Nonwoven membranes are highly engineered fibrous materials that can be manufactured on a large scale from a wide range of different polymers, and their surfaces can be modified using a large variety of different chemistries and ligands. The fiber diameters, surface areas, pore sizes, total porosities, and thicknesses of the nonwoven mats can be carefully controlled, providing many opportunities for creative approaches for the development of novel membranes with unique properties to meet the needs of the future of downstream processing. Fibrous membranes are already finding use in ultrafiltration, microfiltration, depth filtration, and, more recently, in membrane chromatography for product capture and impurity removal. This article summarizes the various methods of manufacturing nonwoven fabrics, and the many methods available for the modification of the fiber surfaces. It also reviews recent studies focused on the use of nonwoven fabric devices in membrane chromatography and provides some perspectives on the challenges that need to be overcome to increase binding capacities, decrease residence times, and reduce pressure drops so that eventually they can replace resin column chromatography in downstream process operations.

The methodology for production of biologics is going through a paradigm shift from batch-wise operation to continuous production. Lot of efforts are focused on integration, intensification and continuous operation for decreased foot-print, material, equipment and increased productivity and product quality. These integrated continuous processes with on-line analytics becomes complex processes, which requires automation, monitoring and control of the operation, even unmanned or remote, which means bioprocesses with high level of automation or even autonomous capabilities. The development of these digital solutions becomes an important part of the process development and needs to be assessed early in the development chain. This work discusses a platform that allow fast development, advanced studies and validation of digital solutions for integrated continuous downstream processes. It uses an open, flexible and extendable real-time supervisory controller, called Orbit, developed in Python. Orbit makes it possible to communicate with a set of different physical setups and on the same time perform real-time execution. Integrated continuous processing often imply parallel operation of several setups and network of Orbit controllers makes it possible to synchronize complex process system. Data handling, storage and analysis are important properties for handling heterogeneous and asynchronous data generated in complex downstream systems. Digital twin applications, such as advanced model-based and plant-wide monitoring and control, are exemplified using computational extensions in Orbit, exploiting data and models. Examples of novel digital solutions in integrated downstream processes are automatic operation parameter optimization, Kalman filter monitoring and model-based batch-to-batch control.

Pre-packed chromatography columns and cassette filtration units offer many advantages in bioprocessing. These include reduced labor costs and processing times, ease of storage, and enhanced process flexibility. Rectangular formats are particularly attractive as they can be stacked and multiplexed together for continuous processing. Cylindrical chromatography beds have historically been favored even though their bed support and pressure-flow performance vary with bed dimensions. This work presents the performance of rectangular chromatography devices with novel internally supported beds. They are compatible with existing chromatography workstations and can be packed with any standard commercial resin. The devices offer pressure-flow characteristics independent of container-volume, simple multiplexing, and separation performance comparable to cylindrical columns. Their internal bed support allows mechanically less-rigid resins to be used at up to 4 times higher maximal linear velocities, and productivities approaching 200 g/L/h for affinity resins, compared to the 20 g/L/h typical of many column-based devices. Three 5 L devices should allow processing of up to 3 kg of monoclonal antibody per hour.

Genome-scale metabolic models (GSMMs) and flux balance analysis (FBA) have been extensively used to model and design bacterial fermentation. However, FBA-based metabolic models designed for simulating the dynamics of co-culture with quantitative accuracy are still uncommon, which is particularly true for lactic acid bacteria (LAB) used for yogurt fermentation. To investigate metabolic interactions in yogurt starter culture of Streptococcus thermophilus (ST) and Lactobacillus delbrueckii subsp. bulgaricus (LB), this study built a dynamic community-level GSMM based on metagenomic analysis. We first assessed the accuracy of the model by comparing predicted bacterial growth, consumption of lactose and production of lactic acid with reference experimental data, and then used it to predict the impact of different initial ST:LB inoculation ratios (gDW/gDW) on acidification. The dynamic simulation demonstrated the mutual dependence of ST and LB during the yogurt fermentation process. The modeling pipeline presented in this work provided a basis for the computer-aided process design and control of the production of fermented dairy products, contributing to the development of precision fermentation in the food industry.

