TRLUP – ML Insights and Metrics

More than 90% of pharmaceuticals fail to impact fail to pass regulatory approval due to toxicity or lack of effectiveness. 50% of those failures have the potential to impact patient lives and existing therapeutics can be repurposed to expand therapeutic options if they can be delivered in smaller dosages to where they are needed most. The project aims to accelerate the development of nanoparticles that are modified to deliver therapeutics to where they are needed most (targeted therapeutic delivery).

The technology uses AI to extract and index the qualitative and quantitative data about how nanoparticles are made and how they interact or impact cells and in animal models once administered. The data presented in an analytics platform and search engine, allows users to perform head-to-head comparisons of their formulations with peer-reviewed literature. This allows users to have confidence in their work and gain insights on how to design or make nanoparticles suitable for their therapeutic of interest and application.

If successful, this solution will generate enough data to train AI models that can simplify the R&D process by predicting how to make nanoparticles for a specific purpose; to load and deliver a therapeutic to where it is needed most by accounting for safety and how it will interact in the body. This will provide Canada the tools to accelerate the creation of safer and more effective solutions for patients – providing more therapeutic options for tailored therapies for patients.

Faculty Supervisor:

Jolen Galaugher;Ralph Dueck

Student:

Partner:

North Forge

Discipline:

Computer science

Sector:

Education; Management of companies and enterprises; Professional, scientific and technical services

University:

Red River College Polytechnic

Program: