A multisensor approach for improved protein A load phase monitoring by conductivity-based background subtraction of UV spectra.

Abstract

Real-time monitoring and control of protein A capture steps by process analytical technologies (PATs) promises significant economic benefits due to the improved usage of the column's binding capacity, by eliminating time-consuming off-line analytics and costly resin lifetime studies, and enabling continuous production. The PAT method proposed in this study relies on ultraviolet (UV) spectroscopy with a dynamic background subtraction based on the leveling out of the conductivity signal. This point in time can be used to collect a reference spectrum for removing the majority of spectral contributions by process-related contaminants. The removal of the background spectrum facilitates chemometric model building and model accuracy. To demonstrate the benefits of this method, five different feedstocks from our industry partner were used to mix the load material for a case study. To our knowledge, such a large design space, which covers possible variations in upstream condition besides the product concentration, has not been disclosed yet. By applying the conductivity-based background subtraction, the root mean square error of prediction (RMSEP) of the partial least squares (PLS) model improved from 0.2080to0.0131 g . Finally, the potential of the background subtraction method was further evaluated for single wavelength-based predictions to facilitate implementation in production processes. An RMSEP of 0.0890 g with univariate linear regression was achieved, showing that by subtraction of the background better prediction accuracy is achieved then without subtraction and a PLS model. In summary, the developed background subtraction method is versatile, enables accurate prediction results, and is easily implemented into existing chromatography setups with typically already integrated sensors.