Economic evaluation of the primary recovery of tetracycline with traditional and novel aqueous two-phase systems.

Antibiotics are a key pharmaceutical to inhibit growth or kill microorganisms. They represent a profitable market and, in particular, tetracycline has been listed as an essential medicine by the WHO. Therefore it is important to improve their production processes. Recently novel and traditional aqueous two-phase systems for the extraction have been developed with positive results. The present work performs an economic analysis of the production and recovery of tetracycline through the use of several ATPS through bioprocess modeling using specialized software (BioSolve, Biopharm Services Ltd, UK) to determine production costs per gram (CoG/g). First, a virtual model was constructed using published data on the recovery of tetracycline and extended to incorporate uncertainties. To determine how the model behaved, a sensitivity analysis and Monte Carlo simulations were performed. Results showed that ATPS formed by cholinium chloride/K3PO4 was the best option to recover tetracycline, as it had the lowest CoG/g (US$ 672.83/g), offered the highest recovery yield (92.42%), second best sample input capacity (45% of the ATPS composition) and one of the lowest materials contribution to cost. The ionic liquid-based method of ATPS is a promising alternative for recovering tetracycline from fermentation broth.

Effects of bed compression on protein separation on gel filtration chromatography at bench and pilot scale.

BACKGROUND: Poorly packed chromatography columns are known to reduce drastically the column efficiency and produce broader peaks. Controlled bed compression has been suggested to be a useful approach for solving this problem. Here the relationship between column efficiency and resolution of protein separation are examined when preparative chromatography media were compressed using mechanical and hydrodynamic methods. Sepharose CL-6B, an agarose based size exclusion media was examined at bench and pilot scale. The asymmetry and height equivalent of a theoretical plate (HETP) was determined by using 2% v/v acetone, whereas the void volume and intraparticle porosity (ϵ p) were estimated by using blue dextran. A protein mixture of ovalbumin (chicken), bovine serum albumin (BSA) and γ'- globulin (bovine) with molecular weights of 44, 67 and 158 kDa, respectively, were used as a 'model' separation challenge. RESULTS: Mechanical compression achieved a reduction in plate height for the column with a concomitant improvement in asymmetry. Furthermore, the theoretical plate height decreased significantly with mechanical compression resulting in a 40% improvement in purity compared with uncompressed columns at the most extreme conditions of compression used. CONCLUSION: The results suggest that the mechanical bed compression of Sepharose CL-6B can be used to improve the resolution of protein separation. © 2017 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Integration of host strain bioengineering and bioprocess development using ultra-scale down studies to select the optimum combination: an antibody fragment primary recovery case study.

An ultra scale-down primary recovery sequence was established for a platform E. coli Fab production process. It was used to evaluate the process robustness of various bioengineered strains. Centrifugal discharge in the initial dewatering stage was determined to be the major cause of cell breakage. The ability of cells to resist breakage was dependant on a combination of factors including host strain, vector, and fermentation strategy. Periplasmic extraction studies were conducted in shake flasks and it was demonstrated that key performance parameters such as Fab titre and nucleic acid concentrations were mimicked. The shake flask system also captured particle aggregation effects seen in a large scale stirred vessel, reproducing the fine particle size distribution that impacts the final centrifugal clarification stage. The use of scale-down primary recovery process sequences can be used to screen a larger number of engineered strains. This can lead to closer integration with and better feedback between strain development, fermentation development, and primary recovery studies.

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.

Ultra scale-down of protein refold screening in microwells: challenges, solutions and application.

