Towards continuous biomanufacturing a computational approach for the intensification of monoclonal antibody production

Current industrial trends encourage the development of sustainable, environmentally friendly processes with reduced energy and raw material consumption. Meanwhile, the increasing market demand as well as the tight regulations in product quality, necessitate efficient operating procedures that guarantee products of high purity. In this direction, process intensification via continuous operation paves the way for the development of novel, eco-friendly processes, characterized by higher productivity compared to batch (Nicoud, 2014). The shift towards continuous operation could advance the market of high value biologics, such as monoclonal antibodies (mAbs), as it would lead to shorter production times, decreased costs, as well as significantly less energy consumption (Konstantinov and Cooney, 2015, Xenopoulos, 2015). In particular, mAb production comprises two main steps: the culturing of the cells (upstream) and the purification of the targeted product (downstream). Both processes are highly complex and their performance depends on various parameters. In particular, the efficiency of the upstream depends highly on cell growth and the longevity of the culture, while product quality can be jeopardized in case the culture is not terminated timely. Similarly, downstream processing, whose main step is the chromatographic separation, relies highly on the setup configuration, as well as on the composition of the upstream mixture. Therefore, it is necessary to understand and optimize both processes prior to their integration. In this direction, the design of intelligent computational tools becomes eminent. Such tools can form a solid basis for the: (i) execution of cost-free comparisons of various operating strategies, (ii) design of optimal operation profiles and (iii) development of advanced, intelligent control systems that can maintain the process under optimal operation, rejecting disturbances. In this context, this work focuses on the development of advanced computational tools for the improvement of the performance of: (a) chromatographic separation processes and (b) cell culture systems, following the systematic PAROC framework and software platform (Pistikopoulos et al., 2015). In particular we develop model-based controllers for single- and multi-column chromatographic setups based on the operating principles of an industrially relevant separation process. The presented strategies are immunized against variations in the feed stream and can successfully compensate for time delays caused due to the column residence time. Issues regarding the points of integration in multi-column systems are also discussed. Moreover, we design and test in silico model-based control strategies for a cell culture system, aiming to increase the culture productivity and drive the system towards continuous operation. Challenges and potential solutions for the seamless integration of the examined bioprocess are also investigated at the end of this thesis.Open Acces

Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

Multiple Intelligent Agents for Manufacturing Intensification (MIAMI): A Platform for Ranking Clonal Variation in Upstream Bioprocess Development

