Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data.

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.

Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology.

In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and implementation of Process Analytical Technology (PAT), which aims to develop rapid and high-throughput strategies for real-time monitoring of bioprocesses key variables, in order to improve their quality control lines. In this context, spectroscopic techniques for data generation in combination with chemometrics represent alternative analytical methods for on-line critical process variables prediction. In this work, a novel multivariate calibration strategy for the at-line prediction of etanercept, a recombinant protein produced in a mammalian cells-based perfusion process, is presented. For data generation, samples from etanercept processes were daily obtained, from which fluorescence excitation-emission matrices were generated in the spectral ranges of 225.0 and 495.0 nm and 250.0 and 599.5 nm for excitation and emission modes, respectively. These data were correlated with etanercept concentration in supernatant (measured by an off-line HPLC-based reference univariate technique) by implementing different chemometric strategies, in order to build predictive models. Partial least squares (PLS) regression evidenced a non-linear relation between signal and concentration when observing actual vs. predicted concentrations. Hence, a non-parametric approach was implemented, based on a multilayer perceptron artificial neural network (MLP). The MLP topology was optimized by means of the response surface methodology. The prediction performance of MLP model was superior to PLS, since the first is able to cope with non-linearity in calibration models, reaching percentage mean relative error in predictions of about 7.0% (against 12.6% for PLS). This strategy represents a fast and inexpensive approach for etanercept monitoring, which conforms the principles of PAT.