Quantitative structure–property relationships (QSPRs) were developed for predicting the solubility enhancement (expressed as log S/S0) of compounds in 45% (w/v) aqueous solution of HP--CD.Aset of 25 structurally different drugs, whose log S/S0 values were taken from literature, was used as a training set for building the computational models. Thirteen molecular descriptors, including parameters for size, lipophilicity, cohesive energy density and hydrogen-bonding capacity, were calculated and together with the experimental melting point (MP), used in multivariate analysis. Eight pertinent variables were detected after looking at the results of principal component analysis (PCA) and cluster analysis, and two reliable four-descriptor models generated by multiple linear regression (MLR) and by the partial least squares-projection to latent structures (PLS) methods. In both cases, satisfactory coefficients of determination values were obtained (i.e., R2 equal to 0.793 or 0.763 for MLR and PLS, respectively). The models were validated using a test set of six compounds. The equations generated can predict the aqueous solubility increase of poorly soluble compounds by complexation in 45% (w/v) aqueous solution of HP--CD with a reasonable accuracy. These equations can allow formulation scientists to rapidly estimate, at the early stage of drug development, the potential of HP--CD in increasing solubility of poorly water-soluble drugs.
A rapid screening tool for estimating the potential of 2-hydroxypropyl-beta-cyclodextrin complexation for solubilization purposes
TRAPANI, ADRIANA;LOPEDOTA, Angela Assunta;DENORA, NUNZIO;LAQUINTANA, VALENTINO;FRANCO, Massimo;TRAPANI, Giuseppe;
2005-01-01
Abstract
Quantitative structure–property relationships (QSPRs) were developed for predicting the solubility enhancement (expressed as log S/S0) of compounds in 45% (w/v) aqueous solution of HP--CD.Aset of 25 structurally different drugs, whose log S/S0 values were taken from literature, was used as a training set for building the computational models. Thirteen molecular descriptors, including parameters for size, lipophilicity, cohesive energy density and hydrogen-bonding capacity, were calculated and together with the experimental melting point (MP), used in multivariate analysis. Eight pertinent variables were detected after looking at the results of principal component analysis (PCA) and cluster analysis, and two reliable four-descriptor models generated by multiple linear regression (MLR) and by the partial least squares-projection to latent structures (PLS) methods. In both cases, satisfactory coefficients of determination values were obtained (i.e., R2 equal to 0.793 or 0.763 for MLR and PLS, respectively). The models were validated using a test set of six compounds. The equations generated can predict the aqueous solubility increase of poorly soluble compounds by complexation in 45% (w/v) aqueous solution of HP--CD with a reasonable accuracy. These equations can allow formulation scientists to rapidly estimate, at the early stage of drug development, the potential of HP--CD in increasing solubility of poorly water-soluble drugs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
