Biotechnology Progress最新文献

Despite significant advances in continuous manufacturing of monoclonal antibodies, the implementation of continuous virus inactivation (CVI) remains challenging due to standardization gaps that could compromise product quality and safety. This study identified limitations in minimum residence time (mRT) prediction for packed bed reactors (PBR) utilized for CVI. This work focused on characterizing the residence time distribution (RTD) behavior of tracers with varying molecular properties in four PBR configurations. The results demonstrated that tracer molecular size impacted mRT prediction, with larger molecules showing shorter residence times than smaller molecule tracers under identical conditions. During scale-up from 16 to 26 mm diameter columns, mRT was not maintained, suggesting that traditional chromatography scale-up principles may not be directly applicable to CVI using PBRs. Overall, this work established a helpful foundational understanding of how process material properties impact mRT prediction-a critical process parameter that would directly impact virus inactivation efficacy in integrated CVI systems.

In the biopharmaceutical industry, effective process control strategies are essential for enhancing drug substance quality and yield. This study presents dielectric spectroscopy as a novel approach for in-line monitoring of cell apoptosis, enabling earlier detection of apoptotic events and related cellular changes. Utilizing permittivity measurements, we assessed Cole-Cole parameters, specifically critical frequency (f_c) and delta epsilon (Δε), as key performance indicators (KPIs) for real-time monitoring of cell health in CHO cell cultures during batch and perfusion processes. Our findings demonstrate that variations in these parameters correlate with cellular stress responses, such as nutrient limitation and high shear conditions, providing timely signals for process monitoring and control. By integrating these in-line measurements, we can enhance feeding strategies, ultimately improving cell viability and productivity. This approach not only streamlines the monitoring process but also offers a robust framework for proactive adjustments in bioprocessing, thereby optimizing overall performance and resource utilization.

Hydrophobic interaction chromatography (HIC) provides a powerful alternative impurity control method for antisense oligonucleotide purification relative to traditionally used anion exchange (AEX) and/or reverse phase methods. HIC is particularly effective in clearing process-related solvents and small molecules by ≥3 log₁₀ as well as failure sequences (sometimes called early eluting impurities (EEIs) by ≥90%). Additionally, HIC reduces harder to remove product-related impurities. These include branchmers (late eluting impurities (LEIs), oligonucleotides missing a single nucleotide (N-1 impurities), oligonucleotides lacking appropriate phosphorothioate sulfurization (P = O₁ impurity), and other synthesis-related impurities. To optimize the purification process, variables such as resin ligand, salt types, processing conditions, types of gradients, and loading ratios were systematically evaluated to achieve 90% yield and maximal impurity resolution. Loading the column at 32%-78% of its dynamic binding capacity (DBC), combined with stepwise wash and elution gradients, provided effective resolution of impurities in crude oligonucleotide mixtures. The desorption of the purified product was achieved in low lyotropic salt concentrations (typically ≤50 mM) using a stepwise gradient. This approach retained non-polar impurities such as LEIs within the column. When properly designed, HIC is an all-aqueous, scalable, cost effective and predictable purification process. It can be implemented as a stand-alone method or integrated into a dual-column process alongside orthogonal techniques, such as AEX, to achieve even higher levels of product purity.

Continued advancements in recombinant CHO expression of therapeutic mAbs have led to improved productivity but have also increased the HCP burden on the downstream purification process. In this work, we developed an in silico mediated workflow to facilitate the rapid development of non-protein A three-step processes for the effective removal of HCPs from a CHO-derived mAb therapeutic. Null CCF and pure mAb retention patterns were generated using linear gradient screens on a set of strategically selected resins, membrane adsorbers, and novel adsorbents. HCP characterization of key fractions was then carried out using RPLC "HCP fingerprinting" and the resulting retention database was processed using an in silico tool to generate a list of all possible three-step sequences subject to design constraints. Top-ranked processes generated by the tool were then evaluated and refined at the bench scale to produce several successful processes consisting of bind-elute capture followed by either a bind-elute and flowthrough step (91.4 ppm HCP with a cumulative product yield of 78.7%) or two flowthrough steps with no salt (96.1 ppm HCP with a cumulative yield of 81.4%).

Clonally derived cell lines generated from Chinese hamster ovary (CHO) cells encounter numerous stressors when cultured in high-intensity perfusion bioreactors leading to poor process performance. To circumvent this, the ability of CHO cells to adapt to different culture environments was exploited. Here host cells were selected in the presence of physical and chemical stressors associated with a perfusion environment by culturing at a high cell density in a perfusion bioreactor for 30 days. Following recovery and expansion, the performance of the resulting perfusion-evolved host was evaluated using stable transfectant pools and clones expressing biotherapeutics of different formats. Cell lines generated from the perfusion host outperformed the parental host at several fundamental stages of the clone selection process. Perfusion host-derived pools showed elevations in productivity, cell-specific productivity, end-of-run viability, and reduced lactate production in fed-batch culture. Use of the perfusion host for cell line generation resulted in an increased frequency of high-producing clones. Moreover, the perfusion host-derived clones demonstrated 30% higher productivity and improved mannose profile in the perfusion environment compared to the clones from the parental host. Furthermore, a comparative proteomic analysis between the two host types revealed unique regulatory networks that allowed us to gain insights into the underlying molecular processes influencing production performance. Taken together, the results suggest that the perfusion host may not only increase the efficiency of the cell line development process but may also serve as an efficient tool for improvement in production capability in the perfusion platform.

