Microfiltration is an essential step during biopharmaceutical manufacturing. However, unexpected flux decay can occur. Although the flux decay profile and initial flux are important factors determining microfiltration filterability, predicting them accurately is challenging since the root cause of unexpected flux decay remains elusive. In this study, the methodology for developing a prediction model of flux decay profiles was established. First, the filtration profiles of different monodisperse polystyrene latex and silica beads of various sizes were evaluated. These results revealed that the size and surface electrostatic properties of the beads affect the flux decay profile. Taking the size and surface electrostatic properties of protein aggregates into account, we constructed a predictive model using model bead filtration profiles. We showed that this methodology was applicable to two different microfiltration filters to predict the flux decay profile of therapeutic proteins. Since this prediction model is based on normalized flux, the initial flux must be predicted. We therefore successfully developed a method to predict initial flux based on the Hagen-Poiseuille equation using sample viscosity values for both filters. These prediction models can be used for effective microfiltration scale-up assessment and can be applied during early stage of process development.

Monoclonal antibodies (MAbs) are powerful therapeutic tools in modern medicine and represent a rapidly expanding multi-billion USD market. While bioprocesses are generally well understood and optimized for MAbs, online quality control remains challenging. Notably, N-glycosylation is a critical quality attribute of MAbs as it affects binding to Fcγ receptors (FcγR), impacting the efficacy and safety of MAbs. Traditional N-glycosylation characterization methods are ill-suited for online monitoring of a bioreactor; in contrast, surface plasmon resonance (SPR) represents a promising avenue, as SPR biosensors can record MAb-FcγR interactions in real-time and without labelling. In this study, we produced five lots of differentially glycosylated Trastuzumab (TZM) and finely characterized their glycosylation profile by HILIC-UPLC chromatography. We then compared the interaction kinetics of these MAb lots with four FcγRs including FcγRIIA and FcγRIIB at 5 oC and 25 oC. When interacting with FcγRIIA/B at low temperature, the differentially glycosylated MAb lots exhibited distinct kinetic behaviours, contrary to room-temperature experiments. Galactosylated TZM (1) and core fucosylated TZM (2) could be discriminated and even quantified using an analytical technique based on the area under the curve (AUC) of the signal recorded during the dissociation phase of a SPR sensorgram describing the interaction with FcγRIIA (1) or FcγRII2B (2). Because of the rapidity of the proposed method (less than 5 minutes per measurement) and the small sample concentration it requires (as low as 30 nM, exact concentration not required), it could be a valuable process analytical technology for MAb glycosylation monitoring.

Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.

Synthetic mRNA is currently produced in standardised 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 optimisation of product yield and quality. Herein, we describe for the first time development of an in vivo mRNA manufacturing platform, utilising an E. 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 in large- or small-scale processes. Accordingly, this study introduces a new mRNA production technology, expanding the solution space available for mRNA manufacturing.

The in vitro transcription (IVT) reaction used in the production of mRNA vaccines and therapies remains poorly quantitatively understood. Mechanistic modeling of IVT could inform reaction design, scale up, optimization, and control. In this work, we develop a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA. A novel quantitative description is included for the rate of transcription as a function of target sequence length, DNA concentration, and T7 polymerase concentration. The model explains previously unexplained trends in IVT data and quantitatively predicts the effect of adding the pyrophosphatase enzyme to the reaction system. The model is validated on additional literature data showing an ability to predict transcription rates as a function of RNA sequence length.

The recent uptick in the approval of ex vivo cell therapies highlight the relevance of Lentivirus (LV) as an enabling viral vector of modern medicine. As labile biologics, however, LVs pose critical challenges to industrial biomanufacturing. In particular, LV purification – currently reliant on filtration and anion-exchange or size-exclusion chromatography – suffers from long process times and low yield of transducing particles, which translate in high waiting time and cost to patients. Seeking to improve LV downstream processing, this study introduces peptides targeting the enveloped protein Vesicular stomatitis virus G (VSV-G) to serve as affinity ligands for the chromatographic purification of LV particles. An ensemble of candidate ligands was initially discovered by implementing a dual-fluorescence screening technology and a targeted in silico approach designed to identify sequences with high selectivity and tunable affinity. The selected peptides were conjugated on Poros resin and their LV binding-and-release performance was optimized by adjusting the flow rate, composition, and pH of the chromatographic buffers. Ligands GKEAAFAA and SRAFVGDADRD were selected for their high product yield (50-60% of viral genomes; 40-50% of HT1080 cell-transducing particles) upon elution in PIPES buffer with 0.65 M NaCl at pH 7.4. The peptide-based adsorbents also presented remarkable values of binding capacity (up to 3·10 9 TU per mL of resin at the residence time of 1 min) and clearance of host cell proteins (up to 220-fold reduction of HEK293 HCPs). Additionally, GKEAAFAA demonstrated high resistance to caustic cleaning-in-place (0.5 M NaOH, 30 min) with no observable loss in product yield and quality.

