2.7. In Silico Prediction of Blood–Brain Barrier (BBB) Penetration

Blood–brain barrier (BBB) penetration was predicted using the ADME descriptors protocol for Biovia Discovery Studio Client v18.1. This protocol contains a quantitative linear regression model for the prediction of blood–brain penetration, as well as 95% and 99% confidence ellipses derived from the correlation between polar surface area (PSA-2D) and atom-based LogP (AlogP98) parameters derived from over 800 compounds known to enter the CNS after oral administration [57]. BBB penetration is predicted in terms of logBB values as base 10 logarithm of brain concentration/blood concentration. There are four prediction levels within the 95% and 99% confidence ellipsoids with logBB values: 0 (very high penetrants, with logBB ≥ 0.7, where the concentration of a compound in the brain is at least five times higher than in the blood), 1 (high penetrants, with 0 ≤ logBB < 0.7), 2 (medium penetrants with −0.52 < logBB < 0), 3 (low penetrants, with logBB ≤ −0.52, where the brain-blood ratio is less than 0.3:1), and 4 (undefined, outside the confidence ellipsoids).