an industrial E. coli case study

The authors thank Chi Chung (Tim) Choi and Paul Rice for their collaboration in batch production and performing off-line sample analysis and Carl Hémond for providing the RP-HPLC assay and results. The authors thank Paul Rice also for critical reading of the manuscript. © Copyright 2017 Elsevier B.V., All rights reserved.Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.publishersversionpublishe

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization

Predictive macroscopic modeling of Chinese hamster ovary cells in fed-batch processes

This thesis focuses on developing a systematic modeling method that can capture the essential features for prediction of cell metabolism, growth and monoclonal antibody (mAb) production in Chinese Hamster Ovary (CHO) cells. In a first step all specific consumption rates are calculated based on time courses of extracellular metabolites, viable cell density and mAb. Then the metabolic phases within which the metabolic pseudo-steady state approximation is verified are identified. In a third step, all metabolic rates are expressed as a function of the specific growth rate within each metabolic phase. We have applied this method to a set of small bioreactor data and have shown that the model obtained can predict specific conversion rates both small and also at large scale. In the second part of this thesis, a kinetic model of the cell growth has been developed. Together with previously described methodology, this kinetic model results in a predictive metabolic model for each experimental cell growth data are not required. The kinetic model is based on Monod kinetics with a few modifications such as a varying the maximum specific growth rate as a function of the integral viable cell density. The full kinetic model can be used off line to design optimal feeding profiles. The results of this thesis demonstrate that rich knowledge can be derived from macroscopic data that can then be used to predict new production conditions in an industrial environment at small and large scale.Der Schwerpunkt dieser Dissertation liegt auf der systematischen Entwicklung Modellen für die Vorhersage des zellulären Stoffwechsels, des Wachstums und der Produktion von monoklonalen Antikörpern (mAb) in Kulturen von Chinesischen Hamster-Ovarzellen (CHO). Zunächst wurden mit segmentierter linearer Regression metabolischer Phasen identifiziert. Diese Identifizierung beruht auf der Annahme eines pseudo-stationären Zustands und somit, dass in einer Phase alle Raten linear miteinander korreliert waren. Die spezifischen Raten wurden aus den Zeitverläufen der Konzentrationen der Metabolite und des mAb sowie der Lebendzellzahl bestimmt. Durch die Korrelation konnten alle Raten über die Wachstumsrate im 2 L und im 2000 L Maßstab berechnet werden. Danach wurde ein kinetisches Modell des Wachstums der Zellen etabliert, was die Vorhersage aller Raten auch in fed-batch Kulturen erlaubt. Die Kinetik basiert auf der Monod-Kinetik modifiziert mit einer variablen maximalen spezifischen Wachstumsrate. Das kinetische Modell erlaubt eine rechnerische Optimierung der Substratzuführung für eine maximale Produktion. Damit wurde gezeigt, dass aus makroskopischen Daten, d.h. ohne intrazelluläre Messungen, wesentliche Informationen erhalten werden können, mit denen neue Experimente in einem industriellen Umfeld vorhergesagt werden können. Diese innovative und systematische Vorgangsweise eröffnet neue Perspektiven für die Reduzierung von Kosten und für eine Beschleunigung der Prozessentwicklung

Model based process design for a monoclonal antibody-producing cell line :optimisation using hybrid modelling and an agent based system

PhD ThesisThe biopharmaceutical industry has seen rapid growth over the last 10 years in the area of therapeutic medicines. These include products such as monoclonal antibodies (mAbs) produced using mammalian cell lines such as Chinese Hamster Ovary (CHO). In order to comply with the regulatory authority (FDA) Quality by Design (QbD) and Process Analytical Technology (PAT) requirements, modelling can be used in the development and operation of the bioprocess. A model can assist in both the design, scale up and control of these complex, non-linear processes. A predictive model can be used to identify optimal operating conditions, which is vital for a contract manufacturer. Traditionally industry has approached modelling through the one-unit-at-a-time method, which can fail to capture unit interactions. The research reported in this work addresses this issue by using a whole system approach, which can also capture the interactions between units. Predictive models for each of the process units are combined within an overall framework allowing for the integration of the models, predicting how changes in the output of one unit influence the performance of subsequent units. These predictions can serve as the basis for the modifications to the standard operating procedures to achieve the required performance of the whole process. In this thesis three distinct studies are presented; the first utilises a hybridoma data set and presents a model to predict and characterise the various critical quality attributes (CQAs), such as final product glycosylation profile, and critical process parameters (CPPs) including titre and viable cell count. The second data set concerns the purification of lactoferrin using ion-exchange chromatography as a model system for developing downstream iii processing models. The output of this data set varied widely, and has led to the development of a novel peak isolation methodology, which can ultimately be used to characterise the elution. The final data set contains various CQAs and CPPs for multiple units within one process. This data set has been employed within a proof of concept study to show how an agent based framework can be developed to allow for overall process optimisation. The results showed that it is possible to link process units using a common CPP or CQA. This work shows that using a agent based system of two layers of modelling i.e. individual process unit models connected with a higher level agent model that links via a common measurement allows for the influences between units to be considered. The model presented in this work considers the use of titre, HCP, measure of heterogeneity, and molecular weight as the common measurement. It is shown that it is possible to link the units in this way with the goal of predicting and controlling the glycosylation profile of the Bulk Drug Substance (BDS)