions, ranging from Multiple Sclerosis to Alzheimer鈥檚 disease. We previously uncovered genes that can prevent neuroinflammation and provide new opportunities for drug discovery for a variety of clinical indications. Here, we propose to specifically inhibit the ubiquitin-specific protease 15 (USP15), in view of managing and treating neuroinflammation. Our project will expand the therapeutic field for USP15, map out USP15 pathways and associated partners in immune cells, better understand the functional role of USP15 in brain and in immune cells during neuroinflammation and pursue the development and validation of small molecule USP15 inhibitors. The whole project will build value through identification of small molecule leads for modulating USP15 for neuroinflammation and bring it closer to the clinic.

HBHL Research Theme: 2

Principal Investigator: Philippe Gros

Funding Received from HBHL: $550,000

Advanced Nystagmus System (ANS鈩�) as objective diagnostic tool for mild traumatic brain injury (mTBI) and concussion: a validation study using structural and functional magnetic resonance imaging

Annually, over 2 million individuals in North America will suffer from mild traumatic brain injury (mTBI) or concussion to a cost of over $75 billion dollars in lost productivity (CDC). In Canada alone, 200,000 professional and amateur athletes, children and elderly, drivers and pedestrians, civilians and soldiers will suffer from mild brain trauma. An objective evaluation is required in terms of its capacity to distinguish concussed patients from healthy controls, and its ability to predict which individuals will develop chronic symptoms from data acquired in the acute stage. A number of brain areas are particularly susceptible to mTBI, among these are the frontal lobes, portions of the corpus callosum, and the thalamus. These brain regions are made up of a number of structures that play key roles in oculomotor movement. It is therefore logical to expect that visual deficits would follow mTBI. Our Advanced Nystagmus System (ANSTM) offers a quantitative and automated solution to the analysis of eye and head coordination, and will be tested on concussed patients to uncover novel markers to assist in the localization of damaged neural centres. The results will be validated using functional and structural MRI protocol that is sensitive to mTBI/concussion.

HBHL Research Theme: 3

Principal Investigator: Alain Ptito

Funding Received from HBHL: $51,322

Evaluation of a radiomic approach based on hyperspectral retinal imaging to predict the cerebral amyloid status for the diagnosis of Alzheimer鈥檚 disease

The proposed project aims at validating and further developing a novel technology to predict the presence of significant amyloid (A尾) deposition in the brain from a simple, non-invasive hyperspectral retina scan with Optina Diagnostics鈥� Metabolic Hyperspectral Retinal Camera (MHRC) in combination with an artificial intelligence algorithm. Accumulation of A尾 plaques in the brain is a key hallmark of Alzheimer鈥檚 disease (AD), but current methods to evaluate its presence in vivo (A尾 positron emission tomography imaging and quantification of A尾 proteins in the cerebrospinal fluid obtained) are not practically implementable as screening methods due to cost, availability and/or invasiveness nature. A simpler and cheaper method is urgently needed to help in the recruitment of asymptomatic, amyloid positive, subjects to test new disease modifying therapies for AD, and eventually to help clinicians confirm a diagnosis of AD in a subject presenting with cognitive impairment.

HBHL Research Theme: 2

Principal Investigator: Jean-Paul Soucy

Funding Received from HBHL: $208,757

Implementation of a digital mental health tool in an Urban and Rural Setting 鈥� Optimizing Usability and Data Quality

Depression is the leading cause of disability worldwide. Despite the existence of best-practice guidelines which recommend that patients be treated using measurement-based care (MBC), few clinicians employ this effective practice due to a lack of effective MBC tools. In addition, data derived from patients being treated using MBC can be used to produce computational models that can improve the personalization of depression treatment, but such high quality data is rarely collected in clinical practice. Our project has two major aims: the first is to support the implementation of a digital mental health tool that facilitates the practice of MBC at two McGill-affiliated sites (one urban and one regional), using this experience to work with patients and clinicians to optimize the experience and ensure patient and clinician adherence. The second goal will be to study the data collected and the data collection process in order to ensure that the data quality is optimized. We will use an existing machine learning model of treatment selection which will be incorporated into the tool at a later date. We will also focus on optimizing of the tools from the patient perspective and to support behavioral change towards better health. Successful completion of this project will result in a tool that can be easily accessed by clinicians, patients and, with the acceptance of the patient, a selected family member, which in turn will help improve the treatment of depression on a population scale. This in turn will make a significant difference in the lives of millions of people suffering from depression. This project is directly aligned with HBHL鈥檚 mission to improve neuroinformatics and computational modelling to optimize our models of depression treatment; by aiming to address such a large-scale problem, the project will also support HBHL鈥檚 aim of improving population health.听

HBHL Research Theme: 4

Principal Investigator: Manuela Ferrari

Funding Received from HBHL: $21,000

Data-driven Biomarker Based Alzheimer鈥檚 Disease Progression Composite Score

Dementia due to Alzheimer鈥檚 disease (AD) is a major health concern for which there are still no curative treatments. The underlying causes are complex and multifactorial. It is known that cerebral changes such as the accumulation of amyloid plaques and early signs of neurodegeneration (mild atrophy on structural MRI) are present several years prior to the onset of clinical symptoms. Despite this improved biological understanding, in clinical practice and pharmacological research disease staging is still based on clinical observation (subjective symptom severity rating and cognitive tests). In addition, physicians have no tools to predict future progression of symptoms, which is a major source of anxiety for patients and their families. This project aims to improve this situation by developing a data-driven composite AD progression score combining structural MRI scans analysis to amyloid markers using advanced statistical models. It is based on a collaboration between an academic research group and Arctic Fox AI, a young start-up company applying artificial intelligence to brain MRI for neurodegenerative diseases. Our objective is to develop and validate an objective and simple AD progression score which could be rapidly used in clinic and research to better track the disease and predict future course of symptoms in recently diagnosed patients.

