![]() In this work, we address the rapid selection of optimal resins for integrated chromatographic separations by proposing the use of mathematical programming techniques. It is critical to use a systematic approach to select promising resins for integrated chromatographic separations. Thus, the best resin for one separation step may not be the best choice when the whole sequence is considered, since performance is also related to the resins used at the other steps in the chromatographic sequence, their operating conditions and performance. However, in practice at industrial scale, a chromatography sequence, with two to four chromatographic separation steps, is usually implemented. Each microscale experiment is capable of being implemented for only a single resin, and hence the optimal resin is only the best one for the specific conditions tested in that experiment. To deal with the substantial volume of data generated from such microscale HTS experiments, rapid analysis using a systematic methodology to focus on the conditions that result in optimal overall process performance can become therefore critical.Īn additional concern is that current HTS methods optimize a chromatographic step irrespective of the rest of the chromatographic steps. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 In HTS, the combination of robotic methods, parallel processing, and the miniaturization of bioprocess unit operations allows for a large number of potential process parameters to be examined within a short time, and also results in the generation of large amounts of data for evaluation. ![]() 1 Platforms that have a capacity for high‐throughput screening (HTS) are commonly used to identify the most promising candidates for further investigation, in terms of key criteria of large scale purification, like yield, purity, and productivity. In the early stages of purification process development, different types of resins need to be tested at small scale (1.5–5000 µL) under various operating conditions, including different pH values, salt concentrations, and flow rates, to establish the resin most suited for process application at large scale. on behalf of American Institute of Chemical Engineers Biotechnol. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. 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. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming model. A multiobjective mixed integer nonlinear programming model is developed and solved using the ε‐constraint method. 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. This method enabled > 90 % overall recovery of unformulated DS at ≥ 150 g/L.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. Most of the rinse was combined with the retentate to hit the target protein concentration. After the retentate was collected, a minimal volume of buffer was added for the UF rinse. ![]() During the UF/DF process, the antibody was initially concentrated to 90 g/L, diafiltered, and concentrated to ≥ 180 g/L, then the retentate was collected. Analysis showed that the Capto S run removed the excipients with yields of ≥ 96%. In addition, Capto S has lower resin costs, takes less time to process, and uses milder elution conditions. A Capto S column was chosen over Protein A chromatography to remove excipients from formulated drug substance because of its higher binding capacity. Fortunately, a sufficient supply of formulated DS was available for reprocessing. Since the pilot plants were not available for large-scale campaigns, a creative alternative was needed to produce 2 kg of antibody from formulated DS for these studies. During process scale-up, the project team decided to make a high-concentration mAb drug substance for subcutaneous injection and change the formulation. The scope of this work is two-fold: 1) excipients removal from formulated mAb drug substance by Capto S chromatography and 2) UF/DF process development to make high-concentration drug substance (DS) for subcutaneous injection. ![]()
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