Endotoxins are considered as the major contributors to the pyrogenic response observed with contaminated pharmaceutical products. Recombinant biopharmaceutical products are manufactured using living organisms, including gram-negative bacteria. Upon the death of a gram-negative bacteria, endotoxins (also known as lipopolysaccharides; LPS) in the outer cell membrane are released into the lysate where it can interact with and form bonds with biomolecules, including target therapeutic compounds. Endotoxin contamination of biologic products may also occur through water, raw materials such as excipients, media, additives, sera, equipment, containers closure systems, and expression systems used in manufacturing. The manufacturing process is therefore in critical need to reduce and remove endotoxins by monitoring raw materials and in-process intermediates at critical steps, in addition to final drug product release testing. In this review, a discussion regarding the progression of endotoxin detection techniques, from crude to refined are presented. We provide a brief overview of the upstream processed used to manufacture therapeutic products and then discuss various downstream purification techniques widely used to purify the products off endotoxins. Finally, we investigate the effectiveness of endotoxin purification processes, both from a perspective of precision as well as cost-effectiveness.
The Vero cell line is the most used continuous cell line in viral vaccine manufacturing. This adherent cell culture platform requires the use of surfaces to support cell growth, typically roller bottles or microcarriers. We have recently compared the production of rVSV-ZEBOV on Vero cells between microcarrier and fixed-bed bioreactors. However, suspension cultures are considered superior with regards to process scalability. Therefore, we further explore the Vero suspension system for rVSV-vectored vaccine production. Previously, this suspension cell line was only able to be cultivated in a proprietary medium. Here, we expand the adaptation and bioreactor cultivation to a serum-free commercial medium. Following small scale optimization and screening studies, we demonstrate bioreactor productions of highly relevant vaccines and vaccine candidates against Ebola virus disease, HIV and COVID-19 in the Vero suspension system. rVSV-ZEBOV, rVSV-HIV and rVSVInd-msp-SF-Gtc can replicate to high titers in the bioreactor, reaching 3.87 × 107 TCID50/mL, 2.12 × 107 TCID50/mL and 3.59 × 109 TCID50/mL, respectively. Further, we compare cell specific productivities, and the quality of the produced viruses by determining the ratio of total viral particles to infectious viral particles
Sulfate-reducing prokaryotes (SRPs) are crucial participants in the cycling of sulfur, carbon, and various metals in the natural environment and in engineered systems. Despite recent advances in genetics and molecular biology bringing a huge amount of information about the energy metabolism of SRPs, little effort has been made to link this important information with their biotechnological studies. This study aims to construct multiple metabolic models of SRPs that systematically compile genomic, genetic, biochemical, and molecular information about SRPs to study their energy metabolism. Pan-genome analysis is conducted to compare the genomes of SRPs, from which a list of orthologous genes related to central and energy metabolism is obtained. 24 SRP metabolic models via the inference of pan-genome analysis are constructed efficiently. The reference model of the well-studied model SRP Desulfovibrio vulgaris Hildenborough (DvH) is validated via Flux balance analysis (FBA). The DvH model predictions match reported experimental growth and energy yields, which demonstrates that the core metabolic model works successfully. Further, steady-state simulation of SRP metabolic models under different growth conditions shows how the use of different electron transfer pathways leads to energy generation. Three energy conservation mechanisms are identified, including menaquinone-based redox loop, hydrogen cycling, and proton pumping. Flavin-based electron bifurcation (FBEB) is also demonstrated to be an essential mechanism for supporting energy conservation. The developed models can be easily extended to other species of SRPs not examined in this study. More importantly, the present work develops an accurate and efficient approach for constructing metabolic models of multiple organisms, which can be applied to other critical microbes in environmental and industrial systems, thereby enabling the quantitative prediction of their metabolic behaviors to benefit relevant applications.
The control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations. This potential has not been realized due to unresolved challenges: (1) the models assume an optimal and highly efficient metabolism, and therefore tend to underestimate amino acid consumption, and (2) the models assume a steady state, and therefore have a short forecast range. We address these challenges by integrating machine learning with the metabolic models. Through this we demonstrate accurate and time-course dependent prediction of individual amino acid concentration in culture medium throughout the production process. Thus, these models can be deployed to control nutrient feeding to avoid premature nutrient depletion or provide early predictions of failed bioreactor runs.
