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Updated: Feb 14, 2026

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CAR T細胞生産中の代謝状態の動的推定
N Suhas Jagannathan1, Wei-Xiang Sin2, Denise Bei Lin Teo2
1Critical Analytics for Manufacturing Personalized-Medicine (CAMP), Singapore-MIT Alliance for Research and Technology Centre (SMART), Singapore 138602, Singapore; Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore 169857, Singapore.
Cell reports methods
|February 12, 2026
まとめ
私たちは,ex vivo拡張中のT細胞代謝率を推定するためのリアルタイムモデリングツールを開発しました. このアプローチは,初期代謝データを最終製品の属性と相関させ,適応的なプロセス制御を可能にすることで,細胞療法の一貫性を改善するのに役立ちます.
科学分野:
- バイオテクノロジー バイオテクノロジー
- 細胞生物学 細胞生物学
- プロセスエンジニアリング プロセスエンジニアリング
背景:
- T細胞のex vivo膨張は,細胞療法において極めて重要です.
- 拡張中の細胞代謝のモニタリングは,品質管理の鍵です.
- 現在の方法では,細胞毎の代謝率をリアルタイムで推定できない.
研究 の 目的:
- T細胞培養における細胞毎の代謝率の推定をリアルタイムにするための新しいモデリングフレームワークを提示する.
- モデルの正確性と適用性を異なる原子炉タイプで検証するためです.
- 初期の代謝プロファイルと最終的なT細胞製品特性の間の相関を証明するために.
主な方法:
- 代謝率の推定のための計算モデリングフレームワークの開発.
- メタボリックアッセイを用いた検証と,個々のT細胞現象型へのデコンボリューション.
- 健康なT細胞と患者からのT細胞を搭載した perfusion ベースのマイクロバイオリアクターへの適用.
主要な成果:
- 細胞毎の代謝率のリアルタイム推定に成功しました.
- メタボリックアッセイに対する推定率の検証.
- 早期代謝率とT細胞の成長,分化,枯渇の間の相関が実証されました.
- perfusion ベースのマイクロバイオリアクターに適用するための原理の証明.
結論:
- 開発されたモデリングフレームワークは,ex vivo T細胞の精密なリアルタイム代謝率モニタリングを可能にします.
- 早期の代謝の洞察は,最終的な細胞製品の重要な属性を予測することができます.
- このツールは,細胞療法の一貫性を高めるための適応プロセス制御をサポートします.
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