Viral vectors for gene therapy, such as recombinant Adeno-Associated Viruses (rAAV), are produced in Human Embryonic Kidney (HEK) 293 cells. However, the presence of the SV40 T-antigen-encoding CDS SV40GP6 and SV40GP7 in the HEK293T genome raises safety issues when these cells are used in manufacturing for clinical purposes. We developed a new T-antigen-negative HEK cell line from ExcellGene’s proprietary HEKExpress®, using the CRISPR-Cas9 strategy. We obtained a high number of clonally-derived cell populations and all of them were demonstrated T-antigen negative. Stability study and AAV production evaluation showed that the deletion of the T-antigen-encoding locus did not impact neither cell growth nor viability nor productivity. The resulting CMC-compliant cell line, named HEKzeroT®, is able to produce high AAV titers, from small to large scale.

Oxygen and extracellular matrix (ECM)-derived biopolymers play vital roles in regulating many cellular functions in both the healthy and diseased liver. This study reveals the importance of synergistically tuning the internal microenvironment to enhance oxygen availability alongside phenotypic ECM ligand presentation to promote native metabolic functions of human liver three-dimensional (3D) cell aggregates. First, fluorinated (PFC) chitosan microparticles (MPs) were generated with a microfluidic chip, then their oxygen transport properties were studied using a custom ruthenium-based oxygen sensing approach. Next, to allow for integrin engagements the surfaces of these MPs were functionalized using liver ECM proteins including fibronectin, laminin-111, laminin-511, and laminin-521. These MPs were used to assemble heterogeneous composite spheroids composed of human hepatocytes and human hepatic stellate cells. After in vitro culture, liver-specific functions and cell adhesion patterns were compared between groups and cells showed enhanced liver phenotypic responses in response to laminin-511 and 521 as evidenced via enhanced E-cadherin and vinculin expression as well as albumin and urea secretion. Furthermore, hepatocytes and stellate cells arranged in more phenotypic arrangements when cocultured with laminin-511 and 521 modified MPs providing clear evidence that specific ECM proteins have distinctive roles in the phenotypic regulation of liver cells in engineering 3D spheroids. This study advances efforts to create more physiologically relevant organ models allowing for well-defined conditions and phenotypic cell signaling which together improve the relevance of 3D spheroid and organoid models.

Chondroitin sulfate A (CSA) is a valuable glycosaminoglycan that has great market demand. However, current synthetic methods are limited by requiring the expensive sulfate group donor 3′-phosphoadenosine-5′-phosphosulfate (PAPS) and inefficient enzyme carbohydrate sulfotransferase 11 (CHST11). Herein, we report the design and integration of the PAPS synthesis and sulfotransferase pathways to realize whole-cell catalytic production of CSA. Using mechanism-based protein engineering, we improved the thermostability and catalytic efficiency of CHST11; its T m and half-life increased by 6.9°C and 3.5 h, respectively, and its specific activity increased 2.1-fold. Via cofactor engineering, we designed a dual cycle strategy of regenerating ATP and PAPS to increase the supply of PAPS. Through surface display engineering, we realized the outer membrane expression of CHST11 and constructed a whole-cell catalytic system of CSA production with a 89.5% conversion rate. This whole-cell catalytic process provides a promising method for the industrial production of CSA.

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve manual calibration effort by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions using controlled mixing. We used this automated system to calibrate multi-product quality attribute models that accurately measured product concentration and aggregation every 9.3 seconds using an in-line flow-cell. We demonstrate the application of a non-linear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GMP manufacturing as well as to increase CMC understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

Biofilms can increase pathogenic contamination of drinking water, cause biofilm-related diseases, alter the sediment erosion rate, and degrade contaminants in wastewater. Compared with mature biofilms, biofilms in the early-stage have been shown to be more susceptible to antimicrobials and easier to remove. Mechanistic understanding of physical factors controlling early-stage biofilm growth is critical to predict and control biofilm development, yet such understanding is currently incomplete. Here, we reveal the impacts of hydrodynamic conditions and microscale surface roughness on the development of early-stage Pseudomonas putida biofilm through a combination of microfluidic experiments, numerical simulations, and fluid mechanics theories. We demonstrate that early-stage biofilm growth is suppressed under high flow conditions and that the critical local velocity for early-stage P. putida biofilms to develop is about 50 μm/s, similar to P. putida’s swimming speed. We further illustrate that microscale surface roughness promotes the growth of early-stage biofilms by increasing the area of the low-flow region. Furthermore, we show that the critical average shear stress, above which early-stage biofilms cease to form, is 0.9 Pa for rough surfaces, three times as large as the value for flat or smooth surfaces (0.3 Pa). The important control of flow conditions and microscale surface roughness on early-stage biofilm development, characterized in this study, will facilitate future predictions and managements of early-stage P. putida biofilm development on the surfaces of drinking water pipelines, blood vessels, and sediments in aquatic environments.

