Process analytical technique (PAT) miniaturization for monoclonal antibody aggregate detection in continuous downstream processing

Abstract

The transition to continuous biomanufacturing is considered the next step to reduce costs and improve process robustness in the biopharmaceutical industry, while also improving productivity and product quality. The platform production process for monoclonal antibodies (mAbs) is eligible for continuous processing to lower manufacturing costs due to patent expiration and subsequent growing competition. One of the critical quality attributes of interest during mAb purification is aggregate formation, with several processing parameters and environmental factors known to influence antibody aggregation. Therefore, a real-time measurement to monitor aggregate formation is crucial to have immediate feedback and process control and to achieve a continuous downstream processing. Miniaturized biosensors as an in-line process analytical technology tool could play a pivotal role to facilitate the transition to continuous manufacturing. In this review, miniaturization of already wellestablished methods to detect protein aggregation, such as dynamic light scattering, Raman spectroscopy and circular dichroism, will be extensively evaluated for the possibility of providing a real-time measurement of mAb aggregation. The method evaluation presented in this review shows which limitations of each analytical method still need to be addressed and provides application examples of each technique for mAb aggregate characterization. Additionally, challenges related to miniaturization are also addressed, such as the design of the microfluidic chip and the microfabrication material. The evaluation provided in this review shows why the development of microfluidic biosensors is considered the key for real-time measurement of mAb aggregates and how it can contribute to the transition to a continuous processing.

Converting a mAb downstream process from batch to continuous using process modeling and process analytical technology

“…[52] Currently, various PAT applications in upstream have been reported such as Raman spectrum, viable cell sensor, oxygen and carbon dioxide sensors, [53] but PAT devices and applications for the downstream process are very limited. [54][55][56][57][58] In this study, a customized in-line protein quantity monitoring prototype system was developed to check the load mass of the CEX column and integrated with the DeltaV system to control load mass within a validated range. Previously, the mAb concentration was measured off-line and load volume was calculated manually.…”

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

“…Besides the conventionally used UV absorbance at 280 nm as one-dimensional data or as a multiwavelength method, variable pathlength UV spectroscopy allows the accurate detection of analytes in a high concentration range [ 65 ]. Furthermore, several analytical techniques are used to detect aggregates in a wide size range, from which only a few can be integrated as an in-line PAT tool (e.g., light scattering methods) [ 66 , 67 ]. When there is no available in-line analytical tool for monitoring a CQA/CPP, an automated, sterile sampling system can be integrated into the process.…”

Recent Advances and Future Directions in Downstream Processing of Therapeutic Antibodies

“…A more modern approach called Process Analytical Technology (PAT) involves at-line (analysis performed with a sample that is removed from the process stream but is in proximity to the unit operation), on-line (analysis performed using a sample that is diverted from and may be returned to the process stream), or in-line analysis (direct monitoring using a sensor located in the process stream) and sampling. This allows for real-time or near real-time analytical data on product critical quality attributes (CQAs) (reviewed in [ 56 , 57 , 58 , 59 , 60 ]. PAT takes advantage of modern sensors, sampling, and analytical technologies in combination with data management and data mining tools and strategies to gain deeper and more thorough product and process understanding, consistent with QbD expectations.…”