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Mass Spectrometry: A Powerful Analytical Tool in Biopharma

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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 according to molecular mass and signal intensity. Fragmentation can be used to examine the structure of large molecules, determine precursor molecules and identify modifications, such 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 identification of biomolecules and tracking of chemical reactions and molecular modifications.

MS on its own will only reveal the mass-to-charge ratio, or m/z. Consequently, 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 process of biopharmaceutical development, from initial target identification and proteomics to toxicology and industrial quality control, which will be explored in this article.

MS: An important technique for biopharma analysis and development

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

Drug discovery involves three key elements: determining the mechanism of 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 drug mechanisms of action (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 biotherapeutics and/ or their targets – to determine 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 especially useful for this, as it can use sequential fragmentations to give insight into higher-order structure. Monoclonal antibodies (mAbs) are a type of protein biotherapeutic that are analyzed and characterized by these techniques during development and industrial manufacturing.3

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Nature’s drug cabinet

MS can be used in the identification of active compounds from traditional herbal remedies, to develop regulated biotherapies. Direct analysis in real-time MS (DART-MS) has been used to quantify coumarins and other active agents in traditional Chinese medicines (TCM) as a means of quality control.4 In another study, a specific TCM treatment for depression and insomnia, Baihe Dihuang decoction, was evaluated in mice to determine the active ingredients. LC-MS was used to determine that many of the decoction’s compounds were absorbed by the brain and bound to serotonin and melatonin receptors.5

An alternative source for biotherapeutics are biosynthetic gene clusters that produce natural products in microbes and fungi. “These are nature’s drug cabinet,” says Dr. Neil Kelleher, the Walter and Mary Glass Elizabeth professor of life sciences at Northwestern University. Kelleher’s lab uses ultra high-resolution LC-MS/MS to run expression screening on thousands of cultivable microbes to discover natural products and the genes that encode their expression. These new chemicals average over five stereocenters. They can then be analyzed with bioassays to determine their effects and pharmaceutical potential. Kelleher’s lab has used these strategies in metabologenomics to probe the biosynthetic mechanisms for stravidins and biotin.6

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

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 byproducts and other impurities in the large-scale manufacture of biotherapeutics. It is also used to track expected drug metabolites during clinical trials and to monitor potential toxic derivatives.8

MS is often used in proteomics to track enzymatic modifications of proteins, such as glycosylation and phosphorylation, which can alter the function of the biopharmaceutical compound and produce adverse effects. Like proteins, 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 the characterization and analysis of critical quality attributes for glycosylated biotherapeutics.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 laboratory, which focuses on the use of MS to characterize post-translational modifications and the biosynthesis of small molecule therapeutics, as well as develop MS tools to structurally characterize molecules involved in metabolic exchange. In the paper, the team demonstrated that some drugs taken orally or systematically can diffuse through the skin and be detected on the epidermis.10 This shows a potential to use MS data from non-invasive samples for a variety of clinical trial purposes, such as tracking the metabolization of therapeutics. “You can monitor drug adherence by simple skin swabs,” says Bittremieux.

Technological advancements address MS challenges

Challenges in MS analysis include the time and processing power needed for data interpretation and the necessity of sample preparation.

The Global Natural Product Social and Molecular Network (GNPS) was established by the Dorrestein laboratory in 2016 to address the former issue.11 It’s an MS data repository and analysis platform hosted on UCSD’s 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 solved by existing data. Bittremieux’s skin swab data came from the GNPS.

The sample preparation necessary for MS systems (often including purification or separation, resulting in a loss of material) is a barrier to natural products analysis and subsequent drug discovery. High-resolution mass spectrometry (HRMS) helps to alleviate this barrier by allowing precise analysis of small amounts of material and is used to evaluate metabolites and biomarkers in pharmaceutical whole systems analysis.12

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

The future of mass spectrometry

As with other analytical technologies, MS equipment is developing higher resolution and further miniaturization that leads to ease of use and an increase in data obtained. This then leads to ease of sample prep for native state protein analysis and natural products drug discovery.

Advancements 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 to develop 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 previously with more standard bioinformatics approaches,” he adds. Techniques like this are already being used in genomics research, and fields like metabolomics and proteomics are catching up quickly. These data and analyses will provide phenotypical information that will have many applications in biotherapeutics and precision medicine.