Plasma metabolomic signatures after oral administration of ritonavir in COVID-19 treatment via chemometrics-assisted UPLC/Q-TOF/MS/MS


DEMİRKAYA MİLOĞLU F., BAYRAK B., YÜKSEL B., Demir S. N., Gundogdu G., KADIOĞLU Y., ...Daha Fazla

Journal of Pharmaceutical and Biomedical Analysis, cilt.255, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 255
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.jpba.2024.116638
  • Dergi Adı: Journal of Pharmaceutical and Biomedical Analysis
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Analytical Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, International Pharmaceutical Abstracts, Veterinary Science Database
  • Anahtar Kelimeler: Metabolomics, Multivariate data analysis, Pathway analysis, Ritonavir, UPLC/Q-TOF/MS/MS
  • Atatürk Üniversitesi Adresli: Evet

Özet

Understanding the pharmacodynamics of ritonavir through metabolomics offers insights into its side effects and helps in the development of safer therapies. This study aimed to investigate the effects of ritonavir treatment on the metabolic profiles of rabbits via a metabolomics approach, with the objective of elucidating its impact on various biochemical pathways and identifying relevant biomarkers. The rabbits were divided into control and ritonavir-treated groups, and their plasma samples were analyzed via ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF/MS/MS). Metabolites were identified on the basis of the mass[sbnd]charge ratio (m/z) and validated via XCMS software. Metabolites with a fold change ≥ 1.5 and P ≤ 0.01 were analyzed via principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) to distinguish between the groups. MetaboAnalyst 6.0 was used for pathway analysis to identify metabolic pathways affected by ritonavir. The PCA and OPLS-DA models revealed clear separation between the control and ritonavir-treated groups, with high R² and Q² values indicating robust model performance. Pathway analysis revealed that ritonavir treatment significantly affected several metabolic pathways, including those related to ether lipid, phenylalanine, sphingolipid, and glycerophospholipid metabolism. Particularly significant changes were observed in metabolites related to lipid metabolism, oxidative stress responses and cellular signaling. Ritonavir significantly impacts metabolic pathways, particularly those involved in lipid metabolism, and oxidative stress responses, which may influence immune responses and drug interactions. This study also highlights the potential of integrating metabolomics with personalized medicine approaches to optimize ritonavir treatment strategies and reduce adverse effects. These findings indicate that ritonavir significantly influences cellular homeostasis and metabolic processes in addition to its antiviral properties. This highlights the necessity of comprehending the metabolic effects of ritonavir to enhance its clinical application, especially in the management of COVID-19. Further research is warranted to explore these alterations and their implications for therapeutic strategies.