Purification processes of live virus vaccine candidates expressed in adherent Vero cell lines via multimodal chromatography in flowthrough mode.

Live virus vaccine (LVV) purification, employing chromatography, can be challenged by low binding capacities and elution yields. Alternatively, processes relying solely on enzymatic digestion steps and size-based membrane separations can be limited by suboptimal reduction of process related impurities and poorly scalable unit operations. Here, we demonstrate that the combination of flowthrough mode chromatography and an ultrafiltration/diafiltration (UF/DF) unit operation delivers a purification process for two different LVV candidates, V590 and Measles, expressed in adherent Vero cells. For V590, chromatography with mixed mode cation exchange resins returned final product yields of ∼50% and logarithmic reduction values (LRVs) of 1.7->3.4 and 2.5-3.0 for host cell DNA (hcDNA) and host cell proteins (HCPs), respectively. For Measles, chromatography with mixed mode anion exchange resins returned final product yields of ∼50% and LRVs of 1.6 and 2.2 for hcDNA and HCPs, respectively. For both V590 and Measles processing, the employed resins cleared a key HCP, fibronectin, which could foul the UF/DF unit operation, and thusly enabling it to further reduce HCPs and to formulate the final LVV products. This integrated purification process utilizes the complementary action of the two unit operations and its applicability across LVVs supports its consideration for their processing.

Identifying analytics for high throughput bioprocess development studies.

In recent years, high throughput screening (HTS) studies have been increasingly employed as an integral element of bioprocess development activities. These studies are often limited by an analytical bottleneck; they generate multiple samples for analysis and the available analytical methods cannot always cope with the added analytical burden. A potential solution to this challenge is offered by the deployment of appropriate analytics. This article outlines features of analytical methods that affect their fit to high throughput (HT) applications. These are discussed for a range of analytics frequently used in bioprocess development studies of monoclonal antibodies. It then outlines how these features need to be considered in order to classify analytical methods in terms of their particular application in high throughput scenarios.

Application of cation exchange chromatography in bind and elute and flowthrough mode for the purification of enteroviruses.

Members of the enterovirus genus are promising oncolytic agents. Their morphogenesis involves the generation of both genome-packed infectious capsids and empty capsids. The latter are typically considered as an impurity in need of removal from the final product. The separation of empty and full capsids can take place with centrifugation methods, which are of low throughput and poorly scalable, or scalable chromatographic processes, which typically require peak cutting and a significant trade-off between purity and yield. Here we demonstrate the application of packed bed cation exchange (CEX) column chromatography for the separation of empty capsids from infectious virions for a prototype strain of Coxsackievirus A21. This separation was developed using high throughput chromatography techniques and scaled up as a bind and elute polishing step. The separation was robust over a wide range of operating conditions and returned highly resolved empty and full capsids. The CEX step could be operated in bind and elute or flowthrough mode with similar selectivity and returned yields greater than 70% for full mature virus particles. Similar performance was also achieved using a selection of other bead based CEX chromatography media, demonstrating general applicability of this type of chromatography for Coxsackievirus A21 purification. These results highlight the wide applicability and excellent performance of CEX chromatography for the purification of enteroviruses, such as Coxsackievirus A21.

Regeneration of Capto Core 700 resin through high throughput and laboratory scale studies and impact on production of a SARS-CoV-2 vaccine candidate.

During the development of a SARS-CoV-2 vaccine candidate, at the height of the COVID-19 pandemic, raw materials shortages, including chromatography resins, necessitated the determination of a cleaning in place (CIP) strategy for a multimodal core-shell resin both rapidly and efficiently. Here, the deployment of high throughput (HT) techniques to screen CIP conditions for cleaning Capto Core 700 resin exposed to clarified cell culture harvest (CCCH) of a SARS-CoV-2 vaccine candidate produced in Vero adherent cell culture are described. The best performing conditions, comprised of 30% n-propanol and ≥0.75 N NaOH, were deployed in cycling experiments, completed with miniature chromatography columns, to demonstrate their effectiveness. The success of the CIP strategy was ultimately verified at the laboratory scale. Here, its impact was assessed across the entire purification process which also included an ultrafiltration/diafiltration step. It is shown that the implementation of the CIP strategy enabled the re-use of the Capto Core 700 resin for up to 10 cycles without any negative impact on the purified product. Hence, the strategic combination of HT and laboratory-scale experiments can lead rapidly to robust CIP procedures, even for a challenging to clean resin, and thus help to overcome supply shortages.

The simplex algorithm for the rapid identification of operating conditions during early bioprocess development: case studies in FAb' precipitation and multimodal chromatography.

