Modeling the Residence Time Distribution of Integrated Continuous Bioprocesses

In response to the biopharmaceutical industry advancing from traditional batch operation to continuous operation, the Food and Drug Administration (FDA) has published a draft for continuous integrated biomanufacturing. This draft outlines the most important rules for establishing continuous integration. One of these rules is a thorough understanding of mass flows in the process. A computer simulation framework is developed for modeling the residence time distribution (RTD) of integrated continuous downstream processes based on a unit‐by‐unit modeling approach in which unit operations are simulated one‐by‐one across the entire processing time, and then combined into an integrated RTD model. The framework allows for easy addition or replacement of new unit operations, as well as quick adjustment of process parameters during evaluation of the RTD model. With this RTD model, the start‐up phase to reach steady state can be accelerated, the effects of process disturbances at any stage of the process can be calculated, and virtual tracking of a section of the inlet material throughout the process is possible. A hypothetical biomanufacturing process for an antibody was chosen for showcasing the RTD modeling approach.     

Biological hydrogen production by dark fermentation: challenges and prospects towards scaled-up production

Among different technologies of hydrogen production, bio-hydrogen production exhibits perhaps the greatest potential to replace fossil fuels. Based on recent research on dark fermentative hydrogen production, this article reviews the following aspects towards scaled-up application of this technology: bioreactor development and parameter optimization, process modeling and simulation, exploitation of cheaper raw materials and combining dark-fermentation with photo-fermentation. Bioreactors are necessary for dark-fermentation hydrogen production, so the design of reactor type and optimization of parameters are essential. Process modeling and simulation can help engineers design and optimize large-scale systems and operations. Use of cheaper raw materials will surely accelerate the pace of scaled-up production of biological hydrogen. And finally, combining dark-fermentation with photo-fermentation holds considerable promise, and has successfully achieved maximum overall hydrogen yield from a single substrate. Future development of bio-hydrogen production will also be discussed.     

Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.     

Chemometrics applications in biotech processes: a review

Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations amongst them. A typical biotech process starts with the vial of the cell bank, ends with the final product, and has anywhere from 15 to 30 such unit operations in series. The aforementioned parameters can impact process performance and product quality and also interact amongst each other. Chemometrics presents one effective approach to gather process understanding from such complex data sets. The increasing use of chemometrics is fuelled by the gradual acceptance of quality by design and process analytical technology amongst the regulators and the biotech industry, which require enhanced process and product understanding. In this article, we review the topic of chemometrics applications in biotech processes with a special focus on recent major developments. Case studies have been used to highlight some of the significant applications.     

Performance modeling and simulation of biochemical process sequences with interacting unit operations

Many biochemical processes consist of a sequence of operations for which optimal operating conditions (setpoints) have to be determined. If such optimization is performed for each operation separately with respect to objectives defined for each operation individually, overall process performance is likely to be suboptimal. Interactions between unit operations have to be considered, and a unique objective has to be defined for the whole process. This paper shows how a suitable optimization problem can be formulated and solved to obtain the best overall set of operating conditions for a process. A typical enzyme production process has been chosen as an example. In order to arrive at a demonstrative model for the entire sequence of unit operations, it is shown how interaction effects may be accommodated in the models. Optimal operating conditions are then determined subject to a global process objective and are shown to be different from those resulting from optimization of each separate operation. As this strategy may result in an economic benefit, it merits further research into interaction modeling and performance optimization.     

Very large scale monoclonal antibody purification: the case for conventional unit operations

Technology development initiatives targeted for monoclonal antibody purification may be motivated by manufacturing limitations and are often aimed at solving current and future process bottlenecks. A subject under debate in many biotechnology companies is whether conventional unit operations such as chromatography will eventually become limiting for the production of recombinant protein therapeutics. An evaluation of the potential limitations of process chromatography and filtration using today's commercially available resins and membranes was conducted for a conceptual process scaled to produce 10 tons of monoclonal antibody per year from a single manufacturing plant, a scale representing one of the world's largest single-plant capacities for cGMP protein production. The process employs a simple, efficient purification train using only two chromatographic and two ultrafiltration steps, modeled after a platform antibody purification train that has generated 10 kg batches in clinical production. Based on analyses of cost of goods and the production capacity of this very large scale purification process, it is unlikely that non-conventional downstream unit operations would be needed to replace conventional chromatographic and filtration separation steps, at least for recombinant antibodies.     

