Background/Objectives: To date, nearly 300 lentiviral-based gene therapy clinical trials have been conducted, with eight therapies receiving regulatory approval for commercialization. These advances, along with the increased number of advanced-phase clinical trials, have prompted contract development and manufacturing organizations (CDMOs) to develop innovative strategies to address the growing demand for large-scale batches of lentiviral vectors (LVVs). Consequently, manufacturers have focused on optimizing processes under good manufacturing practices (GMPs) to improve cost-efficiency, increase process robustness, and ensure regulatory compliance. Nowadays, the LVV production process mainly relies on the transient transfection of four plasmids encoding for the lentiviral helper genes and the transgene. While this method is efficient at small scales and has also proven to be scalable, the industry is exploring alternative processes due to the high cost of GMP reagents, and the batch-to-batch variability predominantly attributed to the transfection step. Methods: Here, we report the development and implementation of a reliable and clinical-grade envisioned platform based on the generation of stable producer cell lines (SCLs) from an initial well-characterized lentiviral packaging cell line (PCL). Results: This platform enables the production of VSV-G-pseudotyped LVVs through a fully transfection-free manufacturing process. Our data demonstrate that the developed platform will facilitate successful technological transfer to large-scale LVV production for clinical application. Conclusions: With this simple and robust stable cell line generation strategy, we address key concerns associated with the costs and reproducibility of current manufacturing processes.

Perinatal asphyxia represents a major medical disorder and is related to around a fourth of all neonatal deaths worldwide. Specific thresholds for lactate or pH levels define the gold standard for detecting hypoxic-ischemic events as physiological abnormalities. In contrast to current gold standard, we analyze the systemic picture, represented by the whole set of biochemical parameters from blood gas analysis, by multiparametric machine learning algorithms. In a swine model with 22 objects, we investigate the impact of neonatal hypoxic-ischemic encephalopathy on 18 individual physiological parameters. In a first approach, we study the statistical significance of individual parameters by univariate analysis methods. In a second approach, we take the most relevant parameters as input for the development of predictive models by different hybrid and non hybrid classification algorithms. The predictive power of our multiparametric models outperforms by far the limited performance of pH and lactate as reliable indicators, despite strong correlation with hypoxic-ischemic events. We have been able to detect hypoxic-ischemic events even one hour after the episode, with accuracies close to 100% in contrast to pH or lactate-based diagnosis with 62% and 78%, respectively. By all machine learning algorithms, lactate is recognized as the main contributor due to its longer-term evidence of hypoxia-ischemia episodes. However, substantial improvement of the diagnosis is achieved by predictions based on a systemic picture of different physiological parameters. Our results prove the potential applicability of our method as a support tool for decision-making that will allow obstetricians to identify hypoxic ischemic episodes more accurately during labor.