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A LSER-based model to predict the solubilizing effect of drugs by inclusion with cucurbit[7]uril.

Enping ChengYangyan ZengYan HuangTiezhu SuYang YangLi PengJun Li
Published in: RSC advances (2020)
A large number of traditional drugs and the development of new drugs often encounter the problem of poor water solubility. Cucurbit[7]uril, a novel macrocyclic host, has attracted great interest in this field. Investigating the solubilizing effect of drugs by inclusion with cucurbit[7]uril could provide guidance for drug solubilization. In this work, the interactions of drugs with cucurbit[7]uril, drugs with water and the inclusion complexes with water, and the properties of drugs and inclusion complexes, are considered to establish a linear solvation energy relationships (LSER)-based model. This model could be applied to predicting the solubility of drugs with cucurbit[7]uril in water. Density functional theory (DFT) is employed to obtain the properties and interaction parameters. The multi-parameter solubility model obtained by stepwise regression shows good fitting and predicting results. And the surface area of inclusion complexes ( A 3 ), the LUMO energy of inclusion complexes ( E 3LUMO ), the polarity index of inclusion complexes ( I 3 ), the electronegativity of drugs ( χ 1 ), and the oil-water partition coefficient of drugs (log  p 1w ) are effective parameters related to the solubilization of drugs with cucurbit[7]uril. Futhermore, the model could be extended to calculate the solubilizing effect of other macrocycles.
Keyphrases
  • density functional theory
  • drug induced
  • magnetic resonance
  • molecular dynamics
  • magnetic resonance imaging
  • molecular docking
  • contrast enhanced
  • neural network