Mass spectrometry: a powerful analytical tool in biopharmaceuticals

Mass spectrometry (MS) is an analytical technique that measures the mass-to-charge ratio of molecular ions or their fragments. Samples are injected into the mass spectrometer, ionized, fragmented and detected based on molecular mass and signal intensity. Fragmentation can be used to examine the structure of large molecules, determine precursor molecules and identify modifications, as in the case of proteins. Scientists using MS can identify molecular changes down to the isotopes of individual atoms, making it a powerful analytical technique for identifying biomolecules and tracking chemical reactions and molecular changes.

MS alone will only reveal the mass to charge ratio, or m/z. Therefore, it is often used in combination with a variety of other analytical tools, such as liquid chromatography (LC-MS/MS) or matrix-assisted laser desorption/ionization (MALDI) coupled with a time-of-flight detector. (MALDI-TOF). MS is used for analysis throughout the biopharmaceutical development process, from initial target identification and proteomics to toxicology and industrial quality control, which will be explored in this article.

MS: an important technique for biopharmaceutical analysis and development

MS has been essential for the characterization of large biomolecules such as proteins and DNA, and for the analysis of whole systems.

Drug discovery involves three key elements: determining the mechanism of the disease, identifying molecular targets for treatment and developing bioactive compounds to act on these targets.1 As proteins are the most common drug targets and are also increasingly used as biotherapeutics, MS-based techniques in proteomics and chemoproteomics are essential for drug discovery and development. Proteome profiling, combined with affinity probes and other chemoproteomics techniques, is used in target deconvolution to identify both drug targets and the molecules that affect their activity. Thermal profiling combined with high-resolution MS is used to determine the mechanisms of action of drugs (MoA) and the stability of their protein targets.2

MS to determine higher order structures

Proteomics MS methods include both top-down (little or no fragmentation) and bottom-up (high fragmentation) analysis of large biomolecules – such as biotherapy and/or their targets – to determine the primary and higher order structure. This is essential for determining the structure-function relationship of proteins and the mechanism of action of their related drugs. Coupled MS/MS is particularly useful for this, as it can use sequential fragmentations to provide insight into higher-order structure. Monoclonal antibodies (mAbs) are a type of biotherapeutic proteins that are analyzed and characterized by these techniques during development and industrial manufacturing.3

Cutting-edge technology to accelerate the development of next-generation biotherapy

Watch this webinar to learn about the latest technologies specifically designed to meet the challenges of the biopharmaceutical industry. Learn how a multicapillary electrophoresis system allows researchers to analyze multiple samples much faster than traditional methods, enabling high-throughput analysis of protein purity and stability, as well as genome integrity and the purity of AAV.

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Nature’s Medicine Cabinet

An alternative source of biotherapy are biosynthetic gene clusters that produce natural products in microbes and fungi. “These are nature’s drugs,” says Dr. Neil Kelleher, Walter and Mary Glass Elizabeth Professor of Life Sciences at Northwestern University. Kelleher’s lab uses ultra-high resolution LC-MS/MS to perform expression screening on thousands of culturable microbes to discover natural products and the genes that code for their expression. These new chemicals have on average more than five stereocenters. They can then be analyzed with bioassays to determine their effects and pharmaceutical potential. Kelleher’s lab has used these strategies in metabogenomics to probe the mechanisms of biosynthesis of stravidins and biotin.6

MS is particularly useful when detecting compounds in solution with other nearly identical molecules. This is the case for the quantification of mAbs, used to treat autoimmune diseases and monitored in solution with endogenous immunoglobulins.7 High-resolution MS techniques can distinguish masses down to several decimal places, allowing better identification of solution components.

MS-Based Methods for Biopharmaceutical Quality Control and Clinical Research

Industrial pharmaceutical manufacturing uses the high resolution capabilities of MS for quality control and toxicology purposes. It enables the detection of unwanted by-products and other impurities in the large-scale manufacturing of biotherapeutic products. It is also used to track expected drug metabolites during clinical trials and to monitor potential toxic derivatives.8

SEP is often used in proteomics to track enzymatic changes in proteins, such as glycosylation and phosphorylation, which can alter the function of the biopharmaceutical compound and produce undesirable effects. Like proteins, the polysaccharides used in glycosylation are large, complicated biomolecules that benefit from the use of high-resolution MS to determine size and structure. Pharmacology labs and manufacturers use MS for characterization and analysis of critical quality attributes of glycosylated biotherapies.9

A 2022 paper by Dr. Wout Bittremieux used a machine learning program to analyze human skin swab MS data. Bittremieux is a postdoctoral researcher in the Dorrestein lab, focusing on the use of MS to characterize post-translational modifications and biosynthesis of small molecule therapeutics, as well as the development of MS tools to structurally characterize the molecules involved. in metabolic exchanges. In the paper, the team demonstrated that certain drugs taken orally or systemically can diffuse through the skin and be detected on the epidermis.ten This shows potential for using MS data from non-invasive samples for various clinical trial purposes, such as monitoring the metabolism of therapeutics. “You can monitor medication adherence through simple skin swabs,” says Bittremieux.

Advances in technology meet the challenges of MS

The challenges of MS analysis include the time and processing power needed to interpret the data and the need for sample preparation.

The Global Natural Product Social and Molecular Network (GNPS) was created by the Dorrestein lab in 2016 to solve the first problem.11 It’s an MS data repository and analysis platform hosted on UCSD servers, “a search engine for untargeted metabolomics,” says Bittremieux. Research groups around the world can then use this data for reanalysis and reinterpretation, allowing new research questions to be answered from existing data. Data from skin samples from Bittremieux come from the GNPS.

The sample preparation required for MS systems (often including purification or separation, resulting in loss of material) is a barrier to natural product analysis and subsequent drug discovery. High-Resolution Mass Spectrometry (HRMS) helps mitigate this barrier by allowing precise analysis of small amounts of material and is used to assess metabolites and biomarkers in the analysis of whole pharmaceutical systems.12

Kelleher says the most exciting recent development in top-down proteoform measurement is single-molecule MS, which his lab helped develop. “This is a major, non-incremental advance in the ability to characterize dilute complex mixtures and do so with single-molecule resolution,” he says. Also known as individual ion MS, it allows the charge state of each ion to be determined, greatly simplifying mass assignment for highly modified proteins, their complexes, and other large molecules.

The future of mass spectrometry

As with other analytical technologies, MS equipment develops higher resolution and further miniaturization which leads to ease of use and increased data obtained. This then leads to ease of sample preparation for native-state protein analysis and natural product drug discovery.

Advances in MS equipment result in the generation of massive amounts of data stored in databases like GNPS. To process this data, machine learning and deep learning approaches will become much more common, says Bittremieux: “The availability of large [amounts of] training data is a necessary requirement for developing machine learning models, and the field is now really starting to explore these options.

These models will allow researchers to “really dig deeper into the data than could be done before with more standard bioinformatics approaches,” he adds. Techniques like this are already used in genomics research, and areas like metabolomics and proteomics are rapidly catching up. These data and analyzes will provide phenotypic information that will have many applications in biotherapy and precision medicine.