Biology subjects

Wolfrum, J. M.

Publications and source records attributed to Wolfrum, J. M..

3 recordsLinked to original sources

bioRxiv2023

Mechanistic Modeling Explains the Production Dynamics of Recombinant Adeno-Associated Virus with the Baculovirus Expression Vector System

The demand for recombinant adeno-associated virus (rAAV) for gene therapy is expected to soon exceed current manufacturing capabilities, considering the expanding number of approved products and of pre-clinical and clinical stage studies. Current rAAV manufacturing processes have less-than-desired yields and produce a significant amount of empty capsids. Recently, FDA approved the first rAAV-based gene therapy product manufactured in the baculovirus expression vector system (BEVS). The BEVS technology, based on an invertebrate cell line derived from Spodoptera frugiperda, demonstrated scalable production of high volumetric titers of full capsids. In this work, we develop a mechanistic model describing the key extracellular and intracellular phenomena occurring during baculovirus infection and rAAV virion maturation in the BEVS. The predictions of the model show good agreement with experimental measurements reported in the literature on rAAV manufacturing in the BEVS, including for TwoBac, ThreeBac, and OneBac constructs. The model is successfully validated against measured concentrations of structural and non-structural protein components, and of vector genome. We carry out a model-based analysis of the process, to provide insights on potential bottlenecks that limit the formation of full capsids. The analysis suggests that vector genome amplification is the limiting step for rAAV production in TwoBac. In turn, vector genome amplification is limited by low Rep78 levels. For ThreeBac, low vector genome amplification dictated by Rep78 limitation appears even more severe than in TwoBac. Transgene expression in the insect cell during rAAV manufacturing is also found to negatively influence the final rAAV production yields.

molecular biology↗

bioRxiv2022

Machine-learning based detection of adventitious microbes in T-cell therapy cultures using long read sequencing

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP<71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimised experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 CFU / mL using metagenomic classification. Machine learning can be coupled with long read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP<71> organisms to 10 CFU / mL. ImportanceThis research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long read Nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

bioinformatics↗

bioRxiv2021

Weighing the DNA content of Adeno-Associated Virus vectors with zeptogram precision using nanomechanical resonators

Quantifying the composition of viral vectors used in vaccine development and gene therapy is critical for assessing their functionality. Adeno-Associated Virus (AAV) vectors, which are the most widely used viral vectors for in-vivo gene therapy, are typically characterized using PCR, ELISA, and Analytical Ultracentrifugation which require laborious protocols or hours of turnaround time. Emerging methods such as Charge-Detection Mass Spectroscopy, Static Light Scattering, and Mass Photometry offer turnaround times of minutes for measuring AAV mass, but mostly require purified AAV-based reference materials for calibration. Here, we demonstrate a method for using Suspended Nanomechanical Resonators (SNR) to directly measure both AAV mass and aggregation from a few microliters of sample within minutes. We achieve a resolution near 10 zeptograms which corresponds to 1% of the genome holding capacity of the AAV capsid. Our results show the potential of our method for providing real-time quality control of viral vectors during biomanufacturing.

bioengineering↗