Raman-Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

IF 3.9 4区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Jeppe Hagedorn, Guilherme Ramos, Miguel Ressurreição, Ernst Broberg Hansen, Michael Sokolov, Carlos Casado Vázquez, Christos Panos
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Abstract

Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell-free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real-time, on-line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae-based biopharmaceutical production.

Abstract Image

生物制药酿酒酵母发酵过程中蛋白质含量和代谢物的拉曼预测。
拉曼光谱是一种强大的非侵入性分析方法,在监测生物制药生产过程中显示出巨大的潜力。它能够提供有关分子振动的详细信息,使其成为复杂生物制药混合物中治疗性蛋白质和关键控制参数的检测和定量的理想选择。然而,它在酿酒酵母发酵中的应用一直受到细胞固有的强荧光背景的阻碍。这种荧光干扰拉曼信号,影响光谱数据的准确性。在本研究中,我们提出了一种方法,通过在无细胞介质样品上部署拉曼光谱,结合先进的化学计量学建模,缓解了这一问题。该方法能够准确预测蛋白质浓度和关键工艺参数,为生物制药发酵过程的控制和优化奠定了基础。利用预测中的变量重要性(VIP)进一步增强了模型的鲁棒性,从而降低了六个研究目标的相对预测均方根误差(RMSEP)值。我们的研究结果突出了拉曼光谱在复杂微生物发酵过程的实时、在线监测和控制方面的潜力,从而显著提高了酿酒葡萄球菌生物制药生产的效率和质量。
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来源期刊
Engineering in Life Sciences
Engineering in Life Sciences 工程技术-生物工程与应用微生物
CiteScore
6.40
自引率
3.70%
发文量
81
审稿时长
3 months
期刊介绍: Engineering in Life Sciences (ELS) focuses on engineering principles and innovations in life sciences and biotechnology. Life sciences and biotechnology covered in ELS encompass the use of biomolecules (e.g. proteins/enzymes), cells (microbial, plant and mammalian origins) and biomaterials for biosynthesis, biotransformation, cell-based treatment and bio-based solutions in industrial and pharmaceutical biotechnologies as well as in biomedicine. ELS especially aims to promote interdisciplinary collaborations among biologists, biotechnologists and engineers for quantitative understanding and holistic engineering (design-built-test) of biological parts and processes in the different application areas.
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