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Driving Forces and Current Trends in Biopharmaceutical Analysis

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Driving Forces and Current Trends in Biopharmaceutical Analysis

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The accurate characterization of proteins and other compounds is a major determinant of success in biopharmaceutical development. Molecular insights obtained by analytical technologies guide the entire drug discovery and development process, from candidate screening and in vitro metabolism studies to bioprocess optimization and drug release testing.1 Sensitive analytical technologies are critical to answering key questions about the structure and activity of complex biotherapeutics and associated impurities. To meet the diverse and growing needs across biopharmaceutical R&D, a wide range of analytical tools continue to evolve.

Biopharmaceutical analysis is central to all stages of R&D

In early discovery phases, separation and analytical techniques are used to identify promising drug targets and candidates with the highest therapeutic potential. During Phase I trials, analytical tools facilitate the assessment of product safety and support production on a relatively small scale. Eventually, a bioprocess will be upscaled to meet commercial demands and defined according to regulatory requirements. There is a strong incentive to upscale as early and as quickly as possible, to minimize costs and provide greater confidence in the comparability of the final product with what was used in pivotal trials.2

With major advances in mass spectrometry (MS), performing detailed product characterization alongside bioprocess and formulation process development is now common practice.3
Dame Carol Robinson, professor in the Department of Chemistry at the University of Oxford, expects the criteria for such characterization to become increasingly demanding: “We anticipate that in years to come, it will be critical to routinely define modifications and small molecules that adhere to proteins, as these properties could affect outcomes in clinical trials.” With deep characterization capabilities highly relevant later in the pipeline, it is not unusual for the same technology to feature in both academic and industry settings. To provide a greater context of how different analytical technologies can be applied within the biopharmaceutical industry, examples are outlined in Table 1. 

Stage of research or development

Analytical technology 


Drug target characterization
Native MS
Elucidate membrane protein stoichiometry and ligand binding interactions4
Studying the function of large protein complexes
High resolution MS combined with halo affinity capture and chemical crosslinking
Capture information about neighboring surfaces within a large protein complex5
Method development for monoclonal antibody production
Automated high-throughput flow cytometry
Identify monoclonal antibody-producing hybridoma cells6
Biotherapeutic candidate selection
Surface plasmon resonance
Determine antibody binding affinities to select optimal antibody-drug conjugates7
Understanding drug metabolism and pharmacokinetics
Liquid chromatography and mass spectrometry (LC-MS)
Detect metabolites and characterize their structure8
Purification optimization during process development
Native MS
Identify ligands that may have co-purified with the protein sample4
Quality control
Nucleic acid amplification techniques
Detect mycoplasma in CAR (chimeric antigen receptor)-T cells9

Table 1. Opportunities for the application of analytical technologies within biopharmaceutical R&D.

Drivers of technological developments in biopharma

Today’s analytical methods have been shaped by the need to quantify and characterize complex protein therapeutics, which represent a growing portion of the market share.10 Protein therapeutics are highly heterogeneous; much of which can be attributed to the variability caused by
post-translational modification (PTM) including phosphorylation, glycosylation, and deamidation. While some PTMs are acceptable from a quality control (QC) perspective, others can significantly influence a product’s efficacy and immunogenicity and must be monitored during manufacturing. Bottom-up peptide mapping approaches have been employed to examine the primary structure of biopharmaceuticals, whereby proteins or peptides are digested, separated and analyzed via ultraviolet detection and/or MS technology.11

However, there is more to understanding protein characteristics than studying just the proteins themselves; other ligands bound to proteins are potential regulators of protein function. Along with her colleagues, Dame Robinson developed a top-down method for defining small molecules and very large protein complexes in the same protein assembly, within the same experiment.4 Dubbed “nativeomics”, the approach unifies “omics” analysis with native MS and enables ligand discovery without the need for prior knowledge of ligand chemistry. “We maintained the essential link between the protein and its ligand by introducing them into the gas phase of the mass spectrometer together,” Dame Robinson explains. “No solution phase separation was used. Instead, we used MS capabilities to activate the complex and performed multiple rounds of MS/MS to release the ligand for fragmentation and identification in databases.” The
new approach to native MS provides an enriched and high-resolution view of protein complexes and holds many potential applications.

Isothermal Titration Calorimetry for Enzyme Analysis

Enzymes are proteins that function as biological catalysts, which play crucial roles in the biochemical processes that occur in living organisms. Understanding how enzymes function, and how to activate or inhibit their activity, is a core research focus for biochemists that has broad applications. Isothermal titration calorimetry (ITC) is a key technique adopted here. Download this whitepaper and tech note for an in-depth understanding of ITC and its applications.