Steps for the refolding of proteins from solubilized inclusion bodies or misfolded product often represent bottlenecks in process development, where optimal conditions are typically derived empirically. To expedite refolding optimization, microwell screening may be used to test multiple conditions in parallel. Fast, accurate, and reproducible assays are required for such screening processes, and the results derived must be representative of the process at full scale. This article demonstrates the use of these microscale techniques to evaluate the effects of a number of additives on the refolding of IGF-1 from denatured inclusion bodies, using an established HPLC assay for this protein. Prior to this, microwell refolding was calibrated for scale-up using hen egg-white lysozyme (HEWL) as an initial model protein, allowing us to implement and compare several assays for protein refolding, including turbidity, enzyme activity, and chromatographic methods, and assess their use for microwell-based experimentation. The impact of various microplate types upon protein binding and loss is also assessed. Solution mixing is a key factor in protein refolding, therefore we have characterized the effects of different methods of mixing in microwells in terms of their impact on protein refolding. Our results confirm the applicability and scalability of microwell screening for the development of protein refolding processes, and its potential for application to new inclusion body-derived protein products.

High throughput process development workflow with advanced decision-support for antibody purification.

Chromatography remains the workhorse in antibody purification; however process development and characterisation still require significant resources. The high number of operating parameters involved requires extensive experimentation, traditionally performed at small- and pilot-scale, leading to demands in terms of materials and time that can be a challenge. The main objective of this research was the establishment of a novel High Throughput Process Development (HTPD) workflow combining scale-down chromatography experimentation with advanced decision-support techniques in order to minimise the consumption of resources and accelerate the development timeframe. Additionally, the HTPD workflow provides a framework to rapidly manipulate large datasets in an automated fashion. The central component of the HTPD workflow is the systematic integration of a microscale chromatography experimentation strategy with an advanced chromatogram evaluation method, design of experiments (DoE) and multivariate data analysis. The outputs of this are leveraged into the screening and optimisation components of the workflow. For the screening component, a decision-support tool was developed combining different multi-criteria decision-making techniques to enable a fair comparison of a number of CEX resin candidates and determine those that demonstrate superior purification performance. This provided a rational methodology for screening chromatography resins and process parameters. For the optimisation component, the workflow leverages insights provided through screening experimentation to guide subsequent DoE experiments so as to tune significant process parameters for the selected resin. The resulting empirical correlations are linked to a stochastic modelling technique so as to predict the optimal and most robust chromatographic process parameters to achieve the desired performance criteria.

A methodical approach to ultra-scale-down of process sequences: application to casein removal from the milk of transgenic animals.

Scale-down models of individual operations are widely used in biopharmaceutical process development to obtain information about the performance of production-scale equipment on the basis of inexpensive and efficient laboratory-scale tests, for the purposes of validation or optimization or characterization studies. We have investigated the ability of scale-down models of whole process sequences to provide reliable information for process scale-up from laboratory- to pilot-scales of operation. Using the example of the recovery of a protein from transgenic milk, we have conducted an a priori scale-down analysis of a projected pilot-scale process sequence. A systematic approach was developed to ensure that all critical aspects of process behavior were included in the scale-down model, resulting in the creation of an accurate and reliable scale-down representation of the pilot-scale process. The data from scale-down process trials conducted at 70 and 200 mL scales of operation served to highlight crucial factors determining process performance, and proved reliable in predicting the performance of the pilot-scale process over a scaling factor of 1000.

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.

Decision-support software for the industrial-scale chromatographic purification of antibodies.

The high therapeutic and financial value offered by polyclonal antibodies and their fragments has prompted extensive commercialization for the treatment of a wide range of acute clinical indications. Large-scale manufacture typically includes antibody-specific chromatography steps that employ custom-made affinity matrices to separate product-specific IgG from the remainder of the contaminating antibody repertoire. The high cost of such matrices necessitates efficient process design in order to maximize their economic potential. Techniques that identify the most suitable operating conditions for achieving desired levels of manufacturing performance are therefore of significant utility. This paper describes the development of a computer model that incorporates the effects of capacity changes over consecutive chromatographic operational cycles in order to identify combinations of protein load and loading flowrate that satisfy preset constraints of product yield and throughput. The method is illustrated by application to the manufacture of DigiFab, an FDA-approved polyclonal antibody fragment purified from ovine serum, which is used to treat digoxin toxicity (Protherics U.K. Limited). The model was populated with data obtained from scale-down experimental studies of the commercial-scale affinity purification step, which correlated measured changes in matrix capacity with the total protein load and number of resin re-uses. To enable a tradeoff between yield and throughput, output values were integrated together into a single metric by multi-attribute decision-making techniques to identify the most suitable flowrate and feed concentration required for achieving target levels of DigiFab yield and throughput. Results indicated that reducing the flowrate by 70% (from the current level) and using a protein load at the midpoint of the range currently employed at production scale (approximately 200-500 g/L) would provide the most satisfactory tradeoff between yield and throughput.

