Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

Abstract

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

Raman Spectroscopy for Quantitative Analysis in the Pharmaceutical Industry

“…All these variants were carefully developed aiming to overcome the limitations of the conventional Raman spectroscopy. By using chemometric treatments, it is possible to improve analytical models for the quantification of drugs substances and excipients in multicomponent formulas (10,16,26), as presented in Table 1.…”

“…This concept allows risk management in the development of new medicines (54)(55)(56)(57)(58). Raman spectroscopy allows for identifying critical variables and their clinical relevance related to quality and safety for patients (26,50,59,60). Table 3 presents 14 studies in the last five years, related to the use of this technique in the product development phase.…”

A Chemometric Tool to Monitor and Predict Cell Viability in Filamentous Fungi Bioprocesses Using UV Chromatogram Fingerprints

“…In general, the first PCs explain most of the variance in the chromatographic dataset. PCA has been widely reviewed for applications in process development and production [37,39,40].…”

Implementing Process Analytical Technology for the Production of Recombinant Proteins in Escherichia coli Using an Advanced Controller Scheme

“…One possible approach is to control the process along the desired trajectory using advanced controllers that have proven performance. Process controllers improve productivity and process robustness without accruing cost to the bioprocess …”

“…Process controllers improve productivity and process robustness without accruing cost to the bioprocess. [8,9] The initiative of quality by design (QbD) has been suggested by regulatory agencies to encourage the utilization of expert domain knowledge in developing and improving processes in a manner that ensures the achievement of desired product qualities. [10][11][12] Further, the aspects of process control and its role in delivering consistent product quality are addressed by a subcomponent of QbD known as process analytical technology (PAT).…”