Fluorescence excitation-emission matrix combined with chemometric modelling for upstream monitoring of SARS-CoV-2 spike protein production

Abstract

Biotherapeutic production is inherently complex, requiring multiple unit operations and analytical methods compared to small-molecule drugs. In this context, Quality by Design (QbD) and Process Analytical Technology (PAT) play key roles in ensuring the quality, safety, and efficacy of biotherapeutics. This work presents a fluorescence excitation-emission matrix (EEM) combined with PARAFAC (Parallel Factor) model under non-negativity constraint for the off-line quantitative prediction of the SARS-CoV-2 Spike ectodomain glycoprotein (S-ED), a COVID-19 subunit vaccine candidate, in HEK293 perfusion bioreactor cultures. Design of experiments (DoE) approach was applied to optimize the sandwich enzyme-linked immunosorbent assay (ELISA) method, whose validation was crucial to assess the accuracy and consistency of the results. Principal component analysis (PCA) was used for outlier detection in spectral data, while an appropriate chemical rank estimation strategy was implemented to determine the number of fluorescent responsive components. Subsequently, PARAFAC modelling of the three-way data array enabled the off-line prediction of S-ED in bioreactor samples. This multivariate calibration model, offering simplicity, accuracy, and precision, aligns with PAT guidelines by monitoring critical process parameters (CPPs) such as S-ED concentration in bioreactor samples. It provides an efficient alternative to traditional analytical methods, enhancing process monitoring and improving the overall S-ED production workflow.