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Halogenation effects of pheniramines on the complexation with beta-cyclodextrin.
1Department of Applied Chemistry and Material Science, Fooyin University, Kaohsiung 831, Taiwan, ROC. sc112@mail.fy.edu.tw
Halogenation significantly impacts beta-cyclodextrin inclusion complex stability, with brompheniramine showing the strongest binding. This research clarifies intermolecular forces influencing drug-cyclodextrin interactions.
Area of Science:
- Supramolecular Chemistry
- Computational Chemistry
- Medicinal Chemistry
Background:
- Cyclodextrins are widely used in drug formulation to enhance solubility and stability.
- Pheniramine and its halogenated derivatives are common antihistamines.
- Understanding host-guest interactions is crucial for drug design.
Purpose of the Study:
- To investigate the formation and stability of beta-cyclodextrin inclusion complexes with pheniramine and its halogenated derivatives.
- To characterize the influence of phenyl ring halogenation on intermolecular interactions.
- To correlate experimental findings with theoretical calculations.
Main Methods:
- Experimental methods: Fourier transform infrared spectroscopy and Nuclear Magnetic Resonance (NMR) spectroscopy.
- Theoretical methods: ONIOM(B3LYP/6-31G(d):PM3) hybrid method for geometry optimization.
- Natural Bond Orbital (NBO) analysis for electronic delocalization and intermolecular forces.
Main Results:
- Evidence of inclusion complex formation confirmed by FTIR and NMR.
- Apparent binding constants evaluated, showing a stability order: brompheniramine > chlorpheniramine > pheniramine.
- Calculated binding energies and NBO analysis revealed electronic delocalizations as key drivers of binding strength, with S-enantiomers more stable than R-enantiomers.
Conclusions:
- Halogenation of the phenyl ring in pheniramine derivatives significantly enhances binding affinity to beta-cyclodextrin.
- Intermolecular electronic delocalizations are the primary forces governing the stability of these inclusion complexes.
- Computational methods accurately predict experimental observations, providing a valuable tool for future drug design.
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