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首页> 外文期刊>Biotechnology Progress >Optimization of a Saccharomyces Cerevisiae Fermentation Process for Productior of a Therapeutic Recombinant Protein Using a Multivariate Bayesian Approach
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Optimization of a Saccharomyces Cerevisiae Fermentation Process for Productior of a Therapeutic Recombinant Protein Using a Multivariate Bayesian Approach

机译：利用多变量贝叶斯方法优化酿酒酵母发酵生产重组蛋白的工艺

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摘要

Various approaches have been applied to optimize biological product fermentation processes and define design space. In this article, we present a stepwise approach to optimize a Saccharomyces cerevisiae fermentation process through risk assessment analysis, statistical design of experiments (DoE), and multivariate Bayesian predictive approach. The critical process parameters (CPPs) were first identified through a risk assessment. The response surface for each attribute was modeled using the results from the DoE study with consideration given to interactions between CPPs. A multivariate Bayesian predictive approach was then used to identify the region of process operating conditions where all attributes met their specifications simultaneously. The model prediction was verified by twelve consistency runs where all batches achieved broth titer more than 1.53 g/L of broth and quality attributes within the expected ranges. The calculated probability was used to define the reliable operating region. To our knowledge, this is the first case study to implement the multivariate Bayesian predictive approach to the process optimization for the industrial application and its corresponding verification at two different production scales: This approach can be extended to other fermentation process optimizations and reliable operating region quantitation.
机译：已应用各种方法来优化生物产品发酵过程并定义设计空间。在本文中，我们提出了一种通过风险评估分析，实验统计设计（DoE）和多元贝叶斯预测方法来优化酿酒酵母发酵过程的逐步方法。关键过程参数（CPP）首先通过风险评估确定。考虑到CPP之间的相互作用，使用DoE研究的结果对每个属性的响应面进行建模。然后使用多元贝叶斯预测方法来识别所有属性同时满足其规格的过程操作条件区域。通过十二次一致性运行验证了模型预测，其中所有批次的肉汤滴度均超过1.53 g / L的肉汤，并且品质属性在预期范围内。计算出的概率用于定义可靠的工作区域。据我们所知，这是第一个将多元贝叶斯预测方法用于工业应用的过程优化及其在两个不同生产规模下的相应验证的案例研究：该方法可以扩展到其他发酵过程优化和可靠的操作区域定量。

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