Incurable breast cancer bone metastasis causes widespread bone loss, resulting in fragility, pain, increased fracture risk, and ultimately increased patient mortality. Increased mechanical signals in the skeleton are anabolic and protect against bone loss, and they may also do so during osteolytic bone metastasis. Skeletal mechanical signals include interdependent tissue deformations and interstitial fluid flow, but how metastatic tumor cells respond to each of these individual signals remains under-investigated, a barrier to translation to the clinic. To delineate their respective roles, we report computed estimates of the internal mechanical field of a bone-mimetic scaffold undergoing combinations of high and low compression and perfusion using multiphysics simulations. Simulations were conducted in advance of multi-modal loading bioreactor experiments with bone metastatic breast cancer cells to ensure that mechanical stimuli occurring internally were physiological and anabolic. Our results show that mechanical stimuli throughout the scaffold were within the anabolic range of bone cells in all loading configurations, were homogenously distributed throughout, and that combined high magnitude compression and perfusion synergized to produce the largest wall shear stresses within the scaffold. These simulations, when combined with experiments, will shed light on how increased mechanical loading in the skeleton may confer anti-tumorigenic effects during metastasis.
Integrated continuous downstream processes with process analytical technology offer a promising opportunity to reduce production costs and increase process flexibility and adaptability. In this case study, an integrated continuous process was used to purify a recombinant protein on laboratory scale in a two-system setup that can be used as a general downstream setup offering multi-product and multi-purpose manufacturing capabilities. The process consisted of continuous solvent/detergent virus inactivation followed by periodic countercurrent chromatography in the capture step, and a final chromatographic polishing step. A real-time controller was implemented to ensure stable operation by adapting the downstream process to external changes. A concentration disturbance was introduced to test the controller. After the disturbance was applied, the product output recovered within 6 hours, showing the effectiveness of the controller. In a comparison of the process with and without the controller, the product output per cycle increased by 27%, the resin utilization increased from 71.4% to 87.9%, and the specific buffer consumption was decreased by 21% with the controller, while maintaining a similar yield and purity as in the process without the disturbance. In addition, the integrated continuous process outperformed the batch process, increasing the productivity by 95% and the yield by 28%.
The heavy metals pollution represents one of the important issues in the environmental field since they are involved in many pathologies from cancer, neurodegenerative and metabolic diseases. We propose an innovative portable biosensor for the determination of traces of trivalent Arsenic (AsIII) and bivalent mercury (HgII) in water. The system implements a strategy combining two advanced sensing modules consisting in (a) a whole cell based on engineered Escherichia coli as selective sensing element towards the metals and (b) an electrochemical miniaturised silicon device with three microelectrodes and a portable reading system. The sensing mechanism relies on the selective recognition from the bacterium of given metals producing the 4-aminophenol (PAP) redox active mediator detected through a cyclic voltammetry analysis. The miniaturized biosensor is able to operate a portable, robust and high-sensitivity detection of AsIII with a sensitivity of 0.122 µA ppb-1, LoD of 1.5 ppb and a LoQ of 5 ppb. The LoD value is one order of magnitude below of the value indicated to WHO to be dangerous (10 μg/L). The system was proved to be fully versatile being effective in the detection of Hg(II) as well. A first study on Hg(II) showed sensitivity value of 2.11 µA/ppb a LOD value of 0.1 ppb and LoQ value of 0.34 ppb. Also in this case, the detected LOD was ten time lower than that indicated by WHO (1 ppb). These results pave the way for advanced sensing strategies suitable for the environmental monitoring and the public safety.
Organs-on-chip are increasingly catching on as a promising and valuable alternative to animal models, in line with the 3Rs initiative, to create 3D tissue microenvironments in which cells behave physiologically and pathologically at unparalleled precision and complexity. Indeed, these platforms offer new opportunities to model human diseases and test the potential therapeutic effect of different drugs as well as their limitations, overtaking the limited predictive accuracy of conventional 2D culture systems. Here, we present a liver-on-a-chip model to investigate the effects of two naturally occurring polyphenols, namely Quercetin and Hydroxytyrosol, on non-alcoholic fatty liver disease (NAFLD) using a method of high-content analysis. NAFLD is currently the most common form of chronic liver disease, whose complex pathogenesis is far from being clear. Besides, no definitive treatment has been established for NAFLD so far. In our experiments, we observed that both polyphenols seem to restrain the progression of the free fatty acid-induced hepatocellular steatosis, showing a cytoprotective effect due to their antioxidant properties. In conclusion, the resulting insights of the present work could guide novel strategies to contrast the onset and progression of NAFLD.
