Computational prediction of the plasma protein-binding percent of diverse pharmaceutical compounds

Abstract

A nonlinear regression analysis has been applied to develop a new method to predict the plasma protein-binding percent of structurally diverse pharmaceutical compounds. The analysis included over 300 launched drugs with experimental human plasma protein binding percent data. These drugs were classified according to protonation state and pharmacophore features. The correlation formula for each class is a simple sigmoidal function of variable LogP or LogD. A correlation formula of variable LogD at pH 7.4 with a good correlation coefficient (R-squared = 0.803) was obtained for neutral and basic drugs, with the exception of zwitterions. A correlation formula using LogP as variable for acidic drugs with one of the specific pharmacophore features gave a good correlation coefficient (R-squared = 0.786). The method was verified using the protein binding data of 20 compounds that had not been included in the data set to configure the formulas. The correlation coefficient (R-squared) between the experimental and predicted protein binding percent was 0.830. In conclusion, the method developed and described in this report can provide precise and useful prediction of plasma protein binding percent for new drug candidates.