Thursday, October 6

Emerging Technological Landscapes in Biomanfacturing and Monoclonal Antibody Screening

Monoclonal antibodies (mAbs) have been used as therapeutic agents over the past several decades. Their specificity and ability to trigger the immune system are useful in fighting diseases such as cancers and autoimmune disorders. Though the concept of immunotherapy is not new, antibody production and characterization landscapes are everchanging. Scientists are constantly innovating newer technologies that allow them to understand nuances within these drugs and find ways to manufacture them more reliably. This article aims to understand the current challenges in biomanufacturing and screening mAb profiles, and how innovative tools allow for greater insight when monitoring the critical attributes of mAbs.

 
Urging the innovation of PAT towards increased product quality
 

In 2004 the US Food and Drug Administration (FDA) issued guidance toward finding innovative solutions for biopharmaceutical manufacturing to increase process knowledge within the production of biologics, such as mAbs.1 Process Analytical Technology (PAT) had already been utilized for small molecule-based manufacturing. However, it has fallen short within the biomanufacturing realm because of the complexities that arise with large molecules, such as post-translational modifications (PTMs), that affect the characterizations of these drugs.


“PAT, in short, is a concept in which one should monitor a biologic for its quality, through measuring critical quality attributes (CQAs) and, if the quality is not sufficient, to then manipulate the process to get the quality required for regulatory specifications,” says Anurag Rathore, professor of chemical engineering at IIT Delhi, a key thought leader who recently published a paper highlighting the importance of PAT for antibody manufacturing.2 “The first problem is quality; the second problem is knowing which – of the many – attributes and PTMs are critical and finding ways to measure them in real-time.”

The greatest shift in mAb manufacturing has been the speed of analysis. PAT has been an enabling tool in helping to close the gap in process development. “For a typical mAb, there are anywhere between 15–20 different quality parameters that impact their efficacy and stability, such as aggregate levels, variant profile and glycosylation.” Hence, current research is finding ways to measure these processes using different tool kits to elucidate the antibody’s characteristics, especially during upstream processing. With the urgency to understand product quality, novel PAT tools are being produced using established technologies, such as high performance liquid chromatography (HPLC) and mass spectrometry (MS), as well as taking these technologies and finding new ways to employ them. Some highlights include automation of CQA analyses such as N-glycosylation and using MS as multi-attribute monitoring (MAM) platform to allow for increased product knowledge rapidly.

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Flow injection-based sample preparation coupled to online HPLC for PAT

The use of flow injection for PAT has slowly garnered attention over the past few years, as it allows for the automation of assays in a streamlined fashion. Although this technology has been around for some time, it has only recently been adopted for use in biopharma. There have been several proof of concepts with the use of flow injection analysis (FIA) with the integration of HPLC, MS and a promising utility with C.E. integration aswell.3,4,5,6


One of the major quality attributes that requires analysis during the bioprocessing of antibodies is the N-glycan profile. It is known to influence the characteristics of the protein, such as stability, drug clearance and immunogenicity. The current major drawback when measuring the glycan profile of antibodies is the lack of an integrated approach. Though strides have been made in creating chemistries for sample prep to speed up processing times, there is still a lack of automation for the sample preparation to allow for online integration into a bioprocess. This lack of integration severely impacts the ability of process scientists to know how the glycosylation of antibodies is behaving throughout their cell culture, up until the end of the culture. This is not conducive to a regulatory environment.

Work in the Chundawat lab at Rutgers University looks to study the N-glycosylation process by building a platform that allows for an integrated and automated approach to glycan monitoring.4 “We built a PAT system that allows for automated, near real-time, quality monitoring of the mAbs, called the N-GLYcanyzer,” explains Shishir P.S. Chundawat, associate professor of chemical and biochemical engineering at Rutgers University, who recently published a paper describing the advances in bioprocess practices.4,7 The innovation demonstrates the use of a flow injection system integrated into a bioreactor and online HPLC. “This system allows us to understand the temporal changes within glycosylation, as the glycosylation process is non-template driven. We need to understand the cellular metabolism and its role in PTMs,” says Chundawat.

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The N-GLYcanyzer has shown reproducibility against a traditional off-line sample preparation procedure, while shortening the sampling time from the traditional two to three days to just three hours, immensely increasing processing knowledge during bioprocessing.

The principles of the N-GLYcanyzer follow a traditional sample preparation procedure for mAb–glycan analysis, with some alterations that allow for the chemistry to work in a flow injection setting. The system allows for automated sampling and sample preparation of glycans from mAbs in cell culture. After sample preparation on the N-GLYcanyzer system, the sample is injected into a HPLC where chromatography is conducted and enables one to elucidate the glycan pattern. This innovation automates and greatly speeds up sample prep time for glycan analysis and has proven to be conducive to helping advance the understanding of mAb bioprocessing through rapid monitoring and screening.

The work aims to create a system that can build control for the antibody glycosylation process. “You do not want to run a multi-week fed-batch culture to find out the biologic is sub-optimal. We need to monitor these aspects of the process to understand the design space,” explains Chundawat. “However, the use of this toolkit also has translational aspects, such as being introduced at different stages of product development, from candidate selection to early and late-stage process development. But also, as a QC tool for real-time release.” Truly a motivating factor is also the movement towards continuous biomanufacturing as well.8


“The next stage is to build out the design space,” Chundawat says. “The tool to monitor the glycosylation processes in near-real-time has been built. The following steps will be to study factors that influence the process to develop strategies based on process models to allow for the glycosylation of biologics to be more heterogeneous and relieve some aspects of regulatory oversight.”

