(247k) Modeling and Prediction of Behavior of Mammalian Cell Culture for Monoclonal Antibody Production - Dual Rate Approach

The demand for high-value biopharmaceuticals is on a rapid rise due to their therapeutic and diagnostic applications. Monoclonal antibodies (MABs) are important reagents used in biomedical research and diagnosis and treatment of diseases and account for nearly 40% of the sale of biologics in the U.S. and are produced in animal cell bioreactors at a variety of scales. Chinese Hamster Ovary (CHO) cells and hybridoma cells have been the popular cell types for production of MABs. The simplest approach for producing MABs in vitro is to grow the mammalian cells in batch and fed-batch cultures, since these are easy to operate and scale up, and recover and purify the MABs from the culture medium. However, under batch conditions, mammalian cells exhibit an inefficient glucose and glutamine metabolism, causing increased production of lactate and ammonia, major waste products of metabolism of glucose and glutamine, respectively. Both lactate and ammonia inhibit growth of mammalian cells and deteriorate MAB production. To reduce formation of these toxic by-products, fed-batch operations with intermittent supplementation of glucose and glutamine have been performed with these cell lines, resulting in a higher volumetric productivity and extended cell viability. Although fed-batch operations have been successfully employed for microbial systems, kinetics of mammalian cell cultures is still under investigation to determine quantitatively as well as qualitatively cost-effective production strategies. Traditional operations of these culture systems at production scales are mainly based on empirical knowledge. There has been limited effort on development of first principle-based mathematical models for these cultures at laboratory scale operations. Due to complexity of the models and uncertainties associated with estimation of model parameters, the first principle models are not suitable for online prediction and control of mammalian cell cultures. Simple data driven models may be more suitable for online prediction and control.

Multi-rate systems, where inputs and/or outputs have different sampling rates, are encountered in many chemical and biological engineering applications. Typically, this occurs in systems where multiple variables are measured. Process variables such as temperature, pressure and pH are measured more frequently, at times nearly continuously, while other variables, such as species concentrations in a reaction mixture, are measured less frequently. An example of such processes is batch and fed-batch mammalian cell cultures, where sampling rates for viable hybridoma cells, nutrients glucose and glutamine, by-products lactate and ammonia, and the target product, a MAB, are significantly different. The simplest way to handle multi-rate systems is to neglect excess data from fast sampling signals and synchronize the signals with the slowest sampling rate. With this method, great amount of data will be discarded, the data that may contain crucial information regarding system dynamics. Therefore, some effort has been devoted to modeling, analysis and identification of multi-rate systems to take advantage of the rich information available in the experimental databases. In all the techniques proposed, multi-rate systems are generally simplified to multiple dual rate systems where the slower sampling rate is a positive integer () multiple of the faster sampling rate. One of the most successful techniques, polynomial transformation technique, can successfully transfer single rate models into dual rate models with ease. The dual rate model, after transformation, can utilize dual rate signals simultaneously. For such model structures, multiple identification techniques have been proposed with good estimation accuracy and convergence rate.

In this work, a second order discrete time system is considered first to demonstrate convergence of the proposed parameter estimation algorithm for a dual rate model. The convergence rates and estimation accuracy for various values are compared to examine efficacy of the dual rate parameter estimation technique. Use of a dual rate model with parameter estimation technique for modeling and prediction of mammalian cell cultures is discussed next. The faster sampling rate corresponds to viable hybridoma cell concentration and the slower sampling rate corresponds to glucose and glutamine concentrations. The sampling rate for monoclonal antibody is much slower than that for glucose and glutamine. Recursive time series models are developed for key culture variables. The model parameters are recursively estimated by using a least square estimation algorithm. Model stability is evaluated and confirmed by converting the time series model into a state space counterpart. The most rapidly measured variable, viable cell concentration in cell culture is expressed by a recursive ARMAX model. The performance of the dual rate model coupled with frequent parameter estimation in representation and prediction of mammalian cell culture producing a MAB is examined in considerable detail.