An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development.

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process.

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.

Multiproduct Resin Reuse for Clinical and Commercial Manufacturing-Methodology and Acceptance Criteria.

Chromatography resins used for purifying biopharmaceuticals are generally dedicated to a single product. In good manufacturing practice (GMP) facilities that manufacture a limited amount of any particular product, this practice can result in the resin being used for a fraction of its useful life. A methodology for extending resin reuse to multiple products is described. With this methodology, resin and column performance, product carryover, and cleaning effectiveness are continually monitored to ensure that product quality is not affected by multiproduct resin reuse (MRR). Resin and column performance is evaluated in terms of (a) system suitability parameters, such as peak-shape and transition, and height equivalent theoretical plate (HETP) data; (b) key operating parameters, such as flow rate, inlet pressure, and pressure drop across the column; and (c) process performance parameters, such as impurity profiles, product quality, and yield. Historical data are used to establish process capability limits (PCLs) for these parameters. Operation within the PCLs provides assurance that column integrity and binding capacity of the resin are not affected by MRR.Product carryover defined as the carryover of the previously processed product (A) into a dose of the subsequently processed product (B) (COA→B), should be acceptable from a predictive patient safety standpoint. A methodology for determining COA→B from first principles and setting acceptance limits for cleaning validation is described.Cleaning effectiveness is evaluated by performing a blank elution run after inter-campaign cleaning and prior to product changeover. The acceptance limits for product carryover (COA→B) are more stringent for MRR than for single-product resin reuse. Thus, the inter-campaign cleaning process should be robust enough to consistently meet the more stringent acceptance limits for MRR. Additionally, the analytical methods should be sensitive enough to adequately quantify the concentration of the previously processed product (A) and its degradants in the eluent.General considerations for designing small-scale chromatographic studies for process development are also described. These studies typically include process-cycling runs with multiple products followed by viral clearance studies with a panel of model viruses. Small-scale studies can be used to optimize cleaning parameters, predict resin performance and product quality, and estimate the number of multiproduct purification cycles that can be run without affecting product quality. The proposed methodology is intended to be broadly applicable; however, it is acknowledged that alternative approaches may be more appropriate for specific scenarios.LAY ABSTRACT: Chromatography resins used for purifying biopharmaceuticals are generally dedicated to a single product. In good manufacturing practice (GMP) facilities that make a limited amount of any particular product, this practice can result in the resin being used for a fraction of its useful life. A methodology for extending resin reuse to multiple products is described. With this methodology, resin and column performance, product carryover, and cleaning effectiveness are continually monitored to ensure that product quality is not affected by multiproduct resin reuse.General considerations for designing small-scale chromatographic studies for process development are described. These studies typically include process-cycling runs with multiple products followed by viral clearance studies with a panel of model viruses. Small-scale studies can be used to optimize cleaning parameters, predict resin performance and product quality, and estimate the number of multiproduct purification cycles that can be run without impacting product quality.The proposed methodology is intended to be broadly applicable; however, it is acknowledged that alternative approaches may be more appropriate for specific scenarios.