Advancing Biopharmaceutical Process Development by System-Level Data Analysis and Integration of Omics Data

Abstract

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential. Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

Cell culture media for recombinant protein expression in Chinese hamster ovary (CHO) cells: History, key components, and optimization strategies

“…Research groups at the Massachusetts Institute of Technology and the University of Minnesota pioneered the use of stoichiometric analysis of cell growth and nutrient utilization to design basal and feed media formulations to feed according to nutrient demand, thereby preventing depletion of key nutrients while minimizing accumulation of toxic metabolic waste . Further optimization of CHO cell culture media and process parameters by leading teams in the biopharmaceutical industry, specifically aimed at commercial manufacturing of mAbs and other recombinant proteins, has resulted in dramatic increases in cell density and protein expression, with titers reaching more than 10 g/L in some cases . Furthermore, several companies specializing in cell culture media have developed and optimized basal and feed media combinations specifically for recombinant CHO manufacturing processes (ThermoFisher, Waltham, MA, USA, GE Healthcare, Waukesha, WI, USA, MilliporeSigma, St. Louis, MO, USA, Lonza, Basel, Switzerland, Irvine Scientific, Santa Ana, CA, USA).…”

Combining mechanistic and data‐driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed‐batch CHO process

“…In the last decades, significant efforts have been made to increase the productivity and robustness of cell culture processes, enabling the accumulation of an extensive knowledge on interactions between process control features, cell physiology and product formation . Thereby, the investigation of cell metabolism has become a key method in bioprocess characterization and optimization . Metabolomic analysis is essential to identify shifts in cell physiology typically occurring during industrial fed‐batch cultivations and to adapt process control to the metabolic phenotype.…”

The use of ‘Omics technology to rationally improve industrial mammalian cell line performance

“…As a result, developing efficient and robust data analysis methods and tools is becoming increasingly important (Casci, 2012;Jayapal, 2007;Kotera et al, 2012;Schaub et al, 2012). A major advantage of this large-scale, systems level approach is the unbiased evaluation of study conditions and high resolution datasets.…”

“…While approaches like multi-factorial design have streamlined some aspects of process optimization, any empirical approach must be applied for every cell line, ultimately requiring significant time and resources (Hacker, 2008). Future progress will require a transition away from a purely empirical approach, and efforts toward rational modification through knowledge of cell biology (Schaub et al, 2012;Schaub et al, 2010;Wuest et al, 2012;Young, 2013). Future progress will require a transition away from a purely empirical approach, and efforts toward rational modification through knowledge of cell biology (Schaub et al, 2012;Schaub et al, 2010;Wuest et al, 2012;Young, 2013).…”