在体外转录的高通量算法优化用于SARS-CoV-2mRNA疫苗生产
Spencer E McMinn1, Danielle V Miller1, Daniel Yur1
1Process Research and Development, Merck & Co., Inc., West Point, Pennsylvania 19486, United States.
Biochemistry
|October 21, 2024
概括
机器学习优化了来自SARS-CoV-2DNA模板的信使核糖核酸 (mRNA) 生产. 这种方法提高了mRNA产量和纯度,同时减少了反应时间和试剂成本.
科学领域:
- 生物技术是生物技术.
- 分子生物学分子生物学
- 机器学习应用 机器学习应用
背景情况:
- 优化体外转录 (IVT) 是生产高质量的信使核糖核酸 (mRNA) 的关键.
- 有效的mRNA生产对于开发疫苗和治疗药物至关重要,特别是对于像SARS-CoV-2 Delta.这样快速出现的病毒变种.
- 目前的IVT方法经常面临产量,纯度,反应时间和试剂成本方面的挑战.
研究的目的:
- 为了优化体外转录 (IVT) 条件,从SARS-CoV-2Delta变异DNA模板中产生信使核糖核酸 (mRNA).
- 使用机器学习和自动化液体处理来提高总mRNA产量和纯度 (百分比完整的mRNA).
- 为了评估商业上可用的T7RNA聚合酶,以改善mRNA合成.
主要方法:
- 利用代贝叶斯优化方法来微调11个关键过程参数.
- 采用自动化,高通量液体处理技术,以实现高效的反应执行.
- 在优化条件下对T7RNA聚合酶进行自动化,高通量选.
主要成果:
- 在总mRNA产量方面实现了12%的改善.
- 减少了50%的反应时间.
- 将昂贵的试剂的使用降低了44%,同时提高了mRNA纯度.
结论:
- 开发了一种新,高效的平台,用于优化用于mRNA生产的IVT反应.
- 证明了机器学习和自动化在增强mRNA合成方面的成功应用.
- 优化过程为mRNA生产提供了显著的产量,速度,成本效益和纯度的改进.
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