High throughput chromatography and analytics can inform viral clearance capabilities during downstream process development for biologics

High throughput process development (HTPD) using liquid handling robotics and RoboColumns is an established methodology in downstream process development to screen chromatography resins and optimize process designs to meet target product profiles. However, HTPD is not yet widely available for use in viral clearance capability of the resin due to a variety of constraints. In the present study, a BSL-1-compatible, non-infectious MVM model, MVM-VLP, was tested for viral clearance assessment with various resin and membrane chromatography operations in a HTPD mode. To detect the MVM-VLP in the high throughput experiments, an electrochemiluminescence immunoassay (ECLIA) assay was developed with up to 5 logs of dynamic range. Storage time suitability of MVM-VLP solutions in various buffer matrices, in the presence or absence of a glycoprotein vaccine candidate, were assessed. Then, MVM-VLP and a test article monoclonal antibody (mAb) were used in a HTPD design that included commercially available ion exchange media chemistries, elucidating a wide variety of viral clearance ability at different operating conditions. The methodologies described herein have the potential to be a part of the process design stage in biologics manufacturing process development, which in turn can reduce risk associated with viral clearance validation studies.     

High‐throughput process development of purification alternatives for the protein avidin

With an increased number of applications in the field of the avidin‐biotin technology, the resulting demand for highly‐purified protein avidin has drawn our attention to the purification process of avidin that naturally occurs in chicken egg white. The high‐throughput process development (HTPD) methodology was exploited, in order to evaluate purification process alternatives to commonly used ion‐exchange chromatography. In a high‐throughput format, process parameters for aqueous two‐phase extraction, selective precipitation with salts and polyethylene glycol, and hydrophobic interaction and mixed‐mode column chromatography experiments were performed. The HTPD strategy was complemented by a high‐throughput tandem high‐performance liquid chromatography assay for protein quantification. Suitable conditions for the separation of avidin from the major impurities ovalbumin, ovomucoid, ovotransferrin, and lysozyme were identified in the screening experiments. By combination of polyethylene glycol precipitation with subsequent resolubilization and separation in a polyethylene glycol/sulfate/sodium chloride two‐phase system an avidin purity of 77% was obtained with a yield >90% while at the same time achieving a significant reduction of the process volume. The two‐phase extraction and precipitation results were largely confirmed in larger scale with scale‐up factors of 230 and 133, respectively. Seamless processing of the avidin enriched bottom phase was found feasible by using mixed‐mode chromatography. By gradient elution a final avidin purity of at least 97% and yield >90% was obtained in the elution pool. The presented identification of a new and beneficial alternative for the purification of the high value protein thus represents a successful implementation of HTPD for an industrially relevant purification task. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:957–973, 2015     

Large scale demonstration of a process analytical technology application in bioprocessing: Use of on‐line high performance liquid chromatography for making real time pooling decisions for process chromatography

Process Analytical Technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community because of the potential for continuous real time quality assurance resulting in improved operational control and compliance. In previous publications, we have demonstrated feasibility of applications involving use of high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) for real‐time pooling of process chromatography column. In this article we follow a similar approach to perform lab studies and create a model for a chromatography step of a different modality (hydrophobic interaction chromatography). It is seen that the predictions of the model compare well to actual experimental data, demonstrating the usefulness of the approach across the different modes of chromatography. Also, use of online HPLC when the step is scaled up to pilot scale (a 2294 fold scale‐up from a 3.4 mL column in the lab to a 7.8 L column in the pilot plant) and eventually to manufacturing scale (a 45930 fold scale‐up from a 3.4 mL column in the lab to a 158 L column in the manufacturing plant) is examined. Overall, the results confirm that for the application under consideration, online‐HPLC offers a feasible approach for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes. The observations demonstrate that the proposed analytical scheme allows us to meet two of the key goals that have been outlined for PAT, i.e., “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions”. The application presented here can be extended to other modes of process chromatography and/or HPLC analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Case study and application of process analytical technology (PAT) towards bioprocessing: Use of tryptophan fluorescence as at‐line tool for making pooling decisions for process chromatography

