Liang George, Sha Sha, Wang Zhao, Liu Huolong, Yoon Seongkyu
Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States.
Front Mol Biosci. 2024 Oct 22;11:1441885. doi: 10.3389/fmolb.2024.1441885. eCollection 2024.
Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models.
有效评估产品质量仍然需要耗费大量时间和资源。在线过程分析技术（PATs），包括实时监测工具和软传感器模型，对于理解过程影响和实时产品质量至关重要。本研究评估了三种用于预测CHO细胞生长、产物、代谢物（细胞外、核苷酸糖供体（NSD）和聚糖谱）的建模方法：基于第一原理米氏动力学（MMK）的机理模型、数据驱动的正交偏最小二乘法（OPLS）和神经网络机器学习（NN）。我们的实验设计涉及半乳糖补料分批培养。MMK在预测生长和产物方面表现出色，证明了其在这些方面的可靠性，并通过减少输入要求减轻了数据负担。然而，它在模拟聚糖谱和细胞内代谢物趋势方面不够精确。相比之下，NN和OPLS在预测精确的聚糖组成方面表现更好，但在准确预测生长和产物方面存在不足。我们利用训练集中的时间来应对NN和OPLS的外推挑战。OPLS和NN模型需要更多广泛的输入来进行类似的细胞内代谢物趋势预测。然而，开发这两种模型所需的时间显著减少。这里提供的指导可以为利用PATs快速开发和应用软传感器模型以实现提高CHO治疗性产品实时质量的目的提供有价值的见解。因此，我们研究了三种模型类型以提高实时产品CHO治疗性产品质量。结合新兴的组学技术，NN和OPLS将受益于大量数据的可用性，并且我们预见会有更强大的预测模型，这可能对动力学或部分动力学（混合）模型有利。