A framework for the prediction of scale-up when using compressible chromatographic packings.

Experimental data are given for the solid pressure distributions in chromatography columns of various column aspect ratios packed with four types of agarose-based resin. The loss of column wall support at large scales can result in unexpectedly high pressures caused by the compression of the matrix via drag forces exerted by fluid flow through the bed. The need for an accurate model to predict flow conditions at increasing scale is essential for the scaling-up of chromatographic processes and for avoiding bed compression during operation. Several studies have generated correlations that allow for the prediction of column pressure drops, but they either are mathematically complex, which impairs their practical use, or require a large number of experiments to be performed before they can be used. In this study an empirical correlation was developed based on a previously proposed model, which links the critical velocity of operation of a chromatographic system (microcrit), to the gravity-settled bed height (L0), the column diameter (D), the feed viscosity (micro), and the compressibility of the chromatographic media used (micro 10%). The methodology developed in this study is straightforward to use and significantly reduces the burden of preceding laboratory-scale experimentation. The approach can be used to predict the critical velocity of any chromatographic system and will be useful in the development of chromatographic operations and for column sizing.

Adapted ultra scale-down approach for predicting the centrifugal separation behavior of high cell density cultures.

The work presented here describes an ultra scale-down (USD) methodology for predicting centrifugal clarification performance in the case of high cell density fermentation broths. Existing USD approaches generated for dilute systems led to a 5- to 10-fold overprediction of clarification performance when applied to such high cell density feeds. This is due to increased interparticle forces, leading to effects such as aggregation, flocculation, or even blanket sedimentation, occurring in the low shear environment of a laboratory centrifuge, which will not be apparent in the settling region of a continuous-flow industrial centrifuge. A USD methodology was created based upon the dilution of high solids feed material to approximately 2% wet wt/vol prior to the application of the clarification test. At this level of dilution cell-cell interactions are minimal. The dilution alters the level of hindered settling in the feed suspensions, and so mathematical corrections are applied to the resultant clarification curves to mimic the original feed accurately. The methodology was successfully verified: corrected USD curves accurately predicted pilot-scale clarification performance of high cell density broths of Saccharomyces cerevisiae and Escherichia coli cells. The USD method allows for the rapid prediction of large-scale clarification of high solids density material using millilitre quantities of feed. The advantages of this method to the biochemical engineer, such as the enabling of rapid process design and scale-up, are discussed.

Evaluation of a novel agarose-based synthetic ligand adsorbent for the recovery of antibodies from ovine serum.

This paper evaluates a prototype agarose-based affinity adsorbent utilizing a bound synthetic ligand designed to replace Protein A as an IgG-affinity capture resin and compares its purification characteristics with four commercially available matrices for the recovery of polyclonal antibodies from crude hyperimmune ovine serum. The novel adsorbent was found to show the highest dynamic capacity (29.2 mg/mL) of all matrices under evaluation--30% higher than the other commercial adsorbents evaluated. When using a post-load caprylic acid wash, IgG yields of over 85% and purities of over 90% were achieved consistently over multiple loading cycles. To evaluate bead diffusion, inverted confocal microscopy was used to visualise fluorescent antibody binding on to individual adsorbent beads in real time. The results indicate that the binding characteristics of the prototype adsorbent are similar to those obtained with Protein G Sepharose. This study indicates that the high-capacity prototype matrix is a feasible and potentially cost-effective alternative for the direct capture of antibodies from crude ovine serum and may therefore also be applicable to the purification of other complex industrial feedstocks such as transgenic milk or monoclonal antibodies expressed using recombinant technologies.