View Whitepaper and Technote

The need for speed in continuous manufacturing

The pressure to reduce manufacturing costs has created a demand for high-throughput techniques at all stages of the pipeline. Fully automated high-throughput flow cytometry12 and machine learning-enabled image analysis13 are examples of technologies that have emerged to support high-throughput demands and the
shift towards phenotypic screening. In biopharmaceutical production, Massimo Morbidelli, professor at the Polytechnic University of Milan and former professor at ETH Zürich, has witnessed the slow, but increasing uptake of continuous manufacturing processes. In contrast to fed-batch processing, continuous processing involves constant harvesting of the product and renewal of cell culture medium.

Towards the end of his 25 years at the Swiss Federal Institute of Technology in Zurich, Morbidelli and his colleagues reported a digitalized and automated end-to-end integrated process for producing an aggregation-sensitive antibody from an unstable cell line.14 Both the perfusion bioreactor and the capture step were operated continuously and showed to be robust when challenged with disturbances and drifts. “The time is now to make this transition,” says Morbidelli, and lists many benefits of continuous bioreactors in his recent publication, including: greater economic benefits, higher purification efficiency and more homogenous product quality.

Morbidelli, recent co-author of several continuous processing books15-16, explains why rapid analysis is so important: “Conceptually, with batch processing, you have all the time you want for analysis by putting everything to the side while you process data. With continuous processing, if you take an hour for analytics, the process would have continued for that hour.” In this context, it may be too late to implement necessary changes to bioprocess parameters, and Morbidelli lists techniques he expects will become more valuable as a result: “Spectroscopic techniques, which provide a very fast response, become even more important for continuous (processing) – because they bring the capability of having a fast response into the game. In particular, Raman spectroscopy, also ultraviolet spectroscopy and near infrared will become particularly important.” Dame Robinson is also well aware of the need for rapid analysis, and envisions a place for nativeomics in this space: “Although not yet primed for continuous processing, nativeomics will be critical for QC of biomolecules, particularly those harboring small molecules that could impair their function,” she says.

The rise of atline and online process analysis

Considering the need for rapid feedback during continuous processing, Morbidelli predicts there will be greater emphasis on atline and online techniques in the future, and outlines the general concepts behind each of the different approaches:

  • “Offline means you take the sample, walk into another laboratory next door, and run analytical experiments (i.e. defined by manual intervention and discontinuous sample preparation and analysis).

  • Online: A machine measures the composition of the material while it flows inside the unit. No sample is removed; instead, the measurement occurs inside the bioreactor.

  • Atline: Sits somewhere in between offline and online, and there are many nuances. In general, there shouldn’t be a requirement for human intervention. There might be an automatic sampling device – so there’s still a sample, which means it’s not quite online. This could be a dedicated HPLC which works only for this line, which is constantly feeding on samples.”

Analytical workhorses remain: MS-based analysis and advanced chromatography techniques

Over the past two decades, MS-based methods have become established as core industry tools, used to identify and locate modifications based on mass differences and the analysis of peptide fragments. Typically coupled with LC or another chromatographic technique, MS has become the gold standard tool for biotherapeutic characterization.17 LC-MS can be combined with other techniques to reveal a range of qualitative and quantitative information.18 In combination with preparative ion exchange chromatography, for example, LC-MS can reveal PTM-induced variations of charge distribution – an important characteristic for safety and regulatory purposes.19

With humble beginnings as a research tool, LC-MS is increasingly becoming adopted across the biopharmaceutical development pipeline and continues to evolve. The multi-attribute method (MAM) is a LC-MS-based approach to peptide mapping, used to confirm amino acid sequences and monitor site-specific modifications related to charge, protein fragments and glycosylation profiles at the amino acid level. MAM is expected to become a major tool for batch release and stability testing in cGMP environments and has the potential to replace a number of quality control tests.20
Recent advances in native MS also provide the opportunity to obtain deeper enable molecular better insights while replacing several other steps. “Before nativeomics, typically you would have to denature proteins, extract small molecules with organic solvents, and carry out experiments to obtain structures of the small molecules,” says Dame Robinson. 

Greater connectivity a common theme in analytical advances

Researchers in biopharmaceutical R&D want to get more from the large amount of data generated by high-throughput technologies, while remaining compliant with data integrity regulations.21 Therefore, for optimal efficiency, modern analytical technology must be supported by software capable of facilitating easy data collection, retrieval, sharing, analysis and visualization. Modern laboratories are increasingly implementing analytical technologies supported by digital solutions to help derive deeper, high-resolution biological insights.