Significant amounts of soluble product aggregates were observed in the low-pH viral inactivation (VI) opertion during an initial scale-up run for an IgG4 monoclonal antibody (mAb IgG4-N1). Being earlier in development, a scale-down model did not exist, nor was it practical to use costly Protein A eluate (PAE) for testing the VI process at scale, thus, a computational fluid dynamics (CFD)-based high molecular weight (HMW) prediction model was developed for troubleshooting and risk mitigation. It was previously reported that the IgG4-N1 molecules upon exposure to low pH tend to change into transient and partially unfolded monomers during VI acidification (i.e., VIA) and form aggregates after neutralization (i.e., VIN) (Jin et al. 2019). Therefore, the CFD model reported here focuses on the VIA step. The model mimics the continuous addition of acid to PAE and tracks acid distribution during VIA. Based on the simulated low-pH zone (≤ pH 3.3) profiles and PAE properties, the integrated low-pH zone (ILPZ) value was obtained to predict HMW level at the VI step. The simulations were performed to examine the operating parameters, such as agitation speed, acid addition rate, and protein concentration of PAE, of the pilot scale (50-200L) runs. The conditions with predictions of no product aggregation risk were recommended to the real scale-up runs, resulted in 100% success rate of the consecutive 12 pilot-scale runs. This work demonstrated that the CFD-based HMW prediction model could be used as a tool to facilitate the scale up of the low-pH VI process directly from bench to pilot/production scale.
Site-specific integration has emerged as a promising strategy for precise Chinese hamster ovary (CHO) cell line engineering and predictable cell line development. CRISPR/Cas9 with homology-directed repair (HDR) pathway enables precise integration of transgenes into target genomic sites. However, inherent recalcitrance to HDR-mediated targeted integration (TI) of transgenes results in low targeting efficiency, thus requires selection process to acquire targeted integrant in CHO cells. Here we explored several parameters that influence the targeting efficiency using the promoter-trap based single or double knock-in (KI) monitoring system. A simple change in the donor template design by adding sgRNA recognition sequences strongly increased KI efficiency by 2.9–36 fold depending on integration sites and culture mode, compared with conventional circular donor plasmids. Furthermore, sequential and simultaneous KI strategies enabled the generation of double KI populations about 1–4% without the need of additional enrichment processes. This simple optimized strategy not only allowed efficient CRISPR/Cas9-mediated TI in CHO cells but also paved the way for the applicability of multiplexed KIs in one experimental step without the requirement of sequential and independent CHO cell line development procedures.
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.
Extracellular production of target proteins simplifies downstream processing due to obsolete cell disruption. However, optimal combinations of a heterologous protein, suitable signal peptide and secretion host can currently not be predicted, resulting in large strain libraries that need to be tested. On the experimental side, this challenge can be tackled by miniaturization, parallelization and automation, which provide high-throughput screening data. These data need to be condensed into a candidate ranking for decision making to focus bioprocess development on the most promising candidates. We screened for Bacillus subtilis signal peptides mediating Sec secretion of two polyethylene terephthalate degrading enzymes (PETases), leaf-branch compost cutinase (LCC) and polyester hydrolase (PE-H) mutants, by Corynebacterium glutamicum. We developed a fully automated screening process and constructed an accompanying Bayesian statistical modeling framework, which we applied in screenings for highest activity in 4-nitrophenyl palmitate degradation. In contrast to classical evaluation methods, batch effects and biological errors are taken into account and their uncertainty is quantified. Within only two rounds of screening, the most suitable signal peptide was identified for each PETase. Results from LCC secretion in microliter-scale cultivation were shown to be scalable to laboratory-scale bioreactors. This work demonstrates an experiment-modeling loop that can accelerate early-stage screening in a way that experimental capacities are focused to the most promising strain candidates. Combined with high-throughput cloning, this paves the way for using large strain libraries of several hundreds of strains in a Design-Build-Test-Learn approach.
Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Y. lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.
Molecular diagnosis is an essential means to detect pathogens. The portable nucleic acid detection chip has excellent prospects in places where medical resources are scarce, and it is also of research interest in the field of microfluidic chips. Here, the paper developed a new type of microfluidic chip for nucleic acid detection where stretching acts as the driving force. The sample entered the chip by applying capillary force. The strain valve was opened under the action of tensile force, and the spring pump generated the power to drive the fluid to flow to the detection chamber in a specific direction. The detection of COVID-19 was realized on the chip. The RT-LAMP amplification system was adopted to observe the liquid color in the detection chamber to decide whether the sample tested positive or negative qualitatively.
