The red alga Porphyridium as a host for molecular farming: Efficient production of immunologically active hepatitis C virus glycoprotein.

Microalgae are promising production platforms for the cost-effective production of recombinant proteins. We have recently established that the red alga Porphyridium purpureum provides superior transgene expression properties, due to the episomal maintenance of transformation vectors as multicopy plasmids in the nucleus. Here, we have explored the potential of Porphyridium to synthesize complex pharmaceutical proteins to high levels. Testing expression constructs for a candidate subunit vaccine against the hepatitis C virus (HCV), we show that the soluble HCV E2 glycoprotein can be produced in transgenic algal cultures to high levels. The antigen undergoes faithful posttranslational modification by N-glycosylation and is recognized by conformationally selective antibodies, suggesting that it adopts a proper antigenic conformation in the endoplasmic reticulum of red algal cells. We also report the experimental determination of the structure of the N-glycan moiety that is attached to glycosylated proteins in Porphyridium. Finally, we demonstrate the immunogenicity of the HCV antigen produced in red algae when administered by injection as pure protein or by feeding of algal biomass.

What is the role of current mass spectrometry in pharmaceutical analysis?

The role of mass spectrometry (MS) has become more important in most application domains in recent years. Pharmaceutical analysis is specific due to its stringent regulation procedures, the need for good laboratory/manufacturing practices, and a large number of routine quality control analyses to be carried out. The role of MS is, therefore, very different throughout the whole drug development cycle. While it dominates within the drug discovery and development phase, in routine quality control, the role of MS is minor and indispensable only for selected applications. Moreover, its role is very different in the case of analysis of small molecule pharmaceuticals and biopharmaceuticals. Our review explains the role of current MS in the analysis of both small-molecule chemical drugs and biopharmaceuticals. Important features of MS-based technologies being implemented, method requirements, and related challenges are discussed. The differences in analytical procedures for small molecule pharmaceuticals and biopharmaceuticals are pointed out. While a single method or a small set of methods is usually sufficient for quality control in the case of small molecule pharmaceuticals and MS is often not indispensable, a large panel of methods including extensive use of MS must be used for quality control of biopharmaceuticals. Finally, expected development and future trends are outlined.

Capillary electrophoresis-mass spectrometry for protein analyses under native conditions: Current progress and perspectives.

Native mass spectrometry is a rapidly emerging technique for fast and sensitive structural analysis of protein constructs, maintaining the protein higher order structure. The coupling with electromigration separation techniques under native conditions enables the characterization of proteoforms and highly complex protein mixtures. In this review, we present an overview of current native CE-MS technology. First, the status of native separation conditions is described for capillary zone electrophoresis (CZE), affinity capillary electrophoresis (ACE), and capillary isoelectric focusing (CIEF), as well as their chip-based formats, including essential parameters such as electrolyte composition and capillary coatings. Further, conditions required for native ESI-MS of (large) protein constructs, including instrumental parameters of QTOF and Orbitrap systems, as well as requirements for native CE-MS interfacing are presented. On this basis, methods and applications of the different modes of native CE-MS are summarized and discussed in the context of biological, medical, and biopharmaceutical questions. Finally, key achievements are highlighted and concluded, while remaining challenges are pointed out.

Raman laser intensity and sample clarification on biochemical monitoring over Zika-VLP upstream stages.

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 105 cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH4+); and 0.0103 g/L and 2 % for potassium (K+). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH4+ presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K+ had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy.

Chemical Modification of Insulin Using Flow Chemistry.