Antibody-based therapeutics are an important class of biotherapeutics for therapeutic applications. With the rising demand and increase in biotherapeutic products on the market, there lies the need for rapid bioprocess development. Clone selection is a critical and time-consuming step in upstream bioprocess development and it is a critical step to execute accurately. A Multiple Intelligent Agents for Manufacturing Intensification (MIAMI) is proposed to process raw data and evaluate clones of three commonly used host cells, Chinese Hamster Ovary (CHO), Escherichia coli (E. coli), and Pichia pastoris (P. pastoris). A search conducted for an IP-free protein sequence yielded the Anti-hepatitis B antibody. The whole antibody sequence was truncated to create a Fab’ fragment. Gene designs for three commonly used host cells, CHO, E. coli, and P. pastoris were created using the IP-free Anti-hepatitis B Fab’ fragment. The development of MIAMI identifies and addresses the necessity of creating a sophisticated code that evaluates clonal ranking based upon data sets. These data sets were collected using the IP-free Anti-hepatitis B gene designs and an existing AV4 gene design. The AV4 gene design was transformed into P. pastoris and repurposed as an inverse methanol detector. In 50mL shake flask culture, green fluorescence protein was detected when cultivating the AV4 strain using glycerol and sorbitol carbon source, while protein transcription was inhibited when using a methanol carbon source. Data collected from cultivating the AV4 strain in 800µL microtiter plates was used to develop the MIAMI software. The Anti-hepatitis B gene designs were established and characterized in 50mL shake flasks for E. coli and P. pastoris and a preliminary attempt to establish the gene design CHO. Using the data collected from automated cultivation of 8 different clones of Anti-hepatitis B E. coli and P. pastoris strains in 800µL microtiter plates scale using the TECAN, a manual ranking of the clones was performed. Scaling the cultivation up to 200mL DASGIPs microbioreactors, clonal ranking for both strains remained unchanged. A code was written in python for the processing of raw data. This was demonstrated on the collected HPLC data sets for the Anti-hepatitis B E. coli and P. pastoris strains, and the flow cytometer data set for the AV4 strain. Multiple agents were created for the development of MIAMI. An assay agent was created for analysing raw HPLC and flow cytometry data to identify and remove unwanted clonal variations. A scanning algorithm calculated the mean and standard deviation of the yields at three consecutive time points to identify a period of stable yield. A ranking algorithm takes into consideration the maximum stable yield achieved and the variability in the data point, giving these two factors a 75% and 25% weighting, MIAMI identifies the best performing clone. The MIAMI ranking came to the same conclusion as manual human ranking. The effectiveness of MIAMI was validated on the Anti-hepatitis B E. coli strain, being able to correctly identify a top performing clone with an optimal induction time, with a conservative estimate of 87% decrease in time taken when compared to manual evaluation. The MIAMI software significantly improved the timeliness of bioprocess development by accurately screening and evaluating clones. This frees up the time of the user while removing potential sources of human error. With the incorporation of further bioprocesses, MIAMI will become a powerful and effective tool for bioprocess development

Digitalisation of Development and Supply Networks: Sequential and Platform-Driven Innovations

We draw from an eight-year dataset of 98 organisational entities involved in pre-competitive innovation networks across the UK pharmaceutical sector. These data map into three networks that are representative of: (i) a product development-led sequential pathway that begins with digitalised product development, followed by digitalisation of supply networks, (ii) a supply network-led sequential pathway that starts with digitalised supply networks, followed by digitalisation of product development, and (iii) a parallel — platform-driven — pathway that enables simultaneous digitalisation of development, production, and supply networks. We draw upon extant literature to assess these network structures along three dimensions — strategic intent, the integrative roles of nodes with high centrality, and innovation performance. We conduct within-case and cross-case analyses to postulate 10 research propositions that compare and contrast modalities for sequential and platform-based digitalisation involving collaborative innovation networks. With sequential development, our propositions are congruent with conventional pathways for mitigating innovation risks through modular moves. On the other hand, we posit that platform-based design rules, rather than modular moves, mitigate the risks for parallel development pathways, and lead to novel development and delivery mechanisms

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market

Scalability of single-use biopharmaceutical manufacturing processes using process analytical technology (PAT) tools

Monoclonal antibodies (mAbs) are key molecules in biopharmaceutical manufacturing with important therapeutic use such as anticancer drugs. Mammalian cells serve as production hosts for mAbs. However, mammalian cell culture processes are complex and development timelines for new processes are long. To overcome these challenges the industry is moving towards high-throughput, single-use bioreactors and intensified processes. The scalability in both directions (scale-down and scale-up) is a key step towards fast and economic process development. Moreover, novel Process Analytical Technology (PAT) tools aim at improving process understanding and establishing process control and automation resulting in high and consistent product quality. In the first part of this PhD thesis a method to transfer an existing Chinese Hamster Ovary (CHO) cell culture fed-batch process platform into a semi-perfusion process with threefold higher cumulative product titers was developed. Design of Experiment (DoE) as a powerful PAT tool to screen medium and feed compositions speeded up significantly the semi-perfusion process development. The process transfer to a small scale, single-use bioreactor enabled process control for important parameters such as pH and dissolved oxygen (DO) while keeping the experimental costs low. However, important process attributes (e.g. Viable Cell Concentrations (VCC)) were measured offline limiting the automation possibilities. The second part of this thesis demonstrated how the implementation of an advanced inline capacitance sensor can support online monitoring of important biomass related changes in cell culture. The sensor implementation was proven to be scale-independent in single-use bioreactors (50 L up to 2000 L). Additionally, the transferability of the method to different CHO fed-batch processes was demonstrated. The Wet Cell Weight (WCW) and Viable Cell Volume (VCV) were predicted for the complete cultivation duration within an acceptance criterion based on the offline reference method. The VCC, however, correlated with the permittivity signal only until the end of the exponential growth phase due to the single-frequency measurement dependency on cell diameter changes. The third part of this PhD thesis successfully tested the inline capacitance sensor in frequency scanning mode to predict VCCs over the complete culture time by establishing a robust Multivariate Data Analysis (MVDA) model. A small scale bioreactor system served as method development tool. Therefore, a fast and economic development of a robust MVDA model was demonstrated, highlighting the benefits of scale-down models in biopharmaceutical manufacturing

