The transition to continuous biomanufacturing is considered the next step to reduce costs and improve process robustness in the biopharmaceutical industry, while also improving productivity and product quality. The platform production process for monoclonal antibodies (mAbs) is eligible for continuous processing to lower manufacturing costs due to patent expiration and subsequent growing competition. One of the critical quality attributes of interest during mAb purification is aggregate formation, with several processing parameters and environmental factors known to influence antibody aggregation. Therefore, a real-time measurement to monitor aggregate formation is crucial to have immediate feedback and process control and to achieve a continuous downstream processing. Miniaturized biosensors as an in-line process analytical technology tool could play a pivotal role to facilitate the transition to continuous manufacturing. In this review, miniaturization of already wellestablished methods to detect protein aggregation, such as dynamic light scattering, Raman spectroscopy and circular dichroism, will be extensively evaluated for the possibility of providing a real-time measurement of mAb aggregation. The method evaluation presented in this review shows which limitations of each analytical method still need to be addressed and provides application examples of each technique for mAb aggregate characterization. Additionally, challenges related to miniaturization are also addressed, such as the design of the microfluidic chip and the microfabrication material. The evaluation provided in this review shows why the development of microfluidic biosensors is considered the key for real-time measurement of mAb aggregates and how it can contribute to the transition to a continuous processing.

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The correlation between the dimensionless retention times (DRT) of proteins in hydrophobic interaction chromatography (HIC) and their surface properties were investigated. A ternary atomic-level hydrophobicity scale was used to calculate the distribution of local average hydrophobicity across the proteins surfaces. These distributions were characterized by robust descriptive statistics to reduce their sensitivity to small changes in the three-dimensional structure. The applicability of these statistics for the prediction of protein retention behaviour was looked into. A linear combination of robust statistics describing the central tendency, heterogeneity and frequency of highly hydrophobic clusters was found to have a good predictive capability (R2  = 0.78), when combined a factor to account for protein size differences. The achieved error of prediction was 35% lower than for a similar model based on a description of the protein surface on an amino acid level. This indicates that a robust and mathematically simple model based on an atomic description of the protein surface can be used for the prediction of the retention behaviour of conformationally stable globular proteins with a well determined 3D structure in HIC. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:372-381, 2016.

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Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious timeconsuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence.

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