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Advancing insights on β-cyclodextrin inclusion complexes with SSRIs through lens of X-ray diffraction and DFT calculation.

ABSTRACT: Depression-the global crisis hastened by the coronavirus outbreak, can be efficaciously treated by the selective serotonin reuptake inhibitors (SSRIs). Cyclodextrin (CD) inclusion complexation is a method of choice for reducing side effects and improving bioavailability of drugs. Here, we investigate in-depth the β-CD encapsulation of sertraline (STL) HCl (1) and fluoxetine (FXT) HCl (2) by single-crystal X-ray diffraction and DFT complete-geometry optimization, in comparison to the reported complex of paroxetine (PXT) base. X-ray analysis unveiled the 2:2 β-CD-STL/FXT complexes with two drug molecules inserting their halogen-containing aromatic ring in the β-CD dimeric cavity, which are stabilized by the interplay of intermolecular O2-H⋯N1-H⋯O3 H-bonds, C3/C5-H⋯π and halogen⋯halogen interactions. Similarly, the 1:1 β-CD-tricyclic-antidepressant (TCA) complexes have an exclusive inclusion mode of the aromatic ring, which is maintained by C3/C5-H⋯π interactions. By contrast, the 2:1 β-CD-PXT complex has a total inclusion that is stabilized by host-guest O6-H⋯N1-H⋯O5 H-bonds and C3-H⋯π interactions. The inherent stabilization energies of 1 and 2 evaluated using DFT calculation suggested that the improved thermodynamic stabilities via CD encapsulation facilitates the reduction of drug side effects. Moreover, the SSRI conformational flexibilities are thoroughly discussed for understanding of their pharmacoactivity.

SUBMITTER: Aree T

PROVIDER: S-EPMC8450047 | 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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Advancing insights on β-cyclodextrin inclusion complexes with SSRIs through lens of X-ray diffraction and DFT calculation.

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