Process analytical technology (PAT) has been gaining momentum in the biopharmaceutical community due to the potential for continuous real time quality assurance resulting in improved operational control and compliance. Two imperatives for implementing any PAT tool are that “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.” Recently, we have been examining the feasibility of applying different analytical tools to bioprocessing unit operations. We have previously demonstarted that commercially available online‐high performance liquid chromatography and ultra performance liquid chromatography systems can be used for analysis that can facilitate real‐time decisions for column pooling based on product quality attributes (Rathore et al., 2008 a,b). In this article, we review an at‐line tool that can be used for pooling of process chromatography columns. We have demonstrated that our tryptophan fluorescence method offers a feasible approach and meets the requirements of a PAT application. It is significantly faster than the alternative of fractionation, offline analysis followed by pooling. Although the method as presented here is not an online method, this technique may offer better resolution for certain applications and may be a more optimal approach as it is very conducive to implementation in a manufacturing environment. This technique is also amenable to be used as an online tool via front face fluorescence measurements done concurrently with product concentration determination by UV. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Defining process design space for a hydrophobic interaction chromatography (HIC) purification step: Application of quality by design (QbD) principles

The concept of design space has been taking root under the quality by design paradigm as a foundation of in‐process control strategies for biopharmaceutical manufacturing processes. This paper outlines the development of a design space for a hydrophobic interaction chromatography (HIC) process step. The design space included the impact of raw material lot‐to‐lot variability and variations in the feed stream from cell culture. A failure modes and effects analysis was employed as the basis for the process characterization exercise. During mapping of the process design space, the multi‐dimensional combination of operational variables were studied to quantify the impact on process performance in terms of yield and product quality. Variability in resin hydrophobicity was found to have a significant influence on step yield and high‐molecular weight aggregate clearance through the HIC step. A robust operating window was identified for this process step that enabled a higher step yield while ensuring acceptable product quality. Biotechnol. Bioeng. 2010;107: 985–997. © 2010 Wiley Periodicals, Inc.     

Defining process design space for biotech products: case study of Pichia pastoris fermentation

The concept of "design space" has been proposed in the ICH Q8 guideline and is gaining momentum in its application in the biotech industry. It has been defined as "the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality." This paper presents a stepwise approach for defining process design space for a biologic product. A case study, involving P. pastoris fermentation, is presented to facilitate this. First, risk analysis via Failure Modes and Effects Analysis (FMEA) is performed to identify parameters for process characterization. Second, small-scale models are created and qualified prior to their use in these experimental studies. Third, studies are designed using Design of Experiments (DOE) in order for the data to be amenable for use in defining the process design space. Fourth, the studies are executed and the results analyzed for decisions on the criticality of the parameters as well as on establishing process design space. For the application under consideration, it is shown that the fermentation unit operation is very robust with a wide design space and no critical operating parameters. The approach presented here is not specific to the illustrated case study. It can be extended to other biotech unit operations and processes that can be scaled down and characterized at small scale.     

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     

A practical strategy for using miniature chromatography columns in a standardized high‐throughput workflow for purification development of monoclonal antibodies

The emergence of monoclonal antibody (mAb) therapies has created a need for faster and more efficient bioprocess development strategies in order to meet timeline and material demands. In this work, a high‐throughput process development (HTPD) strategy implementing several high‐throughput chromatography purification techniques is described. Namely, batch incubations are used to scout feasible operating conditions, miniature columns are then used to determine separation of impurities, and, finally, a limited number of lab scale columns are tested to confirm the conditions identified using high‐throughput techniques and to provide a path toward large scale processing. This multistep approach builds upon previous HTPD work by combining, in a unique sequential fashion, the flexibility and throughput of batch incubations with the increased separation characteristics for the packed bed format of miniature columns. Additionally, in order to assess the applicability of using miniature columns in this workflow, transport considerations were compared with traditional lab scale columns, and performances were mapped for the two techniques. The high‐throughput strategy was utilized to determine optimal operating conditions with two different types of resins for a difficult separation of a mAb monomer from aggregates. Other more detailed prediction models are cited, but the intent of this work was to use high‐throughput strategies as a general guide for scaling and assessing operating space rather than as a precise model to exactly predict performance. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:626–635, 2014     

Strategies for developing design spaces for viral clearance by anion exchange chromatography during monoclonal antibody production

