将数据科学与细胞疗法制造联系起来:我们现在的位置以及我们需要的位置
Bryan Wang1, Rui Qi Chen2, Jing Li2
1Marcus Center for Therapeutic Cell Characterization and Manufacturing, Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA; NSF Engineering Research Center (ERC) for cell Manufacturing Technologies (CMaT), USA.
Cytotherapy
|June 6, 2024
概括
在细胞疗法制造中实施数据科学可以克服诸如变异性和高成本之类的挑战. 这种方法提高了可复制性,降低了风险,并加速向患者提供高质量的细胞疗法.
科学领域:
- 生物技术和生物医学工程 生物技术和生物医学工程
- 计算生物学和数据科学数据科学
背景情况:
- 基于细胞的疗法面临着重要的制造障碍,包括可变性,成本和可扩展性.
- 当前的制造工艺往往缺乏数据驱动的洞察力,导致效率低下和产品质量不一致.
研究的目的:
- 审查数据科学工具在细胞疗法制造中的应用.
- 确定计算方法如何解决当前行业的挑战,并改善治疗细胞的生产.
主要方法:
- 对生物制造中的数据科学应用现有文献的审查.
- 讨论人工智能,机器学习和数据分析等计算工具.
- 确定细胞治疗生产中创新方法的领域.
主要成果:
- 数据科学为提高细胞疗法制造的一致性,降低风险和降低成本提供解决方案.
- 整合数据驱动的预测能力可以优化生产并提高产品质量.
- 人工智能和机器学习可以帮助理解关键的质量属性和过程参数.
结论:
- 在细胞治疗生命周期中整合数据科学对于克服制造和监管障碍至关重要.
- 开发数据驱动的工作流程和更好的数据收集工具对于加速临床翻译至关重要.
- 在数据科学方面接受培训的熟练劳动力对于未来的细胞疗法制造至关重要.
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