A Hybrid Mixture Discriminant Analysis-Random Forest Computational Model for the Prediction of Volume of Distribution of Drugs in Human

摘要

A computational approach is described that can predict the VD_(ss)of new compounds in humans,with an accuracy of within 2-fold of the actual value.A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant analysis-random forest(MDA-RF)model using 31 computed descriptors.Descriptors included terms describing lipophilicity,ionization,molecular volume,and various molecular fragments.For a test set of 23 proprietary compounds not used in model construction,the geometric mean fold-error(GMFE)was 1.78-fold(+-11.4%).The model was also tested using a leave-class out approach wherein subsets of drugs based on therapeutic class were removed from the training set of 384,the model was recast,and the VD_(ss)values for each of the subsets were predicted.GMFE values ranged from 1.46 to 2.94-fold,depending on the subset.Finally,for an additional set of 74 compounds,VD_(ss)predictions made using the computational model were compared to predictions made using previously described methods dependent on animal pharmacokinetic data.Computational VD_(ss)predictions were,on average,2.13-fold different from the VD_(ss)predictions from animal data.The computational model described can predict human VD_(ss)with an accuracy comparable to predictions requiring substantially greater effort and can be applied in place of animal experimentation.