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Repurposing Bedaquiline for Effective Non-Small Cell Lung Cancer (NSCLC) Therapy as Inhalable Cyclodextrin-Based Molecular Inclusion Complexes.

ABSTRACT: There is growing evidence that repurposed drugs demonstrate excellent efficacy against many cancers, while facilitating accelerated drug development process. In this study, bedaquiline (BDQ), an FDA approved anti-mycobacterial agent, was repurposed and an inhalable cyclodextrin complex formulation was developed to explore its anti-cancer activity in non-small cell lung cancer (NSCLC). A sulfobutyl ether derivative of β-cyclodextrin (SBE-β-CD) was selected based on phase solubility studies and molecular modeling to prepare an inclusion complex of BDQ and cyclodextrin. Aqueous solubility of BDQ was increased by 2.8 × 10³-fold after complexation with SBE-β-CD, as compared to its intrinsic solubility. Solid-state characterization studies confirmed the successful incorporation of BDQ in the SBE-β-CD cavity. In vitro lung deposition study results demonstrated excellent inhalable properties (mass median aerodynamic diameter: 2.9 ± 0.6 µm (<5 µm) and fine particle fraction: 83.3 ± 3.8%) of BDQ-CD complex. Accelerated stability studies showed BDQ-CD complex to be stable up to 3 weeks. From cytotoxicity studies, a slight enhancement in the anti-cancer efficacy was observed with BDQ-cyclodextrin complex, compared to BDQ alone in H1299 cell line. The IC₅₀ values for BDQ and BDQ-CD complex were found to be ~40 µM in case of H1299 cell line at 72 h, whereas BDQ/BDQ-CD were not found to be cytotoxic up to concentrations of 50 µM in A549 cell line. Taken together, BDQ-CD complex offers a promising inhalation strategy with efficient lung deposition and cytotoxicity for NSCLC treatment.

SUBMITTER: Parvathaneni V

PROVIDER: S-EPMC8124211 | 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.

Dataset Information

Repurposing Bedaquiline for Effective Non-Small Cell Lung Cancer (NSCLC) Therapy as Inhalable Cyclodextrin-Based Molecular Inclusion Complexes.

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