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.
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.
Rapid analyses of chromatographic steps within a biopharmaceutical manufacturing process are often desirable to evaluate column performance, provide mass balance data and to permit accurate calculations of yields and recoveries. Using SPR (surface plasmon resonance) biosensor (Biacore) technology, we have developed a sandwich immunoassay to quantify polyclonal anti-digoxin Fab fragments used for the production of the FDA (Food and Drug Administration)-approved biotherapeutic DigiFab. The results show that specific Fab may be quantified in all affinity process streams and accurate yield and mass balance data calculated. Control experiments using sheep Fab and Fc indicate that the assay is specific to DigiFab. The quantification of potential leached ligand within chromatographic fractions may also be technically challenging, particularly when low-molecular-mass ligands are covalently coupled with an affinity absorbent. Typical methods to assess ligand leakage such as DDMA (digoxin-dicarboxymethoxylamine; digoxin analogue) often involve the use of labelled ligands and relatively complex and labour-intensive analytical techniques. Using the same analytical methodologies, an assay to detect leached or eluted ligand off the column was developed. The results indicate minimal levels of leached ligand in all chromatographic fractions, with total levels of leached DDMA calculated to be 1.52 microg. This is less than 0.01% of the total amount of DDMA coupled with the laboratory-scale affinity column. The SPR methods described in the present study may be applicable for the rapid in-process analysis of specific polyclonal Fab fragments (within a polyclonal mixture) and to rapidly assess leakage of small molecule ligands covalently attached to chromatographic supports.
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