Sunil Chhatre

A microscale approach for predicting the performance of chromatography columns used to recover therapeutic polyclonal antibodies.

A microscale approach is described which screens conditions for recovering polyclonal antibodies from ovine sera by mixed-mode cation-exchange chromatography. The impact of pH and loading buffer salt concentration were assessed using robotically operated 20microL packed pipette tips. Low salt concentrations delivered capacities up to 41mg/mL, while only half this level was obtained at high salt concentrations. Two of the screened conditions were then tested in a 10mL packed bed and overall trends in capacity, yield and purity were found to be retained. Microscale pipette tips thus provided a useful basis for the rapid, approximate definition of a chromatography design space.

Purification of antibodies using the synthetic affinity ligand absorbent MAbsorbent A2P

A protocol for the purification of polyclonal antibodies from ovine serum using the synthetic protein A absorbent MAbsorbent A2P is described. Clarified serum is loaded directly onto the affinity column without prior adjustment and albumin and unwanted serum components are washed from the column using a sodium octanoate buffer before elution of bound antibodies. MAbsorbent A2P was shown to bind ∼27 mg ml−1 of polyclonal immunoglobulin under overloading conditions, with eluted IgG purities of >90% and minor levels of albumin (∼1%). The anticipated time required to complete the purification protocol is 6–7 h. Although the protocol is similar to methods utilized for antibody purification using chromatography with protein A derived from the cell wall of the microorganism Staphylococcus aureus or protein G from Streptococcus as the affinity ligands, affinity absorbents based on synthetic ligands offer a number of advantages to compounds derived from biological sources, in particular robustness, relatively low cost, ease of sanitization and, in principle, lack of biological contamination.

Ultra scale-down approach to correct dispersive and retentive effects in small-scale columns when predicting larger scale elution profiles

Ultra scale‐down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector, but for them to be of value, scale‐down mimics must predict large‐scale process performance accurately. For example, one application of a scale‐down model involves using it to predict large‐scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large‐scale profiles from data generated by small laboratory columns is complicated, however, by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale‐down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale‐down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory‐ and 18.3 L pilot‐scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger‐scale elution profiles. These data then provide the justification to adjust small‐scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large‐scale and which can then be used for subsequent ultra scale‐down operations.

Global Sensitivity Analysis for the determination of parameter importance in bio-manufacturing processes

The present paper describes the application of GSA (Global Sensitivity Analysis) techniques to mathematical models of bioprocesses in order to rank inputs such as feed titres, flow rates and matrix capacities for the relative influence that each exerts upon outputs such as yield or throughput. GSA enables quantification of both the impact of individual variables on process outputs, as well as their interactions. These data highlight those attributes of a bioprocess which offer the greatest potential for achieving manufacturing improvements. Whereas previous GSA studies have been limited to individual unit operations, this paper extends the treatment to an entire downstream process and illustrates its utility by application to the production of a Fab‐based rattlesnake antivenom called CroFab™ [(Crotalidae Polyvalent Immune Fab (Ovine); Protherics U.K. Limited]. Initially, hyperimmunized ovine serum containing rattlesnake antivenom IgG (product), other antibodies and albumin is applied to a synthetic affinity ligand adsorbent column to separate the antibodies from the albumin. The antibodies are papain‐digested into Fab and Fc fragments, before concentration by ultrafiltration. Fc, residual IgG and albumin are eliminated by an ion‐exchanger and then CroFab‐specific affinity chromatography is used to produce purified antivenom. Application of GSA to the model of this process showed that product yield was controlled by IgG feed concentration and the synthetic‐material affinity columns capacity and flow rate, whereas product throughput was predominantly influenced by the synthetic materials capacity, the ultrafiltration concentration factor and the CroFab affinity flow rate. Such information provides a rational basis for identifying the most promising strategies for delivering improvements to commercial‐scale biomanufacturing processes.

