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.
Bibliographical noteFunding Information:
The partial support by the Barenholz Fund is gratefully acknowledged. The authors would like to thank Dr. David Marcus for the development and implementation of the ISE algorithm. Mr. Theo Walker is acknowledged for his technical support of the Chembench portal. The UNC group acknowledges support from the NIH grant GM066940 .
- Chemical descriptors
- Loading conditions
- Loading efficiency
- Remote loading