1.     Shou WZ. Current status and future directions of high-throughput ADME screening in drug discovery. Journal of Pharmaceutical Analysis. 2020;10(3):201-208. doi:10.1016/j.jpha.2020.05.004

Shukla AA, Rameez S, Wolfe LS, Oien N. High-Throughput Process Development for Biopharmaceuticals. New Bioprocessing Strategies: Development and Manufacturing of Recombinant Antibodies and Proteins. 2017:401-441. doi:10.1007/10_2017_20

Rogers RS, Abernathy M, Richardson DD, et al. A View on the Importance of “Multi-Attribute Method” for Measuring Purity of Biopharmaceuticals and Improving Overall Control Strategy. The AAPS Journal. 2017;20(1). doi:10.1208/s12248-017-0168-3

Gault J, Liko I, Landreh M, et al. Combining native and ‘omics’ mass spectrometry to identify endogenous ligands bound to membrane proteins. Nature Methods. 2020;17(5):505-508. doi:10.1038/s41592-020-0821-0

Banks CAS, Zhang Y, Miah S, et al. Integrative Modeling of a Sin3/HDAC Complex Sub-structure. Cell Reports. 2020;31(2):107516. doi:10.1016/j.celrep.2020.03.080

Listek M, Hönow A, Gossen M, Hanack K. A novel selection strategy for antibody producing hybridoma cells based on a new transgenic fusion cell line. Scientific Reports. 2020;10(1). doi:10.1038/s41598-020-58571-w

Zwaagstra JC, Sulea T, Baardsnes J, et al. Binding and functional profiling of antibody mutants guides selection of optimal candidates as antibody drug conjugates. Silman I, ed. PLOS ONE. 2019;14(12):e0226593. doi:10.1371/journal.pone.0226593

Nedderman ANR. Metabolites in safety testing: metabolite identification strategies in discovery and development. Biopharmaceutics & Drug Disposition. 2009;30(4):153-162. doi:10.1002/bdd.660

Dreolini L, Cullen M, Yung E, et al. A Rapid and Sensitive Nucleic Acid Amplification Technique for Mycoplasma Screening of Cell Therapy Products. Molecular Therapy - Methods & Clinical Development. 2020;17:393-399. doi:10.1016/j.omtm.2020.01.009

‌Protein Therapeutics Market by Product: Global Opportunities and Industry Forecast, 2017–2023. Available at: https://www.alliedmarketresearch.com/protein-therapeutics-market

Mouchahoir T, Schiel JE. Development of an LC-MS/MS peptide mapping protocol for the NISTmAb. Analytical and Bioanalytical Chemistry. 2018;410(8):2111-2126. doi:10.1007/s00216-018-0848-6

Joslin J, Gilligan J, Anderson P, et al. A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery. SLAS DISCOVERY: Advancing the Science of Drug Discovery. 2018;23(7):697-707. doi:10.1177/2472555218773086

Moen E, Bannon D, Kudo T, Graf W, Covert M, Van Valen D. Deep learning for cellular image analysis. Nature Methods. 2019;16(12):1233-1246. doi:10.1038/s41592-019-0403-1

Feidl F, Vogg S, Wolf M, et al. Process‐wide control and automation of an integrated continuous manufacturing platform for antibodies. Biotechnology and Bioengineering. 2020;117(5):1367-1380. doi:10.1002/bit.27296

Wolf M, Bielser JM, Morbidelli M. Perfusion Cell Culture Processes for Biopharmaceuticals 2020, New York, USA: Cambridge University Press

Pfister D, Nicoud L, Morbidelli M. Continuous Biopharmaceutical Processes 2018, New York, USA: Cambridge University Press. 350.

Ren D. Advancing Mass Spectrometry Technology in cGMP Environments. Trends in Biotechnology. 2020;38(10):1051-1053. doi:10.1016/j.tibtech.2020.06.007

Ren D. Advancing Mass Spectrometry Technology in cGMP Environments. Trends in Biotechnology. 2020;38(10):1051-1053. doi:10.1016/j.tibtech.2020.06.007

Leblanc Y, Ramon C, Bihoreau N, Chevreux G. Charge variants characterization of a monoclonal antibody by ion exchange chromatography coupled on-line to native mass spectrometry: Case study after a long-term storage at +5 °C. Journal of Chromatography B. 2017;1048:130-139. doi:10.1016/j.jchromb.2017.02.017

Leblanc Y, Ramon C, Bihoreau N, Chevreux G. Charge variants characterization of a monoclonal antibody by ion exchange chromatography coupled on-line to native mass spectrometry: Case study after a long-term storage at +5 °C. Journal of Chromatography B. 2017;1048:130-139. doi:10.1016/j.jchromb.2017.02.017

Guideline on Data Integrity. World Health Organisation. October 2019. Available at: https://www.who.int/medicines/areas/quality_safety/quality_assurance/QAS19_819_data_integrity.pdf

Meet The Author
Michele Trott, PhD
Michele Trott, PhD