Review of literature
CHAPTER 2 2.2.4 Gaps and challenges
Availability of reliable process measurements with the help of advanced PAT tools could facilitate the real-time monitoring of the CPPs. Therefore, developing a suitable and cost- effective combination of the PAT tools for process monitoring is essential. As discussed in the previous sections, DS can be applied to achieve enhanced monitoring of biomass concentration, which is a key physiological variable. However, there are a few setbacks concerning the same, and they could be highlighted as follows.
• Implementing suitable data filtering techniques to extract valuable information from the capacitance sensor data is the first challenge. Pre-processing is significant, especially with respect to filtering the data from the frequency scans of the capacitance measurements since precise datasets devoid of signal noise are necessary to interpret the physiological changes occurring during the fermentation.
Additionally, since the DS data are used to estimate biomass concentration using different correlation techniques and models, reliable data acquirement and appropriate filtering techniques are crucial.
• Published literature emphasized the possibility of estimation of physiological properties using scanning capacitance data. Since the cultivation of microorganisms for the production of recombinant proteins has significant decision steps like induction and harvest, the estimation of physiological properties can provide insight into the changes happening throughout the fermentation, and the operator can take suitable process decisions based on the same. Further advancement would be achieving this in real-time, which could further be applied for online model-based control strategies. Thus, there is a need for real-time estimation of physiological properties using measured capacitance data for enhanced monitoring and control applications.
• The real-time estimation of physiological properties can be achieved by developing a Cole-Cole model to relate the measured scanning capacitance and conductivity data to the respective properties. However, as discussed in the previous sections, there are a few setbacks regarding the implementation of the Cole-Cole model in a bacterial system for recombinant therapeutic production. Due to the small size of the cells and high signal noise, the challenges associated with Cole-Cole model
2.3 Model development in bioprocesses
development for real-time estimation of physiological properties in a bacterial system remains to be addressed and explored.
• Real-time capacitance data can fingerprint the biomass growth profile accurately and instantaneously during the fermentation. Hence, integration of the measured capacitance data and the estimated biomass concentration in a validated process model would be beneficial for implementing online optimization and control strategies, and there is a scope to explore this notion.
2.3 Model development in bioprocesses
Bioprocess measurement and monitoring involve two main approaches: applying reliable and accurate real-time PAT tools and developing mathematical models that can relate the measured process variables from the sensors to the required quality attributes (Vojinović et al., 2006). The success of a PAT tool depends on the accuracy of the process model that could interpret the measured variables. An essential aspect of PAT implementation is establishing a meaningful relationship between the process conditions and product quality. A meaningful relationship can be achieved with the help of the combination of sensor data from the PAT tools employed in the reactor with a process model to obtain holistic knowledge about the various process variables. Successful process monitoring and control in the bioprocess depends on the measurement and monitoring techniques (Sonnleitner, 2012).
Model development is crucial since it is the backbone for developing and implementing optimization and control strategies for the biotherapeutic production process. Therefore, developing and validating appropriate models that could provide a reliable description of the underlying process is a significant step towards achieving desired objectives. Models can establish a mathematical relationship between the monitored CPP and CQA, which is a significant step before implementing a control strategy. A thorough understanding of the process is necessary to improve the process consistency in biopharmaceutical manufacturing, as emphasized by the recent initiative by the FDA and other regulatory bodies. Hence, mathematical model development would be the primary step (Wechselberger et al., 2010).
A mathematical model can improve the understanding of the living system through means of equations and serves as a mode of communication for knowledge sharing in both industrial and research settings and further aids in expanding the operational horizons
CHAPTER 2 with the help of simulation (Mears et al., 2017a; Proß and Bachmann, 2012). A well- developed representative process model can enable performance estimation and prediction, scheduling and optimization (Montague et al., 2002). The combination of advanced sensor systems and an appropriate mathematical model could enhance the understanding of the underlying process, enable better prediction of the critical quality attribute and, subsequently, enhance the product quality by facilitating a better control strategy implementation (Sommeregger et al., 2017).
2.3.1 Modeling in upstream processes
Model development in the bioprocess perspective is the method by which mathematical representation of the process is developed and validated. Models can enable a convenient understanding of the relationship between different process variables even without performing actual time and resource-consuming experiments. Additionally, models could also provide insights on state variables that cannot be measured directly and provide output variables that are estimated infrequently. Another significant reason behind model development is its subsequent usage in optimization and control studies wherein the controlled variables (CV), and manipulated variables (MV) can be used to establish a suitable model-based control strategy (Luo et al., 2021).
2.3.1.1 Different modeling methods
In bio-manufacturing processes, modeling techniques can be applied for different scenarios like process design, scheduling, economic analysis, process improvement, and many more (Chhatre, 2012). Among these, the process models describing fermentation fall into two broad categories: Mechanistic (white-box/ first-principles) and data-driven (black-box/ empirical). Another type of model, known as semi-parametric or hybrid modeling (grey-box), combines fundamental knowledge-based models and data-driven techniques.
Mechanistic models are mathematical models developed using the detailed description of the underlying process using first principle mechanisms. They describe the physical relationship of the input variables of the fermentation with the state and output variables in the form of an ordinary differential (ODEs) or partial differential equations.
Mechanistic models can enable monitoring of critical process parameters that are not known in real-time and could further be used along with the measured experimental data
2.3 Model development in bioprocesses
Mechanistic models can provide great benefit by attempting to approximate the process conditions to optimize the performance of a fermentation process (Ignova et al., 1998).
The mechanistic models are more predictive and dynamic than the empirical data-driven models, as they can describe the non-linear process efficiently, and hence these models are more suitable for applications in model-based monitoring and control strategy testing (Mears et al., 2017a).
A simple example of a mechanistic process model for a fed-batch fermentation will be based on volume balance concerning feed addition (F). Empirical equations describing the relationship between biomass, substrate and product concentration are developed to represent the kinetics of the process. The generalized equations describing the change in biomass (X), substrate (S) and product (P) concentrations with respect to time of fermentation is presented in equation block (2.8). The typical kinetic model for representing growth rate (μ) in microbial fermentation can be described using Monod's model, which emphasized the significance of dependence of specific growth rate on the growth-limiting substrate concentration as shown in (2.9) (Monod, 1949). The specific growth rate (μ) is a significant process variable since it reflects the physiological characteristic of the organism and is related to the specific product titer. Within the substrate-dependent kinetic model, different substrate limiting and inhibiting models are available. For aerobic microbial cultivations, oxygen concentration is a substantially important variable affecting cell metabolism. Therefore, oxygen concentration could be considered an additional limiting substrate, and suitable kinetic models could be implemented (Kornaros and Lyberatos, 1997).
(
0)
P
XS
dV F
dt
dX F
X X
dt V
dP F
q X P
dt V
dS F
S S m X
dt V Y
=
= −
= −
= − − +
(2.8)
max s
S
K S
=
+ (2.9)
CHAPTER 2