High-throughput Bioreactor Research Articles

A quality-by-design approach to improve process understanding and optimise the production and quality of CAR-T cells in automated stirred-tank bioreactors.

Ex vivo genetically-modified cellular immunotherapies, such as chimeric antigen receptor T cell (CAR-T) therapies, have generated significant clinical and commercial outcomes due to their unparalleled response rates against relapsed and refractory blood cancers. However, the development and scalable manufacture of these novel therapies remains challenging and further process understanding and optimisation is required to improve product quality and yield. In this study, we employ a quality-by-design (QbD) approach to systematically investigate the impact of critical process parameters (CPPs) during the expansion step on the critical quality attributes (CQAs) of CAR-T cells. Utilising the design of experiments (DOE) methodology, we investigated the impact of multiple CPPs, such as number of activations, culture seeding density, seed train time, and IL-2 concentration, on CAR-T CQAs including, cell yield, viability, metabolism, immunophenotype, T cell differentiation, exhaustion and CAR expression. Initial studies undertaken in G-Rex® 24 multi-well plates demonstrated that the combination of a single activation step and a shorter, 3-day, seed train resulted in significant CAR-T yield and quality improvements, specifically a 3-fold increase in cell yield, a 30% reduction in exhaustion marker expression and more efficient metabolism when compared to a process involving 2 activation steps and a 7-day seed train. Similar findings were observed when the CPPs identified in the G-Rex® multi-well plates studies were translated to a larger-scale automated, controlled stirred-tank bioreactor (Ambr® 250 High Throughput) process. The single activation step and reduced seed train time resulted in a similar, significant improvement in CAR-T CQAs including cell yield, quality and metabolism in the Ambr® 250 High Throughput bioreactor, thereby validating the findings of the small-scale studies and resulting in significant process understanding and improvements. This study provides a methodology for the systematic investigation of CAR-T CPPs and the findings demonstrate the scope and impact of enhanced process understanding for improved CAR-T production.

Enhancing early-stage cell culture process development efficiency using an integrated approach of high-throughput miniaturized bioreactors and definitive screening design

Rapidly expanding pipelines in biopharmaceutical industry and increasingly stringent requirements from regulatory agencies call for a more efficient and rational study design at early-stage cell culture process development. Recent developments in high-throughput bioreactor system and design of experiments (DoE) enable cell culture process development sector to meet such demand by integrating both technological advances. In this study, we compared varied forms of fractional factorial designs (FFDs) and definitive screening design (DSD) covering key process parameters such as temperature shift target, temperature shift timing, feed strategy, pH, dissolved oxygen, harvest time and additives in Ambr® 15 bioreactors to better understand the initial cell culture process and thus optimize it. Results showed that DSD was more effective and efficient in detecting both main and quadratic effects comparing to FFDs. Comparison of varied available model development strategies revealed that two-stage forward regression combined with corrected Akaike's information criterion (AICc) and least absolute shrinkage and selection operator (LASSO) regression combined with self-validated ensembled models (SVEM) offered superior predictive capability. Moreover, key DoE regularities was evaluated for their validity. In conclusion, DSD is a suitable experimental design for the early-stage cell culture process development if quadratic effects might exist. Statistical models derived based on DSD-based dataset could be applied for predictive optimization of antibody producing bioprocess while regularities such as effect heredity and effect hierarchy might be relaxed considering higher-order interaction effects are more likely to existed for a cell-based biological system.

Determining appropriate input excitation for model identification of a continuous bio-process

This manuscript addresses the problem of determining input excitation for data driven model identification appropriate for cell culture bio-processes in general, and for an industrial bioreactor used for the production of monoclonal antibodies, in particular. The design space is set up to give us the operating parameters for the key objective of demonstrating the feasibility of using far more perturbations than typically done in bio process identification, although significantly less than other applications, to yield data rich enough for the purpose of data driven modelling (and subsequently, control). A proprietary mechanistic model developed by Sartorius for their Cellca cell line is first introduced to serve as a test bed, based on AMBR 250® (Sartorius registered trademark for integrated high throughput bioreactor systems). Subsequently, this test bed is used to address the question of determining the frequency of input perturbation sufficient to identify a data driven dynamic model. To this end, the test bed is used to generate data at various frequencies and a linear time invariant model identified. The predictive capability of the identified model is used to ascertain the frequency of changes in data generation such that the changes are acceptable from a biological standpoint, and yet generate sufficiently rich data. In particular, a frequency of perturbations at once every three days is found to balance these tradeoffs for the monoclonal antibody process under consideration. The results from the manuscript are meaningful both from a specific results standpoint (as illustrated by subsequent adoption by Sartorius), but also by providing a mechanism to ascertain such information for other bio-processes.