HBHL Research Theme: 1

Principal Investigator: Yasser Iturria-Medina

Funding Received from HBHL: $42,000

The 200-plex nano-ELISA: a next-generation immuno-proteomic technology for biomarker and drug discovery

Parkinson鈥檚 disease (PD) is a debilitating neurodegenerative disorder that affects more than 1% of people over the age of 65, and is characterized by motor handicaps and associated non-motor symptoms. Clinical and preclinical evidence show that neuroinflammatory processes play a critical role in disease development or suppression, suggesting that immune-based therapies could be effective. However, it has traditionally been difficult to monitor the immune response of brain cells because it involves many different proteins at low concentrations, and existing technologies are not capable to detect many proteins simultaneously with the required sensitivity, and are too slow and expensive. We have invented a ground-breaking protein analysis technology, termed CLAMP, that can robustly measure multiple proteins simultaneously, rapidly and with high sensitivity. In this project, brain immune cells will first be produced from a PD patient鈥檚 stem-cells. Next, 1280 drugs of the Library of Pharmacologically Active Compounds (LOPAC) will be applied to these cells. Finally, the cell immune response to each drug will be characterized by measuring 150 relevant immune molecules using CLAMP in the cell secretions. This approach will for the firsttime reveal the effect of drugs on the cell immune response, and could potentially lead to new insights on disease mechanisms, and new strategies and new therapies for PD. The research fits in the priority of the research theme 鈥淢echanistic Models of Neurodegenerative Disorders鈥�, and the project outcome would contribute to HBHL鈥檚 deliverables, as well as its vision of reducing socio-economic burdens, improving mental health and quality of life.

HBHL Research Theme: 2

Principal Investigator: David Juncker

Funding Received from HBHL: $200,000

Development of a high throughput 3D microphysiological platform for rapid automated assessment of human brain organoids response to drugs targeting neurological disorders

Alzheimer's disease (AD), Parkinson鈥檚 disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are the most common and devastating neurological diseases. Nearly 103,000 Canadians suffer from these diseases and approximately a quarter of these patients are Quebeckers. The loss of productivity related to these diseases cost the Quebec health care system an estimated $260 million in 2015. Considering the enormous economic burden of these diseases and the absence of efficacious treatments for them, it is imperative new therapies are developed for the clinic. Sadly, drug development remains a slow, expensive and inefficient process. The failure rate for new drugs is over 95% and the major reason behind failure is efficacy and toxicity [1]. For AD, the success rate for therapies in clinical trials is just 0.4%, suggesting that the current pre-clinical tools are misleading 99.6% of the time. Indeed, current in vitro models are neither organotypic nor systemic and animal models, although systemic are not human. The biopharmaceutical industries need alternative models that better predict the human response to drugs and that are easily scalable to enable High-Throughput Screening (HTS) of small molecules, allowing the rapid interrogation of thousands to hundreds of thousands of small molecules in disease-relevant assays.

HBHL Research Theme: 2

Principal Investigator: Thomas Durcan

Funding Received from HBHL: $600,000

Development of an AI-controlled closed-loop neuromodulation system for chronic conditions

The treatment of chronic conditions accounted for 58% of the annual healthcare spend in Canada in 2012, primarily through the use of pharmaceuticals. However, these are generally best suited to treat acute diseases, as with chronic use, side effects can accumulate over time while therapeutic effects diminish. Neuromodulation of the Peripheral Nervous System (PNS) represents a promising and adaptable treatment alternative to pharmaceuticals in many cases. Such treatments are still in their infancy and are currently dominated (>99.5%) by devices utilizing open-loop stimulation with clinician-led, manual adjustment. A closed-loop system that responds to peripheral nerve activity and other biomarkers in real time would enable dose-sensitive and targeted therapies. However, closed-loop neuromodulation systems face a significant challenge; smart adaptation requires an understanding of how particular nerves encode information to govern the behavior of tissues or organs. New methods must therefore be developed to decode and harness the large volumes of highly complex information transmitted through the PNS. This project will employ the latest findings in machine learning to extract biomarkers from neural data. Semi-supervised training methods will determine how these biomarkers drive physiological responses. The proposed approach will yield methods for robust, real-time calculation of neural biomarkers for targeted nerve stimulation patterns, which will ultimately serve as a data science platform for reliable, chronically implanted neuromodulation devices.

HBHL Research Theme: 1

Principal Investigator: Blake Richards

Funding Received from HBHL: $65,580

The preclinical development of 5 molecules for Amyotrophic Lateral Sclerosis and other neurodegenerative

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease without an effective treatment, that specifically targets motor neurons. ALS patients die of a progressive paralysis ~2-3 years after diagnosis. Here, we will develop 4 different drugs for ALS. These drugs all target different disease mechanisms, and have already been tested in humans and are safe, yet have not been developed or approved for any disease. This provides a unique opportunity to accelerate the development of these compounds to ALS patients. To test whether these drugs can rescue motor neuron death, we will test these drugs, separately and in combinations, in cells from ALS patients and ALS mice. This work will lead to the understanding of how protective each drug is in ALS models, and with this work we will obtain the necessary data to proceed with these drugs to clinical trials to test them in ALS patients.

HBHL Research Theme: 2

Principal Investigator: Thomas Durcan

Funding Received from HBHL: $200,000