Solid-State NMR Characterization of Mefloquine Resinate Complexes Designed for Taste-Masking Pediatric Formulations

Author(s)

Borre, Leandro B. ; Sousa, Eduardo G. R. ; San Gil, Rosane A. S. ; Baptista, Mateus M. ; Leitao, Alexandre A. ; De Almeida, Joao M. A. R. ; Carr, Olivia ; Oliveira, Osvaldo N. ; Shimizu, Flavio M. ; Guimaraes, Thiago F.

• Document type: Journal article
• Source: PHARMACEUTICALS; v. 17, n. 7, p. 16-pg., 2024-07-01.

Abstract

Mefloquine (MQ) is an antimalarial medication prescribed to treat or malaria prevention.. When taken by children, vomiting usually occurs, and new doses of medication frequently need to be taken. So, developing pediatric medicines using taste-masked antimalarial drug complexes is mandatory for the success of mefloquine administration. The hypothesis that binding mefloquine to an ion-exchange resin (R) could circumvent the drug's bitter taste problem was proposed, and solid-state C-13 cross-polarization magic angle spinning (CPMAS) NMR was able to follow MQ-R mixtures through chemical shift and relaxation measurements. The nature of MQ-R complex formation could then be determined. Impedimetric electronic tongue equipment also verified the resinate taste-masking efficiency in vitro. Variations in chemical shifts and structure dynamics measured by proton relaxation properties (e.g., T1 rho(H)) were used as probes to follow the extension of mixing and specific interactions that would be present in MQ-R. A significant decrease in T1 rho(H) values was observed for MQ carbons in MQ-R complexes, compared to the ones in MQ (from 100-200 ms in MQ to 20-50 ms in an MQ-R complex). The results evidenced that the cationic resin interacts strongly with mefloquine molecules in the formulation of a 1:1 ratio complex. Thus, C-13 CPMAS NMR allowed the confirmation of the presence of a binding between mefloquine and polacrilin in the MQ-R formulation studied. (AU)

• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants