Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process.

Longitudinal monitoring of cell metabolism in biopharmaceutical production using label-free fluorescence lifetime imaging microscopy.

Chinese hamster ovary (CHO) cells are routinely used in the biopharmaceutical industry for production of therapeutic monoclonal antibodies (mAbs). Although multiple offline and time-consuming measurements of spent media composition and cell viability assays are used to monitor the status of culture in biopharmaceutical manufacturing, the day-to-day changes in the cellular microenvironment need further in-depth characterization. In this study, two-photon fluorescence lifetime imaging microscopy (2P-FLIM) was used as a tool to directly probe into the health of CHO cells from a bioreactor, exploiting the autofluorescence of intracellular nicotinamide adenine dinucleotide phosphate (NAD(P)H), an enzymatic cofactor that determines the redox state of the cells.

Tuning monoclonal antibody galactosylation using Raman spectroscopy-controlled lactic acid feeding.

A key aspect of large-scale production of biotherapeutics is a well-designed and consistently-executed upstream cell culture process. Process analytical technology tools provide enhanced monitoring and control capabilities to support consistent process execution, and also have potential to aid in maintenance of product quality at desired levels. One such tool, Raman spectroscopy, has matured as a useful technique to achieve real-time monitoring and control of key cell culture process attributes.

A Rational Analysis of Uniformity Risk for Agglomerated Drug Substance Using NIR Chemical Imaging.

Early risk detection and quick diagnosis of manufacturing challenges are necessary to support the accelerated development pace of drug product in the current competitive environment. Analytical tools, such as near-infrared (NIR) chemical imaging (CI), can be employed for alerting drug substance uniformity risks in intermediates and the final product of solid dosage forms. In this particular study, the ability to characterize the behavior of agglomerated drug substance throughout process development was enabled by NIR CI to identify uniformity risks with small sample sizes and short turnaround time.

In-line ATR-UV and Raman Spectroscopy for Monitoring API Dissolution Process During Liquid-Filled Soft-Gelatin Capsule Manufacturing.

Complete dissolution of the active pharmaceutical ingredient (API) is critical in the manufacturing of liquid-filled soft-gelatin capsules (SGC). Attenuated total reflectance UV spectroscopy (ATR-UV) and Raman spectroscopy have been investigated for in-line monitoring of API dissolution during manufacturing of an SGC product. Calibration models have been developed with both techniques for in-line determination of API potency.

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra.

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model.

Synthetic training sets for the development of discriminant functions for the detection of volatile organic compounds from passive infrared remote sensing data.

A novel synthetic data generation methodology is described for use in the development of pattern recognition classifiers that are employed for the automated detection of volatile organic compounds (VOCs) during infrared remote sensing measurements. The approach used is passive Fourier transform infrared spectrometry implemented in a downward-looking mode on an aircraft platform. A key issue in developing this methodology in practice is the need for example data that can be used to train the classifiers.

Airborne passive Fourier transform infrared remote sensing of methanol vapor from industrial emissions.

Passive Fourier transform infrared (FT-IR) remote sensing measurements are applied to the detection of methanol vapor plumes released from a chemical manufacturing facility. With the spectrometer mounted in a downward-looking mode on a fixed-wing aircraft, overflights of the facility are made during the methanol release. Signal processing and pattern recognition methods are applied to the acquired data for the purpose of constructing an automated classification algorithm for the methanol detection.