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Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics

Valerie Y Odeh-Couvertier1, Nathan J Dwarshuis2, Maxwell B Colonna3

  • 1Department of Industrial Engineering University of Puerto Rico Mayagüez Mayagüez Puerto Rico USA.

Bioengineering & Translational Medicine
|May 23, 2022
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Summary

This study introduces an AI-driven platform to predict therapeutic cell manufacturing quality early on. It uses multiomics data to identify critical parameters, ensuring consistent, high-quality cell therapies.

Keywords:
T‐cell memoryartificial intelligencebioprocess optimizationcell therapy manufacturingcytokinesdesign of experimentsmetabolomics

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Area of Science:

  • Biotechnology
  • Cell Therapy Manufacturing
  • Artificial Intelligence in Medicine

Background:

  • Reproducible manufacturing of high-quality therapeutic cells is crucial for clinical success.
  • Challenges in cell therapy production include inherent biological variability and process uncertainties, hindering predictable product quality.
  • Current methods struggle to guarantee consistent quality in living cell products.

Purpose of the Study:

  • To develop an AI-driven platform for predicting end-product quality in therapeutic cell manufacturing.
  • To identify critical process parameters and quality attributes from early-stage manufacturing data.
  • To enable predictable cell-product quality for scalable and accessible cell therapies.

Main Methods:

  • Utilized a degradable microscaffold-based T-cell manufacturing process.
  • Developed an experimental-computational platform integrating artificial intelligence (AI).
  • Employed sequential design-of-experiment studies and an agnostic machine-learning framework on multiomics data.

Main Results:

  • Identified critical process parameters and quality attributes from early manufacturing stages.
  • Early in-culture media assessments accurately predicted end-product CD4/CD8 ratio.
  • Successfully predicted total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+).

Conclusions:

  • The developed AI platform can predict therapeutic cell manufacturing outcomes from early in-process measurements.
  • This approach offers a broadly applicable tool for ensuring consistent quality in cell therapy production.
  • Facilitates the translation of cell therapies by improving manufacturing predictability and accessibility.