Biotechnology Progress最新文献

Plasma expanders (PEs) are commonly used to replace lost blood volume for septic shock patients with increased vascular permeability. Human serum albumin (HSA) is the preferred PE, due to its innate ability to restore blood colloid osmotic pressure (COP). However, HSA is susceptible to protein extravasation under endothelial dysfunction leading to edema and exposing tissue to toxic HSA-bound metabolites. To prevent extravasation, the molecular diameter of HSA has been previously increased through chemical polymerization to yield polymerized HSA (PHSA). In this study, we further optimize PHSA size and COP via polyethylene glycol (PEG) surface conjugation. Previously synthesized PHSA that was size fractionated via tangential flow filtration (TFF) into two brackets (bracket A [500 kDa–0.2 μm] and bracket B [50–500 kDa]) served as precursors for subsequent PEGylation. Each PHSA bracket was thiolated with 2-iminothiolane hydrochloride (IT) and PEGylated with monofunctional 5 kDa maleimide PEG to yield PEGylated PHSA (PPHSA). All PPHSA solutions exhibited increased molecular size, zeta potential, and osmolality compared to their non-PEGylated precursor PHSA. At the same total protein concentration, PPHSA viscosity decreased compared to the precursor PHSA, while the COP remained consistent with HSA, indicating their potential to serve as PEs.

RETRACTION: S. Haseli, M. Pourmadadi, A. Samadi, F. Yazdian, M. Abdouss, H. Rashedi, and M. Navaei-Nigjeh, “ A Novel pH-Responsive Nanoniosomal Emulsion for Sustained Release of Curcumin from a Chitosan-Based Nanocarrier: Emphasis on the Concurrent Improvement of Loading, Sustained Release, and Apoptosis Induction,” Biotechnology Progress 38, no. 5 (2022): e3280, https://doi.org/10.1002/btpr.3280.

The above article, published online on 30 June 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, John A. Morgan; the American Institute of Chemical Engineers; the Society for Biological Engineering; and Wiley Periodicals LLC.

The retraction has been agreed upon following an investigation into concerns raised by a third party, which revealed inappropriate duplication of image panels between this (Figure 4) and another article published by an overlapping group of authors, depicting a different experimental condition.

The partial raw data provided by the authors could not address the original concerns, showed inconsistencies with the published results, and ultimately raised additional doubts about the study's overall reliability. Consequently, the editors have lost confidence in the presented data and decided to retract the paper. The authors’ institute has been informed of the allegations and the decision to retract but remained unresponsive. The authors disagree with the retraction.

Chimeric antigen receptor (CAR) T cell therapy has tremendous potential for the treatment of cancer and other diseases. To manufacture cells of the desired quantity and quality, it is important to expand the CAR T cells ex vivo for an optimal duration. However, identifying the optimal harvest time requires knowledge of the cell concentration during the expansion period. To address this challenge, we have developed a digital shadow of CAR T cell expansion that provides a soft sensor of cell concentration in real-time. Specifically, a novel mechanistic mathematical model of cell growth within a proportional-integral-derivative (PID) controlled perfusion bioreactor has been developed using nonlinear ordinary differential equations. The model is fitted to data generated via bioreactor runs of the Aglaris FACER, in which both donor and patient cells have been expanded in two different media. Off-line data includes the initial and final cell concentrations, and online data includes the glucose and lactate concentrations as well as the perfusion rate. Training the digital shadow utilizes all the off-line and online data for each run. In contrast, real-time testing utilizes only the initial cell concentration and the available online data at the time of model fitting. Real-time testing shows that with at least 2.5 days of online data, the final cell concentration up to 2.5 days later is predicted with a mean relative error of 13% (standard deviation ≈ 6%). Informative real-time predictions of cell concentration via the digital shadow can guide decisions regarding the optimal harvest time of CAR T cells.

