The Research in Motion funding program supports interdisciplinary collaboration and new approaches to research along D2R鈥檚 discovery-to-implementation chain.

In the first funding cycle launched in 2024, 8 applications were received of which 5 received awards. View a summary of the review and selection process.

Funded project听summaries

Lung-targeted lipid nanoparticles to deliver anti-viral RNA against respiratory viral infection

Respiratory viral infections, such as the flu and COVID-19, can pose serious health risks, especially to vulnerable individuals like the elderly and those with pre-existing health conditions. Although vaccines and antiviral treatments are available, they don鈥檛 always work effectively, which highlights the need for new therapies.

Our research focuses on developing a new treatment that can better protect against these viruses. To do this, we are creating special tiny particles called lipid nanoparticles (LNPs) that can deliver anti-viral RNAs directly to the lungs where viral infections occur. These LNPs are designed to be more stable and effective at targeting lung tissues. If successful, this research could lead to a new antiviral therapy that would greatly improve public health and help us be better prepared for future viral outbreaks"

Principal Investigator: Guojun Chen (海角社区)

Co-Investigator(s): Rongtuan Lin (海角社区)

Collaborator(s): Zhenlong Liu (Ebovir Inc)

Project duration: Two-year

D2R Axes: RNA Therapeutics (Axis 2), Bioprocessing, Biomanufacturing, and Nanotechnology (Axis 3)

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Intelligent screening for next generation LNP design and manufacturing

The efficacy of a lipid nanoparticle (LNP) RNA therapeutics is governed by their formulation, but how is poorly understood, and efficacious formulations must be tediously sought via trial and error. We recently developed an automated microscope that can analyze individual LNPs and their content. Here we propose to rapidly and systematically screen thousands of LNP RNA formulations with our microscope, and identify the optimal ones. We will use the data to train an AI algorithm that can predict the optimal formulation, and share it so everyone may rapidly formulate efficacious LNP-based RNA therapeutics.

Principal Investigator: David Juncker (海角社区)

Co-Investigator(s): Thomas Duchaine (海角社区 university), Yaoyao Zhao (海角社区)

Collaborator(s): Julia Burnier (RI-MUHC), Jean Sebastien Delisle (Hopital Maisonneuve Rosemont)

Project duration: Two-year

D2R Axes: Bioprocessing, Biomanufacturing, and Nanotechnology (Axis 3) , RNA Therapeutics (Axis 2), Data Science, Bioinformatics, and Computing in Personalized Medicine (Axis 5)

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CRISPR-guided trans-splicing for RNA treatment of oculopharyngeal muscular dystrophy

Oculopharyngeal muscular dystrophy (OPMD) is an adult-onset rare genetic disease affecting skeletal muscles. The highest prevalence of OPMD is in the French-Canadian population, though the disease is found worldwide. OPMD is caused by mutations in the first exon of the PABPN1 gene. There is no treatment available for OPMD. Using lipid nanoparticles as a delivery tool, we will use the CRISPR-based RNA editing molecules to excise mutated RNA exon 1 and replace it, in a single reaction, with wildtype PABPN1 RNA exon 1 in OPMD patient cells and in a transgenic mouse model, hence correcting the mutations at the RNA level.

Principal Investigator: Guy Rouleau (海角社区)

Co-Investigator(s): Zhou, Yang (MNI, 海角社区)

Collaborator(s): Cullis, Pieter (University of British Columbia), Zhou, Sirui (海角社区), Abu-Baker, Aida (海角社区)

Project duration: Two-year

D2R Axes: RNA Therapeutics (Axis 2), Bioprocessing, Biomanufacturing, and Nanotechnology (Axis 3)

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A citizen science platform for accelerating metagenomic studies in urban built environment

Sequencing technologies are generating massive datasets, but this information must be carefully curated to derive biological observations and AI applications. Unfortunately, manual curation is unrealistic for classical research teams. We will leverage our previous collaborations with video game developers to build a mobile platform that will engage online gamers into environmental metagenomics studies. We will apply our technology to an on-going project (CUBE) monitoring the spread of viruses in congregate settings like hospitals, schools, and libraries. This project will also open new scientific communication channels with a broad public traditionally and contribute to public understanding of metagenomic clinical studies.

Principal Investigator: J茅r么me Waldisp眉hl (海角社区)

Co-Investigator(s): Rees Kassen (海角社区 university)

Collaborator(s): Attila Szantner (Massively Multiplayer Online Science (MMOS)), Laura Hug (University of Waterloo), Alex Wong (Carleton University), Mike Fralick (Mount Sinai Hospital)

Project duration: Two-year

D2R Axes: Data Science, Bioinformatics, and Computing in Personalized Medicine (Axis 5) , Clinical Research, Acceleration, and Implementation (Axis 4) , Ethical, Socioeconomic, and Cultural Dimensions in Genomic Research (Axis 6)

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Development of responsible AI for genomic biomarker identification in the context of the D2R Initiative

This project examines the ethical and social impacts of using AI to identify genomic biomarkers鈥攂iological indicators that help develop personalized therapies for diseases like rare disorders and cancer. While AI offers exciting opportunities to analyze large datasets and find new biomarkers, it also poses risks of exacerbating existing inequities and ethical concerns in healthcare. We will review current AI uses in biomarker identification, analyze relevant policies and laws, interview experts, and conduct a focus group with people from diverse backgrounds. We will produce ethico-legal guidance to inform the responsible use of AI for biomarker selection and identification by D2R researchers.

Principal Investigator: Ma'n Zawati (海角社区)

Collaborator(s): Mathieu Blanchette (海角社区), Yue Li (海角社区), Durhane Wong-Rieger (Canadian Organization for Rare Disorders)

Project duration: Two-year

D2R Axes: Ethical, Socioeconomic, and Cultural Dimensions in Genomic Research (Axis 6), Data Science, Bioinformatics, and Computing in Personalized Medicine (Axis 5)