The quality‐by‐design (QbD) regulatory initiative promotes the development of process design spaces describing the multidimensional effects and interactions of process variables on critical quality attributes of therapeutic products. However, because of the complex nature of production processes, strategies must be devised to provide for design space development with reasonable allocation of resources while maintaining highly dependable results. Here, we discuss strategies for the determination of design spaces for viral clearance by anion exchange chromatography (AEX) during purification of monoclonal antibodies. We developed a risk assessment for AEX using a formalized method and applying previous knowledge of the effects of certain variables and the mechanism of action for virus removal by this process. We then use design‐of‐experiments (DOE) concepts to perform a highly fractionated factorial experiment and show that varying many process parameters simultaneously over wide ranges does not affect the ability of the AEX process to remove endogenous retrovirus‐like particles from CHO‐cell derived feedstocks. Finally, we performed a full factorial design and observed that a high degree of viral clearance was obtained for three different model viruses when the most significant process parameters were varied over ranges relevant to typical manufacturing processes. These experiments indicate the robust nature of viral clearance by the AEX process as well as the design space where removal of viral impurities and contaminants can be assured. In addition, the concepts and methodology presented here provides a general approach for the development of design spaces to assure that quality of biotherapeutic products is maintained. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications

Implementing real‐time product quality control meets one or both of the key goals outlined in FDA's PAT guidance: “variability is managed by the process” and “product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.” The first part of the paper presented an overview of PAT concepts and applications in the areas of upstream and downstream processing. In this second part, we present principles and case studies to illustrate implementation of PAT for drug product manufacturing, rapid microbiology, and chemometrics. We further present our thoughts on how PAT will be applied to biotech processes going forward. The role of PAT as an enabling component of the Quality by Design framework is highlighted. Integration of PAT with the principles stated in the ICH Q8, Q9, and Q10 guidance documents is also discussed. Biotechnol. Bioeng. 2010; 105: 285–295. Published 2009 Wiley Periodicals, Inc.     

An impurity characterization based approach for the rapid development of integrated downstream purification processes

In this study, we describe a new approach for the characterization of process‐related impurities along with an in silico tool to generate orthogonal, integrated downstream purification processes for biological products. A one‐time characterization of process‐related impurities from product expression in Pichia pastoris was first carried out using linear salt and pH gradients on a library of multimodal, salt‐tolerant, and hydrophobic charge induction chromatographic resins. The Reversed‐phase ultra‐performance liquid chromatography (UPLC) analysis of the fractions from these gradients was then used to generate large data sets of impurity profiles. A retention database of the biological product was also generated using the same linear salt and pH gradients on these resins, without fraction collection. The resulting two data sets were then analyzed using an in silico tool, which incorporated integrated manufacturing constraints to generate and rank potential three‐step purification sequences based on their predicted purification performance as well as whole‐process “orthogonality” for impurity removal. Highly ranked sequences were further examined to identify templates for process development. The efficacy of this approach was successfully demonstrated for the rapid development of robust integrated processes for human growth hormone and granulocyte‐colony stimulating factor.     

A single aromatic core mutation converts a designed “primitive” protein from halophile to mesophile folding

The halophile environment has a number of compelling aspects with regard to the origin of structured polypeptides (i.e., proteogenesis) and, instead of a curious niche that living systems adapted into, the halophile environment is emerging as a candidate “cradle” for proteogenesis. In this viewpoint, a subsequent halophile‐to‐mesophile transition was a key step in early evolution. Several lines of evidence indicate that aromatic amino acids were a late addition to the codon table and not part of the original “prebiotic” set comprising the earliest polypeptides. We test the hypothesis that the availability of aromatic amino acids could facilitate a halophile‐to‐mesophile transition by hydrophobic core‐packing enhancement. The effects of aromatic amino acid substitutions were evaluated in the core of a “primitive” designed protein enriched for the 10 prebiotic amino acids (A,D,E,G,I,L,P,S,T,V)—having an exclusively prebiotic core and requiring halophilic conditions for folding. The results indicate that a single aromatic amino acid substitution is capable of eliminating the requirement of halophile conditions for folding of a “primitive” polypeptide. Thus, the availability of aromatic amino acids could have facilitated a critical halophile‐to‐mesophile protein folding adaptation—identifying a selective advantage for the incorporation of aromatic amino acids into the codon table.     

