Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods

We have developed quantitative structure-pharmacokinetic parameters relationship (QSPKR) models using k-nearest-neighbor (k-NN) and partial least-square (PLS) methods to predict the volume of distribution at steady state (Vss) and clearance (CL) of 44 antimicrobial agents in humans. The performance of QSPKR was determined by the values of the internal leave-one-out, crossvalidated coefficient of determination q(2) for the training set and external predictive r(2) for the test set. The best simulated annealing (SA)-kNN model was highly predictive for Vss and provided q(2) and r(2) values of 0.93 and 0.80, respectively. For all compounds, the model produced average fold error values for Vss of 1.00 and for 93% of the compounds provided predictions that were within a twofold error of actual values. The best SA-kNN model for prediction of CL yielded q(2) and r(2) values of 0.77 and 0.94, respectively, and had an average fold rror of 1.05. Use of PLS methods resulted in inferior QSPKR models. The SA-kNN QSPKR approach has utility in drug discovery and development in the identification of compounds that possess appropriate pharmacokinetic characteristics in humans, and will assist in the selection of a suitable starting dose for Phase I, first-time-in-man studies.