In silico prediction of brain and CSF permeation of small molecules using PLS regression models

Computational partial least square (PLS) regression models were developed, which can be applied to predict central nervous system (CNS) penetration of drug-like organic molecules. For modeling, a dataset of 77 structurally diverse compounds was used with reported steady-state rat brain to plasma ratios (BPR). Information on steady-state cerebrospinal fluid distribution (CSF to plasma ratio or CSFPR) was available for 37 of these compounds. The molecules were from different chemical series and included bases, acids, zwitterions and neutral molecules. They were CNS active and were therefore assumed to penetrate the blood-brain barrier and/or the blood-liquor barrier. Using these PLS models, the dataset could be described accurately (r(2)=0.78, StErrorEst=0.30 and r(2)=0.75, StErrorEst=0.28 for BPR and CSFPR, respectively). Molecular descriptors used for the prediction of passive membrane transport were lipophilicity, polar and hydrophobic surface areas as well as structural parameters and net charge at physiological pH. There was no apparent correlation between experimental brain and CSF exposure. Consequently, different PLS models and guiding rules were developed and discussed for the prediction of BPR or CSFPR. The present models provide a cost-effective and efficient strategy to guide synthetic efforts in medicinal chemistry at an early stage of the drug discovery and development process.