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Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.

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Limited manufacturing capacity for chimeric antigen receptor (CAR)-T cell therapies causes patient waitlists and high costs. Developing data analytics tools can improve CAR-T therapy manufacturing, reduce costs, and enhance patient outcomes.

Keywords:
CAR‐Tautomationbiomanufacturingimmunotherapypersonalized medicine

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Area of Science:

  • Oncology
  • Immunotherapy
  • Biotechnology

Background:

  • Autologous chimeric antigen receptor (CAR)-T cell therapies offer a vital treatment option for refractory cancers.
  • Current manufacturing limitations lead to significant patient wait times and high costs.
  • Personalized nature of CAR-T treatments complicates simultaneous patient treatment and extends clinical trials.

Purpose of the Study:

  • To review the development, manufacturing, and treatment of CAR-T therapeutics.
  • To advocate for data analytics-based decision support tools to optimize CAR-T therapy.
  • To highlight the need for a systems approach in CAR-T therapy to improve patient outcomes.

Main Methods:

  • Review of existing literature on CAR-T therapy development and manufacturing.
  • Analysis of factors impacting CAR-T treatment success, including patient health, cell quality, and supply chain.
  • Proposal for integrating data analytics and decision support tools into the CAR-T workflow.

Main Results:

  • Manufacturing delays and failures pose significant risks to patients awaiting CAR-T therapy.
  • Current manufacturing processes are labor-intensive, contributing to high treatment costs (approx. $500,000 per dose).
  • A systems-level perspective is crucial, considering pre-treatment factors, manufacturing, and supply chain logistics.

Conclusions:

  • Enhanced automation and reduced manufacturing times are critical for increasing CAR-T therapy accessibility.
  • Data analytics and multi-scale decision support tools can streamline manufacturing and improve treatment efficacy.
  • Continuous data updates can refine treatment recommendations and improve patient outcomes in CAR-T therapy.