Application of LC–MS in the Development of Natural Product–Based Antidepressant Drugs: A Systematic Literature Review

Penulis

  • Ridel Yosua Rumondor Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Pancasila, Jakarta, Indonesia
  • Novi Yantih Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Pancasila, Jakarta, Indonesia
  • Kholilah Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Pancasila, Jakarta, Indonesia
  • Nur Pujiastuti Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Pancasila, Jakarta, Indonesia

DOI:

https://doi.org/10.58511/v3i2.9690

Kata Kunci:

LC–MS, Natural Products, Antidepressant, Metabolomics, Drug Development

Abstrak

 Due to the shortcomings of existing medications, depression remains a worldwide neuropsychiatric disorder requiring innovative therapeutic strategies. Natural compounds represent promising multi-target antidepressant candidates; however, their chemical complexity necessitates advanced analytical tools. This systematic literature review assessed the application of liquid chromatography–mass spectrometry (LC–MS) in the identification, profiling, and standardization of natural antidepressant compounds. A comprehensive search of major databases was conducted following PRISMA guidelines for studies published between 2014 and 2024, with a total of six eligible studies were included in the qualitative synthesis.. The findings indicate that LC–MS, particularly high-resolution platforms such as QTOF and Orbitrap, is the primary method for metabolite profiling and dereplication of bioactive classes including flavonoids, alkaloids, and phenolic acids. Integration of LC–MS data with in vitro and in vivo models has facilitated the correlation of chemical profiles with pharmacological mechanisms, such as neurotransmitter regulation. Despite its pivotal role in evidence-based natural product research, challenges remain in analytical standardization and clinical translation. In conclusion, LC–MS is an indispensable and transformative tool in the development of natural product-based antidepressants, providing robust chemical characterization that supports pharmacological validation and accelerates drug discovery. Future research should emphasize methodological harmonization and systems biology integration to enhance translational impact. 

Referensi

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

Malhi GS, Mann JJ. Depression. Lancet. 2018;392(10161):2299–2312. doi:10.1016/S0140-6736(18)31948-2.

World Health Organization. Depression. Geneva: World Health Organization; 2023.

Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one

or several treatment steps. Am J Psychiatry. 2006;163(11):1905–1917. doi:10.1176/ajp.2006.163.11.1905.

2018;391(10128):1357–1366. doi:10.1016/S0140-6736(17)32802-7.

doi:10.1038/nchembio.118.

doi:10.4103/0973-7847.125530.

Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of antidepressants in the acute

treatment of major depressive disorder: a systematic review and network meta-analysis. Lancet.

Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682–690.

Bahramsoltani R, Farzaei MH, Rahimi R. Medicinal plants and their natural components as future drugs for the

treatment of depression. Pharmacol Res. 2019;145:104204. doi:10.1016/j.phrs.2019.104204.

Dhingra D, Valecha R. Screening of antidepressant-like activity of herbal drugs. Pharmacogn Rev. 2014;8(15):37–42.

Zhang ZJ, Cheung HY, Lee WK, et al. Natural products for neuropsychiatric disorders: antidepressant mechanisms

and molecular targets. Front Pharmacol. 2021;12:698712. doi:10.3389/fphar.2021.698712.

Wolfender JL, Nuzillard JM, van der Hooft JJJ, et al. Accelerating metabolite identification in natural product

research: toward an ideal combination of LC–HRMS and NMR profiling. Anal Chem. 2019;91(1):704–742.

doi:10.1021/acs.analchem.8b05112.

[10] Niessen WMA. Liquid Chromatography–Mass Spectrometry. 3rd ed. Boca Raton: CRC Press; 2017.

[11] Dunn WB, Ellis DI. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics.

Metabolomics. 2011;7(1):4–16. doi:10.1007/s11306-010-0210-8.

[12] Pan SY, Litscher G, Gao SH, et al. New perspectives on Chinese herbal medicine research and development. Evid

Based Complement Alternat Med. 2020;2020:124641. doi:10.1155/2020/124641.

[13] Wolfender JL, Queiroz EF, Hostettmann K. Accelerating dereplication and metabolite identification in natural

product research by LC–MS. J Chromatogr A. 2015;1382:136–164.

JNPDD 2026; 3(2): 53-61

60

Rumondor et al.

Journal of Natural Product for Degenerative Diseases

Review Article

[14] Theodoridis G, Gika HG, Want EJ, Wilson ID. LC–MS-based global metabolite profiling: a review. Nat Protoc.

2016;11(9):1637–1657.

[15] Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using metabolomics. Nat Rev

Mol Cell Biol. 2019;20(6):353–367.

[16] Kim HK, Verpoorte R. Sample preparation for plant metabolomics. Phytochem Anal. 2015;26(4):215–224.

[17] Cajka T, Fiehn O. Comprehensive lipid analysis by LC–MS. TrAC Trends Anal Chem. 2014;61:192–206.

[18] Wang M, Carver JJ, Phelan VV, et al. GNPS: community curation of mass spectrometry data. Nat Biotechnol.

2016;34(8):828–837.

[19] Blaženović I, Kind T, Ji J, Fiehn O. Software tools for metabolomics data analysis. Metabolites. 2018;8(2):31.

[20] Xia J, Wishart DS. Using MetaboAnalyst for metabolomics data analysis. Curr Protoc Bioinformatics. 2016;55:14.10.1

14.10.91.

[21] Dunn WB, Broadhurst DI, Atherton HJ, et al. Systems-level studies of mammalian metabolomes. Chem Soc Rev.

2017;46(12):387–426.

[22] Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature. 2008;455(7216):1054–1056.

[23] van der Greef J, Hankemeier T, McBurney RN. Metabolomics-based systems biology and personalized medicine.

Pharmacogenomics. 2006;7(7):1087–1094.

[24] Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2017;36(1):1–24.

[25] Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263

269.

[26] Wishart DS, Guo AC, Oler E, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res.

2022;50(D1):D622–D631

metabolic diversity. Chem Soc Rev. 2020;49(11):3297–3314.

[27] Nogales C, Mamdouh ZM, List M, Kiel C, Casas AI, Schmidt HHHW. Network pharmacology: curing causal

mechanisms instead of treating symptoms. Trends in Pharmacological Sciences. 2022;43(2):136–150.

[28] Tebani A, Afonso C, Bekri S. Metabolomics for personalized medicine: the input of analytical chemistry from

biomarker discovery to point-of-care tests. Analytical and Bioanalytical Chemistry. 2021;413(25):6167–6186.

[29] van der Hooft JJJ, Mohimani H, Bauermeister A, et al. Linking genomics and metabolomics to chart specialized

[30] Rinschen MM, Giera M, Siuzdak G. Identification of bioactive metabolites. Nat Rev Mol Cell Biol. 2019;20(6):353–367.

[31] Wolfender JL, Rudaz S, Choi YH, Kim HK. Plant metabolomics. Curr Med Chem. 2016;23(30):3313–3341.

[32] Theodoridis G, Gika HG, Wilson ID. Global metabolomics workflows. Nat Protoc. 2016;11(9):1637–1657.

[33] Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Metabolomics standardization challenges. Nat Rev Mol Cell Biol.

2019;20(6):353–367.

[34] Nicholson JK, Holmes E, Kinross J, et al. Host–gut microbiota metabolic interactions. Science. 2012;336(6086):1262

1267.

Diterbitkan

2026-03-31

Terbitan

Bagian

Articles