Prediction of Blood-Brain Barrier Permeability from Molecular Dynamics Simulations

Abstract

The blood-brain barrier (BBB) is formed by endothelial cells that line brain capillaries. Specialized tight junctions between these cells form a selective barrier between the brain and the rest of the body. A major problem in drug design is the ability of the compound to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body should not cross the BBB in order to avoid possible psychotropic side effects. The task of predicting BBB permeability of new compounds is thus of great importance. Two gold-standard experimental measures of BBB permeability are logBB (concentration of drug in the brain divided by concentration in the blood) and logPS (the permeability-surface area product). Both are time-consuming and expensive to determine, and while logPS is considered more informative, it is lower-throughput, and more resource-intensive. We make computational predictions of these two parameters for a sample of 13 relatively small compounds. Atomistic molecular dynamics (MD) simulations measure the potential of mean force (PMF) for these compounds through a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) bilayer - a system often used as a simple BBB mimetic. Additionally, 1D position-dependent diffusion rates are calculated from the MD trajectories. These diffusion rates are combined with the free energy landscape to calculate permeabilities for each sample compound. The relative values of these permeabilities were compared to logBB and logPS. Our computational predictions correlate remarkably well with experimental values of both logBB (R2 = 0.92) and logPS (R2 = 0.95). Thus, we demonstrate that this approach may have the potential to relatively inexpensively and quickly give reliable, quantitative predictions of BBB permeability. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-644465.