Robotic facilities that can perform advanced cultivation (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioproduct development pipeline in the last decades. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation to implement feedback control methods in miniaturized bioreactor systems, where the observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman filter in a high-throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. This method exploits the information generated by the ammonia-based pH control to enable the continuous estimation of biomass, a critical state to monitor the specific rates of production and consumption in the process. The objective in our case study is to ensure that the selected specific growth rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of antibody fragment.

The therapeutic effects of human mesenchymal stromal cells (MSC) have been attributed mostly to their paracrine activity, exerted through small-secreted extracellular vesicles (EVs) rather than their engraftment into injured tissues. Currently, the production of MSC-derived EVs (MSC-EVs) is performed in laborious static culture systems with limited manufacturing capacity using serum-containing media. In this work, a serum-/xenogeneic-free microcarrier-based culture system was successfully established for bone marrow-derived MSC cultivation and MSC-EV production using a 2 L-scale controlled stirred tank reactor (STR) operated under fed-batch (FB) or fed-batch combined with continuous perfusion (FB/CP). Overall, maximal cell numbers of (3.0±0.12)×10 8 and (5.3±0.32)×10 8 were attained at days 8 and 12 for FB and FB/CP cultures, respectively, and MSC(M) expanded under both conditions retained their immunophenotype. MSC-EVs were identified in the conditioned medium collected from all STR cultures by TEM, and EV protein markers were successfully identified by WB analysis. Overall, no significant differences were observed between EVs isolated from MSC expanded in STR operated under the two feeding approaches. EV mean sizes of 163±5.27 nm and 162±4.44 nm (P>0.05) and concentrations of (2.4±0.35)x10 11 EVs/mL and (3.0±0.48)x10 11 EVs/mL (P>0.05) were estimated by nanoparticle tracking analysis for FB and FB/CP cultures, respectively. The STR-based platform optimized herein represents a major contribution towards the development of human MSC- and MSC-EV-based products as promising therapeutic agents for Regenerative Medicine settings.

The production of high-quality recombinant proteins is critical to maintaining a continuous supply of biopharmaceuticals, such as therapeutic antibodies. Engineering mammalian cell factories presents a number of limitations typically associated with proteotoxic stress induced upon aberrant accumulation of off-pathway protein folding intermediates, which eventually culminate with the induction of apoptosis. Recent progress in mammalian synthetic biology provides unique opportunities to endow cells with programmable, user-defined behaviors, thereby addressing some of the challenges of current methods. In this review, we will discuss advances in synthetic biology to design efficient strategies for biomanufacturing.

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 VLPs. More recently IC-BEVS has been also used as an alternative to produce adeno-associated virus (AAV). However, little is known about the variability of insect cell populations and the potential effect of heterogeneity on product titer and/or quality. In this study, transcriptomics analysis of Sf9 insect cells during the production of recombinant AAV using a low multiplicity of infection, dual-baculovirus system was performed via single-cell RNA-seq (scRNA-seq). Before infection, the principal source of variability in Sf9 insect cells was associated to cell cycle. Over the course of infection, an increase in transcriptional heterogeneity was detected, this being linked to the expression of baculovirus genes as well as to differences in AAV transgenes ( rep, cap and gfp) expression. Noteworthy, at 24 hours post-infection (hpi) only 29 % of cells showed to enclose all three necessary AAV transgenes to produce packed AAV particles, indicating limitations of the dual baculovirus system. In addition, the trajectory analysis herein performed highlighted biological processes such as protein folding, metabolic processes, translation and stress response has 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 for rational cell and process engineering towards improved AAV production in IC-BEVS.

Reducing drug development timelines is an industry-wide goal to bring medicines to patients in need more quickly. This was exemplified in the COVID-19 pandemic where reducing development timelines had a direct impact on the number of lives lost to the disease. The use of drug substance produced using cell pools, as opposed to clones, has the potential to shorten development timelines. Toward this goal, we have developed a novel technology, GPEx® Lightning, that allows for rapid, reproducible, targeted recombination of transgenes into more than 200 Dock sites in the CHO genome. This allows for rapid production of high expressing stable cell pools and clones that reach titers of 4 to 12 g/L in generic fed-batch production. These pools and clones are highly stable in both titer and glycosylation, showing strong similarity in glycosylation profiles.

