Hierarchical time‐series analysis of dynamic bioprocess systems

Abstract

Background: Monoclonal antibodies (mAbs) are leading types of 'blockbuster' biotherapeutics worldwide; they have been successfully used to treat various cancers and chronic inflammatory and autoimmune diseases. Biotherapeutics process development and manufacturing are complicated due to lack of understanding the factors that impact cell productivity and product quality attributes. Understanding complex interactions between cells, media, and process parameters on the molecular level is essential to bring biomanufacturing to the next level. This can be achieved by analyzing cell culture metabolic levels connected to vital process parameters like viable cell density (VCD). However, VCD and metabolic profiles are dynamic parameters and inherently correlated with time, leading to a significant correlation without actual causality. Many time-series methods deal with such issues. However, with metabolic profiling, the number of measured variables vastly exceeds the number of experiments, making most of existing methods ill-suited and hard to interpret. Methods and Major Results:Here we propose an alternative workflow using hierarchical dimension reduction to visualize and interpret the relation between evolution of metabolic profiles and dynamic process parameters. The first step of proposed method is focused on finding predictive relation between metabolic profiles and process parameter at all time points using OPLS regression. For each time point, the p(corr) obtained from OPLS model is considered as a differential metabogram and is further assessed using principal components analysis (PCA).Conclusions: Compared to traditional batch modeling, applying proposed methodology on metabolic data from Chinese Hamster Ovary (CHO) antibody production characterized the dynamic relation between metabolic profiles and critical process parameters.

A hierarchical model combining process and Raman spectroscopic data for real-time monitoring of cell culture process

“…Follow-up studies could provide additional metabolic intermediates data for Raman base model calibration by using metabolomics methods such as 1 H-NMR [16]. When dealing with variable selection for the top BEM, process knowledge is required to include or exclude specific variables.…”

“…In this study, BEM core model was developed to estimate the maturity of processes from the measured parameters and detect variations of parameters from their ideal trajectory [16]. To do this, the N × J × K (i.e.…”

Exploring metabolic responses and pathway changes in CHO‐K1 cells under varied aeration conditions and copper supplementations using 1H NMR‐based metabolomics

“…In the biopharmaceutical sector, living cells constitute the central platform for manufacturing a diverse array of pharmaceutical products known as biologics. [1,2] Chinese hamster ovary (CHO) cells are extensively employed as expression systems for the commercial production of monoclonal antibodies (mAbs). [3] Cultivated within chemically defined serum-free media, CHO cells rely on an array of essential components to drive cell growth, mAb expression, and the control of critical quality attributes (CQAs) like charge variants, glycosylation, and size variants.…”

1H NMR‐based process understanding and biochemical marker identification methodology for monitoring CHO cell culture process during commercial‐scale manufacturing

“…1 H NMR has been applied for predicting the bioprocess performance, and for early identification of potential biochemical markers linked to cell starvation, viability, and expansion, to favor the timely control of culture conditions, through global multi‐component analysis [ 14–17 ] . These previous successful attempts have demonstrated that the 1 H NMR technology does have great application potential in the monitoring and control of bioprocesses [ 18,19 ] . 1 H NMR spectra could provide atomic‐level detail of small organic molecules in mixtures and give quantitative information in a highly reproducible way with very limited sample preparation.…”

“…[14][15][16][17] These previous successful attempts have demonstrated that the 1 H NMR technology does have great application potential in the monitoring and control of bioprocesses. [18,19] 1 H NMR spectra could provide atomic-level detail of small organic molecules in mixtures and give quantitative information in a highly reproducible way with very limited sample preparation.…”