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MAM allows for increased product knowledge in a condensed timeframe  

MAM has amassed attention within the past half-decade for its promise of allowing for an understanding of multiple quality attributes of a mAb – all within one assay. Typically, numerous different processes, such as size exclusion chromatography, ion-exchange chromatography and hydrophobic and hydrophilic interaction liquid chromatography, must be run sequentially to understand each quality attribute per analysis. The aim of MAM methods is to provide MS-based quantitative information using parallel monitoring of multiple quality attributes. “MAM is garnering much attention as it can be used to replace a number of costly assays based runs,” says Craig Jakes, researcher at the Characterization and Comparability Lab (CCL) in the National Institute of Bioprocessing Research and Training (NIBRT) in Dublin, Ireland, who has been working to build MAM tools.9


“MAM is a peptide mapping approach that allows for monitoring multiple attributes and the detection of impurities using a single LC-MS run,” says Jakes. “There are two stages to MAM. The first is termed the discovery and monitoring phase. Here LC-MS/MS is employed to identify product quality attributes and export them into a target peptide workbook. MS-only runs are then employed with a processing method containing this target peptide workbook which will generate a report showing PQA trends over the course of your runs. The second stage is new peak detection (NPD). Here, the chromatogram of a standard is compared to that of your sample, and new peaks are flagged as frames. In this case, you can use this approach to compare batch to batch variability and detect impurities such as host cell proteins.”

A recent case study by Jakes applied a MAM approach to analyze influential quality attributes – including FC N-glycosylation, charge variants and oxidation – of an antibody throughout the culture.

Interestingly, the method was able to pick up host cell protein (HCP) contamination down to 100 ppm, which gives confidence that the MAM tool could eventually replace ELISA for HCP, as ELISA is a cumbersome and lengthy assay. Nuances were also picked up between an innovator and biosimilar within the charge state and glycosylation profile. This is an important finding as sequence mutations due to misincorporation can lead to changes in the physicochemical and or biological properties of the antibody.

The proof-of-concept study by Jakes showed the capabilities of the MAM platform, but these are only a handful of attributes that can be monitored. “The decision of what attributes can be monitored and screened depends entirely upon the product that you wish to monitor,” says Jakes. The quality attributes one would wish to detect and monitor are case-specific, even towards different antibodies with diverse attributes. The other issues that could arise with MAM methods is if an HPLC column is taken from a different lot or a new pump is used. This would require a recalibration of the workbook based on residence times in the chromatography, requiring an advanced skillset to re-setup the system. The forward trend of MAM looks toward integrating these types of tools into an automated format, which would remove the need for manual sampling and sample pretreatments such as trypsin digests. Adopting an automated MAM approach would allow constant sampling and monitoring of antibodies for their quality attributes.

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The future state of antibody monitoring

The largest issue facing antibody production today is still a lack of understanding of the protein production process occurring within a cell line, whether using CHO, HEK or NS0 cell lines. Biologics are products that continue to be produced heterogeneously, and that heterogenous variability can be attributed to biologic variability of the cells used to produce them.

“I want to see automation, and minimal offline testing of products,” says Rathore, when asked about the future state of antibody production and quality. “Automation and continuous processing will allow for more consistency within products and make sure they are safe and efficacious.” However, the industry is not there yet, and much more work is still needed to understand the complexities of cell lines and how they are harnessed to produce therapeutics. Once rapid and robust methods are produced with PAT, mechanistic models can be built to understand the nuances of cell culture and control systems can used to accommodate for biologic heterogeneity, pivoting towards continuous production of safe and efficacious and more cost-effective immunotherapies.

References

1. U.S. Department of Health and Human Services, U.S. Food and Drug Administration. Guidance for industry PAT — A framework for pharmaceutical development, manufacturing, and quality assurance. 2004;(September). https://www.gmp-compliance.org/files/guidemgr/PAT-FDA-6419fnl.pdf

2. Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess control: Current progress and future perspectives. Life. 2021;11(6):557. doi: 10.3390/life11060557

3. Tharmalingam T, Wu C-H, Callahan S, T. Goudar C. A framework for real-time glycosylation monitoring (RT-GM) in mammalian cell culture. Biotechnol Bioeng. 2015;112(6):1146-1154. doi: 10.1002/bit.25520

4. Gyorgypal A, Chundawat SPS. Integrated process analytical platform for automated monitoring of monoclonal antibody N-Linked glycosylation. Anal Chem. 2022;94(19):6986-6995. doi: 10.1021/acs.analchem.1c05396

5. Liu Y, Zhang C, Chen J, et al. A fully integrated online platform for real time monitoring of multiple product quality attributes in biopharmaceutical processes for monoclonal antibody therapeutics. J Pharm Sci. 2022;111(2):358-367. doi: 10.1016/j.xphs.2021.09.011

6. Dvořák M, Miró M, Kubáň P. Automated sequential injection-capillary electrophoresis for dried blood spot analysis: A proof-of-concept study. Anal Chem. 2022;94(13):5301-5309. doi: 10.1021/acs.analchem.1c05130

7. Chopda V, Gyorgypal A, Yang O, et al. Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies. J Chem Technol Biotechnol. 2021;(April):jctb.6765. doi: 10.1002/jctb.6765


8. Khanal O, Lenhoff AM. Developments and opportunities in continuous biopharmaceutical manufacturing. MAbs. 2021;13(1):1903664. doi: 10.1080/19420862.2021.1903664

9. Jakes C, Millán-Martín S, Carillo S, Scheffler K, Zaborowska I, Bones J. Tracking the behavior of monoclonal antibody product quality attributes using a multi-attribute method workflow. J Am Soc Mass Spectrom. 2021;32(8):1998-2012. doi: 10.1021/jasms.0c00432

  



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