Recently, enormous culture profiles and datasets from biomanufacturing processes to produce recombinant therapeutic proteins (RTP) such as monoclonal antibodies (mAbs) could be generated by virtue of the advancement in process analytical techniques and artificial intelligence (AI). Thus, now it is highly necessary to develop AI-based data-driven models (DDMs) and exploit them accordingly in order to further enhance operational efficiency and accelerate reliable product supply. Since bioprocess is a complex and dynamic system, DDMs are practical and particularly useful to describe the intrinsic relationship among biological and process parameters and cell culture conditions by capturing inherent patterns and to produce high-quality RTP under consistent operations as well as to decrease cost and time by predicting incipient or abrupt faults during the cell cultures. In this work, we provide the practical guideline for choosing the best DDM on given mAb-producing Chinese hamster ovary (CHO) cell culture data sets, enabling us to forecast culture performance such as VCD, and mAb titer as well as glucose, lactate and ammonia concentrations in real time manner. Via the case study with 32 fed-batch data sets of CHO cell cultures, we suggested best combination of model elements including AI algorithms and multi-step ahead forecasting strategies, for good prediction in terms of the computational load as well as the model accuracy and reliability, which is applicable to implementation of interactive data-driven model within bioprocess digital twins. We believe this systematic study can help bioprocess engineers to start developing predictive DDMs with their own data and learn how their cell cultures behave in near future, thereby making proactive decision possible.

The challenge of introducing new technologies into established industries is not a problem unique to the biopharmaceutical industry. However, it may be critical to the long-term competitiveness of individual manufacturers and, more importantly, the ability to deliver therapies to patients. This is especially true for new treatment modalities including cell and gene therapies. We review several barriers to technology adoption which have been identified in various public forums including business, regulatory, technology, and people-driven concerns. We also summarize suitable enablers addressing one or more of these barriers along with some suggestions for developing additional synergies.

The integration of a transgene expression construct into the host genome is the initial step for the generation of recombinant cell lines used for biopharmaceutical production. The stability and level of recombinant gene expression in Chinese hamster ovary (CHO) can be correlated to the copy number, its integration site as well as the epigenetic context of the transgene vector. Also, undesired integration events, such as concatemers, truncated and inverted vector repeats, are impacting the stability of recombinant cell lines. Thus, to characterize cell clones and to isolate the most promising candidates it is crucial to obtain information on the site of integration, the structure of integrated sequence and the epigenetic status. Current sequencing techniques allow to gather this information separately but do not offer a comprehensive and simultaneous resolution. In this study, we present a fast and robust nanopore Cas9-targeted sequencing (nCats) pipeline to identify integration sites, the composition of the integrated sequence as well as its DNA methylation status in CHO cells that can be obtained simultaneously from the same sequencing run. A Cas9-enrichment step during library preparation enables targeted and directional nanopore sequencing with up to 724x median on-target coverage and up to 153 Kb long reads. The data generated by nCats provides sensitive, detailed and correct information on the transgene integration sites and the expression vector structure, which could only be partly produced by traditional Targeted Locus Amplification-Seq data. Moreover, with nCats the DNA methylation status can be analyzed from the same raw data without prior DNA amplification.

In recent years, bacteria from genus Clostridia have attracted attention of research community because of their biofuel production capabilities. Present study reports comparative genomic (CG) analysis of 48 genomes of solventogenic and saccharolytic Clostridia. We have focused on central carbon metabolism and general stress response in the analysis. Comprehensive summaries on comparison of general genome features, COG categories, CDSs of the energy, catabolic, and sporulation pathways are given. Furthermore, we have proposed two new genome-scale metabolic (GSM) models iKK848 and iKK1425 for Clostridium pasteurianum DSM 525 = ATCC 6013 and Clostridium acetobutylicum ATCC 824, respectively. These GSM models are most comprehensive in that they account for the largest number of reactions, metabolites, and genes as compared to previous models. Model quality and metabolic flux optimization for biomass growth using iKK1425 and iKK848 are compared with previous literature. Our models had the highest quality score of 61% and 77%.