“Enzyme Test Bench,” a high‐throughput enzyme characterization technique including the long‐term stability

A new high throughput technique for enzyme characterization with specific attention to the long term stability, called “Enzyme Test Bench,” is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests in 96‐well microtiter plates under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimal non‐linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The engineered instrumentation provides for simultaneous automated assaying by fluorescent measurements, mixing and homogenous temperature control in the range of 10–85 ± 0.5°C. A universal fluorescent assay for online acquisition of ester hydrolysis reactions by pH‐shift is developed and established. The developed instrumentation and assay are applied to characterize two esterases. The results of the characterization, carried out in microtiter plates applying short term experiments of hours, are in good agreement with the results of long term experiments at different temperatures in 1 L stirred tank reactors of a week. Thus, the new technique allows for both: the enzyme screening with regard to the long term stability and the choice of the optimal process temperature regarding such process parameters as turn over number, space time yield or optimal process duration. The comparison of the temperature dependent behavior of both characterized enzymes clearly demonstrates that the frequently applied estimation of long term stability at moderate temperatures by simple activity measurements after exposing the enzymes to elevated temperatures may lead to suboptimal enzyme selection. Thus, temperature dependent enzyme characterization is essential in primary screening to predict its long term behavior. Biotechnol. Bioeng. 2009;103: 305–322. © 2008 Wiley Periodicals, Inc.     

“Amyloid‐beta accumulation cycle” as a prevention and/or therapy target for Alzheimer's disease

The cell cycle and its regulators are validated targets for cancer drugs. Reagents that target cells in a specific cell cycle phase (e.g., antimitotics or DNA synthesis inhibitors/replication stress inducers) have demonstrated success as broad‐spectrum anticancer drugs. Cyclin‐dependent kinases (CDKs) are drivers of cell cycle transitions. A CDK inhibitor, flavopiridol/alvocidib, is an FDA‐approved drug for acute myeloid leukemia. Alzheimer's disease (AD) is another serious issue in contemporary medicine. The cause of AD remains elusive, although a critical role of latent amyloid‐beta accumulation has emerged. Existing AD drug research and development targets include amyloid, amyloid metabolism/catabolism, tau, inflammation, cholesterol, the cholinergic system, and other neurotransmitters. However, none have been validated as therapeutically effective targets. Recent reports from AD‐omics and preclinical animal models provided data supporting the long‐standing notion that cell cycle progression and/or mitosis may be a valid target for AD prevention and/or therapy. This review will summarize the recent developments in AD research: (a) Mitotic re‐entry, leading to the “amyloid‐beta accumulation cycle,” may be a prerequisite for amyloid‐beta accumulation and AD pathology development; (b) AD‐associated pathogens can cause cell cycle errors; (c) thirteen among 37 human AD genetic risk genes may be functionally involved in the cell cycle and/or mitosis; and (d) preclinical AD mouse models treated with CDK inhibitor showed improvements in cognitive/behavioral symptoms. If the “amyloid‐beta accumulation cycle is an AD drug target” concept is proven, repurposing of cancer drugs may emerge as a new, fast‐track approach for AD management in the clinic setting.     

Micro sequential injection system as the interfacing device for process analytical applications

It is an important and desirable capability to be able to control the quality and quantity of biological product by maintaining and adjusting bioreactor performance throughout its production duration. Amino acids are the building blocks of proteins. Scientists will need to ensure sufficient supply of amino acids as the substrates in the bioreactors as well as to control the excess level of undesirable free amino acid byproducts to maintain an optimum growth environment for cell culture. We have developed a compact and robust sample preparation platform capable of interfacing with analytical instruments to achieve bioreactor amino acids monitoring. We demonstrated the feasibility of this concept by incorporating an automatic amino acid sample preparation protocol to a micro sequential injection (μSI) system connected to an ultra‐performance liquid chromatography system for real‐time, at‐line amino acid separation, and quantitation. The μSI system was configured into a “platform‐like” sample preparation system that is able to accommodate future wet chemistry‐type sample preparations. Its real‐time amino acid results can be readily available to bioprocess scientists for quick decision making and design of their next experiment. Potential automatic feedback control mechanisms can be established through trigger events based on predetermined analytical signal thresholds so the system can communicate with facility infrastructure to control bioreactors in near real‐time fashion. The proposed μSI system described in this paper can be widely used as an automatic sample preparation system connected to the front‐end of analytical instruments to enable process analytical technology applications. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:607–613, 2015     

