Online monitoring of the cell-specific oxygen uptake rate with an in situ combi-sensor.

In a biotechnological process, standard monitored process variables are pH, partial oxygen pressure (pO2), and temperature. These process variables are important, but they do not give any information about the metabolic activity of the cell. The ISICOM is an in situ combi-sensor that is measuring the cell-specific oxygen uptake rate (qOUR) online. This variable allows a qualitative judgement of metabolic cell activity. The measuring principle of the ISICOM is based on a volume element enclosed into a small measuring chamber. Inside the measuring chamber, the pO2 and the scattered light is measured. Within a defined measuring interval, the chamber closes, and the oxygen supply for the cells is interrupted. The decreasing oxygen concentration is recorded by the pO2 optode. This measuring principle, known as the dynamic method, determines the oxygen uptake rate (OUR). Together with the scattered light signal, the cell concentration is estimated and the qOUR is available online. The design of the ISICOM is focused on functionality, sterility, long-term stability, and response time behavior so the sensor can be used in bioprocesses. With the ISICOM, measurement of online and in situ measurement of the OUR is possible. The OUR and qOUR online measurement of an animal cell batch cultivation is demonstrated, with maximum values of OUR = 2.5 mmol L-1 h-1 and a qOUR = 9.5 pmol cell-1 day-1. Information about limitation of the primary and secondary substrate is derived by the monitoring of the metabolic cell activity of bacteria and yeast cultivation processes. This sensor contributes to a higher process understanding by offering an online view on to the cell behavior. In the sense of process analytical technology (PAT), this important information is needed for bioprocesses to realize a knowledge base process control.

A novel LED-based 2D-fluorescence spectroscopy system for in-line bioprocess monitoring of Chinese hamster ovary cell cultivations-Part II.

This study was performed in order to evaluate a new LED-based 2D-fluorescence spectrometer for in-line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10-L fed-batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D-fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed-batch process was used for model validation and prediction. An almost continuous in-line bioprocess monitoring was realized because 2D-fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D-fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D-fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.

Spectroscopic sensors for in-line bioprocess monitoring in research and pharmaceutical industrial application.

The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.

Sequential/parallel production of potential Malaria vaccines--A direct way from single batch to quasi-continuous integrated production.

An intensification of pharmaceutical protein production processes can be achieved by the integration of unit operations and application of recurring sequences of all biochemical process steps. Within optimization procedures each individual step as well as the overall process has to be in the focus of scientific interest. This paper includes a description of the development of a fully automated production plant, starting with a two step upstream followed by a four step downstream line, including cell clarification, broth cleaning with microfiltration, product concentration with ultrafiltration and purification with column chromatography. Recursive production strategies are developed where a cell breeding, the protein production and the whole downstream is operated in series but also in parallel, each main operation shifted by one day. The quality and reproducibility of the recursive protein expression is monitored on-line by Golden Batch and this is controlled by Model Predictive Multivariate Control (MPMC). As a demonstration process the production of potential Malaria vaccines with Pichia pastoris is under investigation.