PID controls: the forgotten bioprocess parameters

The ambr250 high-throughput bioreactor platform was adopted to provide a highly-controlled environment for a project investigating genome instability in Chinese hamster ovary (CHO) cells, where genome instability leads to lower protein productivity. Development of the baseline (control) and stressed process conditions highlighted the need to control critical process parameters, including the proportional, integral, and derivative (PID) control loops. Process parameters that are often considered scale-independent, include dissolved oxygen (DO) and pH; however, these parameters were observed to be sensitive to PID settings. For many bioreactors, control loops are cascaded such that the manipulated variables are adjusted concurrently. Conversely, for the ambr250 bioreactor system, the control levels are segmented and implemented sequentially. Consequently, each control level must be tuned independently, as the PID settings are independent by control level. For the CHO cell studies, it was observed that initial PID settings did not resulted in a robust process, which was observed as elevated lactate levels; which was caused by the pH being above the setpoint most of the experiment. After several PID tuning iterations, new PID settings were found that could respond appropriately to routine feed and antifoam additions. Furthermore, these new PID settings resulted in more robust cell growth and increased protein productivity. This work highlights the need to describe PID gains and manipulated variable ranges, as profoundly different outcomes can result from the same feeding protocol. Additionally, improved process models are needed to allow process simulations and tuning. Thus, these tuning experiments support the idea that PID settings should be fully described in bioreactor publications to allow for better reproducibility of results.

The Flow Limiting Operator: A New Approach to Environmental Control in Flow Bioreactors.

Bioreactors have become a critical step for the testing of new biomaterials and pharmaceuticals. They need to be controllable, ideally high-throughput, and produce a biologically relevant environment. For example, in the brain, it is essential to recreate multiple flow–pressure profiles daily and mimic brain fluid movement for a bioreactor to be more physiologic. In this study, we demonstrate a scalable system that regulates flow rate, pressure, and pulsation amplitude. We also show that with new microcontroller technology, up to 15 chambers running in parallel is theoretically possible. Our system, the Flow Limiting Operator (FLO), achieves these goals by multiplexing a series of valves and pumps to control pressure and volumetric flow rate instead of relying on head gas pressure. With the ability to control multiple parameters and its ease of use, both scientists and clinicians can use FLO to study the effects of pulsation amplitude of the fluid flow, flow rate, and pressure on intercellular interactions for both biomaterials and pharmaceuticals.

A Fully Automated High-Throughput Bioreactor System for Lung Regeneration.

This work presents methods for ex vivo lung recellularization and biomimetic culture in a high-throughput and consistent manner. These methods allow for the testing of multiple variables, all of which are simultaneously controlled and monitored on a single fully automated pump system, and subsequent assessment of both epithelial and endothelial repair and tissue regeneration. This system provides a controlled environment for tissue repair, wherein key variables can be modified, monitored, reproduced, and optimized to advance the goal of ex vivo tissue regeneration based on native organ scaffolds.

Distributed and Lumped Parameter Models for the Characterization of High Throughput Bioreactors.

Next generation bioreactors are being developed to generate multiple human cell-based tissue analogs within the same fluidic system, to better recapitulate the complexity and interconnection of human physiology [1, 2]. The effective development of these devices requires a solid understanding of their interconnected fluidics, to predict the transport of nutrients and waste through the constructs and improve the design accordingly. In this work, we focus on a specific model of bioreactor, with multiple input/outputs, aimed at generating osteochondral constructs, i.e., a biphasic construct in which one side is cartilaginous in nature, while the other is osseous. We next develop a general computational approach to model the microfluidics of a multi-chamber, interconnected system that may be applied to human-on-chip devices. This objective requires overcoming several challenges at the level of computational modeling. The main one consists of addressing the multi-physics nature of the problem that combines free flow in channels with hindered flow in porous media. Fluid dynamics is also coupled with advection-diffusion-reaction equations that model the transport of biomolecules throughout the system and their interaction with living tissues and C constructs. Ultimately, we aim at providing a predictive approach useful for the general organ-on-chip community. To this end, we have developed a lumped parameter approach that allows us to analyze the behavior of multi-unit bioreactor systems with modest computational effort, provided that the behavior of a single unit can be fully characterized.