An automated packed Protein G micro-pipette tip assay for rapid quantification of polyclonal antibodies in ovine serum

The demands on the biopharmaceutical sector to expedite process development have instigated the deployment of micro-biochemical engineering techniques to acquire manufacturing insight with extremely small sample volumes. In conjunction with automated liquid handlers, this permits the simultaneous evaluation of multiple operating conditions and reduces manual intervention. For these benefits to be sustained, novel ways are now required to accelerate analysis and so prevent this becoming a throughput bottleneck. For example, although Protein G HPLC is used to quantify antibody titres in bioprocess feedstocks, it can be time-consuming owing to the serial nature of its application. Although commercial options are available that can process many samples simultaneously, these require separate, potentially expensive instruments. A more integrated approach is desirable wherein the assay is implemented directly on a robot. This article describes a high-throughput alternative to antibody HPLC analysis which uses an eight-channel liquid handler to control pipette tips packed with 40 μL of Protein G affinity matrix. The linearity, range, limit of detection, specificity and precision of the method were established, with results showing that antibody was detected reliably and specifically between 0.10 and 1.00 mg/mL. Subsequently, the technique was used to quantify the antibody titre in ovine serum, which is used as feed material by BTG PLC for manufacturing FDA-approved polyclonal bio-therapeutics. The mean concentration determined by the tips was comparable to that found by HPLC, but the tip method delivered its results in less than 40% of the time and with the potential for further, substantial time-savings possible by using higher capacity robots.

The integrated simulation and assessment of the impacts of process change in biotherapeutic antibody production

Growing commercial pressures in the pharmaceutical industry are establishing a need for robust computer simulations of whole bioprocesses to allow rapid prediction of the effects of changes made to manufacturing operations. This paper presents an integrated process simulation that models the cGMP manufacture of the FDA‐approved biotherapeutic CroFab, an IgG fragment used to treat rattlesnake envenomation (Protherics U.K. Limited, Blaenwaun, Ffostrasol, Llandysul, Wales, U.K.). Initially, the product is isolated from ovine serum by precipitation and centrifugation, before enzymatic digestion of the IgG to produce FAB and FC fragments. These are purified by ion exchange and affinity chromatography to remove the FC and non‐specific FAB fragments from the final venom‐specific FAB product. The model was constructed in a discrete event simulation environment and used to determine the potential impact of a series of changes to the process, such as increasing the step efficiencies or volumes of chromatographic matrices, upon product yields and process times. The study indicated that the overall FAB yield was particularly sensitive to changes in the digestive and affinity chromatographic step efficiencies, which have a predicted 30% greater impact on process FAB yield than do the precipitation or centrifugation stages. The study showed that increasing the volume of affinity matrix has a negligible impact upon total process time. Although results such as these would require experimental verification within the physical constraints of the process and the facility, the model predictions are still useful in allowing rapid “what‐if” scenario analysis of the likely impacts of process changes within such an integrated production process.

Strategic Assay Selection for analytics in high‐throughput process development: Case studies for downstream processing of monoclonal antibodies

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

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 (∼200–500 g/L) would provide the most satisfactory tradeoff between yield and throughput.

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

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

Integrated Use of Ultra Scale-Down and Financial Modeling to Identify Optimal Conditions for the Precipitation and Centrifugal Recovery of Milk Proteins

This article investigates the integrated application of ultra scale‐down (USD) techniques and economic modeling as a means for identifying optimal bioprocess operating conditions. The benefits of the approach are illustrated for the recovery of lactoperoxidase (LPO) from bovine milk. In the process, milk is skimmed to deplete its lipid content, before being subjected to low pH incubation with acetic acid in order to precipitate the primary impurity (casein). Following removal of the solids by disk stack centrifugation, pH adjustment and filtration, cation exchange chromatography is used as a positive mode column step to bind the LPO before it is polished and freeze dried. An economic model of this process was used to identify where greatest product loss occurs and hence where the largest opportunity cost was being incurred. Scale‐down analysis was used to characterize the influence of the critical steps, identified as precipitation and centrifugation, upon LPO recovery. A mathematical model was used to relate the centrifuge feed flowrate and discharge interval to the supernatant yield, and it was shown that increasing the centrifugal solids residence time achieved superior solids de‐watering and so higher product yield, although this also increased the overall processing time. To resolve this conflict, scale‐down data were used again in conjunction with an economic model to determine the most suitable conditions that maximized annual profit and minimized operating costs. The results demonstrate the power of combining USD data with models of economic and process performance in order to establish the best overall operating strategies for biopharmaceutical manufacture.