This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry.

Hybrid models aim to describe different components of a process in different ways. This makes sense when the corresponding knowledge to be represented is different as well. In this way, the most efficient representations can be chosen and, thus, the model performance can be increased significantly. From the various possible variants of hybrid model, three are selected which were applied for important biotechnical processes, two of them from existing production processes. The examples show that hybrid models are powerful tools for process optimisation, monitoring and control.

An optimized fed-batch cultivation process for the production of the polyoma virus capsid protein VP1 in recombinant Escherichia coli BL21 bacteria is presented. The optimization procedure maximizing the amount of desired protein is based on a mathematical model. The model distinguishes an initial cell growth phase from a protein production phase initiated by inducer injection. A new approach to model the target protein formation rate was elaborated, where product formation is primarily dependent on the specific biomass growth rate. Lower growth rates led to higher specific protein concentrations. The model was identified from a series of fed-batch experiments designed for parameter identification purposes and possesses good prediction quality. Then the model was used to determine optimal open-loop control profiles by manipulating the substrate feed rates in both phases as well as the induction time. Feed-rate optimization has been solved using Pontryagin's maximum principle. The solution was validated experimentally. A significant improvement of the process performance index was achieved.

In this paper a simple design procedure is used to enhance the performance of a biotechnical cultivation process. A model supported approach is proposed. It starts with a simple classical process model obtained from literature data, which is used to design the ®rst experiment. Then, the main procedure is an iteration of (i) improving the model making use of the deviations between the experimental data and the data predicted by the model, (ii) of designing the next experiment by determining the optimal control pro®les from the current model, and (iii) of executing that designed experiment. Important for the success of the procedure is that the model development is oriented at the process performance. The procedure is demonstrated at the simple practical example of a laboratoryscale fed-batch cultivation of Escherichia coli.

BackgroundThe focus of this study is online estimation of biomass concentration in fed-batch cultures. It describes a bioengineering software solution, which is explored for Escherichia coli and Saccharomyces cerevisiae fed-batch cultures. The experimental investigation of both cultures presents experimental validation results since the start of the bioprocess, i.e. since the injection of inoculant solution into bioreactor. In total, four strains were analyzed, and 21 experiments were performed under varying bioprocess conditions, out of which 7 experiments were carried out with dosed substrate feeding. Development of the microorganisms’ culture invariant generic estimator of biomass concentration was the main goal of this research.ResultsThe results show that stoichiometric parameters provide acceptable knowledge on the state of biomass concentrations during the whole cultivation process, including the exponential growth phase of both E. coli and S. cerevisiae cultures. The cell culture stoichiometric parameters are estimated by a procedure based on the Luedeking/Piret-model and maximization of entropy. The main input signal of the approach is cumulative oxygen uptake rate at fed-batch cultivation processes. The developed noninvasive biomass estimation procedure was intentionally made to not depend on the selection of corresponding bioprocess/bioreactor parameters.ConclusionsThe precision errors, since the bioprocess start, when inoculant was injected to a bioreactor, confirmed that the approach is relevant for online biomass state estimation. This included the lag and exponential growth phases for both E. coli and S. cerevisiae. The suggested estimation procedure is identical for both cultures. This approach improves the precision achieved by other authors without compromising the simplicity of the implementation. Moreover, the suggested approach is a candidate method to be the microorganisms’ culture invariant approach. It does not depend on any numeric initial optimization conditions, it does not require any of bioreactor parameters. No numeric stability issues of convergence occurred during multiple performance tests. All this makes this approach a potential candidate for industrial tasks with adaptive feeding control or automatic inoculations when substrate feeding profile and bioreactor parameters are not provided.