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Related Concept Videos

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Nonlinear Pharmacokinetics: Bioavailability and Protein-Drug Binding

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The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...

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Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion.

Junmei Wang1, George Krudy, Xiang-Qun Xie

  • 1Encysive Pharmaceuticals Inc., 7000 Fannin Street, Houston, Texas 77030, USA. jwang@encysive.com

Journal of Chemical Information and Modeling
|November 28, 2006
PubMed
Summary

A genetic algorithm built quantitative structure-property relationship models for drug properties. Key molecular fragments influencing bioavailability were identified, showing distinct trends in drug development and chemical databases.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Pharmacokinetics

Background:

  • Quantitative structure-property relationship (QSPR) models are crucial for predicting drug properties.
  • Understanding molecular descriptors is key to optimizing pharmacokinetic profiles.
  • Genetic algorithms offer a powerful approach for developing predictive models.

Purpose of the Study:

  • To develop QSPR models for human oral bioavailability, plasma protein binding, and urinary excretion.
  • To identify key molecular fragments that influence these pharmacokinetic properties.
  • To validate the predictive power of consensus QSPR models.

Main Methods:

  • Application of a genetic algorithm (GA) to construct QSPR models.
  • Utilizing counts of molecular fragments as descriptors.
  • Employing a consensus scoring approach with multiple models (20-30).
  • Database searches (MDDR, ACD) for identified key fragments.

Main Results:

  • Consensus QSPR models significantly improved correlation coefficients and reduced standard errors.
  • Identification of specific molecular fragments that enhance or reduce pharmacokinetic properties.
  • Bioavailability-boosting fragments showed higher hit rates in the MDDR database compared to reducing fragments.
  • Opposite trends were observed for bioavailability-modulating fragments in the ACD database.

Conclusions:

  • Genetic algorithm-based QSPR models provide reliable predictions for pharmacokinetic properties.
  • Key molecular fragments can be identified to guide drug design and optimization.
  • The predictive utility of identified fragments differs across drug development and chemical compound databases.