This study describes a data-driven algorithm as a rapid alternative to conventional Design of Experiments (DoE) approaches for identifying feasible operating conditions during early bioprocess development. In general, DoE methods involve fitting regression models to experimental data, but if model fitness is inadequate then further experimentation is required to gain more confidence in the location of an optimum. This can be undesirable during very early process development when feedstock is in limited supply and especially if a significant percentage of the tested conditions are ultimately found to be sub-optimal. An alternative approach involves focusing solely upon the feasible regions by using the knowledge gained from each condition to direct the choice of subsequent test locations that lead towards an optimum. To illustrate the principle, this study describes the application of the Simplex algorithm which uses accumulated knowledge from previous test points to direct the choice of successive conditions towards better regions. The method is illustrated by two case studies; a two variable precipitation example investigating how salt concentration and pH affect FAb' recovery from E. coli homogenate and a three-variable chromatography example identifying the optimal pH and concentrations of two salts in an elution buffer used to recover ovine antibody bound to a multimodal cation exchange matrix. Two-level and face-centered central composite regression models were constructed for each study and statistical analysis showed that they provided a poor fit to the data, necessitating additional experimentation to confirm the robust regions of the search space. By comparison, the Simplex algorithm identified a good operating point using 50% and 70% fewer conditions for the precipitation and chromatography studies, respectively. Hence, data-driven approaches have significant potential for early process development when material supply is at a premium.

High-Throughput Process Development for the Chromatographic Purification of Viral Antigens.

Chromatography is a widely used method in the biotechnology industry and functions to separate the desired product from process and product related impurities. There is a multitude of resins available based on different modalities (such as charge, hydrophobicity, and affinity) to provide a spectrum of approaches to meet the separation challenges of the diverse products. The challenge of developing viral antigen purification processes is addressed in this method. A unique feature of this product class is that in order to protect against more than one strain of an antigen, vaccines are often multivalent. This entails multiple production processes for each antigen, all of which will require separate development and validation. Ideally, a universal purification method is sought, but differences in the protein subunits (frequently used as the antigens) make this challenging and often-bespoke purification steps are required. This means process development for the chromatographic stages of these products can be particularly challenging and labour intensive. With the numerous choices available, making critical process decisions that are usually unique to each product, process, and strain, can be costly and time-consuming. To address this, scale down purification at <1.0 mL column volume and automation approaches are increasingly applied to increase throughput. In this work, a method is described wherein a Tecan Freedom EVO® automated liquid handler is deployed for the evaluation of different resin chemistries and buffer conditions to find a suitable purification strategy. This method allows for the rapid evaluation of the separation viral antigens where limited information on chromatography behavior is known at the early stages of process development. Here, we demonstrate the methodology firstly by explaining the automated purification script and secondly by applying the script for an efficient purification development for different serotypes of rotavirus antigens.

Data-driven multi-objective optimization via grid compatible simplex technique and desirability approach for challenging high throughput chromatography applications.

Recently, a grid compatible Simplex variant has been demonstrated to identify optima consistently and rapidly in challenging high throughput (HT) applications in early bioprocess development. Here, this method is extended by deploying it to multi-objective optimization problems. Three HT chromatography case studies are presented, each posing challenging early development situations and including three responses which were amalgamated by the adoption of the desirability approach. The suitability of a design of experiments (DoE) methodology per case study, using regression analysis in addition to the desirability approach, was evaluated for a large number of weights and in the presence of stringent and lenient performance requirements. Despite the adoption of high-order models, this approach had low success in identification of the optimal conditions. For the deployment of the Simplex approach, the deterministic specification of the weights of the merged responses was avoided by including them as inputs in the formulated multi-objective optimization problem, facilitating this way the decision making process. This, and the ability of the Simplex method to locate optima, rendered the presented approach highly successful in delivering rapidly operating conditions, which belonged to the Pareto set and offered a superior and balanced performance across all outputs compared to alternatives. Moreover, its performance was relatively independent of the starting conditions and required sub-minute computations despite its higher order mathematical functionality compared to DoE techniques. These evidences support the suitability of the grid compatible Simplex method for early bioprocess development studies involving complex data trends over multiple responses. © 2018 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:1393-1406, 2018.

Flexible and Accessible Automated Operation of Miniature Chromatography Columns on a Liquid Handling Station.

The High Throughput (HT) investigation of chromatographic separations is an important element of downstream bioprocess development due to the importance of chromatography as a technique for achieving stringent regulatory requirements on product purity. Various HT formats for chromatography exist, but the miniature column approach has characteristics resembling large scale packed bed column chromatography the most. The operation of such columns on robotic stations can be automated, but this is not always a straightforward procedure; the robotic manipulations are highly dependent on the settings of each experiment and the standard commands of the supporting software may not provide readily the required flexibility and accessibility for "plug and play" functionality. These can limit the potential of this technique in laboratories engaging on HT activities. In this work, we present an application which aims to overcome this challenge by providing end-users with a flexible operation of the miniature column technique on an automated liquid handler. The application includes a script which is written on Freedom EVOware, and is supplemented by custom compiled executables. Here, the manipulations carried out by the application are described in detail and its functionality is demonstrated through typical experiments based on bind and elute miniature column chromatography. The application is shown to allow for the unsupervised "on-the-fly" programming of the robotic station and to ultimately make the technique accessible to non-automation experts. This application is therefore well suited to simplifying development activities based on the robotic deployment of the miniature column chromatography technique.

