Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform

被引:0
|
作者
Christie, Loren [1 ,2 ]
Rutherford, Samantha [2 ]
Palmer, David S. [1 ,2 ]
Baker, Matthew J. [1 ,3 ]
Butler, Holly J. [1 ]
机构
[1] Dxcover Ltd, Glasgow, Scotland
[2] Univ Strathclyde, Pure & Appl Chem, Glasgow, Scotland
[3] Univ Cent Lancashire, Sch Med & Dent, Preston, England
关键词
fourier transform infrared spectroscopy (FTIR); bioprocess monitoring; process analytical technology (PAT); metabolite quantification; bioprocessing; OVARY CELL-CULTURE; INFRARED-SPECTROSCOPY; CULTIVATION; TECHNOLOGY; GLUCOSE; LINE;
D O I
10.3389/fbioe.2024.1349473
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.
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页数:12
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