In recent years, environmental DNA (eDNA) has received attention from biologists due to its sensitivity, convenience, labor and material efficiency, and lack of damage to organisms. The extensive application of eDNA has opened avenues for the monitoring and biodiversity assessment of amphibians, which are frequently small and difficult to observe in the field, in areas such as biodiversity survey assessment and detection of specific, rare and endangered, or alien invasive species. However, the accuracy of eDNA can be influenced by factors such as ambient temperature, pH, and false positives or false negatives, which makes eDNA an adjunctive tool rather than a replacement for traditional surveys. This review provides a concise overview of the eDNA method and its workflow, summarizes the differences between applying eDNA for detecting amphibians and other organisms, reviews the research progress in eDNA technology for amphibian monitoring, identifies factors influencing detection efficiency, and discusses the challenges and prospects of eDNA. It aims to serve as a reference for future research on the application of eDNA in amphibian detection.

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model’s performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Hollow fiber-based membrane filtration has emerged as the dominant technology for cell retention in perfusion processes yet significant challenges in alleviating filter fouling remain unsolved. In this work, the benefits of co-current filtrate flow applied to a tangential flow filtration (TFF) module to reduce or even completely remove Starling recirculation caused by the axial pressure drop within the module was studied by pressure characterization experiments and perfusion cell culture runs. Additionally, a novel concept to achieve alternating Starling flow within unidirectional TFF was investigated. Pressure profiles demonstrated that precise flow control can be achieved with both lab-scale and manufacturing scale filters. TFF systems with co-current flow showed up to 40% higher product sieving compared to standard TFF. The decoupling of transmembrane pressure from crossflow velocity and filter characteristics in co-current TFF alleviates common challenges for hollow-fiber based systems such as limited crossflow rates and relatively short filter module lengths, both of which are currently used to avoid extensive pressure drop along the filtration module. Therefore, co-current filtrate flow in unidirectional TFF systems represents an interesting and scalable alternative to standard TFF or alternating TFF operation with additional possibilities to control Starling recirculation flow.

With the further improvement of food safety requirements, the development of fast, high sensitivity, and portability methods for the determination of foodborne hazardous substances has become a new trend in the food industry. In recent years, biosensors and platforms based on functional nucleic acids and a range of signal amplification devices and methods have been established to allow rapid and sensitive determination of specific substances in samples by different methods, opening up a new avenue of analysis and detection. In this paper, functional nucleic acid types including aptamers, deoxyribozymes and G-quadruplexes which are commonly used in the detection of food source pollutants are mainly introduced, as well as nano signal amplification elements including quantum dots, noble metal nanoparticles, magnetic nanoparticles, DNA walkers, DNA logic gates. signal amplification technologies including nucleic acid isothermal amplification, HCR, CHA, biological barcode, and microfluidic system are combined with functional nucleic acid sensors and applied to the detection of many foodborne hazardous substances, such as foodborne pathogens, mycotoxins, residual antibiotics, residual pesticides, industrial pollutants, heavy metals, and allergens.Finally, the potential opportunities and broad prospects of functional nucleic acid biosensors in the field of food analysis are discussed.

Researchers and engineers came together in Lisbon at the 27 th Meeting of the European Society for Animal Cell Technology (ESACT 2022), to discuss the latest advances in technologies associated with protein-based biologics production, new modalities and cell, gene and tissue therapies. Main contributions focused on how the capabilities of production platforms can be enhanced, and how to leverage them to generate new products. Some of the advances that were presented are discussed below, including those related with cell line development, metabolic engineering, analytics, CHO and insect cells platforms engineering, vesicle and viral vector production, and gene and cell therapy, along with some concluding remarks on the future of this important field.

Fermentation monitoring is a powerful tool for bioprocess development and optimisation. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 minutes with a triple quadrupole mass spectrometer. This allowed capturing high time resolution biological data that can provide critical information for process optimisation. For 9 of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate PLS regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

