Global Bioprocess Development, Sanofi, Toronto, ON M2R 3T4, Canada.
Analytical Sciences, Sanofi, Toronto, ON M2R 3T4, Canada.
J Ind Microbiol Biotechnol. 2024 Jan 9;51. doi: 10.1093/jimb/kuae019.
Automation of metabolite control in fermenters is fundamental to develop vaccine manufacturing processes more quickly and robustly. We created an end-to-end process analytical technology and quality by design-focused process by replacing manual control of metabolites during the development of fed-batch bioprocesses with a system that is highly adaptable and automation-enabled. Mid-infrared spectroscopy with an attenuated total reflectance probe in-line, and simple linear regression using the Beer-Lambert Law, were developed to quantitate key metabolites (glucose and glutamate) from spectral data that measured complex media during fermentation. This data was digitally connected to a process information management system, to enable continuous control of feed pumps with proportional-integral-derivative controllers that maintained nutrient levels throughout fed-batch stirred-tank fermenter processes. Continuous metabolite data from mid-infrared spectra of cultures in stirred-tank reactors enabled feedback loops and control of the feed pumps in pharmaceutical development laboratories. This improved process control of nutrient levels by 20-fold and the drug substance yield by an order of magnitude. Furthermore, the method is adaptable to other systems and enables soft sensing, such as the consumption rate of metabolites. The ability to develop quantitative metabolite templates quickly and simply for changing bioprocesses was instrumental for project acceleration and heightened process control and automation.
ONE-SENTENCE SUMMARY: Intelligent digital control systems using continuous in-line metabolite data enabled end-to-end automation of fed-batch processes in stirred-tank reactors.
在发酵罐中实现代谢物控制自动化对于更快速、更稳健地开发疫苗制造工艺至关重要。我们通过创建一个端到端的过程分析技术和以质量源于设计为重点的过程,用高度适应和自动化的系统取代了分批生物过程中代谢物的手动控制,从而实现了这一目标。中红外光谱法结合衰减全反射探头在线使用,并使用 Beer-Lambert 定律进行简单的线性回归,从发酵过程中测量复杂培养基的光谱数据中定量关键代谢物(葡萄糖和谷氨酸)。这些数据与过程信息管理系统进行数字连接,以便使用比例积分微分控制器连续控制进料泵,从而在分批搅拌罐发酵罐过程中维持营养物水平。来自搅拌罐反应器中培养物的中红外光谱的连续代谢物数据使反馈回路和进料泵的控制成为可能,从而实现药物开发实验室中的过程控制。这将营养物水平的过程控制提高了 20 倍,药物产量提高了一个数量级。此外,该方法适用于其他系统,并能够实现软感应,例如代谢物的消耗速率。快速、简单地开发用于不断变化的生物过程的定量代谢物模板的能力对于项目加速和提高过程控制和自动化至关重要。
使用连续在线代谢物数据的智能数字控制系统使搅拌罐反应器中的分批过程实现了端到端自动化。