Quantitative structure-property relationship modeling of remote liposome loading of drugs

J Control Release. 2012 Jun 10;160(2):147-57. doi: 10.1016/j.jconrel.2011.11.029. Epub 2011 Dec 1.

Abstract

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • Chemistry, Pharmaceutical
  • Computer Simulation
  • Decision Trees
  • Drug Carriers / chemistry*
  • Drug Compounding
  • Hydrophobic and Hydrophilic Interactions
  • Membranes, Artificial
  • Models, Chemical*
  • Molecular Structure
  • Pharmaceutical Preparations / administration & dosage*
  • Pharmaceutical Preparations / chemistry*
  • Predictive Value of Tests
  • Quantitative Structure-Activity Relationship
  • Reproducibility of Results
  • Software

Substances

  • Drug Carriers
  • Membranes, Artificial
  • Pharmaceutical Preparations