Chemical modification of proteins is of growing importance to generate new molecular probes for chemical biology and for the development of new biopharmaceuticals. For example, two approved, long-acting insulin variants are lipidated at the LysB29 side-chain. Acylations of proteins have so far been performed in batch-mode. Here we describe the use of flow chemistry for site-selective acylation of a small protein, insulin. To the best of our knowledge this is the first report on flow chemistry for chemical modification of insulin. The first step was to develop reaction conditions for acylation of Lys B29 that gave a soluble mixture and thus was compatible with flow chemistry in a microreactor; this included selection of a soluble base. Secondly, the conditions, such as reagent ratios and flow rate were optimized. Third, the use of these conditions for the acylation with a wide range of acids was demonstrated. Finally, Boc-protected insulins were synthesized. Insulin remained stable towards these flow chemistry conditions. This use of flow chemistry for the chemical modification of insulin opens the prospect of producing chemically modified biopharmaceuticals by flow chemistry with fewer byproducts.

Engineering an Escherichia coli based in vivo mRNA manufacturing platform.

Synthetic mRNA is currently produced in standardized in vitro transcription systems. However, this one-size-fits-all approach has associated drawbacks in supply chain shortages, high reagent costs, complex product-related impurity profiles, and limited design options for molecule-specific optimization of product yield and quality. Herein, we describe for the first time development of an in vivo mRNA manufacturing platform, utilizing an Escherichia coli cell chassis. Coordinated mRNA, DNA, cell and media engineering, primarily focussed on disrupting interactions between synthetic mRNA molecules and host cell RNA degradation machinery, increased product yields >40-fold compared to standard "unengineered" E. coli expression systems. Mechanistic dissection of cell factory performance showed that product mRNA accumulation levels approached theoretical limits, accounting for ~30% of intracellular total RNA mass, and that this was achieved via host-cell's reallocating biosynthetic capacity away from endogenous RNA and cell biomass generation activities. We demonstrate that varying sized functional mRNA molecules can be produced in this system and subsequently purified. Accordingly, this study introduces a new mRNA production technology, expanding the solution space available for mRNA manufacturing.

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy.

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing.

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.

Integration of just-in-time learning with variational autoencoder for cell culture process monitoring based on Raman spectroscopy.

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real-time monitoring is through the application of just-in-time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman-based JITL method relies on the K-nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE-JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables.

Electrostatically Mediated Attractive Self-Interactions and Reversible Self-Association of Fc-Fusion Proteins.

Attractive self-interactions and reversible self-association are implicated in many problematic solution behaviors for therapeutic proteins, such as irreversible aggregation, elevated viscosity, phase separation, and opalescence. Protein self-interactions and reversible oligomerization of two Fc-fusion proteins (monovalent and bivalent) and the corresponding fusion partner protein were characterized experimentally with static and dynamic light scattering as a function of pH (5 and 6.5) and ionic strength (10 mM to at least 300 mM). The fusion partner protein and monovalent Fc-fusion each displayed net attractive electrostatic self-interactions at pH 6.5 and net repulsive electrostatic self-interactions at pH 5. Solutions of the bivalent Fc-fusion contained higher molecular weight species that prevented quantification of typical interaction parameters (B22 and kD). All three of the proteins displayed reversible self-association at pH 6.5, where oligomers dissociated with increased ionic strength. Coarse-grained molecular simulations were used to model the self-interactions measured experimentally, assess net self-interactions for the bivalent Fc-fusion, and probe the specific electrostatic interactions between charged amino acids that were involved in attractive electrostatic self-interactions. Mayer-weighted pairwise electrostatic energies from the simulations suggested that attractive electrostatic self-interactions at pH 6.5 for the two Fc-fusion proteins were due to cross-domain interactions between the fusion partner domain(s) and the Fc domain.

Modeling impact of inflation reduction act price negotiations on new drug pipeline considering differential contributions of large and small biopharmaceutical companies.

