Upskilling the cell therapy manufacturing workforce: design, implementation, and evaluation of a massive open online course.

Cell therapies have gained prominence as a promising therapeutic modality for treating a range of diseases. Despite the recent clinical successes of cell therapy products, very few formal training programs exist for cell therapy manufacturing. To meet the demand for a well-trained workforce, we assembled a team of university researchers and industry professionals to develop an online course on the principles and practice of cell therapy manufacturing. The course covers the basic cell and systems physiology underlying cell therapy products, in addition to explaining end-to-end manufacturing from cell acquisition through to patient treatment, industrialization, and regulatory processes. So far, over 10,000 learners have enrolled in the course, and over 90% of respondents to the course exit survey indicated that they were 'very likely' or 'likely' to recommend the course to a peer. In this paper, we discuss our experience in the collaborative design and implementation of the online course, as well as lessons learned from quantitative and qualitative student feedback. We believe that this course can serve as a model for how academia and industry can collaborate to create innovative, scalable training programs to meet the demands of the modern biotechnology workforce.

Mechanistic modeling explains the production dynamics of recombinant adeno-associated virus with the baculovirus expression vector system.

Current manufacturing processes for recombinant adeno-associated viruses (rAAVs) have less-than-desired yields and produce significant amounts of empty capsids. The increasing demand and the high cost of goods for rAAV-based gene therapies motivate development of more efficient manufacturing processes. Recently, the US Food and Drug Administration (FDA) approved the first rAAV-based gene therapy product manufactured in the baculovirus expression vector system (BEVS), a technology that demonstrated production of high titers of full capsids. This work presents a first mechanistic model describing the key extracellular and intracellular phenomena occurring during baculovirus infection and rAAV maturation in the BEVS. The model predictions are successfully validated for in-house and literature experimental measurements of the vector genome and of structural and non-structural proteins collected during rAAV manufacturing in the BEVS with the TwoBac and ThreeBac constructs. A model-based analysis of the process is carried out to identify the bottlenecks that limit full capsid formation. Vector genome amplification is found to be the limiting step for rAAV production in Sf9 cells using either the TwoBac or ThreeBac system. In turn, vector genome amplification is hindered by limiting Rep78 levels. Transgene and non-essential baculovirus protein expression in the insect cell during rAAV manufacturing also negatively influences the rAAV production yields.

Development and Application of a Data-Driven Signal Detection Method for Surveillance of Adverse Event Variability Across Manufacturing Lots of Biologics.

INTRODUCTION: Postmarketing drug safety surveillance research has focused on the product-patient interaction as the primary source of variability in clinical outcomes. However, the inherent complexity of pharmaceutical manufacturing and distribution, especially of biologic drugs, also underscores the importance of risks related to variability in manufacturing and supply chain conditions that could potentially impact clinical outcomes. We propose a data-driven signal detection method called HMMScan to monitor for manufacturing lot-dependent changes in adverse event (AE) rates, and herein apply it to a biologic drug. METHODS: The HMMScan method chooses the best-fitting candidate from a family of probabilistic Hidden Markov Models to detect temporal correlations in per lot AE rates that could signal clinically relevant variability in manufacturing and supply chain conditions. Additionally, HMMScan indicates the particular lots most likely to be related to risky states of the manufacturing or supply chain condition. The HMMScan method was validated on extensive simulated data and applied to three actual lot sequences of a major biologic drug by combining lot metadata from the manufacturer with AE reports from the US FDA Adverse Event Reporting System (FAERS). RESULTS: Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. Applying the HMMScan method to FAERS data, two of the three actual lot sequences examined exhibited evidence of potential manufacturing or supply chain-related variability. CONCLUSIONS: HMMScan could be utilized by both manufacturers and regulators to automate lot variability monitoring and inform targeted root-cause analysis. Broad application of HMMScan would rely on a well-developed data input pipeline. The proposed method is implemented in an open-source GitHub repository.

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 optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (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. IMPORTANCE This 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.

The ratio of nicotinic acid to nicotinamide as a microbial biomarker for assessing cell therapy product sterility.

Controlling microbial risks in cell therapy products (CTPs) is important for product safety. Here, we identified the nicotinic acid (NA) to nicotinamide (NAM) ratio as a biomarker that detects a broad spectrum of microbial contaminants in cell cultures. We separately added six different bacterial species into mesenchymal stromal cell and T cell culture and found that NA was uniquely present in these bacteria-contaminated CTPs due to the conversion from NAM by microbial nicotinamidases, which mammals lack. In cells inoculated with 1 × 104 CFUs/mL of different microorganisms, including USP <71> defined organisms, the increase in NA to NAM ratio ranged from 72 to 15,000 times higher than the uncontaminated controls after 24 h. Importantly, only live microorganisms caused increases in this ratio. In cells inoculated with 18 CFUs/mL of Escherichia coli, 20 CFUs/mL of Bacillus subtilis, and 10 CFUs/mL of Candida albicans, significant increase of NA to NAM ratio was detected using LC-MS after 18.5, 12.5, and 24.5 h, respectively. In contrast, compendial sterility test required >24 h to detect the same amount of these three organisms. In conclusion, the NA to NAM ratio is a useful biomarker for detection of early-stage microbial contaminations in CTPs.

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 using optical or charge properties of AAV. Here, we demonstrate an orthogonal method where suspended nanomechanical resonators (SNR) are used to directly measure both AAV mass and aggregation from a few microliters of sample within minutes. We achieve a precision 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.

Model-based control for column-based continuous viral inactivation of biopharmaceuticals.

Batch low-pH hold is a common processing step to inactivate enveloped viruses for biologics derived from mammalian sources. Increased interest in the transition of biopharmaceutical manufacturing from batch to continuous operation resulted in numerous attempts to adapt batch low-pH hold to continuous processing. However, control challenges with operating this system have not been directly addressed. This article describes a low-cost, column-based continuous viral inactivation system constructed with off-the-shelf components. Model-based, reaction-invariant pH controller is implemented to account for the nonlinearities with Bayesian estimation addressing variations in the operation. The residence time distribution is modeled as a plug flow reactor with axial dispersion in series with a continuously stirred tank reactor, and is periodically estimated during operation through inverse tracer experiments. The estimated residence time distribution quantifies the minimum residence time, which is used to adjust feed flow rates. Controller validation experiments demonstrate that pH and minimum residence time setpoint tracking and disturbance rejection are achieved with fast and accurate response and no instability. Viral inactivation testing demonstrates tight control of logarithmic reduction values over extended operation. This study provides tools for the design and operation of continuous viral inactivation systems in service of increasing productivity, improving product quality, and enhancing patient safety.