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

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.

Figure 1

Remote loading of amphipathic weak bases and acids into liposomes using an ion gradient. (A) loading of amphipathic weak base by transmembrane ammonium sulfate gradient. (B) Loading of amphipathic weak acid by calcium acetate gradient. Concentration of (NH₄)₂SO4 or Ca(C₂H₃O₂)₂ in the liposomes is 1000-fold greater than that in the extraliposomal medium. D = drug, AcH= acetic acid, Ac⁻= Acetate. Un-ionized drug base (D–N) or acid (D–COOH) crosses the liposomal membrane and is trapped inside after it is ionized; in some cases insoluble salt is formed with the counterion inside the liposome (sulfate and Ca ion respectively). This Figure was taken as is from[8] with permission.

Figure 2

External sets index distribution obtained using ISE

Figure 3

Comparison between predicted vs observed D/L values obtained for the external validation tests a. based on SVR models, b. based on kNN models. The empty triangles represent outlier points