Large scale demonstration of a process analytical technology application in bioprocessing: Use of on‐line high performance liquid chromatography for making real time pooling decisions for process chromatography

Abstract

Process Analytical Technology (PAT) has been gaining a lot of momentum in the biopharmaceutical community because of the potential for continuous real time quality assurance resulting in improved operational control and compliance. In previous publications, we have demonstrated feasibility of applications involving use of high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) for real-time pooling of process chromatography column. In this article we follow a similar approach to perform lab studies and create a model for a chromatography step of a different modality (hydrophobic interaction chromatography). It is seen that the predictions of the model compare well to actual experimental data, demonstrating the usefulness of the approach across the different modes of chromatography. Also, use of online HPLC when the step is scaled up to pilot scale (a 2294 fold scale-up from a 3.4 mL column in the lab to a 7.8 L column in the pilot plant) and eventually to manufacturing scale (a 45930 fold scale-up from a 3.4 mL column in the lab to a 158 L column in the manufacturing plant) is examined. Overall, the results confirm that for the application under consideration, online-HPLC offers a feasible approach for analysis that can facilitate real-time decisions for column pooling based on product quality attributes. The observations demonstrate that the proposed analytical scheme allows us to meet two of the key goals that have been outlined for PAT, i.e., "variability is managed by the process" and "product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions". The application presented here can be extended to other modes of process chromatography and/or HPLC analysis.

Chemometrics applications in biotech processes: A review

“…Although chemometrics is not used here in the same manner as in the upstream applications, process modeling is an integral part of these applications involving pooling of process chromatography columns as despite the faster analysis, the analysis lags elution by at least one fraction and so a model is needed to be able to predict the purity of the (n þ 1)th fraction based on the purity data available till nth fraction. [54][55][56] Case study illustrating use of chemometrics for process modeling A study involving monitoring of fractionation of a whey protein isolate (WPI) during dead-end membrane filtration using fluorescence and chemometric tools was published recently. 67 The model system of WPI consisted of a-lactalbumin (a-LA), b-lactoglobulin (b-LG), and a small proportion of bovine serum albumin (BSA).…”

“…56 In a more recent publication, a combination of the use of monolithic columns and UPLC was able to reduce the analysis time to 1.3 min. 54 This study involved pooling of a hydrophobic interaction process chromatography column. The control scheme was demonstrated to work at pilot scale (a 2,294-fold scale-up from a 3.4-mL column in the lab to a 7.8-L column in the pilot plant) and eventually to manufacturing scale (a 45,930-fold scale-up from a 3.4-mL column in the lab to a 158-L column in the manufacturing plant).…”

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time

“…Actions in DSP have to be placed within seconds, and the measurement has to be suitably fast. At‐line high‐performance liquid chromatography (HPLC) measurements were proposed by different research groups . However, such HPLC measurements require automated sampling and subsequent data analyses to obtain results fast enough to give feedback information.…”

A novel toolbox for E. coli lysis monitoring

“…In contrast, spectroscopic methods, such as RAMAN and near infrared spectroscopy (NIR), can be used for online monitoring [4, 5]. Owing to the high magnitude of multi-dimensional data derived from these methods, multivariate data analysis (MVDA) is used for data interpretation [6, 7]. However, the continuously changing media background, changing morphologies, as well as changing process parameters (e.g., aeration) cause inaccuracy in measurements and thus limit the applications of these methods.…”