Scaled production of cultivated meat (CM) will co-produce large volumes of spent media. Recycling of abundant metabolites such as lactic acid in spent media offers an opportunity for valorization and reduction of the carbon footprint of CM production; however, the feasibility has yet to be examined. We modeled a conceptual five-step lactic acid recovery process integrated into a previously modeled CM facility and analyzed the corresponding cost and environmental impacts of recovering an 88% lactic acid solution. At an anticipated lactic acid concentration in spent media of 3 g/L, we found the net cost of recovery would be $0.71/kg lactic acid, with a 7.5-year simple payback period. Sales of lactic acid as a co-product could offset $0.06/kg of the cost of CM production. Depending on allocation scenarios, the environmental impact of CM production with an integrated recovery process had a -1.0 to +0.2 kg CO₂ eq effect on the carbon footprint and a -22 to +3 MJ effect on cumulative energy demand per kg of CM. Recovery of lactic acid from spent media also had a 25% lower carbon footprint than conventional fermentation processes. These model results suggest that recovery of lactic acid may be an economically viable and environmentally beneficial practice if validated in future CM production facilities. This original study provides crucial guidance for lactic acid valorization and other media recycling strategies that can be broadly applied to animal cell biomanufacturing industries.

Perfusion technologies play a growing role in the implementation of continuous processes for biotherapeutics production in mammalian-based manufacturing. However, their application to alternative production hosts is limited. Cell retention systems are of key importance for the efficiency of perfusion bioreactors. In this study, we investigate two cell retention technologies for the development of lab-scale Komagataella phaffii continuous processes. An acoustic-based process (AP) and a membrane-based process (MP) were developed using an acoustic cell separator (ACS) and a vibrating membrane filtration (VMF) device, respectively. Both systems allowed for continuous cell recycle and production of scFv13R4 antibody fragment for 8 days (AP) and 9 days (MP), without loss in productivity, while maintaining high viability (greater than 90%). Higher volumetric and specific productivities were achieved during the AP process, namely 50.63 ± 1.63 mg L^-1 day^-1 and 1.09 ± 0.07 mg g^-1 day^-1, against the 32.29 ± 1.21 mg L^-1 day^-1 and 0.44 ± 0.02 mg g^-1 day^-1 afforded by the MP process. The VMF device provided 100% separation efficiency with biomass accumulating up to concentrations of 74.1 ± 0.1 g L^-1 dry cell weight (DCW), whereas the acoustic device reached 55.1 ± 0.47 g L^-1 DCW at 98% separation efficiency. The acoustic device showed selectivity towards larger and more complex cells in the yeast population, which might be linked to the observed higher productivities for the AP process. This study discusses the advantages and drawbacks of both cell retention technologies and provides an outlook towards their future investigation in K. phaffii perfusion processes.

Chinese hamster ovary (CHO) cells have emerged as the predominant mammalian host for the production of therapeutic recombinant proteins, including monoclonal antibodies (mAbs), bispecific antibodies (bsAbs), and fusion proteins. To meet the growing demand for biologics and reduce manufacturing costs, the exploitation of efficient cell line development platforms is essential. Over the past decades, various selection markers, such as dihydrofolate reductase (DHFR), glutamine synthetase (GS), and antibiotic resistance genes, have been widely utilized in the development of production cell lines. In this study, we introduce the proline selection system, an alternative metabolic selection strategy, as an efficient approach to optimize our CHO cell line development platform. By employing yeast PRO1 and PRO2 genes as selection markers, proline selection effectively complements GS selection to establish high-producing cell lines for both mAbs and bsAbs. In particular, the integration of PRO1 and PRO2 genes into a single plasmid, in conjunction with the GS gene, significantly enhances productivity for asymmetric molecules. Optimized chain configuration across proline and GS selection plasmids can further boost protein yield. Additionally, the overexpression of regulator proteins can be leveraged with proline selection to enhance antibody production or fine-tune product quality. Taken together, the incorporation of proline selection into CHO cell line development, particularly when combined with GS selection, provides a consistent and streamlined strategy to meet the growing demand for high-quality biologics in the pharmaceutical industry.

Measurement and imaging of intra-matrix protein therapeutics diffusion is important due to the emergence of injectable biologics currently in various stages of research and clinical testing. These therapeutics are developed for delivery to hyaluronic acid (HA)-rich anatomical sites such as subcutaneous tissue, the vitreous humor, and knee joints, depending on the target tissue. Understanding their diffusion behavior is essential for optimizing drug delivery strategies. Our work presents an image analysis method suited for tracking IgG diffusion in low viscosity HA matrices representative of the vitreous humor, where diffusion occurs more rapidly unlike a previously reported analysis method for higher viscosity matrices where protein diffusion is significantly slower. The current method utilizes scanner images at 6.3 MP resolution, and an algorithm that removes background and calculates protein mass and concentration measured directly within matrices formulated to represent HA in an intravitreal environment. We report and demonstrate a robust method for predicting protein diffusion coefficient from images of label-free protein diffusing in a low viscosity HA matrix.

Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs. Therefore, applications based on artificial intelligence are essential for successfully manufacturing this new class of biologics more quickly and more economically. Some biologics manufacturers, academic researchers, and young entrepreneurs have already begun implementing artificial intelligence-based applications to increase operational efficiency, enhance process understanding, improve process monitoring, and achieve better regulatory compliance. Regulatory guidance from health agencies on the use of artificial intelligence and machine learning is acting as a catalyst in the adoption process of these new technologies by the biopharmaceutical industry. Research in artificial intelligence and machine learning has also advanced significantly in the last decade. At the same time, new cloud technologies have made the development and deployment of machine learning applications much easier. Several examples of artificial intelligence and machine learning applications in monoclonal antibodies manufacturing already exist. Cell and gene therapy, which present the future of medicine, will also benefit from this new technology. Overall, advancements in this domain will essentially help better serve patients' needs.