Sales Kevin C, Rosa Filipa, Sampaio Pedro N, Fonseca Luís P, Lopes Marta B, Calado Cecília R C
Engineering Faculty, Catholic University of Portugal, Estrada Octávio Pato, 2635-631, Rio de Mouro, Portugal.
Appl Spectrosc. 2015 Jun;69(6):760-72. doi: 10.1366/14-07588. Epub 2015 May 1.
The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coli cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R(2)) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.
生物制药生产工艺的发展面临着关键限制，其中主要限制在于活细胞合成这些分子，由于它们对培养环境中的微小波动高度敏感，会呈现出固有的行为变异性。为了加快开发过程并控制这一关键生产步骤，分别开发高通量和原位监测技术是很有必要的。在此，对脱水细胞沉淀的高通量中红外（MIR）光谱分析和全培养液的原位近红外（NIR）光谱分析进行了比较，以监测重组大肠杆菌培养物中的质粒生产。基于MIR或NIR光谱数据建立了良好的偏最小二乘（PLS）回归模型，得到了较高的决定系数（R²）和较低的预测误差（均方根误差，即RMSE），用于估计宿主细胞生长、质粒生产、碳源消耗（葡萄糖和甘油）以及副产物乙酸的生产和消耗。基于MIR数据的生物量、质粒、葡萄糖、甘油和乙酸的预测误差分别为0.7 g/L、9 mg/L、0.3 g/L、0.4 g/L和0.4 g/L，而基于NIR数据获得的预测误差分别为0.4 g/L、8 mg/L、0.3 g/L、0.2 g/L和0.4 g/L。所获得的模型具有鲁棒性，因为它们对于使用不同培养基组成和不同培养策略（分批和补料分批）进行的培养是有效的。除了使用消毒光纤探头进行原位测量外，近红外光谱法还能够建立用于估计质粒、葡萄糖和乙酸的PLS模型，其准确性与从高通量MIR装置获得的模型相当，并且在估计生物量和甘油方面有更好的模型，与MIR装置相比，均方根误差分别降低了57%和50%。然而，在优化方案的情况下，由于可能存在空间限制或使用多光纤探头用于多生物反应器的成本较高，中红外光谱法可能是一种有效的替代方法。在这种情况下，中红外光谱法可以以高通量方式进行，以快速和自动的模式分析数百个培养样品。