Medical progress as enabled by early plasma products has also revealed biological safety challenges. The combination of donor selection, donation testing and virus reduction processes has effectively addressed these concerns, and today medicinal plasma products feature significant safety margins. The safety tripod concept has since been adapted to biotechnology manufacturing platforms and has also ensured the safety of these products. However, cell-based manufacturing processes have occasionally been exposed to adventitious viruses, leading to manufacturing interruptions and unstable supply situations. The rapid progress of advanced therapy medicinal products (ATMPs) needs an innovative approach to ensure the learnings from more traditional biotechnology help to avoid any unwelcome reminder of the universal presence of viruses. The introduction of up-stream virus clearance steps has already been shown to be valuable for any products too complex for down-stream interventions in the sense of both assuring product safety and continuous supply. The gentlest method being virus filtration – the development of which is presented here. The experiments investigated the feasibility of implementing culture media virus filtration with respect to their virus clearance capacities under extreme conditions such as very high process feed loading (up to ~ 19,000 L/m 2), very long duration (up to 31 days), and multiple process interruptions (up to 21, with cumulative interruptions of over 92 hours). Minute virus of mice (MMV) was used as a relevant target virus, and in general, as a model small non-enveloped virus, as these viruses are the main challenge for the investigated virus filters with a stipulated pore-size of about 20 nm. It was found that certain filters – especially of the newer 2 nd generation – are capable of effective virus clearance despite the harsh regimen they were subjected to. At the same time the investigation of biochemical parameters for un-spiked control runs showed the filters to have no measurable impact on the composition of the culture media. Based on these findings, this technology seems to be quite feasible for large volume pre-manufacturing process culture media preparations.

Zymomonas mobilis is an emerging chassis for being engineered to produce bulk products due to its glycolysis through the Entner-Doudoroff pathway with less ATP produced for lower biomass accumulation and higher yields with targeted products. When self-flocculated, the bacterial cells are more productive and tolerant to stresses for high product titers, but this morphology needs to be controlled properly to avoid internal mass transfer limitation associated with strong flocculation. Herewith we explored the regulation of cyclic diguanosine monophosphate (c-di-GMP) on self-flocculation of the bacterial cells through cellulose biosynthesis. While ZMO1365 and ZMO0919 with GGDEF domains for diguanylate cyclase activities catalyze c-di-GMP biosynthesis, ZMO1487 with an EAL domain for phosphodiesterase activities catalyzes c-di-GMP degradation, but ZMO1055 and ZMO0401 contain the dual domains with phosphodiesterase activities predominated. Since c-di-GMP is synthesized from GTP, the intracellular accumulation of this signal molecule through deactivating the activity of phosphodiesterase is preferred for activating cellulose biosynthesis to flocculate the bacterial cells, since such a strategy exerts less perturbance on intracellular processes regulated by GTP. These discoveries are significant not only for engineering unicellular Z. mobilis strains with the self-flocculating morphology to boost production, but also for understanding mechanism underlying c-di-GMP biosynthesis and degradation in the bacterium.

The biopharmaceutical industry is still running in batch mode, mostly because it is a highly regulated industry sector. In the past, sensors were not readily available and in-process control was mainly executed off-line. The most important product parameters are quantity, purity and potency besides adventitious agents and bioburden. There is increasing economic pressure on time-to-market and also on the environmental sustainability of biopharmaceutical manufacturing. New concepts for manufacturing using disposable single-use technologies and integrated bioprocessing will dominate the future of bioprocessing. In order to ensure the quality of pharmaceuticals initiatives such as Process Analytical Technologies, Quality by Design and Continuous Integrated Manufacturing have been established. The vision must be that these initiatives together with technology development pave the way for process automation and autonomous bioprocessing without any human intervention. Then a real-time release would be realized leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in context of monitoring and control. Starting from statistical treatment of single and multiple sensors, establishing soft sensors with predictive chemometrics and hybrid models. A scenario is described how to integrate soft sensors and predictive chemometrics into modern process control. This will be exemplified by selective downstream processing steps such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

Chromatographic data processing has garnered attention due to multiple FDA 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly benefit from the adoption of digital technologies, but the process of implementing these technologies can be slow and complex. This article presents a “Digital by Design” managerial approach, adapted from pharmaceutical quality by design principles, for designing and implementing an artificial intelligence (AI)-based solution for chromatography peak integration process in the healthcare industry. We report the use of a convolutional neural network model to predict analytical variability for integrating chromatography peaks and propose a potential GxP framework for using artificial intelligence in the healthcare industry that includes elements on data management, model management, and human-in-the-loop processes. The component on analytical variability prediction has a great potential to enable Industry 4.0 objectives on real-time release testing, automated quality control, and continuous manufacturing.

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS has revolutionized the design and monitoring of bioprocesses.

In the production of biopharmaceuticals depth filters followed by sterile filters are often employed to remove residual cell debris present in the feed stream. In the back drop of a global pandemic, supply chains associated with the production of biopharmaceuticals have been constrained. These constraints have limited the available amount of depth filters for the manufacture of biologics. This has placed manufacturing facilities in a difficult position having to choose between running processes with reduced number of depth filters and risking a failed batch or the prospect of plants going into temporary shutdown until the depth filter resources are replenished. This communication describes a modeling based method that leverages manufacturing scale filtration data to predict the depth filter performance with a reduced number of filters and an increased operational flux. This method can be used to quantify the acceptable level of area reduction before which the filtration process performance is affected. This enables facilities to manage their filter inventory avoiding potential plant shutdowns and reduces the risks of negative depth filter performance.