Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T...

Area of Science:

• Biotechnology and Pharmaceutical Manufacturing
• Cell Therapy Production
• Machine Learning in Healthcare

Background:

• Chimeric Antigen Receptor (CAR)-T cell therapy utilizes living cells as active pharmaceutical ingredients, posing manufacturing challenges.
• Current quality control (QC) methods for CAR-T cells rely on end-point testing, leading to high failure rates and product heterogeneity.
• Real-time process monitoring is crucial for improving CAR-T cell production but is hindered by analytical tool limitations with heterogeneous cell products.

Purpose of the Study:

• To demonstrate unsupervised image-based machine learning as a Process Analytical Tool (PAT) for near real-time monitoring of CAR-T cell production.
• To quantitatively track morphological changes in T cells during CAR-T cell manufacturing using machine learning.
• To assess the utility of machine learning for monitoring patient-to-patient variability and detecting process deviations.

Main Methods:

• Collected flow imaging microscopy (FIM) images of T cells from nine healthy donors throughout CAR-T cell production stages (activation, transduction, expansion).
• Trained a Variational Autoencoder (VAE) model on FIM images to quantitatively track cell morphology changes.
• Correlated VAE-derived cell population density with transduction efficiency measured by traditional flow cytometry.

Main Results:

• A novel, transient cell population was identified in CAR-T cells expressing the CAR protein, absent in non-transduced cells.
• The density of this new population correlated directly with transduction efficiency, as confirmed by flow cytometry.
• The VAE model effectively tracked patient-to-patient variability in cell morphology during CAR-T cell production.

Conclusions:

• Unsupervised machine learning, specifically VAEs applied to FIM data, serves as a valuable PAT for CAR-T cell manufacturing.
• This approach enables near real-time monitoring of cell morphology, facilitating early detection of process deviations and potential failures.
• The VAE-based method can quantify patient-specific cellular changes, improving understanding and control over manufacturing variability.