Traditional biologics process development, including antibody and recombinant protein production, typically relies on labor-intensive, iterative cell culture optimization to determine optimal process parameters. To address this inefficiency, we introduced the Industrial Smart Lab Framework for Cell Culture (ISLFCC), an autonomous laboratory that combines deep learning and robotic experimentation to enhance cell culture processes. In this system, robotic arms sample various bioreactors for analysis, and the IoT system transmits these analysis results to decoder-only transformer deep learning models. Based on these analysis results, these models predict future cell states and recommend optimal actions, which are then executed by automation devices through the IoT system, such as adjusting nutrient feeds and temperature shifts. In a comparative case study, our AI-driven process development for three different cell clones resulted in an average titer increase of 26.8% and maintained lactate levels below 1 g/L without rebound in the late phase within just a single batch, surpassing traditional three-stage empirical process development methods. Moreover, our approach has greatly automated cell culture to ensure enhanced reproducibility, data accuracy, adaptability to various cell lines, and seamless scalability across production scales, marking the first implementation of high-throughput automated cell culture in 3 and 15 L bioreactors. By merging AI with robotic execution, ISLFCC provides a transformative framework that accelerates biologics development, representing a paradigm shift towards autonomous, data-driven biomanufacturing.

In-line Raman spectroscopy combined with chemometric modeling is a valuable process analytical technology (PAT) providing real-time quantitative information on cell culture compounds. Considering that compound quantification through chemometric models depends on pre-processing to maintain consistent changes in intensity at certain wavenumbers, all causes of signal distortion should be well understood to prevent quantification inaccuracies. This work investigated spectral distortion caused by the changing bioreactor parameters temperature, bubble quantity, and medium viscosity. In addition, the isolated spectral contribution of Saccharomyces cerevisiae cells in suspension was also determined. A temperature range from 20 to 40°C resulted in peak shifts up to 0.8 cm⁻¹ to lower wavenumbers, bubbles generated under standard bioreactor operation conditions led to signal attenuation of up to 7.93% reduction in peak intensity, and changes in liquid viscosity resulted in complex peak shift behavior. Isolated biomass concentrations reaching 5 g/L caused up to 44.6% reduction in distinct peak intensity, which was similar to spectra from batch process fermentations. Correcting for the attenuation revealed spectral features of biomass associated with proteins and lipids in the 1000–1500 cm⁻¹ region. However, the spectral contribution of yeast biomass is dominated by signal extinction, which attenuates Raman spectra in a non-linear manner as biomass accumulates. The obtained knowledge on different sources of spectral distortion aids in the development of robust pre-processing and modeling strategies to obtain chemometric models applicable across experimental setups.

Integrated Continuous Biomanufacturing reduces manufacturing costs while maintaining product quality. A key contributor to high biopharmaceutical costs, specifically monoclonal antibodies (mAbs), is chromatography. Protein A ligands are usually preferred but still expensive in the manufacturing context, and batch chromatography under-utilizes the columns' capacity, compromising productivity to maintain high yields. Continuous chromatography increases columns' Capacity Utilization (CU) without sacrificing yield or productivity. This work presents the in-silico optimization of a 3 Column Periodic Counter-current Chromatography (3C-PCC) of a capture and polishing step for mAbs from a high titer harvest (c_mAb = 5 g/L). The 3C-PCC was modeled and Pareto-fronts for continuous and batch modes were used to optimize the 3C-PCC steps varying the flow rate and percentage of breakthrough achieved in the interconnected loading, maximizing Productivity and CU, for varying concentrations of mAb (batch mode concentration of 5 g/L and continuous mode concentration of 2.5, 5, 7.5, and 10 g/L). The shape of the breakthrough curve significantly impacts the optimization of 3C-PCC. The model output was validated for three different protein A ligands using a pure mAb solution. MAb Select SuRe pcc was selected to continuously capture mAb from a high-titer clarified cell culture supernatant (harvest). The product eluates were pooled and used for continuous polishing using a Cation-Exchange resin (CaptoS ImpAct). Experimental results validated model predictions (<7% deviation in the worst case) and a process with two 3C-PCC in sequence was proposed, with a productivity of approximately 100 mg/mL res/h.

Schistosoma mansoni infection and other neglected diseases pose significant challenges in diagnosis and treatment, particularly in resource-constrained regions. Despite being useful, traditional techniques lack sensitivity, offering frequent false-positive results, highlighting the emergence of innovative tools such as genosensors as a promising solution to this dilemma. In this work, we developed a simple electrochemical biosensor platform based on electropolymerized films of polythiophene acetic acid (PTAA) and a specific DNA probe for the detection of S. mansoni. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and atomic force microscopy (AFM) were used to assess the assembly process of the genosensor, as well as to evaluate biodetection assays. The developed biosensor was found to be effective in detecting the target analyte in pure and complex samples such as cerebrospinal fluid, urine, and plasma from infected patients at different concentrations. CV and EIS were extremely useful in the evaluation of the detection process based on the electron kinetics and charge transfer resistance (R_CT) in the interface of the biosensor, where the hybridization with the target single-stranded S. mansoni DNA resulted in the variation of these parameters. The genosensor exhibited high sensitivity and selectivity, with a limit of detection of 0.451 pg.μL⁻¹. As genosensors continue to evolve, they promise to revolutionize the field of neglected disease management, providing hope for improved healthcare outcomes worldwide.

