Monoclonal antibodies (mAbs) are nowadays fundamental in treating a wide range of severe diseases, including cancer, infections, or autoimmune disorders. Due to their high specificity, potent activity, and fewer side effects compared to small molecular drugs, the market for mAbs is growing continuously. Consequently, there is an increasing demand for process intensification technologies to increase the mAb throughput. This study introduces a novel integrated continuous biomanufacturing (ICB) process at lab-scale as a tool for process development. The ICB comprises a perfusion cultivation as an upstream process (USP) as well as a continuous multi-column chromatography capture step using membrane adsorbers (RC-BioSMB) and a continuous virus inactivation (VI) approach for the subsequent downstream processing. The process was continuously operated for 4 days. USP variations, like changes in titer and permeate flow rate, were successfully addressed by an adaptive control of the flow rates through all unit operations. The small-scale ICB was used to establish an adaptive control of the RC-BioSMB loading volume. A novel approach for the subsequent continuous VI was developed to enable processing at lab-scale with the associated very low flow rates. Throughout the lab-scale ICB process, a high overall yield of 88% was obtained with simultaneous high removal of process-related impurities like host cell proteins (3.4 log removal to 73 ppm) and DNA (2.9 log removal to 0.8 ppm).
Extracellular vesicles (EVs) derived from adherent cells are promising therapeutics for a wide variety of diseases. Previous studies have shown that mesenchymal stem cell (MSC)-derived EVs have many applications in wound healing and regenerative medicine. Specifically, MSC-derived EVs are safer than cell-based therapies because EV treatments do not involve the administration of live cells to patients. However, a lack of scalable workflows for producing EVs from 2D adherent sources is a major current limitation of the field. One proposed method for culture scale-up is to encapsulate MSCs in a gelatin methacryloyl (GelMA) hydrogel, which provides a 3D matrix that better mimics the in vivo microenvironment of human cells and can be shaped into spherical beads or thin films to support the growth of shear-sensitive cells inside bioreactors. To establish proof of concept, we embedded MSCs in a layer of GelMA hydrogel to assess the production rate, molecular properties, and functional characteristics of EVs collected from 3D cultures. Hydrogel-encapsulated MSCs yielded a greater number of EVs per volume of culture compared to traditionally grown unencapsulated MSCs, and 3D cultures produced EVs with improved functionality in a scratch assay relative to vehicle treatment. These findings support the hypothesis that GelMA can be used to support scalable manufacturing of bioactive EVs from adherent cell sources.
In the realm of clinical surgery, the unceasing pursuit of hemostatic materials with rapid hemostasis, excellent biocompatibility, degradability, wound-healing promotion, and economic viability is of utmost importance. This study, inspired by the preparation and hemostatic mechanism of Surgicel®, a prevalent surgical hemostatic agent, low-cost silk fibroin (SF) was modified. By adding carboxyl groups while maintaining the SF micron-fiber structure, the modified SF showed enhanced water and blood absorption, as well as a significantly improved Ca²⁺ transport capacity compared to unmodified SF. In vitro coagulation tests revealed that the modified SF had excellent hemostatic efficacy, with the quickest time at 76.75 ± 2.17 s. In the animal bleeding model, its fastest hemostatic time was 93 ± 9.63 s, with a mean blood loss of 0.28 ± 0.05 g, similar to Surgicel® (100.64 ± 2.87 s, 0.24 ± 0.05 g). Moreover, the modified SF hemostatic yarns exhibited favorable blood, cell, and biocompatibility, making them a promising option for surgical wound hemostasis, especially in organ surgery.
Psilocybin, an indole alkaloid of psychedelic mushrooms, has the potential to sustainably improve the treatment of several psychiatric diseases. So far, the psilocybin demand for clinical trials has been met by chemical synthesis. In this study, we pursued the biotechnological approach to develop a psilocybin production process utilizing an overproduction strain of Aspergillus nidulans. The developed shake flask cultivation regime was characterized rheologically and was evaluated concerning the sensitivity to changes in oxygen availability and power input. Due to the strong impact of power input on viscosity and thus, (oxygen) mass transfer and mixing of the filamentous culture broth, the bioprocess was scaled up from shake flask to 7 L stirred tank reactor according to the specific power input. Utilizing a pressure reactor, the oxygen supply of the viscous culture broth was enhanced. Subsequently, the nitrogen limitation was addressed by supplementing the cultivation medium with additional ammonium sulfate to provide sufficient building blocks for protein biosynthesis. By producing 542 mg L-1 psilocybin within 68 h from glucose, a robust and efficient batch bioprocess for psilocybin production was developed to potentially contribute to the future supply of psilocybin for pharmaceutical purposes. Moreover, we demonstrated the suitability of pressurized bioprocesses to counteract oxygen limitations for shear-sensitive, filamentous organisms.
