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Density functional theory based quantitative structure-property relationship studies on coumarin-based prodrugs.

Xinying Yang1, Xuben Hou, Binghe Wang

  • 1Department of Medicinal Chemistry, Key Laboratory of Chemical Biology-Ministry of Education, School of Pharmacy, Shandong University, Ji'nan, Shandong, China.

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Researchers developed quantitative structure-property relationship (QSPR) models to predict the release kinetics of coumarin-based prodrugs. These models can guide the design of new esterase-sensitive prodrug candidates.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Design

Background:

  • Coumarin-based prodrugs are crucial for developing esterase-sensitive prodrugs of amines and peptides.
  • Developing predictive models is essential for efficient drug design and optimization.

Purpose of the Study:

  • To establish quantitative structure-property relationship (QSPR) models for coumarin-based prodrugs.
  • To predict the release kinetics and guide the design of novel coumarin-based prodrug candidates.

Main Methods:

  • Calculated electronic structures of 27 coumarin-based prodrugs using B3LYP/6-31+G (d,p) level with Gaussian 03.
  • Developed five QSPR models, including linear (SMLR, PLS) and nonlinear (Polynomial Neural Network - PNN) approaches.
  • Utilized calculated structural parameters as theoretical descriptors for model development.

Main Results:

  • Developed five QSPR models with varying predictive capabilities (linear models: q² up to 0.584, r² up to 0.663; nonlinear PNN models: q² up to 0.692, r² up to 0.700).
  • Nonlinear PNN models demonstrated superior performance in predicting prodrug release kinetics.
  • Identified theoretical descriptors that correlate with prodrug behavior.

Conclusions:

  • The developed QSPR models, particularly the PNN models, are effective for predicting the release kinetics of coumarin-based prodrugs.
  • These models serve as valuable tools for designing and optimizing new coumarin-based prodrug candidates.
  • The study highlights the utility of computational chemistry in accelerating drug discovery efforts.