Simplex-based optimization of numerical and categorical inputs in early bioprocess development: Case studies in HT chromatography.

Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter-plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex-based method was further shown to be of equivalent efficiency to a DoE-based approach, represented here by D-Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex-variant is suited to development activities involving numerical and categorical inputs in early bioprocess development.

High-throughput investigation of single and binary protein adsorption isotherms in anion exchange chromatography employing multivariate analysis.

The combination of multi-well plates and automated liquid handling is well suited to the rapid measurement of the adsorption isotherms of proteins. Here, single and binary adsorption isotherms are reported for BSA, ovalbumin and conalbumin on a strong anion exchanger over a range of pH and salt levels. The impact of the main experimental factors at play on the accuracy and precision of the adsorbed protein concentrations is quantified theoretically and experimentally. In addition to the standard measurement of liquid concentrations before and after adsorption, the amounts eluted from the wells are measured directly. This additional measurement corroborates the calculation based on liquid concentration data, and improves precision especially under conditions of weak or moderate interaction strength. The traditional measurement of multicomponent isotherms is limited by the speed of HPLC analysis; this analytical bottleneck is alleviated by careful multivariate analysis of UV spectra.

Application of simplex-based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing.

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression-based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression-based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex-based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression-based methods to reach favorable operating conditions. The Simplex-variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex-variant is ideally suited to rapid optimization in early-phase process development. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:404-419, 2016.

The hybrid experimental simplex algorithm--an alternative method for 'sweet spot' identification in early bioprocess development: case studies in ion exchange chromatography.

The capacity to locate efficiently a subset of experimental conditions necessary for the identification of an operating envelope is a key objective in many studies. We have shown previously how this can be performed by using the simplex algorithm and this paper now extends the approach by augmenting the established simplex method to form a novel hybrid experimental simplex algorithm (HESA) for identifying 'sweet spots' during scouting development studies. The paper describes the new algorithm and illustrates its use in two bioprocessing case studies conducted in a 96-well filter plate format. The first investigates the effect of pH and salt concentration on the binding of green fluorescent protein, isolated from Escherichia coli homogenate, to a weak anion exchange resin and the second examines the impact of salt concentration, pH and initial feed concentration upon the binding capacities of a FAb', isolated from E. coli lysate, to a strong cation exchange resin. Compared with the established algorithm, HESA was better at delivering valuable information regarding the size, shape and location of operating 'sweet spots' that could then be further investigated and optimized with follow up studies. To test how favorably these features of HESA compared with conventional DoE (design of experiments) methods, HESA results were also compared with approaches including response surface modeling experimental designs. The results show that HESA can return 'sweet spots' that are equivalently or better defined than those obtained from DoE approaches. At the same time the deployment of HESA to identify bioprocess-relevant operating boundaries was accompanied by comparable experimental costs to those of DoE methods. HESA is therefore a viable and valuable alternative route for identifying 'sweet spots' during scouting studies in bioprocess development.

Strategic Assay Selection for analytics in high-throughput process development: case studies for downstream processing of monoclonal antibodies.

During bioprocess development a potentially large number of analytes require measurement. Selection of the best set of analytical methods to deploy can reduce the analytical requirements for process investigation but currently relies on application of heuristics. This paper introduces a generic methodology, Strategic Assay Selection, for screening a large number of analytical methods to produce a subset of analytics that best suit high-throughput studies. The methodology uses a stochastic ranking approach where analytics are ranked based on their holistic performance in a set of criteria. Strategic Assay Selection can be used to help minimizing the impact of analytics in the generation of bottlenecks often encountered during high-throughput process development studies. This is illustrated by using a typical downstream purification process for a monoclonal antibody product. A list of assays is populated for routinely measured analytes across the different units of operation followed by the calculation of their performances in four criteria. The methodology is then applied to select analytics testing for three analytes and the results are analyzed to demonstrate how it can lead to the selection of analytical methods with the most favorable features.

Strategic assay deployment as a method for countering analytical bottlenecks in high throughput process development: case studies in ion exchange chromatography.

High throughput approaches to facilitate the development of chromatographic separations have now been adopted widely in the biopharmaceutical industry, but issues of how to reduce the associated analytical burden remain. For example, acquiring experimental data by high level factorial designs in 96 well plates can place a considerable strain upon assay capabilities, generating a bottleneck that limits significantly the speed of process characterization. This article proposes an approach designed to counter this challenge; Strategic Assay Deployment (SAD). In SAD, a set of available analytical methods is investigated to determine which set of techniques is the most appropriate to use and how best to deploy these to reduce the consumption of analytical resources while still enabling accurate and complete process characterization. The approach is demonstrated by investigating how salt concentration and pH affect the binding of green fluorescent protein from Escherichia coli homogenate to an anion exchange resin presented in a 96-well filter plate format. Compared with the deployment of routinely used analytical methods alone, the application of SAD reduced both the total assay time and total assay material consumption by at least 40% and 5%, respectively. SAD has significant utility in accelerating bioprocess development activities.