A prototype software methodology for the rapid evaluation of biomanufacturing process options.

A three-layered simulation methodology is described that rapidly evaluates biomanufacturing process options. In each layer, inferior options are screened out, while more promising candidates are evaluated further in the subsequent, more refined layer, which uses more rigorous models that require more data from time-consuming experimentation. Screening ensures laboratory studies are focused only on options showing the greatest potential. To simplify the screening, outputs of production level, cost and time are combined into a single value using multi-attribute-decision-making techniques. The methodology was illustrated by evaluating alternatives to an FDA (U.S. Food and Drug Administration)-approved process manufacturing rattlesnake antivenom. Currently, antivenom antibodies are recovered from ovine serum by precipitation/centrifugation and proteolyzed before chromatographic purification. Alternatives included increasing the feed volume, replacing centrifugation with microfiltration and replacing precipitation/centrifugation with a Protein G column. The best alternative used a higher feed volume and a Protein G step. By rapidly evaluating the attractiveness of options, the methodology facilitates efficient and cost-effective process development.

Optimization-based framework for resin selection strategies in biopharmaceutical purification process development.

This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high-throughput screening exercise during the early stages of purification process development. An optimization-based decision support framework is proposed to process the data generated from microscale experiments to identify the best resins to maximize key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming model is developed and solved using the ε-constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with eight and three candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1116-1126, 2017.

Medium term planning of biopharmaceutical manufacture with uncertain fermentation titers.

The growing trend of employing multiproduct manufacturing facilities along with the randomness inherent in the biopharmaceutical manufacturing environment is creating significant scheduling and planning challenges for the biopharmaceutical industry. This work focuses on capturing the effect of uncertainty in fermentation titers when optimizing the planning of biopharmaceutical manufacturing campaigns. A mixed integer linear programming (MILP) model based on previous work is derived via chance constrained programming (CCP). The methodology is applied to two illustrative examples, and the results are compared with those from the deterministic model and a multiscenario model accompanied by an iterative construction algorithm. The computational results indicate that the proposed methodology offers significant improvements in solution quality over the compared approaches and presents an opportunity for biopharmaceutical manufacturers to make better medium term planning decisions, particularly under uncertain manufacturing conditions.

Life-cycle and cost of goods assessment of fed-batch and perfusion-based manufacturing processes for mAbs.

Life-cycle assessment (LCA) is an environmental assessment tool that quantifies the environmental impact associated with a product or a process (e.g., water consumption, energy requirements, and solid waste generation). While LCA is a standard approach in many commercial industries, its application has not been exploited widely in the bioprocessing sector. To contribute toward the design of more cost-efficient, robust and environmentally-friendly manufacturing process for monoclonal antibodies (mAbs), a framework consisting of an LCA and economic analysis combined with a sensitivity analysis of manufacturing process parameters and a production scale-up study is presented. The efficiency of the framework is demonstrated using a comparative study of the two most commonly used upstream configurations for mAb manufacture, namely fed-batch (FB) and perfusion-based processes. Results obtained by the framework are presented using a range of visualization tools, and indicate that a standard perfusion process (with a pooling duration of 4 days) has similar cost of goods than a FB process but a larger environmental footprint because it consumed 35% more water, demanded 17% more energy, and emitted 17% more CO2 than the FB process. Water consumption was the most important impact category, especially when scaling-up the processes, as energy was required to produce process water and water-for-injection, while CO2 was emitted from energy generation. The sensitivity analysis revealed that the perfusion process can be made more environmentally-friendly than the FB process if the pooling duration is extended to 8 days. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1324-1335, 2016.

Integrated economic and experimental framework for screening of primary recovery technologies for high cell density CHO cultures.

