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Paper ID:IJSREM66605

Bio-analytical Method Development and Validation for Estimation of Metoprolol in Human K2EDTA Plasma by using LC-MS/MS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66605

Volume:

10 |

8 |

Aug 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66605

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Abhishek Gaikwad

Abhishek Gaikwad

Pharmaceutical Quality Assurance

Dattakala college of pharmacy Bhigwan

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OA

Onkar Arekar

Pharmaceutics

Dattakala college of pharmacy Bhigwan

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DSN

Dr. Sudarshan Nagrale

Pharmaceutical Chemistry

Dattakala college of pharmacy Bhigwan

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Mentors

DVB

Dr. Vishal Babar

Pharmaceutical chemistry

Dattakala college of Pharmacy Bhigwan

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Abstract

The present study outlines a comprehensive framework for the development and validation of a sensitive, selective, rapid, and robust bioanalytical method designed to estimate Metoprolol in human K2EDTA plasma using Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS). Metoprolol is a selective β1-adrenergic receptor blocker frequently prescribed for the management of cardiovascular conditions such as hypertension, angina pectoris, and heart failure, making its accurate quantification critical for therapeutic drug monitoring and pharmacokinetic profiling. While LC-MS/MS provides inherent advantages in sensitivity and specificity over traditional chromatographic methods, the proposed methodology specifically seeks to optimize sample extraction procedures, mass spectrometric parameters, and analytical run times to handle high-throughput clinical demands. The method incorporates a rigorous, hypothetical validation plan strictly adhering to regulatory bioanalytical guidelines to evaluate parameters including linearity, precision, accuracy, matrix effects, and stability. Ultimately, this approach aims to deliver a highly reproducible and efficient analytical paradigm suitable for routine bioequivalence studies and comprehensive clinical investigations.

KEYWORDS

β1-adrenergic receptor blocker Cardiovascular therapeutics Hypertension angina heart failure Therapeutic drug monitoring (TDM) LC-MS/MS (Liquid Chromatography–Tandem Mass Spectrometry) K2EDTA plasma Sample preparation (protein precipitation SPE)

Cite This Article

Abhishek Gaikwad, Onkar Arekar, Dr. Sudarshan Nagrale, Dr. Vishal Babar, "Bio-analytical Method Development and Validation for Estimation of Metoprolol in Human K2EDTA Plasma by using LC-MS/MS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-5, Aug 2026. https://doi.org/10.55041/IJSREM66605

Full Article/Conclusion

This study establishes a comprehensive framework for the development and validation of an advanced LC-MS/MS method tailored for the precise estimation of Metoprolol in human K2EDTA plasma. By systematically optimizing extraction protocols, chromatographic conditions, and tandem mass spectrometric parameters, the proposed methodology addresses the limitations of conventional analytical approaches, specifically targeting improvements in throughput, sensitivity, and linearity. The rigorous validation plan ensures that the method conforms strictly to regulatory standards, providing a reliable roadmap for assessing precision, accuracy, and matrix stability. Ultimately, the successful execution of this bioanalytical approach holds substantial promise for clinical and pharmaceutical applications. Offering a rapid, reproducible, and highly specific means of quantifying Metoprolol, this method stands to greatly enhance the efficiency of pharmacokinetic profiling and therapeutic drug monitoring. As clinical demands for robust and high-throughput drug quantification continue to rise, methodologies anchored in carefully validated LC-MS/MS techniques will remain indispensable to the advancement of personalized medicine and cardiovascular therapeutics.

References

1.      MacCoss, Michael J., Alfaro, Javier, Wanunu, Meni, Faivre, Danielle A., & Slavov, Nikolai (2022). Sampling the proteome by emerging single-molecule and mass-spectrometry methods. Nat Methods 20, 339--346 (2023). https://doi.org/10.1038/s41592-023-01802-5   2.      Barbieri, Maria Vittoria, Postigo, Cristina, Monllor-Alcaraz, Simon, Barcelo, Damia, & Alda, Miren Lopez de (2020). Reliable LC-MS/MS-based method for trace level determination of 50 medium to highly polar pesticide residues in sediments and ecological risk assessment. Anal Bioanal Chem. 2019 Dec;411(30):7981-7996. https://doi.org/10.1007/s00216-019-02188-0   3.      Francotte, Antoine, Esson, Raphael, Abachin, Eric, Vanhamme, Melissa, Dobly, Alexandre, Carpick, Bruce, Uhlrich, Sylvie, Dierick, Jean-François, & Vanhee, Celine (2022). Development and validation of a targeted LC-MS/MS quantitation method to monitor cell culture expression of tetanus neurotoxin during vaccine production. Talanta vol. 236 (2022): 122883. https://doi.org/10.1016/j.talanta.2021.122883   4.      Rupprecht, Florian, Enge, Sören, Schmidt, Kornelius, Gao, Wei, Kirschbaum, Clemens, & Miller, Robert (2021). Automating LC-MS/MS mass chromatogram quantification. Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods. Biomedical Signal Processing and Control, 71, 2022, 103211. https://doi.org/10.1016/j.bspc.2021.103211   5.      Milac, Thomas I., Randolph, Timothy W., & Wang, Pei (2011). Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies. https://arxiv.org/pdf/1111.4721v1   6.      Schmitt, Uwe, Hemmann, Jethro L., Zamboni, Nicola, Vorholt, Julia A., & Kiefer, Patrick (2025). eMZed 3: flexible and interactive development of scalable LC-MS/MS data analysis workflows in Python. https://doi.org/10.1093/bioadv/vbag138   7.      Manjrekar, Mrunali, Wang, Runz(8), Goldman, Samuel, Fromer, Jenna C., & Coley, Connor W. (2026). Generative structural elucidation from mass spectra as an iterative optimization problem. https://arxiv.org/pdf/2602.07709v1   8.      Hong, Hansol, Lee, Sangwon, Ha, Jang-Ho, Chu, Sung-June, An, So-Hee, Paek, Woo-Hyun, Chung, Gyuhwa, & No, Kyoung Tai (2025). MS2toImg: A Framework for Direct Bioactivity Prediction from Raw LC-MS/MS Data. https://arxiv.org/pdf/2510.09716v1  

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8/8/2026

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