A scalable software framework for data integration in bioprocess development.

Effectiveness in lab workflows-despite progresses made in automation and lab informatics-is often hindered by insufficient integration of devices and data. The iLAB software framework, a middleware connecting and integrating devices and data, provides a plugin architecture that can be adapted to individual lab environments. Integration of devices is preferably based on standardized data and communication protocols. In addition to device integration, process data and result data from different sources (e.g. readers) are converted to a standard format and administered by a powerful database for further processing. In this paper, the use of iLAB in a bioprocess development application is described. Process parameters and measured values of two high-throughput bioreactor systems (96-well plates and 10 mL reactor vessels) are collected in the iLAB database. Data from screening experiments and offline data are visualized, analyzed, and compared. A filter algorithm allows searching for matching parameters in experiments as well as the comparison of correlated datasets, independently from the used bioreactor system. The database model enables the consolidation of data, the transition from data to information, and a solid base for management decisions on enterprise level.

Media optimization for CHO fed-batch processes using a DoE approach in automated high-throughput single use ambr15 bioreactors

Background Chinese Hamster Ovary (CHO) derived cells are the most commonly used cell lines for the production of biopharmaceuticals. We developed a plug and play method to optimize the growth medium for a given CHO production cell line. The experiment consists of a Mixture Design of Experiments (DOE) approach using different basal media to select the optimized formulations for a specific CHO cell line. The package for media optimization consists of different base media together with the ambr15 high throughput bioreactor and integrated MODDE DoE software. Methods and results A suspension adapted CHO DG44 cell line expressing a monoclonal antibody was used for the experiments. Media mixes composed of differing concentrations of nutrients were used in a mixture DOE with 20 different media and 3 center points. All conditions were inoculated in the ambr at the same starting density of 2.0xE5 cells/mL. Dissolved oxygen, pH, and temperature control were maintained throughout the batch process. Each bioreactor was sampled for cell count and viability. Metabolites as well as IgG titers were measured. Peak Viable Cell Density (VCD), Doubling Time (DT), and IgG titer results were

Advances in clone selection using high‐throughput bioreactors

Effective clone selection is a crucial step toward developing a robust mammalian cell culture production platform. Currently, clone selection is done by culturing cells in well plates and picking the highest producers. Ideally, clone selection should be done in a stirred tank bioreactor as this would best replicate the eventual production environment. The actual number of clones selected for future evaluation in bioreactors at bench-scale is limited by the scale-up and operational costs involved. This study describes the application of miniaturized stirred high-throughput bioreactors (35 mL working volume; HTBRs) with noninvasive optical sensors for clone screening and selection. We investigated a method for testing several subclones simultaneously in a stirred environment using our high throughput bioreactors (up to 12 clones per HTBR run) and compared it with a traditional well plate selection approach. Importantly, it was found that selecting clones solely based on results from stationary well plate cultures could result in the chance of missing higher producing clones. Our approach suggests that choosing a clone after analyzing its performance in a stirred bioreactor environment is an improved method for clone selection.

Macroporous Monolithic Polymers: Preparation and Applications

In the last years, macroporous monolithic materials have been introduced as a new and useful generation of polymers used in different fields. These polymers may be prepared in a simple way from a homogenous mixture into a mold and contain large interconnected pores or channels allowing for high flow rates at moderate pressures. Due to their porous characteristics, they could be used in different processes, such as stationary phases for different types of chromatography, high-throughput bioreactors and in microfluidic chip applications. This review reports the contributions of several groups working in the preparation of different macroporous monoliths and their modification by immobilization of specific ligands on the products for specific purposes.

