A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product-related impurity removal

Integrated designs of chromatographic processes for purification of biopharmaceuticals provides potential gains in operational efficiency and reductions of costs and material requirements. We describe a combined method using screening and in silico algorithms for ranking chromatographic steps to rapidly design orthogonally selective integrated processes for purifying protein therapeutics from both process- and product-related impurities. IFN-α2b produced in Pichia pastoris containing a significant product variant challenge was used as a case study. The product and product-related variants were screened on a set of 14 multimodal, ion exchange, and hydrophobic charge induction chromatography resins under various pH and salt linear gradient conditions. Data generated from reversed-phase chromatography of the fractions collected were used to generate a retention database for IFN-α2b and its variants. These data, in combination with a previously constructed process-related impurity database for P. pastoris, were input into an in silico process development tool that generated and ranked all possible integrated chromatographic sequences for their ability to remove both process and product-related impurities. Top-ranking outputs guided the experimental refinement of two successful three step purification processes, one comprising all bind-elute steps and the other having two bind-elute steps and a flowthrough operation. This approach suggests a new platform-like approach for rapidly designing purification processes for a range of proteins where separations of both process- and product-related impurities are needed.     

Adaptive combinatorial design to explore large experimental spaces: approach and validation

Systems biology requires mathematical tools not only to analyse large genomic datasets, but also to explore large experimental spaces in a systematic yet economical way. We demonstrate that two-factor combinatorial design (CD), shown to be useful in software testing, can be used to design a small set of experiments that would allow biologists to explore larger experimental spaces. Further, the results of an initial set of experiments can be used to seed further 'Adaptive' CD experimental designs. As a proof of principle, we demonstrate the usefulness of this Adaptive CD approach by analysing data from the effects of six binary inputs on the regulation of genes in the N-assimilation pathway of Arabidopsis. This CD approach identified the more important regulatory signals previously discovered by traditional experiments using far fewer experiments, and also identified examples of input interactions previously unknown. Tests using simulated data show that Adaptive CD suffers from fewer false positives than traditional experimental designs in determining decisive inputs, and succeeds far more often than traditional or random experimental designs in determining when genes are regulated by input interactions. We conclude that Adaptive CD offers an economical framework for discovering dominant inputs and interactions that affect different aspects of genomic outputs and organismal responses.     

Determination of robustness and optimal work conditions for a purification process of a therapeutic recombinant protein using response surface methodology

A typical chromatographic purification step has numerous operating parameters that can impact its performance. As it is not feasible to evaluate the influence of each one, the current practice in biopharmaceutical industry is to apply risk analysis approach to identify process parameters that should be examined during process characterization. Once these parameters are identified, a response surface study can be run to help understand the relationship between critical inputs and outputs. We performed a study comprising optimization and robustness determination for a Blue-Sepharose purification step of rhEPO, a well-known therapeutic glycoprotein. Initially, risk analysis was fulfilled to identify key parameters. A small-scale model was created and qualified before its use in experimental studies, given by a Box-Behnken design with three factors. This method proved to be a very useful tool in bioprocess validation studies in which many input variables can affect product quality and safety.     

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     

Defining process design space for monoclonal antibody cell culture

The concept of design space has been taking root as a foundation of in‐process control strategies for biopharmaceutical manufacturing processes. During mapping of the process design space, the multidimensional combination of operational variables is studied to quantify the impact on process performance in terms of productivity and product quality. An efficient methodology to map the design space for a monoclonal antibody cell culture process is described. A failure modes and effects analysis (FMEA) was used as the basis for the process characterization exercise. This was followed by an integrated study of the inoculum stage of the process which includes progressive shake flask and seed bioreactor steps. The operating conditions for the seed bioreactor were studied in an integrated fashion with the production bioreactor using a two stage design of experiments (DOE) methodology to enable optimization of operating conditions. A two level Resolution IV design was followed by a central composite design (CCD). These experiments enabled identification of the edge of failure and classification of the operational parameters as non‐key, key or critical. In addition, the models generated from the data provide further insight into balancing productivity of the cell culture process with product quality considerations. Finally, process and product‐related impurity clearance was evaluated by studies linking the upstream process with downstream purification. Production bioreactor parameters that directly influence antibody charge variants and glycosylation in CHO systems were identified. Biotechnol. Bioeng. 2010;106: 894–905. © 2010 Wiley Periodicals, Inc.     