Designing appliances for mobile commerce and retailtainment

In the emerging world of the new consumer and the `anytime, anywhere' mobile commerce, appliances are located at the collision point of the retailer and consumer agendas. The consequence of this is twofold: on the one hand appliances that were previously considered plain and utilitarian become entertainment devices and on the other, for the effective design of consumer appliances it becomes paramount to employ multidisciplinary expertise. In this paper, we discuss consumer perceptions of a retailtainment commerce system developed in collaboration between interactivity designers, information systems engineers, hardware and application developers, marketing strategists, product development teams, social scientists and retail professionals. We discuss the approached employed for the design of the consumer experience and its implications for appliance design

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

International audienceSoil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture

The innovation system vs. cluster process: common contributive elements towards regional development

Recent approaches to the study of innovations enhance some similar aspects of the innovation process in knowledge-based economies: (i) the systemic and interrelated nature of innovation and (ii) its geographic and inter-economic activities density of networking. One perspective is linked to the innovation systems approach at the national, regional and local level. What we know so far is that the most specialized forms of knowledge are becoming a short lived resource, in face of the (increasingly) fast changes that are occurring in the global economy; it’s the ability to learn permanently and to adapt to this fast changing scenario that determines the innovative performance of firms, regions and countries. Another approach is to be found in the research on cluster development, where proximity and interrelated technical/technological linkage are the main features to take under consideration. Although these two approaches operate at slightly different spatial scale of analysis, they both allow the identification of a set of key factors that contribute to understand the way in which institutions and actors, considering the innovation system or the cluster process, participate in the innovation atmosphere and in the economic growth. Nevertheless, both approaches show the same limitation: they tend to focalise into the descriptive and analytical level, disregarding the explanatory level. Local and regional authorities are, mainly, interested in the process of cluster intensification in the local and regional economies context. This feature stress out one other controversy level: are the “hard” location factors (the concrete tangible location factors) more important than the “soft” location factors (qualitative, intangible factors) or vice-versa? This paper aims to explore the current knowledge about this process and to open some fields of future research.

Oscillation dynamics of embolic microspheres in flows with red blood cell suspensions

Dynamic nature of particle motion in blood flow is an important determinant of embolization based cancer therapy. Yet, the manner in which the presence of high volume fraction of red blood cells influences the particle dynamics remains unknown. Here, by investigating the motions of embolic microspheres in pressure-driven flows of red blood cell suspensions through capillaries, we illustrate unique oscillatory trends in particle trajectories, which are not observable in Newtonian fluid flows. Our investigation reveals that such oscillatory behavior essentially manifests when three simultaneous conditions, namely, the Reynolds number beyond a threshold limit, degree of confinement beyond a critical limit, and high hematocrit level, are fulfilled simultaneously. Given that these conditions are extremely relevant to fluid dynamics of blood or polymer flow, the observations reported here bear significant implications on embolization based cancer treatment as well as for complex multiphase fluidics involving particle