Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development.

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

Cardiac Operative Risk in Latin America: A Comparison of Machine Learning Models vs EuroSCORE-II.

BACKGROUND: Machine learning is a useful tool for predicting medical outcomes. This study aimed to develop a machine learning-based preoperative score to predict cardiac surgical operative mortality. METHODS: We developed various models to predict cardiac operative mortality using machine learning techniques and compared each model to European System for Cardiac Operative Risk Evaluation-II (EuroSCORE-II) using the area under the receiver operating characteristic (ROC) and precision-recall (PR) curves (ROC AUC and PR AUC) as performance metrics. The model calibration in our population was also reported with all models and in high-risk groups for gradient boosting and EuroSCORE-II. This study is a retrospective cohort based on a prospectively collected database from July 2008 to April 2018 from a single cardiac surgical center in Bogotá, Colombia. RESULTS: Model comparison consisted of hold-out validation: 80% of the data were used for model training, and the remaining 20% of the data were used to test each model and EuroSCORE-II. Operative mortality was 6.45% in the entire database and 6.59% in the test set. The performance metrics for the best machine learning model, gradient boosting (ROC: 0.755; PR: 0.292), were higher than those of EuroSCORE-II (ROC: 0.716, PR: 0.179), with a P value of .318 for the AUC of the ROC and .137 for the AUC of the PR. CONCLUSIONS: The gradient boosting model was more precise than EuroSCORE-II in predicting mortality in our population based on ROC and PR analyses, although the difference was not statistically significant.