Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.@en

Purification of recombinantly produced biopharmaceuticals involves removal of host cell material, such as host cell proteins (HCPs). For lysates of the common expression host Escherichia coli (E. coli) over 1500 unique proteins can be identified. Currently, understanding the behavior of individual HCPs for purification operations, such as preparative chromatography, is limited. Therefore, we aim to elucidate the elution behavior of individual HCPs from E. coli strain BLR(DE3) during chromatography. Understanding this complex mixture and knowing the chromatographic behavior of each individual HCP improves the ability for rational purification process design. Specifically, linear gradient experiments were performed using ion exchange (IEX) and hydrophobic interaction chromatography, coupled with mass spectrometry-based proteomics to map the retention of individual HCPs. We combined knowledge of protein location, function, and interaction available in literature to identify trends in elution behavior. Additionally, quantitative structure–property relationship models were trained relating the protein 3D structure to elution behavior during IEX. For the complete data set a model with a cross-validated R2 of 0.55 was constructed, that could be improved to a R2 of 0.70 by considering only monomeric proteins. Ultimately this study is a significant step toward greater process understanding.@en

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R² of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

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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 well-established 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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Imaging is increasingly more utilized as analytical technology in biopharmaceutical formulation research, with applications ranging from subvisible particle characterization to thermal stability screening and residual moisture analysis. This review offers a comprehensive overview of analytical imaging for scientists active in biopharmaceutical formulation research and development, where it presents the unique information provided by the ultraviolet (UV), visible (Vis), and infrared (IR) sections in the electromagnetic spectrum. The main body of this review consists of an outline of UV, Vis, and IR imaging techniques for several (bio)physical properties that are commonly determined during protein-based biopharmaceutical formulation characterization and development studies. The review concludes with a future perspective of applied imaging within the field of biopharmaceutical formulation research.

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The protein cloud-point temperature (TCloud) is a known representative of protein–protein interaction strength and provides
valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A
high-throughput low volume TCloud detection method was introduced in preceding work, where it was concluded that the
extracted value is an apparent TCloud (TCloud,app). As an understanding of the apparent nature is imperative to facilitate inter-
study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strate-
gies and 2 experimental parameters (sample volume and cooling rate) on TCloud,app detection of lysozyme. Different image
analysis strategies showed that TCloud,app is detectable by means of total pixel intensity difference and the total number of
white pixels, but the latter is also able to extract the ice nucleation temperature. Experimental parameter variation showed a
TCloud,app depression for increasing cooling rates (0.1–0.5 °C/min), and larger sample volumes (5–24 μL). Exploratory ther-
mographic data indicated this resulted from a temperature discrepancy between the measured temperature by the cryogenic
device and the actual sample temperature. Literature validation confirmed that the discrepancy does not affect the relative
inter-study comparability of the samples, regardless of the image analysis strategy or experimental parameters. Additionally,
high measurement precision was demonstrated, as TCloud,app changes were detectable down to a sample volume of only 5 μL
and for 0.1 °C/min cooling rate increments. This work explains the apparent nature of the TCloud detection method, showcases
its detection precision, and broadens the applicability of the experimental setup@en

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 time-consuming 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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Abstract: Short-term parameters correlating to long-term protein stability, such as the protein cloud point temperature (T_cloud), are of interest to improve efficiency during protein product development. Such efficiency is reached if short-term parameters are obtained in a low volume and high-throughput (HT) manner. This study presents a low volume HT detection method for (sub-zero) T_cloud determination of lysozyme, as such an experimental method is not available yet. The setup consists of a cryogenic device with an automated imaging system. Measurement reproducibility (median absolute deviation of 0.2 °C) and literature-based parameter validation (Pearson correlation coefficient of 0.996) were shown by a robustness and validation study. The subsequent case study demonstrated a partial correlation between the obtained apparent T_cloud parameter and long-term protein stability as a function of lysozyme concentration, ion type, ionic strength, and freeze/thaw stress. The presented experimental setup demonstrates its ability to advance short-term strategies for efficient protein formulation development. Graphical Abstract: [Figure not available: see fulltext.].