BACKGROUND/AIMS: Provisions of the Inflation Reduction Act mandating drug price negotiation by the Centers for Medicare & Medicaid Services have been criticized as a threat to pharmaceutical innovation. This study models potential impacts of the Inflation Reduction Act on drug approvals based on the differential contributions of large pharmaceutical companies and smaller biotechnology firms to clinical trials and the availability of capital. METHODS: This study examined research and development expense, revenue, and new investment (sale of common and preferred stock) by public biopharmaceutical companies and sponsorship of phased clinical trials in ClinicalTrials.gov. Financial data were incorporated in a model that estimates the number of drugs in each phase and approvals from reported phase-specific costs and transition rates, proportional sponsorship of trials by companies of different size, projected reductions in research and development spending based on company size, and three scenarios by which large companies may allocate reductions in research and development spending among clinical phases: (1) research and development proportionally reduced across phases; (2) research and development disproportionally reduced in phases 2-3; and (3) research and development disproportionately reduced in phases 1-2. RESULTS: Financial data were examined for 1378 public biopharmaceutical companies (2000-2018). Research and development expense was associated with revenue for 79 large companies with market capitalization ≥$7 billion with a 10% reduction in revenue reducing research and development expense by 8.4%. For 1299 smaller companies with market capitalization <$7 billion, research and development was associated with new investment, but not revenue. Smaller companies sponsored 55.2% of phase 1, 55.6% of phase 2, and 49.8% of phase 3 trials in ClinicalTrials.gov 2013-2018. In a model of clinical development that apportions clinical trials between large and smaller companies and determines the number of trials based on research and development resources, 400 drugs entering development produced 47.3 approvals (11.83% rate). A 10% reduction in revenue, reflecting the upper boundary of observed changes 2000-2018, with (1) proportional reduction across phases 1-3 produced 45.1 approvals (4.61% reduction); (2) disproportional reduction of phases 2-3 produced 42.8 approvals (9.55% reduction); and (3) disproportional reduction of phases 1-2 produced 46.9 approvals (0.95% reduction). CONCLUSION: This work suggests that the drug price negotiation provisions of the Inflation Reduction Act could have little or no impact on the number of drug approvals. While large pharmaceutical companies may reduce research and development spending, continued research and development by smaller companies and strategic allocation of research and development resources by large companies may mitigate any negative effects of the Inflation Reduction Act.

pH inactivation of SARS-CoV-2 and SARS-CoV in virus spiked protein A eluates from a mAb purification process.

Biopharmaceutical manufacturing processes may include a low pH treatment step as a means of inactivating enveloped viruses. Small scale virus clearance studies are routinely performed using model enveloped viruses such as murine leukemia virus to assess inactivation at the pH range used in the downstream manufacturing process. Further, as a means of bioburden reduction, chromatography resins may be cleaned and stored using sodium hydroxide and this can also inactivate viruses. The susceptibility of SARS-CoV-2 and SARS-CoV to low pH conditions using protein A eluate derived material from a monoclonal antibody production process as well as high pH cleaning conditions was addressed. SARS-CoV-2 was effectively inactivated at pH 3.0, moderately inactivated at pH 3.4, but not inactivated at pH 3.8. Low pH was less effective at inactivating SARS-CoV. Both viruses were inactivated at a high pH of ca.13.4. These studies provide important information regarding the effectiveness of viral clearance and inactivation steps of novel coronaviruses when compared to other enveloped viruses.

Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models.

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects.

Amelioration of tableting properties and dissolution rate of naproxen co-grinded with nicotinamide: preparation and characterization of co-grinded mixture.

