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Related Experiment Videos

Modeling drug albumin binding affinity with e-state topological structure representation.

L Mark Hall1, Lowell H Hall, Lemont B Kier

  • 1Hall Associates Consulting, 2 Davis Street, Quincy, Massachusetts 02170-2818, USA.

Journal of Chemical Information and Computer Sciences
|November 25, 2003
PubMed
Summary

This study models drug binding affinity to human serum albumin using molecular structure. Topological descriptors accurately predict binding, aiding drug design by identifying key molecular features for enhanced affinity.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Human serum albumin (HSA) is a key determinant of drug pharmacokinetics.
  • Accurate prediction of drug-HSA binding affinity is crucial for drug development.
  • Quantitative Structure-Activity Relationship (QSAR) models can predict molecular properties.

Purpose of the Study:

  • To develop a predictive model for drug binding affinity to HSA.
  • To identify molecular descriptors that influence drug-HSA interactions.
  • To utilize topological descriptors for QSAR modeling of drug binding.

Main Methods:

  • Modeled binding affinity for 94 drugs using HPLC retention index on immobilized albumin.
  • Employed topological descriptors, including electrotopological state (E-State) and molecular connectivity chi indices.

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  • Utilized MDL QSAR software for data management, descriptor creation, and modeling.
  • Performed leave-group-out (LGO) cross-validation and external validation with a 10-compound set.

    • Main Results:

    • Achieved a satisfactory model with R(2) = 0.77 and cross-validated q(2) = 0.70.
    • External validation yielded a mean absolute error (MAE) of 0.31 and q(2) = 0.74.
    • Identified positive factors for binding affinity: electron accessibility, aromatic rings, aliphatic CH groups, halogens, -OH groups, and six-membered heteroatomic rings.
    • Identified negative factors: five-membered heteroatomic rings.

    Conclusions:

    • The developed topological QSAR model is a useful tool for estimating drug-HSA binding affinity.
    • The model demonstrates good predictive performance through internal and external validation.
    • Findings provide insights into molecular features that enhance or reduce drug binding to HSA, aiding drug design.