Constraint-based reconstruction and analysis (COBRA) is a powerful systems biology approach for computational bioengineering. Synechococcus elongatus PCC 11801 and PCC 11802 are fast-growing, stress-tolerant cyanobacteria that are promising platforms for photosynthetic biomanufacturing. Here, we present constraint-based models (CBMs) iLV1052 and iLV1087 of PCC 11801 and PCC 11802, respectively, to facilitate and streamline strain engineering efforts. Following draft reconstruction using a template model, the models underwent extensive manual curation to reduce redundancy, and verification using BiGG, KEGG, and BRENDA databases. We added 281 and 69 new reactions for PCC 11801 and PCC 11802, respectively, associated with stress tolerance, growth stability, antioxidant defense, energy regulation, and sulfur acquisition. The models were refined through iterative debugging and validation using flux balance analysis, flux variability analysis, and single gene/reaction deletion analysis. Gene essentiality predictions gave 69% accuracy for PCC 11801 and 83% for PCC 11802. The flux maps captured key features of cyanobacterial metabolism, including an incomplete TCA cycle. The final PCC 11801 CBM contained 1130 reactions, 1052 genes, and 930 metabolites, while the PCC 11802 CBM included 1199 reactions, 1087 genes, and 951 metabolites. The simulations predicted that succinic acid exhibited the highest theoretical yield among the tested target products in both strains. Using the Optknock framework, phosphoenolpyruvate carboxylase was identified as a metabolic hotspot for future bioengineering efforts aimed at the production of valuable products like ethanol, butanol, and butanediol.
Continuous bioprocessing with Protein A affinity chromatography has demonstrated great potential to increase productivity and reduce the cost of goods in monoclonal antibody (mAb) production. However, maintaining process stability and responding to dynamic changes remains significant challenges, particularly in the real-time optimization and control of multi-column periodic counter-current chromatography (PCC) for Protein A affinity chromatography, due to the computational complexity of rapidly solving mechanistic models. To address this challenge, this study developed distilled physics-informed neural networks (PINNs) based on the general rate model (GRM) to accelerate and enhance the breakthrough curve fitting and four-column PCC (4C-PCC) process optimization. The distilled PINNs achieved a balance between prediction accuracy and computational speed. The 157k-parameter distilled PINN enabled the breakthrough curve fitting and 4C-PCC process optimization approximately 10 times faster than numerical methods while improving accuracy by about 40%. A smaller 2k-parameter model achieved a 22-fold acceleration with an acceptable trade-off in accuracy, and the optimization time was reduced to 1.44 s. Explainability analyses confirmed the PINN's capability to capture nonlinear and interactive effects among key process parameters. The PINN-accelerated GRM was then integrated with real-time model predictive control (MPC) and applied to a lab-scale continuous manufacturing process. PINN-based MPC maintained robust control of binding capacity and yield, achieving a productivity of 35 g/L resin/h and resin capacity utilization of 90%, despite resin capacity decay and upstream variability. This work demonstrates that the PINNs can provide a computationally efficient and physically consistent framework for real-time optimization and control of continuous processes. Integrating a mechanistic model with neural networks can enhance process understanding and robustness, supporting the implementation of continuous biomanufacturing for therapeutic proteins.
Patients around the world, especially in low- or medium-income countries (LMICs), are unable to afford the prohibitively high cost of monoclonal antibodies (mAbs). One promising approach to this issue of accessibility and affordability is intensifying the manufacturing process by employing continuous processing technology, which improves sustainability, efficiency, and process compactness, while reducings the operational expenses without sacrificing product safety or quality. Even though the biomanufacturing sector has been discussing continuous processing for more than 15 years, its adoption has not been as rapid as expected, with most companies still relying on traditional batch-based production systems. This brings up important inquiries: is it necessary to have a completely end-to-end continuous processing, or are hybrid and selectively intensified methods enough? In this article, we offer a perspective on the current status of continuous biomanufacturing, challenges associated with it and the production costs of five different intensification scenarios using a process simulation tool, incorporating both traditional batch processing and fully integrated continuous processing, and identifying the manufacturing hot-spots that result in the significant cost savings. Our findings suggest that instead of completely replacing the batch-process equipment, mAb manufacturing should strategically engage continuous technologies when they deliver clear value. Sustainable and efficient biopharmaceutical production that enables broad access and affordabilty is achievable through an evolutionary strategy based on planned intensification and risk-managed implementation. So, the question is not whether end-to-end continuous processing is required, but rather how to maximize its advantages while efficiently handling its complexity.
The competition in the biopharmaceutical market is increasing due to the market entry of biosimilars and rising costs in research and development of new drugs. Hence, continuous manufacturing gained significant attention due to its potential in reducing production cycle times and costs, as well as the possibility of real-time release testing. As a consequence, active monitoring and/or control systems are required for quantitative product quality measurements and in-process control. Process analytical technology emerged as a robust strategy for the development and implementation of in situ real-time testing, instead of the standard batch testing of end product. Through the evaluation of state-of-the-art applications, this review highlights future opportunities in the field of quantitative real-time analytical techniques for the characterization of monoclonal antibodies in continuous downstream biomanufacturing.
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