Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor.

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Biotechnology Progress Pub Date : 2024-07-02 DOI:10.1002/btpr.3493
Amir M Behdani, Yuxiang Zhao, Grace Yao, Dhanuka Wasalathanthri, Eric Hodgman, Michael Borys, Gloria Li, Anurag Khetan, Dayanjan Wijesinghe, Anthony Leone
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Abstract

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

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利用基于机器学习模型的软传感器在细胞培养过程中快速监测总硅酸。
生物治疗蛋白质中的总硅酸含量(TSA)通常是影响药物疗效的关键质量属性。传统的湿化学分析方法是在细胞培养过程中量化生物治疗蛋白质中的 TSA,由于该方法非常复杂,需要从相关蛋白质中分离出硅酸,然后进行样品制备和色谱分离分析,因此通常需要几个小时或更长时间。在此,我们开发了一种基于机器学习模型的技术,利用通常测量的过程参数快速预测细胞培养过程中的 TSA。该技术拥有一个用户界面,用户只需上传细胞培养过程参数作为输入变量,TSA 值就会根据模型预测结果即时显示在仪表板平台上。在这项研究中,我们对数据集上的多种机器学习算法进行了评估,发现随机森林模型是最有前途的模型。随机森林模型的特征重要性分析表明,活力细胞密度(VCD)、谷氨酸、铵、磷酸盐和基础培养基类型等属性对预测至关重要。值得注意的是,虽然该模型在第 14 天的观测中表现出很强的可预测性,但在预测校准范围边缘的 TSA 值方面仍然存在挑战。这项研究不仅强调了机器学习和软传感器在生物处理中的变革能力,还为硅酸预测引入了一种快速高效的工具,标志着生物处理领域的重大进步。未来的工作可能会侧重于数据增强,以进一步提高模型精度和探索过程控制能力。
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来源期刊
Biotechnology Progress
Biotechnology Progress 工程技术-生物工程与应用微生物
CiteScore
6.50
自引率
3.40%
发文量
83
审稿时长
4 months
期刊介绍: Biotechnology Progress , an official, bimonthly publication of the American Institute of Chemical Engineers and its technological community, the Society for Biological Engineering, features peer-reviewed research articles, reviews, and descriptions of emerging techniques for the development and design of new processes, products, and devices for the biotechnology, biopharmaceutical and bioprocess industries. Widespread interest includes application of biological and engineering principles in fields such as applied cellular physiology and metabolic engineering, biocatalysis and bioreactor design, bioseparations and downstream processing, cell culture and tissue engineering, biosensors and process control, bioinformatics and systems biology, biomaterials and artificial organs, stem cell biology and genetics, and plant biology and food science. Manuscripts concerning the design of related processes, products, or devices are also encouraged. Four types of manuscripts are printed in the Journal: Research Papers, Topical or Review Papers, Letters to the Editor, and R & D Notes.
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Non-thermal plasma decontamination of microbes: a state of the art. Mechanistic model of minute virus of mice elution behavior in anion exchange chromatography purification. Comparing in silico flowsheet optimization strategies in biopharmaceutical downstream processes. General strategies for IgG-like bispecific antibody purification. Issue Information
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