Increases in mammalian cell culture titres and densities have placed significant demands on primary recovery operation performance. This article presents a methodology which aims to screen rapidly and evaluate primary recovery technologies for their scope for technically feasible and cost-effective operation in the context of high cell density mammalian cell cultures. It was applied to assess the performance of current (centrifugation and depth filtration options) and alternative (tangential flow filtration (TFF)) primary recovery strategies. Cell culture test materials (CCTM) were generated to simulate the most demanding cell culture conditions selected as a screening challenge for the technologies. The performance of these technology options was assessed using lab scale and ultra scale-down (USD) mimics requiring 25-110mL volumes for centrifugation and depth filtration and TFF screening experiments respectively. A centrifugation and depth filtration combination as well as both of the alternative technologies met the performance selection criteria. A detailed process economics evaluation was carried out at three scales of manufacturing (2,000L, 10,000L, 20,000L), where alternative primary recovery options were shown to potentially provide a more cost-effective primary recovery process in the future. This assessment process and the study results can aid technology selection to identify the most effective option for a specific scenario.

Economic analysis of uricase production under uncertainty: Contrast of chromatographic purification and aqueous two-phase extraction (with and without PEG recycle).

Uricase is the enzyme responsible for the breakdown of uric acid, the key molecule leading to gout in humans, into allantoin, but it is absent in humans. It has been produced as a PEGylated pharmaceutical where the purification is performed through three sequential chromatographic columns. More recently an aqueous two-phase system (ATPS) was reported that could recover Uricase with high yield and purity. Although the use of ATPS can decrease cost and time, it also generates a large amount of waste. The ability, therefore, to recycle key components of ATPS is of interest. Economic modelling is a powerful tool that allows the bioprocess engineer to compare possible outcomes and find areas where further research or optimization might be required without recourse to extensive experiments and time. This research provides an economic analysis using the commercial software BioSolve of the strategies for Uricase production: chromatographic and ATPS, and includes a third bioprocess that uses material recycling. The key parameters that affect the process the most were located via a sensitivity analysis and evaluated with a Monte Carlo analysis. Results show that ATPS is far less expensive than chromatography, but that there is an area where the cost of production of both bioprocesses overlap. Furthermore, recycling does not impact the cost of production. This study serves to provide a framework for the economic analysis of Uricase production using alternative techniques.

Decision-support tool for assessing biomanufacturing strategies under uncertainty: stainless steel versus disposable equipment for clinical trial material preparation.

This paper presents the application of a decision-support tool, SIMBIOPHARMA, for assessing different manufacturing strategies under uncertainty for the production of biopharmaceuticals. SIMBIOPHARMA captures both the technical and business aspects of biopharmaceutical manufacture within a single tool that permits manufacturing alternatives to be evaluated in terms of cost, time, yield, project throughput, resource utilization, and risk. Its use for risk analysis is demonstrated through a hypothetical case study that uses the Monte Carlo simulation technique to imitate the randomness inherent in manufacturing subject to technical and market uncertainties. The case study addresses whether start-up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The effects of fluctuating product demands and titers on the performance of a biopharmaceutical company manufacturing clinical trial material are analyzed. The analysis highlights the impact of different manufacturing options on the range in possible outcomes for the project throughput and cost of goods and the likelihood that these metrics exceed a critical threshold. The simulation studies highlight the benefits of incorporating uncertainties when evaluating manufacturing strategies. Methods of presenting and analyzing information generated by the simulations are suggested. These are used to help determine the ranking of alternatives under different scenarios. The example illustrates the benefits to companies of using such a tool to improve management of their R&D portfolios so as to control the cost of goods.

Combining multiple quantitative and qualitative goals when assessing biomanufacturing strategies under uncertainty.

This paper reports how financial and operational results from bioprocess simulations can be combined with other criteria pertinent to decision-making predictions to provide a more holistic approach to the evaluation of biomanufacturing alternatives. The classical additive weighting method, which is a multiattribute decision-making technique that can account for both the quantitative and qualitative parameters that ultimately need to be considered, is used. Its application is demonstrated through a case study that addresses whether start-up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The technique is extended to allow for uncertainty in parameters. An illustration of its use to compare alternatives based on cumulative frequency curves of the aggregate scores is provided. For cases where it is difficult to discriminate between the options, plots of risk versus reward are shown to be useful for identifying the best alternative based on the risk preference of the company's management.