Directeur.e(s) de recherche
Jacques Corbeil
Pascal Germain
Elsa Rousseau
Start date
Title of the research project
Development of an in-process quality monitoring technology for plants during pharmaceutical manufacturing using high throughput mass spectrometry coupled to machine learning approaches
Description

Process efficacy and robustness are crucial to assure productivity and predictability in pharmaceutical manufacturing. Medicago’s vaccine manufacturing technology uses plants for production and our aim is to develop a system capable of predicting and monitoring plant’s fitness for production early in the process, from plant seedling to harvest of producing leaves.

To this end, we must identify the factors that regulate the production level for each plant. We plan to measure a great deal of molecules, called metabolites, to determine the optimal conditions for the plants to generate the maximal amount of each product. Since the quantity of measurements is large, we will use machine learning to design an artificial intelligence capable of understanding and identifying the potentially highly complex patterns of metabolites and/or biomarkers that are correlated with the optimal production.

Discover

Featured project

Prediction and early identification of stroke is crucial to prevent emergency department (ED) revisits and initiate treatment, reducing morbidity and mortality.

Read more