Scale‐down of the inactivated polio vaccine production process

The anticipated increase in the demand for inactivated polio vaccines resulting from the success in the polio eradication program requires an increase in production capacity and cost price reduction of the current inactivated polio vaccine production processes. Improvement of existing production processes is necessary as the initial process development has been done decades ago. An up‐to‐date lab‐scale version encompassing the legacy inactivated polio vaccine production process was set‐up. This lab‐scale version should be representative of the large scale, meaning a scale‐down model, to allow experiments for process optimization that can be readily applied. Initially the separate unit operations were scaled‐down at setpoint. Subsequently, the unit operations were applied successively in a comparative manner to large‐scale manufacturing. This allows the assessment of the effects of changes in one unit operation to the consecutive units at small‐scale. Challenges in translating large‐scale operations to lab‐scale are discussed, and the concessions that needed to be made are described. The current scale‐down model for cell and virus culture (2.3‐L) presents a feasible model with its production scale counterpart (750‐L) when operated at setpoint. Also, the current scale‐down models for the DSP unit operations clarification, concentration, size exclusion chromatography, ion exchange chromatography, and inactivation are in agreement with the manufacturing scale. The small‐scale units can be used separately, as well as sequentially, to study variations and critical product quality attributes in the production process. Finally, it is shown that the scale‐down unit operations can be used consecutively to prepare trivalent vaccine at lab‐scale with comparable characteristics to the product produced at manufacturing scale. Biotechnol. Bioeng. 2013; 110: 1354–1365. © 2012 Wiley Periodicals, Inc.     

Prediction of the pilot-scale recovery of a recombinant yeast enzyme using integrated models

This article describes the rapid prediction of recovery process performance for a new recombinant enzyme product on the basis of a broad portfolio of computer models and highly targeted experimentation. A process model for the recombinant system was generated by linking unit operation models in an integrated fashion, with required parameter estimation and physical property determination accomplished using data from scale-down studies. This enabled the generic modeling framework established for processing of a natural enzyme from bakers' yeast to be applied. An experimental study of the same operations at the pilot scale showed that the process model gave a conservative prediction of recombinant enzyme recovery. The model successfully captured interactions leading to a low overall product yield and indicated the need for further study of precipitate breakage in the feed zone of a disc stack centrifuge in order to improve performance. The utility of scale-down units as an aid to fast model generation and the advantage of integrating computer modeling and scale-down studies to accelerate bioprocess development are highlighted.     

Extreme scale-down of expanded bed adsorption: Purification of an antibody fragment directly from recombinant E. coli culture

Scale-down is a methodology that combines the use of very small volumes of process fluid in dedicated devices to predict accurately the behaviour of process-scale biotechnological unit operations and for the production of comparable material for use in further devices which, taken together, facilitate the mimic of a complete full-scale process. This article provides the rationale behind the development of a small-scale mimic and demonstrates the use of a highly scaled-down expanded bed to predict hydrodynamic, kinetic, and adsorptive performance using less than 5-mL sample volumes. Data acquired on a specially developed 1.9 mm ID column was compared with that obtained in a standard 25 mm ID column. A homogenised E. coli system expressing an antibody fragment (F(ab)) adsorbed onto an rProtein A matrix was used to characterise the full adsorptive performance. Breakthrough curve studies using BSA in buffer were used to characterise binding kinetics. Performance at the two scales was comparable both in terms of expansion, axial dispersion, binding isotherms, and elution behaviour of the antibody fragment. The eluted F(ab) material was further purified by ion exchange chromatography to demonstrate the similarity between the profile of the product material obtained at both scales. The high level of scale-down (approximately 200-fold) provides for rapid process evaluation early in development, where material is at a premium and where a fast appreciation of the likely merits of one process strategy will lead to greater confidence in process selection and more robust flowsheets.     

