This study presents novel QSAR models for the prediction of two key pharmacokinetic properties of drugs � the extent of plasma protein binding (PPB) and apparent volume of distribution (Vd) in humans. Experimental PPB data were represented by almost 1500 percentage bound values and about 300 human serum albumin affinity constants. Predictive models were developed using recently introduced GALAS modeling methodology that allows estimating reliability of resulting predictions and provides the basis for model trainability. Vd was modeled using mechanistic approach accounting for drug binding in both plasma and tissues. 800 original Vd values collected from literature were corrected for free fraction in plasma yielding 'unbound Vd' (Vdu) values that represent drugs' affinity to tissues. pVdu was then described by a nonlinear model in terms of simple physicochemical properties (logP and pKa). Validation results indicate good predictive power of the obtained model with RMSE of pVdu prediction being 0.4 log units.

Trainable QSAR model of plasma protein binding and its application for predicting volume of distribution

240th National Meeting (2010)

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