Fed-batch recombinant therapeutic protein (RTP) production processes utilizing Chinese Hamster Ovary (CHO) cells can take a long period of time (>10 days). Within this period, not all critical features may be measured routinely, and in fact, some are only measured once the process is terminated, complicating decision making. As a consequence, utilizing routine current day bioreactor online data to aid in next day predictions is a promising strategy for model predictive control-based feeding strategies. The article details the development of a proposed soft sensor that merges current day bioreactor online data and offline historical sampling data to generate predictions about the next day of the production process. This approach demonstrated the ability to track product titer, cell growth, key metabolites, and cumulative glucose consumption across the 17-day process with low normalized root mean squared error (nRMSE = 0.24) and low normalized mean absolute error (nMAE = 0.18) as well as high linearity with respect to ground data (average R² = 0.97). It was also demonstrated that the same model architecture could effectively soft sense product titer and metabolic profiles (glucose, lactate, ammonia) without having sampling day's offline data as inputs to the model. This suggests that the proposed model could act as a true soft sensor of hard-to-determine variables such as the trimeric SARS-CoV-2 spike protein that relies on end-of-process measurements to acquire the data (labor-intensive semi-quantitative SDS-PAGE gels or ELISA assay). Instantaneous specific glucose consumption rates were also predicted and showed good agreement with experimental measurements, further offering opportunities for online glucose control.

Long-term functional hepatocyte and reproducible cultures are required in pharmaceutical industries to model chronic liver disorders and to perform associated drug testing. In this frame, we have investigated the behavior of the HepaSH cells when cultivated in liver Biochips, in 3D Spheroids, and in Petri for 20 days. HepaSH is a newly developed humanized hepatocyte harvested from chimeric mice. After the cells' harvesting and inoculation, the HepaSH were successfully maintained in cultures in Petri dishes, spheroids, and Biochips for 20 days. The immunostaining confirmed the expressions of albumin, CYP1A2, and CYP3A4 in all conditions. Furthermore, the CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2D6, and CYP3A4 activities were successfully detected in all conditions after 20 days of cultures. Continuous production of albumin and biliary acids was detected in Biochips, Spheroids, and Petri, among which Biochip culture showed the highest albumin secretion level. The RNA sequencing analysis revealed that Biochips and Spheroids cultures enriched hepatic maturation, xenobiotic, lipid, small molecule, steroid, and alcohol metabolisms compared to Petri cultures. Overall, our data demonstrated the feasibility of cultivating the HepaSH cells in Petri, Biochips, and Spheroids for 20 days in the presented protocol, while keeping important liver functions. Biochip and Spheroids cultures show advantages in hepatic maturation, drug metabolism-related gene expression, and albumin secretion (in biochips) compared with conventional Petri culture.

The removal of host cell proteins (HCPs) is crucial in biopharmaceutical production, as residual impurities can impact product safety and efficacy. While a number of studies have demonstrated that depth filtration can provide significant HCP removal, there is little information on its effectiveness in removing specific HCPs. This study examines the application of liquid chromatography-mass spectrometry (LC-MS) to track HCP removal during depth filtration, providing a detailed analysis of HCP behavior with two commercial depth filters. Our findings reveal significant variability in HCP breakthrough behavior, with transmission patterns showing minimal correlation with either the protein isoelectric point or hydrophobicity, highlighting the unique behavior of individual HCPs. Both the X0SP and X0HC depth filters achieved almost complete removal of Lipoprotein Lipase, and the X0SP filter also effectively removed Lysosomal Acid Lipase (LAL), both known to degrade polysorbate in monoclonal antibody formulations. However, neither filter provided significant removal of Alpha-enolase, Carboxypeptidase D, Glutathione S-transferase, or Phospholipase B-like 2. The X0SP filter showed equal or better removal for 18 out of 20 problematic HCPs, with greater HCP removal seen at lower conductivity. This work provides a detailed framework for understanding and optimizing depth filtration processes, offering insights into the effectiveness of depth filters for removal of problematic HCPs.