Process characterization strategy for a precipitation step for host cell protein reduction

Process characterization using QbD approaches has rarely been described for precipitation steps used for impurity removal in biopharmaceutical processes. We propose a two-step approach for process characterization in which the first step focuses on product quality and the second focuses on process performance. This approach provides an efficient, streamlined strategy for the characterization of precipitation steps under the Quality by Design paradigm. This strategy is demonstrated by a case study for the characterization of a precipitation using sodium caprylate to reduce host cell proteins (HCP) during a monoclonal antibody purification process. Process parameters were methodically selected through a risk assessment based on prior development data and scientific knowledge described in the literature. The characterization studies used two multivariate blocks to decouple and distinguish the impact of product quality (e.g., measured HCP of the recovered product from the precipitation) and process performance (e.g., step yield). Robustness of the precipitation step was further demonstrated through linkage studies across the overall purification process. HCP levels could be robustly reduced to ≤100 ppm in the drug substance when the precipitation step operated within an operation space of ≤1% (m/v) sodium caprylate, pH 5.0–6.0, and filter flux ≤300 L/m²-hr for a load HCP concentration up to 19,000 ppm. This two-step approach for characterization of precipitation steps has several advantages, including tailoring of the experimental design and scale-down model to the intended purpose for each step, use of a manageable number of experiments without compromising scientific understanding, and limited time and material consumption.     

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.     

Aqueous two-phase systems extraction of alpha-toxin from Clostridium perfringens type A

Two sequential half-fraction designs were applied to studying the alpha-toxin partition produced by Clostridium perfringens type A in aqueous two phase systems (ATPS), as a function of four factors: PEG molar mass and concentration, phosphate concentration and pH. The highest purification factor, yield and partition coefficient results were obtained with PEG 8000 (15%, w/w), phosphate at 20% (w/w) and pH 8.0. This system allows, in a single step, an alpha-toxin purification of 4.6-fold with final activity yield of 230% and partition coefficient of 113.9 in the PEG rich phase.     

Designing experiments that aid in the identification of regulatory networks.

Predictive mathematical models of the interactions of a genetic network can provide insight into the mechanisms of gene regulation, the role of various genes within a network and how multiple genes interact leading to complex traits. However, identification of the parameters and interactions is currently a limiting step in the development of such models. This work reviews the state of the art for design of experiments in biological systems and demonstrates the need for improved design of experiments through the use of a model system. Appropriate design of experiments has a profound impact on the ability to identify a model and on the quality of resulting identified model. Key issues include the selection of appropriate input sequences (e.g. random, independent multivariate inputs) and the selection of the sampling frequencies. This work demonstrates that these issues are especially important in the identification of biochemical networks and that the traditional biochemical approach is incapable of truly identifying the behavior present in such networks.     

Application of surface response analysis to the optimization of penicillin acylase purification in aqueous two-phase systems

Penicillin acylase purification from an Escherichia coli crude extract using PEG 3350–sodium citrate aqueous two-phase systems (ATPS) was optimized. An experimental design was used to evaluate the influence of PEG, sodium citrate and sodium chloride on the purification parameters. A central composite design was defined centred on the previously found conditions for highest purification from an osmotic shock extract. Mathematical models for the partition coefficient of protein and enzyme, balance of protein and enzyme, yield and purification were calculated and statistically validated. Analysis of the contours of constant response as a function of PEG and sodium citrate concentrations for three different concentrations of NaCl revealed different effects of the three factors on the studied parameters. A maximum purification factor of 6.5 was predicted for PEG 3350, sodium citrate and NaCl concentrations of 15.1, 11.0 and 8.52% respectively. However, under these conditions the predicted yield was 61%. A better compromise between these two parameters can be found by superimposing the contour plots of the purification factor and yield for 10.3% NaCl. A region in the experimental space can be defined where the purification factor is always higher than 5.5 with yields exceeding 80%.     