Chemometrics applications in biotech processes: assessing process comparability

A typical biotech process starts with the vial of the cell bank, ends with the final product and has anywhere from 15 to 30 unit operations in series. The total number of process variables (input and output parameters) and other variables (raw materials) can add up to several hundred variables. As the manufacturing process is widely accepted to have significant impact on the quality of the product, the regulatory agencies require an assessment of process comparability across different phases of manufacturing (Phase I vs. Phase II vs. Phase III vs. Commercial) as well as other key activities during product commercialization (process scale-up, technology transfer, and process improvement). However, assessing comparability for a process with such a large number of variables is nontrivial and often companies resort to qualitative comparisons. In this article, we present a quantitative approach for assessing process comparability via use of chemometrics. To our knowledge this is the first time that such an approach has been published for biotech processing. The approach has been applied to an industrial case study involving evaluation of two processes that are being used for commercial manufacturing of a major biosimilar product. It has been demonstrated that the proposed approach is able to successfully identify the unit operations in the two processes that are operating differently. We expect this approach, which can also be applied toward assessing product comparability, to be of great use to both the regulators and the industry which otherwise struggle to assess comparability.     

Use of glucose consumption rate (GCR) as a tool to monitor and control animal cell production processes in packed-bed bioreactors

For animal cell cultures growing in packed-bed bioreactors where cell number cannot be determined directly, there is a clear need to use indirect methods that are not based on cell counts in order to monitor and control the process. One option is to use the glucose consumption rate (GCR) of the culture as an indirect measure to monitor the process in bioreactors. This study was done on a packed-bed bioreactor process using recombinant CHO cells cultured on Fibra-Cel disk carriers in perfusion mode at high cell densities. A key step in the process is the switch of the process from the cell growth phase to the production phase triggered by a reduction of the temperature. In this system, we have used a GCR value of 300 g of glucose per kilogram of disks per day as a criterion for the switch. This paper will present results obtained in routine operations for the monitoring and control of an industrial process at pilot-scale. The process operated with this GCR-based strategy yielded consistent, reproducible process performance across numerous bioreactor runs performed on multiple production sites.     

Toward biotherapeutic product real-time quality monitoring

Biotherapeutics, such as those derived from monoclonal antibodies (mAbs), are industrially produced in controlled multiunit operation bioprocesses. Each unit operation contributes to the final characteristics of the bioproduct. The complexity of the bioprocesses, the cellular machinery, and the bioproduct molecules, typically leads to inherent heterogeneity and variability of the final critical quality attributes (CQAs). In order to improve process control and increase product quality assurance, online and real-time monitoring of product CQAs is most relevant. In this review, the recent advances in CQAs monitoring of biotherapeutic drugs, with emphasis on mAbs, and throughout, the different bioprocess unit operations are reviewed. Recent analytical techniques used for assessment of product-related CQAs of mAbs are considered in light of the analytical speed and ability to measure different CQAs. Furthermore, the state of art modeling approaches for CQA estimation in real-time are presented as a viable alternative for real-time bioproduct CQA monitoring under the process analytical technology and quality-by-design frameworks in the biopharmaceutical industry, which have recently been demonstrated.     

New developments in solid-state fermentation: II. Rational approaches to the design, operation and scale-up of bioreactors

Over the last decade there has been a significant improvement in understanding how to design, operate and scale-up solid-state fermentation bioreactors. The key to these advances has been the application of mathematical modeling techniques to describe the biological and transport phenomena within the system. This review focuses on the advances in understanding that have come from this modeling work, and the insights it has given us into bioreactor design, operation and scale-up. It also highlights two promising bioreactor designs that have emerged over the last decade or so. For processes in which the substrate bed must remain static throughout the fermentation, the most promising design is the Zymotis design of ORSTOM at Montpellier, France, which involves closely spaced internal heat transfer plates within a packed-bed bioreactor. For those processes in which mixing can be tolerated, the stirred bioreactor developed at INRA, in Dijon, France, has been successfully demonstrated at scales of 1–25 t of substrate. Theoretical work suggests that mathematical models will be useful tools in the scale-up process, however, there are no reports that they have been used in the development of any current large-scale process. Rather, the models have been validated against data obtained from laboratory-scale bioreactors. There is an urgent need to test the accuracy and robustness of the models by applying them within real process development.     

