适应性机器学习框架使得基因治疗的腺相关病毒载体具有前所未有的产量和纯度
Kelvin P Idanwekhai1,2, Shriarjun Shastry3,4, Arianna Minzoni3
1Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
bioRxiv : the preprint server for biology
|June 6, 2025
概括
机器学习优化了腺相关病毒 (AAV) 载体净化,增强了基因疗法生产. 这种人工智能驱动的方法显著提高了AAV的产量和纯度,使治疗更容易获得.
科学领域:
- 生物技术是生物技术.
- 基因治疗 基因治疗
- 过程优化 过程优化
背景情况:
- 腺相关病毒 (AAV) 载体对于基因疗法至关重要,但它们的净化是一个重要的瓶.
- 目前优化AAV净化的方法是低效,昂贵和耗时的.
- 提高AAV产量,纯度和安全性对于临床翻译和患者可访问性至关重要.
研究的目的:
- 开发和验证用于优化AAV净化过程的机器学习框架.
- 系统地完善亲和色谱参数,以提高AAV矢量生产.
- 证明该框架能够在多种AAV血清型中提高产量,纯度和转导效率.
主要方法:
- 使用贝叶斯优化的机器学习框架的开发.
- 应用框架以优化亲和色谱参数 (样本负载,流量,介质配方).
- 代闭环工作流程用于AAV2,AAV5和AAV9血清型的提炼净化.
主要成果:
- 在贝叶斯优化三个周期后,产量从70%增加到99%.
- 在测试的AAV血清型中,宿主细胞杂质减少了230至400倍.
- 始终生产高纯度的AAV载体,保持转导活性,证明血清型的多功能性.
结论:
- 机器学习框架有效地优化了AAV净化,解决了基因疗法制造中的关键瓶.
- 这种适应性方法加速了流程开发,降低了成本,并提高了AAV生产的可扩展性.
- 优化的净化过程提高了载体质量和一致性,支持基于AAV的基因疗法的临床转化.
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