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Redesigning existing food protein formulations is necessary in situations where food authorities propose dose adjustments or removal of currently employed additives. Redesigning formulations involves evaluating substitute additives to obtain similar long-term physical stability as the original formulation. Such formulation screening experiments benefit from comprehensive data visualization, understanding the effects of substitute additives on long-term physical stability, and identification of short-term optimization targets. This work employs empirical phase diagrams to reach these benefits by combining multidimensional long-term protein physical stability data with short-term empirical protein properties. A case study was performed where multidimensional protein phase diagrams (1152 formulations) allowed for identification of stabilizing effects as a result of pH, methionine, sugars, salt, and minimized glycerol content. Corresponding empirical protein property diagrams (144 formulations) resulted in the identification of normalized surface tension as a short-term empirical protein property to reach long-term physical stability presumably similar to the original product, namely via preferential hydration. Additionally, changes in pH and salt were identified as environmental optimization targets to reach stability via repulsive electrostatic forces. This case study shows the applicability of the empirical phase diagram method to rationally perform formulation redesign screenings, while simultaneously expanding knowledge on protein long-term physical stability.

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Identification of long-term stable biopharmaceutical formulations is essential for biopharmaceutical product development. Reduction of the number of long-term storage experiments and a well-defined formulation search space requires knowledge-based formulation screenings and a detailed protein phase behavior understanding. To achieve this, short-term analytical techniques can serve as predictors for long-term protein phase behavior. Protein phase behavior studies that investigate this concept commonly display shortcomings such as limited and small datasets, sample adjustments, or simplistic data analysis. To overcome these shortcomings, 150 unique lysozyme solutions were analyzed using six different short-term analytical techniques. Lysozyme's structural properties, conformational stability, colloidal stability, surface charge, and surface hydrophobicity were obtained directly after formulation preparation. Employing the empirical phase diagram method, this short-term data was correlated to long-term physical stability data obtained during 40 days of storage. Short-term protein properties showed partial correlation to long-term phase behavior. Structural differences, changing surface properties, colloidal stability, and conformation stability as a function of formulation conditions were observed. This study contributes to long-term protein phase behavior research by presenting a systematic, data-dependent, and multidimensional data evaluation workflow to create a comprehensive overview of short-term protein analytics in relation to long-term protein phase behavior.

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Knowledge-based experimental design can aid biopharmaceutical high-throughput screening (HTS) experiments needed to identify critical manufacturability parameters. Prior knowledge can be obtained via computational methods such as protein property extraction from 3-D protein structures. This study presents a high-throughput 3-D structure preparation and refinement pipeline that supports structure screenings with an automated and data-dependent workflow. As a case study, three chimeric virus-like particle (VLP) building blocks, hepatitis B core antigen (HBcAg) dimers, were constructed. Molecular dynamics (MD) refinement quality, speed, stability, and correlation to zeta potential data was evaluated using different MD simulation settings. Settings included 2 force fields (YASARA2 and AMBER03) and 2 pKa computation methods (YASARA and H++). MD simulations contained a data-dependent termination via identification of a 2 ns Window of Stability, which was also used for robust descriptor extraction. MD simulation with YASARA2, independent of pKa computation method, was found to be most stable and computationally efficient. These settings resulted in a fast refinement (6.6–37.5 h), a good structure quality (−1.17–−1.13) and a strong linear dependence between dimer surface charge and complete chimeric HBcAg VLP zeta potential. These results indicate the computational pipeline's applicability for early-stage candidate assessment and design optimization of HTS manufacturability or formulability experiments.@en

Protein phase diagrams are a tool to investigate the cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphologic features, such as crystal size, as well as kinetic features, such as crystal growth time. Commonly used data visualization techniques include individual line graphs or phase diagrams based on symbols. These techniques have limitations in terms of handling large data sets, comprehensiveness or completeness. To eliminate these limitations, morphologic and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram method. Morphologic features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength, and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the empirical phase diagram method can support high-throughput crystallization experiments in its data amount as well as its data complexity.

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