OBJECTIVE AND SIGNIFICANCE: Reducing the dimensions, when other additives are present, shows potential as a method to improve the dissolution and solubility of biopharmaceutical classification system class II drugs that have poor solubility. In this investigation, the process involved grinding naproxen with nicotinamide with the aim of improving solubility and the rate of dissolution. METHODS: Naproxen was subjected to co-milling with urea, dimethylurea, and nicotinamide using a planetary ball mill for a duration of 90 min, maintaining a 1:1 molar ratio for the excipients (screening studies). The co-milled combinations, naproxen in its pure milled form, and a physical mixture were subjected to analysis using X-ray powder diffraction (XRPD), scanning electron microscopy (SEM), and solubility assessment. The mixture displaying the highest solubility (naproxen-nicotinamide) was chosen for further investigation, involving testing for intrinsic dissolution rate (IDR) and Fourier-transform infrared spectroscopy (FTIR) after co-milling for both 90 and 480 min. RESULTS AND CONCLUSION: The co-milled combination, denoted as S-3b and consisting of the most substantial ratio of nicotinamide to naproxen at 1:3, subjected to 480 min of milling, exhibited a remarkable 45-fold increase in solubility and a 9-fold increase in IDR. XRPD analysis of the co-milled samples demonstrated no amorphization, while SEM images portrayed the aggregates of naproxen with nicotinamide. FTIR outcomes negate the presence of any chemical interactions between the components. The co-milled sample exhibiting the highest solubility and IDR was used to create a tablet, which was then subjected to comprehensive evaluation for standard attributes. The results revealed improved compressibility and dissolution properties.

Chondroitin Sulfate for Cartilage Regeneration, Administered Topically Using a Nanostructured Formulation.

In the pharmaceutical sector, solid lipid nanoparticles (SLN) are vital for drug delivery incorporating a lipid core. Chondroitin sulfate (CHON) is crucial for cartilage health. It is often used in osteoarthritis (OA) treatment. Due to conflicting results from clinical trials on CHON's efficacy in OA treatment, there has been a shift toward exploring effective topical systems utilizing nanotechnology. This study aimed to optimize a solid lipid nanoparticle formulation aiming to enhance CHON permeation for OA therapy. A 3 × 3 × 2 Design of these experiments determined the ideal parameters: a CHON concentration of 0.4 mg/mL, operating at 20,000 rpm speed, and processing for 10 min for SLN production. Transmission electron microscopy analysis confirmed the nanoparticles' spherical morphology, ensuring crucial uniformity for efficient drug delivery. Cell viability assessments showed no significant cytotoxicity within the tested parameters, indicating a safe profile for potential clinical application. The cell internalization assay indicates successful internalization at 1.5 h and 24 h post-treatment. Biopharmaceutical studies supported SLNs, indicating them to be effective CHON carriers through the skin, showcasing improved skin permeation and CHON retention compared to conventional methods. In summary, this study successfully optimized SLN formulation for efficient CHON transport through pig ear skin with no cellular toxicity, highlighting SLNs' potential as promising carriers to enhance CHON delivery in OA treatment and advance nanotechnology-based therapeutic strategies in pharmaceutical formulations.

A Comprehensive Analysis of Biopharmaceutical Products Listed in the FDA's Purple Book.

Although biopharmaceuticals constitute around 10% of the drug landscape, eight of the ten top-selling products were biopharmaceuticals in 2023. This study did a comprehensive analysis of the FDA's Purple Book database. Firstly, our research uncovered market trends and provided insights into biologics distributions. According to the investigation, although biotechnology has advanced and legislative shifts have made the approval process faster, there are still challenges to overcome, such as molecular instability and formulation design. Moreover, our research comprehensively analyzed biological formulations, pointing out significant strategies regarding administration routes, dosage forms, product packaging, and excipients. In conjunction with biologics, the widespread integration of innovative delivery strategies will be implemented to confront the evolving challenges in healthcare and meet an expanding array of treatment needs.

Exploring Co-Amorphous Formulations Of Nevirapine: Insights From Computational, Thermal, And Solubility Analyses.