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

Chromatography operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. This becomes even more critical with increasing product titers that result in higher mass loads onto chromatography columns, potentially causing capacity bottlenecks. In this work, a mixed‐integer nonlinear programming (MINLP) model was created and applied to an industrially relevant case study to optimize the design of a facility by determining the most cost‐effective chromatography equipment sizing strategies for the production of mAbs. Furthermore, the model was extended to evaluate the ability of a fixed facility to cope with higher product titers up to 15 g/L. Examination of the characteristics of the optimal chromatography sizing strategies across different titer values enabled the identification of the maximum titer that the facility could handle using a sequence of single column chromatography steps as well as multi‐column steps. The critical titer levels for different ratios of upstream to dowstream trains where multiple parallel columns per step resulted in the removal of facility bottlenecks were identified. Different facility configurations in terms of number of upstream trains were considered and the trade‐off between their cost and ability to handle higher titers was analyzed. The case study insights demonstrate that the proposed modeling approach, combining MINLP models with visualization tools, is a valuable decision‐support tool for the design of cost‐effective facility configurations and to aid facility fit decisions. © 2013 The Authors. Published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 29:1472–1483, 2013     

Resolution of heterogeneous charged antibody aggregates via multimodal chromatography: A comparison to conventional approaches

Clearance of aggregates during protein purification is increasingly paramount as protein aggregates represent one of the major impurities in biopharmaceutical products. Aggregates, especially dimer species, represent a significant challenge for purification processing since aggregate separation coupled with high purity protein recovery can be difficult to accomplish. Biochemical characterization of the aggregate species from the hydrophobic interaction and cation exchange chromatography elution peaks revealed two different charged populations, i.e. heterogeneous charged aggregates, which led to further challenges for chromatographic removal. This paper compares multimodal versus conventional cation exchange or hydrophobic chromatography methodologies to remove heterogeneous aggregates. A full, mixed level factorial design of experiment strategy together with high throughput experimentation was employed to rapidly evaluate chromatographic parameters such as pH, conductivity, and loading. A variety of operating conditions were identified for the multimodal chromatography step, which lead to effective removal of two different charged populations of aggregate species. This multimodal chromatography step was incorporated into a monoclonal antibody purification process and successfully implemented at commercial manufacturing scale. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:636–645, 2014     

Demonstration of robust host cell protein clearance in biopharmaceutical downstream processes

Residual host cell protein impurities (HCPs) are a key component of biopharmaceutical process related impurities. These impurities need to be effectively cleared through chromatographic steps in the downstream purification process to produce safe and efficacious protein biopharmaceuticals. A variety of strategies to demonstrate robust host cell protein clearance using scale-down studies are highlighted and compared. A common strategy is the "spiking" approach, which is widely employed in clearance studies for well-defined impurities. For HCPs this approach involves spiking cell culture harvest, which is rich in host cell proteins, into the load material for all chromatographic steps to assess their clearance ability. However, for studying HCP clearance, this approach suffers from the significant disadvantage that the vast majority of host cell protein impurities in a cell culture harvest sample are not relevant for a chromatographic step that is downstream of the capture step in the process. Two alternative strategies are presented here to study HCP clearance such that relevance of those species for a given chromatographic step is taken into consideration. These include a "bypass" strategy, which assumes that some of the load material for a chromatographic step bypasses that step and makes it into the load for the subsequent step. The second is a "worst-case" strategy, which utilizes information obtained from process characterization studies. This involves operating steps at a combination of their operating parameters within operating ranges that yield the poorest clearance of HCPs over that step. The eluate from the worst case run is carried forward to the next chromatographic step to assess its ability to clear HCPs. Both the bypass and worst-case approaches offer significant advantages over the spiking approach with respect to process relevance of the HCP impurity species being studied. A combination of these small-scale validation approaches with large-scale HCP clearance data from clinical manufacturing and manufacturing consistency runs is used to demonstrate robust HCP clearance for the downstream purification process of an Fc fusion protein. The demonstration of robust HCP clearance through this comprehensive strategy can potentially be used to eliminate the need for routine analytical testing or for establishing acceptance criteria for these impurities as well as to demonstrate robust operation of the entire downstream purification process.     

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799–812, 2016     

A unifying family of group sequential test designs

Currently, the design of group sequential clinical trials requires choosing among several distinct design categories, design scales, and strategies for determining stopping rules. This approach can limit the design selection process so that clinical issues are not fully addressed. This paper describes a family of designs that unifies previous approaches and allows continuous movement among the previous categories. This unified approach facilitates the process of tailoring the design to address important clinical issues. The unified family of designs is constructed from a generalization of a four-boundary group sequential design in which the shape and location of each boundary can be independently specified. Methods for implementing the design using error-spending functions are described. Examples illustrating the use of the design family are also presented.     

