Correlation and prediction of a large blood-brain distribution data set - an LFER study

We report linear free energy relation (LFER) models of the equilibrium dist ribution of molecules between blood and brain, as log BB values. This metho d relates log BB values to fundamental molecular properties, such as hydrog en bonding capability, polarity/polarisability and size. Our best model of this form covers 148 compounds, the largest set of log BB data yet used in such a model, resulting in R-2 = 0.745 and e.s.d. = 0.343 after inclusion o f an indicator variable for carboxylic acids. This represents rather better accuracy than a number of previously reported models based on subsets of o ur data. The model also reveals the factors that affect log BB: molecular s ize and dispersion effects increase brain uptake, while polarity/polarisabi lity and hydrogen-bond acidity and basicity decrease it. By splitting the f ull data set into several randomly selected training and test sets, we conc lude that such a model can predict log BB values with an accuracy of less t han 0.35 log units. The method is very rapid-log BB can be calculated from structure at a rate of 700 molecules per minute on a silicon graphics O-2. (C) 2001 Editions scientifiques ct medicales Elsevier SAS.