Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

Abstract

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Amino Acid Requirements of the Chinese Hamster Ovary Cell Metabolism during Recombinant Protein Production

“…Majority of biopharmaceutical drugs are produced by Chinese hamster ovary (CHO) cells, which have remained the standard industry host for the past three decades [4]. CHO cells are predominantly used as expression hosts for recombinant monoclonal antibody (mAb) production [1,5], which is also the fastest growing segment of the biopharmaceutical industry [6]. The reason for this wide use of CHO cells is mainly their ability to perform post-translational modifications and correctly fold recombinant proteins, as well as their proven track-record of being safe hosts [4,5].…”

“…Two common experimental strategies used for this purpose are statistical design, which allows for statistical analysis and control of external variables, and metabolic profiling, which provides information on the physiology of the cell. These types of experimental methods can often be resource-intensive and require large amount of experiments [6]. Therefore, there is emerging interest in using modelling approaches to assist and guide the laboratory experiments to achieve further improvements.…”

Kinetic Modeling of Mammalian Cell Culture Bioprocessing: The Quest to Advance Biomanufacturing

“…Here, it should be highlighted that although some kinetic models include detailed metabolic reaction kinetics, they are clearly different from static metabolic flux‐based models (see Terminology), which can be constructed based on reaction stoichiometry without considering enzyme kinetics . These flux models reduce the metabolite mass balances to linear algebraic equations by assuming steady state with no accumulation, and thus can be solved easily unlike kinetic models comprising non‐linear equations, which often require more complicated solution procedures . Despite such complexity, kinetic modeling is, however, the most suitable approach to characterize the dynamic behavior of mammalian cell cultures in terms of cellular growth and metabolic utilization .…”

“…[6,7] These flux models reduce the metabolite mass balances to linear algebraic equations by assuming steady state with no accumulation, [8,9] and thus can be solved easily unlike kinetic models comprising non-linear equations, which often require more complicated solution procedures. [10] Despite such complexity, kinetic modeling is, however, the most suitable approach to characterize the dynamic behavior of mammalian cell cultures in terms of cellular growth and metabolic utilization. [11] The model quality can be further enhanced through the validation of models which can be iteratively refined by reducing the discrepancy between model predictions and experimentally observed states.…”

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

“…Liu et al [13] presented a method for estimating one-dimensional discrete chaotic system based on mean value function. In addition, some intelligent optimization algorithms have been proposed for the parameter identification, such as genetic algorithm (GA) [14], particle swarm optimization (PSO) [15][16][17][18], differential evolution (DE) [19], ant swarm optimization algorithm (AS) [20], bat algorithm (BA) [21], cuckoo search optimization algorithm (CS) [22], and teaching learning based optimization (TLBO) [23]. However, research on the influence of time series on the estimation accuracy of multidimensional nonlinear system is rare.…”