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The biopharmaceutical industry is at a turning point moving toward a more customized and patient-oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high-throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry. [ABSTRACT FROM AUTHOR]

The identification of optimal process parameters for the isolation of a target component from multicomponent mixtures is especially challenging in industrial applications. With constantly increasing time-to-market pressure, screening a large parameter space is not feasible and design-of-experiment approaches with few experiments might fail due to dynamic and nonlinear reactions to small parameter changes. Model-based optimization can determine optimal operating conditions, once the model has been calibrated to the specific process step. In this work, parameters for the steric mass action model were estimated for the target protein and three impurities of an industrial antibody cation-exchange purification step using only chromatograms at different wavelengths and additional fraction analyses with size exclusion chromatography. Information on the molar or mass concentrations in the feed are not available. The model-based optimization results coincide with conventional chromatogram-based optimization. [ABSTRACT FROM AUTHOR]

UV absorbance measurements play an important role in bioprocess development. Yield and purity are often evaluated in terms of peak percentages in analytical SEC or ion-exchange chromatography. Also, industrial chromatography steps are usually controlled based on UV data with pooling decisions according to absorbance thresholds. Model-based process development would make elaborate screening experiments redundant, once the model has been calibrated to the specific process step. So far, absorbance measurements could not be used directly for modeling chromatography steps as the commonly applied models rely on mass or molar concentration. This study presents mechanistic modeling of an industrially relevant chromatography setting without any knowledge of the feed composition. The model equations were rewritten to employ boundary conditions in UV absorbance units, the absorption coefficients were shifted into the isotherm, and standard parameter estimation procedures could be applied. An anion-exchange chromatography case study of a target protein expressed in Escherichia coli and 11 lumped impurity peaks demonstrated practical applicability. The target protein concentration in the feed material was estimated from chromatograms. Using this method, initially unknown feed concentrations can be determined a posteriori for ion-exchange and multimodal chromatography from single-component absorbance curves. [ABSTRACT FROM AUTHOR]