Process development in the QbD paradigm: Role of process integration in process optimization for production of biotherapeutics

Biotherapeutics have become the focus of the pharmaceutical industry due to their proven effectiveness in managing complex diseases. Downstream processes of these molecules consist of several orthogonal, high resolution unit operations designed so as to be able to separate variants having very similar physicochemical properties. Typical process development involves optimization of the individual unit operations based on Quality by Design principles in order to define the design space within which the process can deliver product that meets the predefined specifications. However, limited efforts are dedicated to understanding the interactions between the unit operations. This paper aims to showcase the importance of understanding these interactions and thereby arrive at operating conditions that are optimal for the overall process. It is demonstrated that these are not necessarily same as those obtained from optimization of the individual unit operations. Purification of Granulocyte Colony Stimulating Factor (G‐CSF), a biotherapeutic expressed in E. coli., has been used as a case study. It is evident that the suggested approach results in not only higher yield (91.5 vs. 86.4) but also improved product quality (% RP‐HPLC purity of 98.3 vs. 97.5) and process robustness. We think that this paper is very relevant to the present times when the biotech industry is in the midst of implementing Quality by Design towards process development. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:355–362, 2016     

Stem cell culture engineering - process scale up and beyond

Advances in stem cell research and recent work on clinical trials employing stem cells have heightened the prospect of stem cell applications in regenerative medicine. The eventual clinical application of stem cells will require transforming cell production from laboratory practices to robust processes. Most stem cell applications will require extensive ex vivo handling of cells, from isolation, cultivation, and directed differentiation to product cell separation, cell derivation, and final formulation. Some applications require large quantities of cells in each defined batch for clinical use in multiple patients; others may be for autologous use and require only small-scale operations. All share a common requirement: the production must be robust and generate cell products of consistent quality. Unlike the established manufacturing process of recombinant protein biologics, stem cell applications will likely see greater variability in their cell source and more fluctuations in product quality. Nevertheless, in devising stem cell-based bioprocesses, much insight could be gained from the manufacturing of biological materials, including recombinant proteins and anti-viral vaccines. The key to process robustness is thus not only the control of traditional process chemical and physical variables, but also the sustenance of cells in the desired potency or differentiation state through controlling non-traditional variables, such as signaling pathway modulators.     

Intensified Downstream Processing of Monoclonal Antibodies Using Membrane Technology

The need to intensify downstream processing of monoclonal antibodies to complement the advances in upstream productivity has led to increased attention toward implementing membrane technologies. With the industry moving toward continuous operations and single use processes, membrane technologies show promise in fulfilling the industry needs due to their operational flexibility and ease of implementation. Recently, the applicability of membrane-based unit operations in integrating the downstream process has been explored. In this article, the major developments in the application of membrane-based technologies in the bioprocessing of monoclonal antibodies are reviewed. The recent progress toward developing intensified end-to-end bioprocesses and the critical role membrane technology will play in achieving this goal are focused upon.     

Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties.     

Mechanistic modeling,simulation, and optimization of mixed-mode chromatography for an antibody polishing step

Mixed-mode chromatography combines features of ion-exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow-through operations on traditional unmixed resins or as a pH-controlled bind-and-elute step, the use of both interaction modes promises a better removal of product-specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer-optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion-exchange chromatography. In a case study, a model for a pH-controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed-mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.     

Literature mining, ontologies and information visualization for drug repurposing

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.