A bone regeneration scaffold is typically designed as a platform to effectively heal a bone defect while preventing soft tissue infiltration. Despite the wide variety of scaffold materials currently available, such as collagen, critical problems in achieving bone regeneration remain, including a rapid absorption period and low tensile strength as well as high costs. Inspired by extracellular matrix protein and topographical cues, we developed a polycaprolactone-based scaffold for bone regeneration using a soluble eggshell membrane protein (SEP) coating and a nanotopography structure for enhancing the physical properties and bioactivity. The scaffold exhibited adequate flexibility and mechanical strength as a biomedical platform for bone regeneration. The highly aligned nanostructures and SEP coating were found to regulate and enhance cell morphology, adhesion, proliferation, and differentiation in vitro. In a calvarial bone defect mouse model, the scaffolds coated with SEP applied to the defect site promoted bone regeneration along the direction of the nanotopography in vivo. These findings demonstrate that bone-inspired nanostructures and SEP coatings have high potential to be applicable in the design and manipulation of scaffolds for bone regeneration.
The regulation of cell density is an important segment in microfluidic cell culture, particularly in the repeated assays. Traditionally, the consistent cell density is difficult to achieve owing to the inaccurate regulation of cell density with manual feedback. A novel microfluidic culture method with automatic feedback is proposed for real-time regulation of cell density in this paper. Here, an integrated microfluidic system combining cell culture, density detection and control of proliferation rate was developed. Interdigital electrode structures (IDES) are sputtered on the microchannel for automatically providing the real-time feedback information of impedance. The most sensitive frequency is studied to improve the detection resolution of the sensing chip. Cells were cultured on the chip surface and the cell density was detected by monitoring the alternation of the impedance. The feedback controller is established by least squares support vector machines (LS-SVM). Then, the cell proliferation rate was automatically controlled using the feedback controller to achieve the desired cell density in the repeated assays. The results show that the standard error of this method is 2.8% indicating that the method can keep consistency of cell density in the repeated assays. This study provides a basis for improving the accuracy and repeatability in the further assays of finding the optimal drug concentration.
In this study, the binding of multimodal chromatographic ligands to the IgG1 FC domain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated 15N-labeled FC domain indicated that while single mode ion exchange ligands interacted very weakly throughout the FC surface, multimodal ligands interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the Fc. The multimodal ligand binding sites on the FC were concentrated in the hinge region and near the interface of the CH2 and CH3 domains. Further, the multimodal binding sites were primarily composed of positively charged, polar and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on FC corroborated molecular level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand-FC binding in these preferred regions was shown to be electrostatic interactions and pi-pi stacking of surface exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular level understanding of multimodal ligand-FC interactions and sets the stage for future analyses of even more complex biotherapeutics.
E. coli BL21 (DE3) is an excellent and widely used host for recombinant protein production. Many variant hosts were developed from from BL21 (DE3), but improving the expression of specific proteins remains a major challenge in biotechnology. In this study, we found that when BL21 (DE3) overexpressed glucose dehydrogenase (GDH), a significant industrial enzyme, serious autolysis was induced. Subsequently, we observed this phenomenon in the expression of 10 other recombinant proteins. This precludes a further increase of the produced enzyme activity by extending the fermentation time, which is not conducive to the reduction of industrial enzyme production costs. The membrane structure and mRNA expression analysis showed that cells suffered programmed cell death (PCD) during autolysis period. However, blocking three known PCD pathway in BL21 (DE3) cannot alleviate autolysis completely. Furthermore, we attempted to develop a strong expression host resistant to autolysis by controlling the speed of recombinant protein expression. To find a more suitable protein expression rate, the high- and low-strength promoter lacUV5 and lac were shuffled and recombined to yield the promoter variants lacUV5-1A and lac-1G. The results showed that only one base in lac promoter needs to be changed, and the A at the +1 position was changed to a G, resulting in a host of BL21 (DE3-lac1G), which successfully withstand the PCD of the host. The GDH activity at 43h was greatly increased from 37.5 U/mL to 452.0 U/mL. In scale-up fermentation, the new host was able to produce the model enzyme with a high rate of 89.55 U/mL/h at 43h, compared to the 3 U/mL/h of BL21 (DE3). Importantly, BL21 (DE3-lac1G) also successfully improved the production of other 10 enzymes. The engineered E. coli strain in the study conveniently optimizes recombinant protein overexpression by suppressing cell autolysis, and shows potential industrial applications.