Towards a metabolic engineering strain “commons”: An Escherichia coli platform strain for ethanol production

In the genome‐engineering era, it is increasingly important that researchers have access to a common set of platform strains that can serve as debugged production chassis and the basis for applying new metabolic engineering strategies for modeling and characterizing flux, engineering complex traits, and optimizing overall performance. Here, we describe such a platform strain of E. coli engineered for ethanol production. Starting with a fully characterized host strain (BW25113), we site‐specifically integrated the genes required for homoethanol production under the control of a strong inducible promoter into the genome and deleted the genes encoding four enzymes from competing pathways. This strain is capable of producing >30 g/L of ethanol in minimal media with <2 g/L produced of any fermentative byproduct. Using this platform strain, we tested previously identified ethanol tolerance genes and found that while tolerance was improved under certain conditions, any effect on ethanol production or tolerance was lost when grown under production conditions. Thus, our findings reinforce the need for a metabolic engineering “commons” that could provide a set of platform strains for use in more sophisticated genome‐engineering strategies. Towards this end, we have made this production strain available to the scientific community. Biotechnol. Bioeng. 2013; 110: 1520–1526. © 2013 Wiley Periodicals, Inc.     

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed‐batch fermentations

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical “quality by testing” to rational, knowledge‐based “quality by design” approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real‐time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed‐batch production processes with varying cultivation conditions employing a comprehensive on‐ and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two‐dimensional multi‐wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.     

Design of experiments applications in bioprocessing: Chromatography process development using split design of experiments

Development of a chromatographic step in a time and resource efficient manner remains a serious bottleneck in protein purification. Chromatographic performance typically depends on raw material attributes, feed material attributes, process factors, and their interactions. Design of experiments (DOE) based process development is often chosen for this purpose. A challenge is, however, in performing a DOE with such a large number of process factors. A split DOE approach based on process knowledge in order to reduce the number of experiments is proposed. The first DOE targets optimizing factors that are likely to significantly impact the process and their effect on process performance is unknown. The second DOE aims to fine-tune another set of interacting process factors, impact of whom on process performance is known from process understanding. Furthermore, modeling of a large set of output response variables has been achieved by fitting the output responses to an empirical equation and then using the parametric constants of the equation as output response variables for regression modeling. Two case studies involving hydrophobic interaction chromatography for removal of aggregates and cation exchange chromatography for separation of charge variants and aggregates have been utilized to illustrate the proposed approach. Proposed methodology reduced total number of experiments by 25% and 72% compared to a single DOE based on central composite design and full factorial design, respectively. The proposed approach is likely to result in a significant reduction in resources required as well as time taken during process development. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2730, 2019     

A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity

Within the Quality by Design (QbD) framework proposed by the International Conference on Harmonisation (ICH), high‐throughput process development (HTPD) and mechanistic modeling are of outstanding importance for future biopharmaceutical chromatography process development. In order to compare the data derived from different column scales or batch chromatographies, the amount of adsorber has to be quantified with the same noninvasive method. Similarly, an important requirement for the implementation of mechanistic modeling is the reliable determination of column characteristics such as the ionic capacity Λ for ion‐exchange chromatography with the same method at all scales and formats. We developed a method to determine the ionic capacity in column and batch chromatography, based on the adsorption/desorption of the natural, uv‐detectable amino acid histidine. In column chromatography, this method produces results comparable to those of classical acid?base titration. In contrast to acid?base titration, this method can be adapted to robotic batch chromatographic experiments. We are able to convert the adsorber volumes in batch chromatography to the equivalent volume of a compressed column. In a case study, we demonstrate that this method increases the quality of SMA parameters fitted to batch adsorption isotherms, and the capability to predict column breakthrough experiments. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:666–677, 2016