Comparisons of optically monitored small-scale stirred tank vessels to optically controlled disposable bag bioreactors

BackgroundUpstream bioprocesses are extremely complex since living organisms are used to generate active pharmaceutical ingredients (APIs). Cells in culture behave uniquely in response to their environment, thus culture conditions must be precisely defined and controlled in order for productivity and product quality to be reproducible. Thus, development culturing platforms are needed where many experiments can be carried out at once and pertinent scale-up information can be obtained.ResultsHere we have tested a High Throughput Bioreactor (HTBR) as a scale-down model for a lab-scale wave-type bioreactor (CultiBag). Mass transfer was characterized in both systems and scaling based on volumetric oxygen mass transfer coefficient (kLa) was sufficient to give similar DO trends. HTBR and CultiBag cell growth and mAb production were highly comparable in the first experiment where DO and pH were allowed to vary freely. In the second experiment, growth and mAb production rates were lower in the HTBR as compared to the CultiBag, where pH was controlled. The differences in magnitude were not considered significant for biological systems.ConclusionSimilar oxygen delivery rates were achieved in both systems, leading to comparable culture performance (growth and mAb production) across scales and mode of mixing. HTBR model was most fitting when neither system was pH-controlled, providing an information-rich alternative to typically non-monitored mL-scale platforms.

A High-Throughput Bioreactor System for Simulating Physiological Environments

The optimization of in vitro cell culture for tissue engineering, pharmacological, or metabolic studies requires a large number of experiments to be performed under varying conditions. In this paper, we describe a high-throughput bioreactor system that allows the conduction of parallel experiments in a simulated in vivo-like environment. Our bioreactors consist of tissue-, organ-, or system-specific culture chambers and a mixing device controlled by an embedded system that regulates the insertion of gas in the culture medium in order to control pH and pressure. Each culture chamber and mixing device possesses an autonomous control system that is able to ensure an optimal environment for cells. A computer communicates with the embedded system to acquire data and set up experimental variables. With this apparatus, we can perform a high-throughput experiment controlling several bioreactors working in parallel. In this paper, we discuss the architecture and design of the system, and the results of some experiments which simulate physiological and pathological conditions are presented.

Validation of an optical sensor-based high-throughput bioreactor system for mammalian cell culture

Cell culture optimization is a labor-intensive process requiring a large number of experiments to be conducted under varying conditions. Here we describe a high-throughput bioreactor system that allows 12 mini stirred-tank bioreactors to be operated simultaneously. All bioreactors are monitored by low-cost minimally invasive optical sensors for pH and dissolved oxygen. The sensors consist of single-use patches affixed inside the bioreactors and monitored optically from the outside. Experimental results show that different sensing patches with the same composition respond consistently. The discrepancy between different pH sensors is less than 0.1 pH units over most of their responsive range. The discrepancy between different dissolved oxygen sensors is less than 10% over the whole range from 0% to 100% dissolved oxygen. The consistency of the sensing system ensures that only an initial one-time calibration is required for the sensing patches. After that, a calibration code is generated and sensing patches of the same composition can be used directly. This greatly reduces the time and cost required for monitored multi-bioreactor operations. We used SP2/0 myeloma/mouse hybridoma cell cultures to demonstrate reactor performance consistency. Transcriptional profiling, HPLC analysis, viable cell count, and viability inspection show that the presence of sensing patches and the use of optical monitoring have no apparent effect on the metabolism of the cells.

Design of reactive porous polymer supports for high throughput bioreactors: poly(2-vinyl-4,4-dimethylazlactone-co-acrylamide- co-ethylene dimethacrylate) monoliths.

Enzymatic bioreactors with both high flow characteristics and mechanical stability based on macroporous poly(2-vinyl-4, 4-dimethylazlactone-co-acrylamide-co-ethylene dimethacrylate) monoliths have been prepared. Covalent immobilization of trypsin on these support is achieved in a single reaction step using the azlactone functional groups. Optimization of hydrophilic/hydrophobic properties of the monolith affords a support that does not shrink in water and leads to immobilized enzyme that shows high activity in the hydrolysis of both low and high molecular weight substrates such as L-benzoyl arginine ethyl ester and casein. The catalytic activity of the monolithic reactor is maintained even at a flow velocity of 180 cm/min, which substantially exceeds those reported in the literature for packed bed reactors.