Synchronous Fluorescence Spectroscopy to Monitor Relevant Biochemicals over Zika-Virus-Like Particles' Production
Vinícius Aragão Tejo Dias1, Júlia Dezanetti da Silva1, Júlia Públio Rabello1
1Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências e Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio 1000, CEP 03828-000, São Paulo, SP, Brazil.
Journal of Fluorescence
|October 8, 2025
Summary
Synchronous fluorescence spectroscopy effectively monitors Zika virus-like particle (Zika-VLP) production. This method, using chemometric models like Artificial Neural Network (ANN), accurately predicts key biochemical parameters and viral titer.
Area of Science:
- Biotechnology
- Analytical Chemistry
- Bioprocess Engineering
Background:
- Monitoring biochemical parameters is crucial for optimizing viral vector production.
- Traditional methods for monitoring bioprocesses can be time-consuming and labor-intensive.
- Zika virus-like particle (Zika-VLP) production requires precise control over multiple biochemical factors.
Purpose of the Study:
- To evaluate synchronous fluorescence spectroscopy (SFS) combined with chemometric modeling for real-time monitoring of Zika-VLP production.
- To predict key biochemical parameters including lactate, glutamine, glutamate, ammonium, total protein, viable cell density, cell viability, and viral titer.
- To compare the predictive performance of Partial Least Squares (PLS) and Artificial Neural Network (ANN) models.
Main Methods:
- Synchronous fluorescence spectra were acquired at various wavelength differences (Δλ) during bioreactor cultivation.
- Standard offline methods were used for initial biochemical parameter quantification.
- PLS and ANN chemometric models were developed to correlate spectral data with biochemical parameters and viral titer.
- Transmission electron microscopy (TEM) was used to confirm Zika-VLP morphology.
Main Results:
- ANN models generally outperformed PLS models, demonstrating higher accuracy and lower error rates.
- Spectra at Δλ = 80 nm provided the best predictive results for most parameters.
- Highly accurate predictions were achieved for glutamate (1.1% MRE), glutamine (1.6% MRE), ammonium (4.0% MRE), and viral titer (5.8% MRE) using ANN.
- Successful production of Zika-VLPs was confirmed via TEM.
Conclusions:
- Synchronous fluorescence spectroscopy coupled with chemometric modeling (particularly ANN) is a viable and accurate technique for monitoring multiple critical parameters in Zika-VLP production.
- This approach offers a potential for enhanced process understanding and control in biopharmaceutical manufacturing.
- SFS provides a rapid, non-invasive analytical tool for bioprocess monitoring.