Decomposition-based qualitative experiment design algorithms for a class of compartmental models.

Qualitative experiment design, to determine experimental input/output configurations that provide identifiability for specific parameters of interest, can be extremely difficult if the number of unknown parameters and the number of compartments are relatively large. However, the problem can be considerably simplified if the parameters can be divided into several groups for separate identification and the model can be decomposed into smaller submodels for separate experiment design. Model decomposition-based experiment design algorithms are proposed for a practical class of large-scale compartmental models representative of biosystems characterized by multiple input sources and unidirectional interconnectivity among subsystems. The model parameters are divided into three types, each of which is identified consecutively, in three stages, using simpler submodel experiment designs. Several practical examples are presented. Necessary and sufficient conditions for identifiability using the algorithm are also discussed.     

Sequentially Generated Designs

Procedures for sequential generation of nearly D-optimal designs are described. Two kinds of designs can be obtained: symmetrical block designs and nonsymmetrical ones. It is shown that in a special case when the number of the support points of a continuous D-optimal design equals to the number of regression coefficients the sequential designs can be constructed very easy without use of a computer. A Catalogue containing 135 designs has been developed by use of these procedures. 34 of them can be used for experiments in cuboidal factor space and the remaining for experiments with mixture and process variables. Comparison with other designs is done.     

Development of an activated carbon filtration step and high throughput screening method to remove host cell proteins from a recombinant enzyme process

An increasing number of non-mAb recombinant proteins are being developed today. These biotherapeutics provide greater purification challenges where multiple polishing steps may be required to meet final purity specifications or the process steps may require extensive optimization. Recent studies have shown that activated carbon can be employed in downstream purification processes to selectively separate host cell proteins (HCPs) from monoclonal antibodies (mAb). However, the use of activated carbon as a unit operation in a cGMP purification process is relatively new. As such, the goal of this work is to provide guidance on development approaches, insight into operating parameters and solution conditions that can impact HCP removal, as well as further investigate the mechanism of removal by using mass spectrometry. In this work, activated carbon was evaluated to remove HCPs in the downstream purification process of a recombinant enzyme. Impact of process placement, flux (or residence time), and mass loading on HCP removal was investigated. Feasibility of high throughput screening (HTS) using loose activated carbon was assessed to reduce the amount of therapeutic protein needed and enable testing of a larger number of solution conditions. Finally, mass spectrometry was used to determine the population of HCPs removed by activated carbon. Our work demonstrates that activated carbon can be used effectively in downstream processes of biopharmaceuticals to remove HCPs (up to a 3 log₁₀ reduction) and that an HTS format can be implemented to reduce material demands by up to 23x and allow for process optimization of this adsorbent for purification purposes.     

Small molecule clearance in ultrafiltration/diafiltration in relation to protein interactions: Study of citrate binding to a Fab

Ultrafiltration/diafiltration (UFDF) is commonly utilized in the purification of recombinant proteins to concentrate and buffer exchange the product. It is often the final step in the purification process, placing the protein in its final formulation and clearing small molecules introduced in upstream purification steps. This article presents a case study of reduced small molecule clearance in ultrafiltration/diafiltration of an antigen‐binding fragment of a monoclonal antibody. Citrate, a commonly utilized small molecule in downstream processes, is shown to have reduced clearance due to specific interactions with the protein product. The study presents process solutions and utilizes a simple model to characterize clearance of small molecules which exhibit interactions with product protein. Biotechnol. Bioeng. 2009;102: 1718–1722. © 2008 Wiley Periodicals, Inc.     

Fractionation of recombinant tumor necrosis factor using hydrophobic and hydrophilic membranes.

Recombinant tumor necrosis factor (TNF) is expressed in Escherichia coli as a soluble intracellular protein. A purification process is described that incorporates a hydrophilic membrane (cellulosic) separation followed by a hydrophobic one (PTFE). The hydrophilic step is a traditional one in that species are separated primarily on the basis of size. The hydrophobic step separates species on the basis of parameters apparently not related to size. By combining these two steps, an increase in TNF purity of 7-10-fold can be achieved with a yield of 50%. The effects of cellular debris and pH on selectivity and recovery of the hydrophobic filtration step are discussed.