This study aimed to assess the formation of nevirapine (NVP) co-amorphs systems (CAM) with different co-formers (lamivudine-3TC, citric acid-CAc, and urea) through combined screening techniques as computational and thermal studies, solubility studies; in addition to develop and characterize suitable NVP-CAM. NVP-CAM were obtained using the quench-cooling method, and characterized by differential scanning calorimetry (DSC), X-ray diffractometry (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and polarized light microscopy (PLM), in addition to in vitro dissolution in pH 6.8. The screening results indicated intermolecular interactions occurring between NVP and 3TC; NVP and CAc, where shifts in the melting temperature of NVP were verified. The presence of CAc impacted the NVP equilibrium solubility, due to hydrogen bonds. DSC thermograms evidenced the reduction and shifting of the endothermic peaks of NVP in the presence of its co-formers, suggesting partial miscibility of the compounds. Amorphization was proven by XRD and PLM assays. In vitro dissolution study exhibited a significant increase in solubility and dissolution efficiency of NVP-CAM compared to free NVP. Combined use of screening studies was useful for the development of stable and amorphous NVP-CAM, with increased NVP solubility, making CAM promising systems for combined antiretroviral therapy.

Perspectives of cyanobacterial cell factories.

Cyanobacteria are prokaryotic photosynthetic microorganisms that can generate, in addition to biomass, useful chemicals and proteins/enzymes, essentially from sunlight, carbon dioxide, and water. Selected aspects of cyanobacterial production (isoprenoids and high-value proteins) and scale-up methods suitable for product generation and downstream processing are addressed in this review. The work focuses on the challenge and promise of specialty chemicals and proteins production, with isoprenoid products and biopharma proteins as study cases, and the challenges encountered in the expression of recombinant proteins/enzymes, which underline the essence of synthetic biology with these microorganisms. Progress and the current state-of-the-art in these targeted topics are emphasized.

Impact of dual-baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single-cell RNA-seq.

The insect cell-baculovirus expression vector system (IC-BEVS) has shown to be a powerful platform to produce complex biopharmaceutical products, such as recombinant proteins and virus-like particles. More recently, IC-BEVS has also been used as an alternative to produce recombinant adeno-associated virus (rAAV). However, little is known about the variability of insect cell populations and the potential effect of heterogeneity (e.g., stochastic infection process and differences in infection kinetics) on product titer and/or quality. In this study, transcriptomics analysis of Sf9 insect cells during the production of rAAV of serotype 2 (rAAV2) using a low multiplicity of infection, dual-baculovirus system was performed via single-cell RNA-sequencing (scRNA-seq). Before infection, the principal source of variability in Sf9 insect cells was associated with the cell cycle. Over the course of infection, an increase in transcriptional heterogeneity was detected, which was linked to the expression of baculovirus genes as well as to differences in rAAV transgenes (rep, cap and gfp) expression. Noteworthy, at 24 h post-infection, only 29.4% of cells enclosed all three necessary rAAV transgenes to produce packed rAAV2 particles, indicating limitations of the dual-baculovirus system. In addition, the trajectory analysis herein performed highlighted that biological processes such as protein folding, metabolic processes, translation, and stress response have been significantly altered upon infection. Overall, this work reports the first application of scRNA-seq to the IC-BEVS and highlights significant variations in individual cells within the population, providing insight into the rational cell and process engineering toward improved rAAV2 production in IC-BEVS.

Effect of protein fouling on filtrate flux and virus breakthrough behaviors during virus filtration process.

Virus filtration process is used to ensure viral safety in the biopharmaceutical downstream processes with high virus removal capacity (i.e., >4 log10 ). However, it is still constrained by protein fouling, which results in reduced filtration capacity and possible virus breakthrough. This study investigated the effects of protein fouling on filtrate flux and virus breakthrough using commercial membranes that had different symmetricity, nominal pore size, and pore size gradients. Flux decay tendency due to protein fouling was influenced by hydrodynamic drag force and protein concentration. As the results of prediction with the classical fouling model, standard blocking was suitable for most virus filters. Undesired virus breakthrough was observed in the membranes having relatively a large pore diameter of the retentive region. The study found that elevated levels of protein solution reduced virus removal performance. However, the impact of prefouled membranes was minimal. These findings shed light on the factors that influence protein fouling during the virus filtration process of biopharmaceutical production.