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Novel β-Cyclodextrin and Catnip Essential Oil Inclusion Complex and Its Tick Repellent Properties.

ABSTRACT: Cyclodextrin inclusion complexes have been successfully used to encapsulate essential oils, improving their physicochemical properties and pharmacological effects. Besides being well-known for its effects on cats and other felines, catnip (Nepeta cataria) essential oil demonstrates repellency against blood-feeding pests such as mosquitoes. This study evaluates the tick repellency of catnip oil alone and encapsulated in β-cyclodextrin, prepared using the co-precipitation method at a 1:1 molar ratio. The physicochemical properties of this inclusion complex were characterized using GC-FID for encapsulation efficiency and yield and SPME/GC-MS for volatile emission. Qualitative assessment of complex formation was done by UV-Vis, FT-IR, ¹H NMR, and SEM analyses. Catnip oil at 5% (v/v) demonstrated significant tick repellency over time, being comparable to DEET as used in commercial products. The prepared [catnip: β-CD] inclusion complex exerted significant tick repellency at lower concentration of the essential oil (equivalent of 1% v/v). The inclusion complex showed that the release of the active ingredient was consistent after 6 h, which could improve the effective repellent duration. These results demonstrated the effective tick repellent activity of catnip essential oil and the successful synthesis of the inclusion complex, suggesting that β-CDs are promising carriers to improve catnip oil properties and to expand its use in repellent formulations for tick management.

SUBMITTER: Hogenbom J

PROVIDER: S-EPMC8659168 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn this study we investigated the predictability of three thermodynamic quantities related to complex formation. As a model system we chose the host-guest complexes of beta-cyclodextrin (beta-CD) with different guest molecules. A training dataset comprised of 176 beta-CD guest molecules with experimentally determined thermodynamic quantities was taken from the literature. We compared the performance of three different statistical regression methods - principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression combined with forward feature selection (SVMR/FSS) - with respect to their ability to generate predictive quantitative structure property relationship (QSPR) models for DeltaG degrees, DeltaH degrees and DeltaS degrees on the basis of computed molecular descriptors.ResultsWe found that SVMR/FFS marginally outperforms PLSR and PCR in the prediction of DeltaG degrees, with PLSR performing slightly better than PCR. PLSR and PCR proved to be more stable in a nested cross-validation protocol. Whereas DeltaG degrees can be predicted in good agreement with experimental values, none of the methods led to comparably good predictive models for DeltaH degrees. In using the methods outlined in this study, we found that DeltaS degrees appears almost unpredictable. In order to understand the differences in the ease of predicting the quantities, we performed a detailed analysis. As a result we can show that free energies are less sensitive (than enthalpy or entropy) to the small structural variations of guest molecules. This property, as well as the lower sensitivity of DeltaG degrees to experimental conditions, are possible explanations for its greater predictability.ConclusionThis study shows that the ease of predicting DeltaG degrees cannot be explained by the predictability of either DeltaH degrees or DeltaS degrees. Our analysis suggests that the poor predictability of TDeltaS degrees and, to a lesser extent, DeltaH degrees has to do with a stronger dependence of these quantities on the structural details of the complex and only to a lesser extent on experimental error.

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Novel β-Cyclodextrin and Catnip Essential Oil Inclusion Complex and Its Tick Repellent Properties.

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