High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Smart Process Development: Application of machine-learning and integrated process modeling for inclusion body purification processes

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time consuming. Here, the conditions established as optimal for one step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a SMART approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using Gradient Boosting Machines and Gaussian Processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a two-fold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50 and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.

Modeling the impact of amino acid substitution in a monoclonal antibody on cation exchange chromatography

A vital part of biopharmaceutical research is decision making around which lead candidate should be progressed in early-phase development. When multiple antibody candidates show similar biological activity, developability aspects are taken into account to ease the challenges of manufacturing the potential drug candidate. While current strategies for developability assessment mainly focus on drug product stability, only limited information is available on how antibody candidates with minimal differences in their primary structure behave during downstream processing. With increasing time-to-market pressure and an abundance of monoclonal antibodies (mAbs) in development pipelines, developability assessments should also consider the ability of mAbs to integrate into the downstream platform. This study investigates the influence of amino acid substitutions in the complementarity-determining region (CDR) of a full-length IgG1 mAb on the elution behavior in preparative cation exchange chromatography. Single amino acid substitutions within the investigated mAb resulted in an additional positive charge in the light chain (L) and heavy chain (H) CDR, respectively. The mAb variants showed an increased retention volume in linear gradient elution compared with the wild-type antibody. Furthermore, the substitution of tryptophan with lysine in the H-CDR3 increased charge heterogeneity of the product. A multiscale in silico analysis, consisting of homology modeling, protein surface analysis, and mechanistic chromatography modeling increased understanding of the adsorption mechanism. The results reveal the potential effects of lead optimization during antibody drug discovery on downstream processing.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

Precipitation of complex antibody solutions: influence of contaminant composition and cell culture medium on the precipitation behavior

Preparative protein precipitation is known as a cost-efficient and easy-to-use alternative to chromatographic purification steps. This said, at the moment, there is no process for monoclonal antibodies (mAb) on the market, although especially polyethylene glycol-induced precipitation has shown great potential. One reason might be the highly complex behavior of each component of a crude feedstock during the precipitation process. For different investigated mAbs, significant variations in the host cell protein (HCP) reduction are observed. In contrast to the precipitation behavior of single components, the interactions and interplay in a complex feedstock are not fully understood yet. This work discusses the influence of contaminants on the precipitation behavior of two different mAbs, an IgG1, and an IgG2. By spiking the mAbs with mock solution, a complex feedstock could successfully be mimicked. Spiking contaminants influenced the yield and purity of the mAbs after the precipitation step, compared to the precipitation behavior of the single components. The mixture showed a decrease in the contaminant and mAb solubility. By re-buffering the mock solution prior to spiking, special salts, small molecules like amino acids, vitamins, or sugars could be depleted while larger ones like HCP or DNA were still present. Therefore, it was possible to distinguish the influence of small molecules and larger ones. Hence, mAb-macromolecular interaction could be identified as a possible reason for the observed higher precipitation propensity, while small molecules of the cell culture medium were identified as solubilisation factors during the precipitation process.

Downstream Processing Technologies/Capturing and Final Purification : Opportunities for Innovation, Change, and Improvement. A Review of Downstream Processing Developments in Protein Purification

Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

Microbioreactor Systems for Accelerated Bioprocess Development

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

A core business in industrial biotechnology using microbial production cell factories is the iterative process of strain engineering and optimization of bioprocess conditions. One important aspect is the improvement of cultivation medium to provide an optimal environment for microbial formation of the product of interest. It is well accepted that the media composition can dramatically influence overall bioprocess performance. Nutrition medium optimization is known to improve recombinant protein production with microbial systems and thus, this is a rewarding step in bioprocess development. However, very often standard media recipes are taken from literature, since tailor-made design of the cultivation medium is a tedious task that demands microbioreactor technology for sufficient cultivation throughput, fast product analytics, as well as support by lab robotics to enable reliability in liquid handling steps. Furthermore, advanced mathematical methods are required for rationally analyzing measurement data and efficiently designing parallel experiments such as to achieve optimal information content. The generic nature of the presented protocol allows for easy adaption to different lab equipment, other expression hosts, and target proteins of interest, as well as further bioprocess parameters. Moreover, other optimization objectives like protein production rate, specific yield, or product quality can be chosen to fit the scope of other optimization studies. The applied Kriging Toolbox (KriKit) is a general tool for Design of Experiments (DOE) that contributes to improved holistic bioprocess optimization. It also supports multi-objective optimization which can be important in optimizing both upstream and downstream processes.

Optimizing recombinant protein expression via automated induction profiling in microtiter plates at different temperatures

Background

Escherichia coli (E. coli) is the most abundant expression host for recombinant proteins. The production efficiency is dependent on a multitude of parameters. Therefore, high-throughput applications have become an increasingly frequent technique to investigate the main factors. Within this study, the effects of temperature, induction time and inducer concentration on the metabolic state and the product formation were extensively examined. Induction profiling of E. coli Tuner(DE3) pRhotHi-2-EcFbFP was performed in 48-well Flowerplates and standard 96-well plates using a robotic platform. In parallel shake flask cultivations, the respiration activity of the microorganisms was analyzed. Therefore, two online-monitoring systems were applied: the BioLector for microtiter plates and the RAMOS-device for shake flasks. The impact of different induction conditions on biomass and product formation as well as on the oxygen transfer rate was surveyed.

Results

Different optimal induction conditions were obtained for temperatures of 28, 30, 34, and 37 °C. The best inducer concentrations were determined to be between 0.05 and 0.1 mM IPTG for all investigated temperatures. This is 10–20 times lower than conventional guidelines suggest. The induction time was less relevant when the correct inducer concentration was chosen. Furthermore, there was a stronger impact on growth and respiration activity at higher temperatures. This indicated a higher metabolic burden. Therefore, lower IPTG concentrations were advantageous at elevated temperatures. Very similar results were obtained in standard 96-well plates.

Conclusion

Two online-monitoring systems were successfully used to investigate the optimal induction conditions for the E. coli Tuner(DE3) pRhotHi-2-EcFbFP strain (lacY deletion mutant) at four different temperatures. The experimental effort was reduced to a minimum by integrating a liquid handling robot. To reach the maximum product formation, a detailed induction analysis was necessary. Whenever the cultivation temperature was changed, the induction conditions have to be adapted. Due to the experimental options provided by the BioLector technology, it was found that the higher the cultivation temperature, the lower the inducer concentration that has to be applied.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0832-4) contains supplementary material, which is available to authorized users.

The fed-batch principle for the molecular biology lab: controlled nutrient diets in ready-made media improve production of recombinant proteins in Escherichia coli

While the nutrient limited fed-batch technology is the standard of the cultivation of microorganisms and production of heterologous proteins in industry, despite its advantages in view of metabolic control and high cell density growth, shaken batch cultures are still the standard for protein production and expression screening in molecular biology and biochemistry laboratories. This is due to the difficulty and expenses to apply a controlled continuous glucose feed to shaken cultures. New ready-made growth media, e.g. by biocatalytic release of glucose from a polymer, offer a simple solution for the application of the fed-batch principle in shaken plate and flask cultures. Their wider use has shown that the controlled diet not only provides a solution to obtain significantly higher cell yields, but also in many cases folding of the target protein is improved by the applied lower growth rates; i.e. final volumetric yields for the active protein can be a multiple of what is obtained in complex medium cultures. The combination of the conventional optimization approaches with new and easy applicable growth systems has revolutionized recombinant protein production in Escherichia coli in view of product yield, culture robustness as well as significantly increased cell densities. This technical development establishes the basis for successful miniaturization and parallelization which is now an important tool for synthetic biology and protein engineering approaches. This review provides an overview of the recent developments, results and applications of advanced growth systems which use a controlled glucose release as substrate supply.

Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches

The biopharmaceutical industry is at a turning point moving toward a more customized and patient-oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high-throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry.

Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography

In chromatographic protein purification, process variations, aging of columns, or processing errors can lead to deviations of the expected elution behavior of product and contaminants and can result in a decreased pool purity or yield. A different elution behavior of all or several involved species leads to a deviating chromatogram. The causes for deviations are however hard to identify by visual inspection and complicate the correction of a problem in the next cycle or batch. To overcome this issue, a tool for root cause investigation in protein chromatography was developed. The tool combines a spectral deconvolution with inverse mechanistic modelling. Mid-UV spectral data and Partial Least Squares Regression were first applied to deconvolute peaks to obtain the individual elution profiles of co-eluting proteins. The individual elution profiles were subsequently used to identify errors in process parameters by curve fitting to a mechanistic chromatography model. The functionality of the tool for root cause investigation was successfully demonstrated in a model protein study with lysozyme, cytochrome c, and ribonuclease A. Deviating chromatograms were generated by deliberately caused errors in the process parameters flow rate and sodium-ion concentration in loading and elution buffer according to a design of experiments. The actual values of the three process parameters and, thus, the causes of the deviations were estimated with errors of less than 4.4%. Consequently, the established tool for root cause investigation is a valuable approach to rapidly identify process variations, aging of columns, or processing errors. This might help to minimize batch rejections or contribute to an increased productivity.

Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles

Recombinant protein-based virus-like particles (VLPs) are steadily gaining in importance as innovative vaccines against cancer and infectious diseases. Multiple VLPs are currently evaluated in clinical phases requiring a straightforward and rational process design. To date, there is no generic platform process available for the purification of VLPs. In order to accelerate and simplify VLP downstream processing, there is a demand for novel development approaches, technologies, and purification tools. Membrane adsorbers have been identified as promising stationary phases for the processing of bionanoparticles due to their large pore sizes. In this work, we present the potential of two strategies for designing VLP processes following the basic tenet of 'quality by design': High-throughput experimentation and process modeling of an anion-exchange membrane capture step. Automated membrane screenings allowed the identification of optimal VLP binding conditions yielding a dynamic binding capacity of 5.7 mg/mL for human B19 parvovirus-like particles derived from Spodoptera frugiperda Sf9 insect cells. A mechanistic approach was implemented for radial ion-exchange membrane chromatography using the lumped-rate model and stoichiometric displacement model for the in silico optimization of a VLP capture step. For the first time, process modeling enabled the in silico design of a selective, robust and scalable process with minimal experimental effort for a complex VLP feedstock. The optimized anion-exchange membrane chromatography process resulted in a protein purity of 81.5%, a DNA clearance of 99.2%, and a VLP recovery of 59%.