Quantification of Biopharmaceuticals in Plasma Using a Multiple Charge Stage Calibration Line

Significance of the topic

The accurate measurement of biopharmaceuticals (proteins, peptides, oligonucleotides, antibody–drug conjugates) in biological matrices is essential for candidate selection, toxicology, pharmacokinetics and clinical studies. Electrospray ionization tandem mass spectrometry (ESI LC-MS/MS) is a leading technique for these assays, but multiply charged precursor ion distributions reduce signal per transition and can change with concentration or solvent composition. Consolidating signal from multiple charge-state MRM transitions into a single calibration improves sensitivity, robustness and dynamic range of quantitative bioanalytical workflows.

Objectives and study overview

This application brief demonstrates the creation and use of a multi-charge-state calibration line by summing MRM signals using waters_connect MSQuan software. The approach is illustrated with quantitative analysis of the antisense oligonucleotide nusinersen in extracted rat plasma across a wide calibration range, highlighting how summed MRM channels mitigate charge-state distribution effects and enhance assay performance.

Methodology

Key experimental and processing steps (summarized):

• Analyte: nusinersen (ASO), average MW ~7127 Da.
• Sample preparation: 100 µL plasma aliquots were denatured, reduced, digested and isolated by solid phase extraction using OligoWorks SPE microplates; final extracts diluted 1:1 and 20 µL injected.
• Chromatography: ion-pair reversed-phase on an ACQUITY Premier Oligonucleotide C18 column (2.1 × 50 mm, 1.7 µm) at 60 °C; 5–45% organic gradient over 3.25 min at 600 µL/min. Mobile phases included HFIP and DIPEA in aqueous and organic mixtures to support oligonucleotide retention and MS responsiveness.
• Mass spectrometry: negative electrospray, tandem quadrupole operated in MRM mode. Multiple precursor charge states (M-7H, M-8H, M-9H) were monitored and individually optimized for precursor→product transitions, cone voltages and collision energies.
• Calibration and QCs: calibration range 0.1–1000 ng/mL with QC levels spanning 0.375–750 ng/mL. Data acquisition and processing were performed with waters_connect MSQuan software.

Used instrumentation

Instrumentation reported and used in the study:

• Xevo TQ Absolute XR triple quadrupole mass spectrometer (negative ESI).
• ACQUITY Premier Oligonucleotide C18 column, 2.1 × 50 mm, 1.7 µm.
• OligoWorks SPE microplate for oligonucleotide extraction.
• waters_connect MSQuan (versions cited) for MRM data processing and creation of summed MRM channels.

Results and discussion

Individual charge-state MRM channels for nusinersen (M-7H, M-8H, M-9H) each produced linear calibration curves across 0.1–1000 ng/mL using 1/x weighting, with high correlation coefficients (R2 ≈ 0.9977–0.9992). However, electrospray charge-state distribution spreads signal across these transitions and can shift with concentration or solvent conditions, reducing per-channel sensitivity and potentially affecting accuracy at low levels.

Using the SUM MRM (summed MRM) feature in waters_connect MSQuan, signals from the selected charge-state MRM transitions were combined into a single data channel. The software merges peak area contributions from all chosen MRMs and treats the result as a conventional quantifier channel (selectable weighting and internal standard handling). Practical benefits observed and reported include improved low-level detectability (example 0.5 ng/mL extracted standard demonstrated) and increased robustness of the calibration against charge-state distribution changes. The summed-channel approach also enables rapid evaluation of different MRM combinations to select the most robust set for routine processing.

Benefits and practical applications

Summing MRM signals across charge states delivers several practical advantages for bioanalysis of large biomolecules:

• Increased sensitivity at low analyte concentrations by aggregating distributed ion current.
• Wider and more reliable dynamic range because calibration is less sensitive to shifts in charge-state distribution.
• Simplified data processing and method evaluation through a single consolidated quantifier channel in the quantitation software.
• Flexibility to test different combinations of charge-state transitions to optimize precision and accuracy.

Future trends and potential applications

Expected developments and broader uses of summed-MRM strategies include:

• Wider adoption in bioanalytical workflows for oligonucleotides, peptides and protein-derived signature peptides where multiple charge states are prevalent.
• Integration with automated method development tools to recommend optimal MRM sets for summation based on signal-to-noise and stability across concentration ranges.
• Combination with high-resolution MS and advanced deconvolution approaches to further improve selectivity while preserving summed-signal sensitivity.
• Standardization of summed-MRM validation practices to support regulatory bioanalytical method submission for preclinical and clinical studies.

Conclusion

Charge-state heterogeneity in ESI LC-MS/MS reduces per-transition signal and can vary with experimental conditions, complicating quantification of biotherapeutics. Creating a summed MRM calibration by combining multiple charge-state transitions is a simple and effective mitigation strategy that improves sensitivity, extends usable dynamic range and increases robustness. The waters_connect MSQuan SUM MRM function provides an accessible implementation of this concept, demonstrated here for nusinersen in plasma, and suitable for broader application across biomolecule quantitation workflows.

References

1. Klont F, Hopfgartner G. Mass spectrometry-based approaches and strategies in bioanalysis for qualitative and quantitative analysis of pharmaceutically relevant molecules. Drug Discov Today Technol. 2021;40:64–68.
2. Sleumer B, Kema IP, van de Merbel NC. Quantitative bioanalysis of proteins by digestion and LC-MS/MS: the use of multiple signature peptides. Bioanalysis. 2023;15(19):1203–1216.
3. Kumar D, Sharma M, Trivedi N. A roadmap guide on bioanalysis challenges and practical solutions for accurate quantification of oligonucleotide-based novel therapeutic modalities using LC-MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2026;1270:124900.
4. Tang L, Swezey RR, Green CE, Mirsalis JC. Enhancement of sensitivity and quantification quality in the LC-MS/MS measurement of large biomolecules with sum of MRM (SMRM). Anal Bioanal Chem. 2022;414(5):1933–1947.
5. Twohig M, Doneanu C, Lee M, Tanna N, Trudeau M. Analytical solutions and method development considerations for quantitative bioanalytical oligonucleotide studies. Waters Application Note. 2025.
6. Plumb R. A streamlined workflow for quantitative bioanalysis using waters_connect for Quantitation Software: a case study using gefitinib. Waters Application Note. 2025.