Toward QbD Process Understanding on DNA Vaccine Purification Using Design of Experiment

Assessment of pDNA isoforms using microfluidic electrophoresis

The recent rise in nucleic acid-based vaccines and therapies has resulted in an increased demand for plasmid DNA (pDNA). As a result, there is added pressure to streamline the manufacturing of these vectors, particularly their design and construction, which is currently considered a bottleneck. A significant challenge in optimizing pDNA production is the lack of high-throughput and rapid analytical methods to support the numerous samples produced during the iterative plasmid construction step and for batch-to-batch purity monitoring. pDNA is generally present as one of three isoforms: supercoiled, linear, or open circular. Depending on the ultimate use, the desired isoform may be supercoiled in the initial stages for cell transfection or linear in the case of mRNA synthesis. Here, we present a high-throughput microfluidic electrophoresis method capable of detecting the three pDNA isoforms and determining the size and concentration of the predominant supercoiled and linear isoforms from 2 to 7 kb. The limit of detection of the method is 0.1 ng/µL for the supercoiled and linear isoforms and 0.5 ng/µL for the open circular isoform, with a maximum loading capacity of 10-15 ng/µL. The turnaround time is 1 min/sample, and the volume requirement is 10 µL, making the method suitable for process optimization and batch-to-batch analysis. The results presented in this study will enhance the understanding of electrophoretic transport in microscale systems dependent on molecular conformations and potentially aid technological advances in diverse areas relevant to microfluidic devices.

Intranasal Ion-Triggered In Situ Delivery System of Virus-like Particles: Development Using the Quality by Design Approach

The rapid growth in the prevalence of infectious diseases requires timely action from drug developers. In recent years, the COVID-19 pandemic has demonstrated the unpreparedness of the population for such emergencies. The introduction of modern methods of Design of Experiments (DoE) is required to accelerate the process of drug development and bring a drug to market. The main objective of this study was to develop an ion-triggered in situ system for intranasal delivery of VLP using a Quality by Design approach. Based on a literature review and initial studies, the key QTPP, CQA, CPP, and CMA were identified to develop a novel delivery system for virus-like particles. As a result of the studies on the quality attributes of the developed delivery system, an ion-triggered in situ gel meeting all the specified parameters was obtained using the Quality by Design method.

Uncertainty quantification for gene delivery methods: A roadmap for pDNA manufacturing from phase I clinical trials to commercialization

The fast-growing interest in cell and gene therapy (C>) products has led to a growing demand for the production of plasmid DNA (pDNA) and viral vectors for clinical and commercial use. Manufacturers, regulators, and suppliers need to develop strategies for establishing robust and agile supply chains in the otherwise empirical field of C>. A model-based methodology that has great potential to support the wider adoption of C> is presented, by ensuring efficient timelines, scalability, and cost-effectiveness in the production of key raw materials. Specifically, key process and economic parameters are identified for (1) the production of pDNA for the forward-looking scenario of non-viral-based Chimeric Antigen Receptor (CAR) T-cell therapies from clinical (200 doses) to commercial (40,000 doses) scale and (2) the commercial (40,000 doses) production of pDNA and lentiviral vectors for the current state-of-the-art viral vector-based CAR T-cell therapies. By applying a systematic global sensitivity analysis, we quantify uncertainty in the manufacturing process and apportion it to key process and economic parameters, highlighting cost drivers and limitations that steer decision-making. The results underline the cost-efficiency and operational flexibility of non-viral-based therapies in the overall C> supply chain, as well as the importance of economies-of-scale in the production of pDNA.

Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach

To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of mRNA-LNP vaccine were evaluated using a definitive screening design (DSD) and machine learning (ML) algorithms. Particle size (PS), PDI, zeta potential (ZP), and encapsulation efficiency (EE) of mRNA-LNP were optimized within a defined constraint (PS 40-100 nm, PDI≤0.30, ZP≥(±)0.30 mV, EE≥70%), fed to ML algorithms (XGBoost, bootstrap forest, support vector machines, k-nearest neighbors, generalized regression-Lasso, ANN) and prediction was compared to ANN-DOE model. Increased FRR decreased the PS and increased ZP, while increased TFR increased PDI and ZP. Similarly, DOTAP and DOTMA produced higher ZP and EE. Particularly, a cationic ionizable lipid with an N/P ratio ≥6 provided a higher EE. ANN showed better predictive ability (R²=0.7269-0.9946), while XGBoost demonstrated better RASE (0.2833-2.9817). The ANN-DOE model outperformed both optimized ML models by R²=1.21% and RASE=43.51% (PS prediction), R²=0.23% and RASE=3.47% (PDI prediction), R²=5.73% and RASE=27.95% (ZP prediction), and R²=0.87% and RASE=36.95% (EE prediction), respectively, which demonstrated that ANN-DOE model was superior in predicting the bioprocess compared to independent models.

Maximization of the Minicircle DNA Vaccine Production Expressing SARS-CoV-2 RBD

Nucleic acid vaccines have been proven to be a revolutionary technology to induce an efficient, safe and rapid response against pandemics, like the coronavirus disease (COVID-19). Minicircle DNA (mcDNA) is an innovative vector more stable than messenger RNA and more efficient in cell transfection and transgene expression than conventional plasmid DNA. This work describes the construction of a parental plasmid (PP) vector encoding the receptor-binding domain (RBD) of the S protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the use of the Design of Experiments (DoE) to optimize PP recombination into mcDNA vector in an orbital shaker. First, the results revealed that host cells should be grown at 42 °C and the Terrific Broth (TB) medium should be replaced by Luria Broth (LB) medium containing 0.01% L-arabinose for the induction step. The antibiotic concentration, the induction time, and the induction temperature were used as DoE inputs to maximize the % of recombined mcDNA. The quadratic model was statistically significant (p-value < 0.05) and presented a non-significant lack of fit (p-value > 0.05) with a suitable coefficient of determination. The optimal point was validated using 1 h of induction, at 30 °C, without the presence of antibiotics, obtaining 93.87% of recombined mcDNA. Based on these conditions, the production of mcDNA was then maximized in a mini-bioreactor platform. The most favorable condition obtained in the bioreactor was obtained by applying 60% pO2 in the fermentation step during 5 h and 30% pO2 in the induction step, with 0.01% L-arabinose throughout 5 h. The yield of mcDNA-RBD was increased to a concentration of 1.15 g/L, when compared to the orbital shaker studies (16.48 mg/L). These data revealed that the bioreactor application strongly incremented the host biomass yield and simultaneously improved the recombination levels of PP into mcDNA. Altogether, these results contributed to improving mcDNA-RBD biosynthesis to make the scale-up of mcDNA manufacture simpler, cost-effective, and attractive for the biotechnology industry.

Design of Experiments to Achieve an Efficient Chitosan-Based DNA Vaccine Delivery System

In current times, DNA vaccines are seen as a promising approach to treat and prevent diseases, such as virus infections and cancer. Aiming at the production of a functional and effective plasmid DNA (pDNA) delivery system, four chitosan polymers, differing in the molecular weight, were studied using the design of experiments (DoE) tool. These gene delivery systems were formulated by ionotropic gelation and exploring the chitosan and TPP concentrations as DoE inputs to maximize the nanoparticle positive charge and minimize their size and polydispersity index (PDI) as DoE outputs. The obtained linear and quadratic models were statistically significant (p-value < 0.05) and non-significant lack of fit, with suitable coefficient of determination and the respective optimal points successfully validated. Furthermore, morphology, stability and cytotoxicity assays were performed to evaluate the endurance of these systems over time and their further potential for future in vitro studies. The subsequent optimization process was successful achieved for the delivery systems based on the four chitosan polymers, in which the smallest particle size was obtained for the carrier containing the 5 kDa chitosan (~82 nm), while the nanosystem prepared with the high molecular weight (HMW) chitosan displayed the highest zeta potential (~+26.8 mV). Delivery systems were stable in the formulation buffer after a month and did not exhibit toxicity for the cells. In this sense, DoE revealed to be a powerful tool to explore and tailor the characteristics of chitosan/pDNA nanosystems significantly contributing to unraveling an optimum carrier for advancing the DNA vaccines delivery field.