In Silico Prediction of Blood−Brain Barrier Permeation Using the Calculated Molecular Cross-Sectional Area as Main Parameter

Abstract

The cross-sectional area, AD, of a compound oriented in an amphiphilic gradient such as the air-water or lipid-water interface has previously been shown to be crucial for membrane partitioning and permeation, respectively. Here, we developed an algorithm that determines the molecular axis of amphiphilicity and the cross-sectional area, ADcalc, perpendicular to this axis. Starting from the conformational ensemble of each molecule, the three-dimensional conformation selected as the membrane-binding conformation was the one with the smallest cross-sectional area, ADcalcM, and the strongest amphiphilicity. The calculated, ADcalcM, and the measured, AD, cross-sectional areas correlated linearly (n=55, slope, m=1.04, determination coefficient, r2=0.95). The calculated cross-sectional areas, ADcalcM, were then used together with the calculated octanol-water distribution coefficients, log D7.4, of the 55 compounds (with a known ability to permeate the blood-brain barrier) to establish a calibration diagram for the prediction of blood-brain barrier permeation. It yielded a limiting cross-sectional area (ADcalcM=70 A2) and an optimal range of octanol-water distribution coefficients (-1.4<or=log D7.4<7.0). The calibration diagram was validated with an independent set of 43 compounds with the known ability to permeate the blood-brain barrier, yielding a prediction accuracy of 86%. The incorrectly predicted compounds exhibited log D7.4 values comprised between -0.6 and -1.4, suggesting that the limitation for log D7.4 is less rigorous than the limitation for AD. An accuracy of 83% has been obtained for a second validation set of 42 compounds which were previously shown to be difficult to predict. The calculated parameters, ADcalcM and log D7.4, thus allow for a fast and accurate prediction of blood-brain barrier permeation. Analogous calibration diagrams can be established for other membrane barriers.

The Role of Size and Charge for Blood–Brain Barrier Permeation of Drugs and Fatty Acids

“…Data for compounds taken from Fischer et al (1998) are shown as circles; data for compounds listed in Table 1 Table 1 The ionization constant (pK a ), the air-water partition coefficient (K aw ), the cross-sectional area (A D ), the concentration of half-maximum activation of Pgp (K 1 ), and the maximum activity (V 1 ) of compounds 1-12 are measured values taken from Gatlik- Landwojtowicz et al (2006). The ionization constant (pK a ) and the cross-sectional area (A D ) of the fatty acid analogs 13-15 are calculated values as described in Gerebtzoff and Seelig (2006) Number Compound pK a K aw [mM The air-water partition coefficient (K aw ) for palmitic acid was measured (X. Li-Blatter, unpublished results). The values of the two analogs were estimated.…”

“…The limitation of intestinal barrier (IB) and blood-brain barrier (BBB) permeation by the size and the charge of molecules has long been observed (Didziapetris et al 2003;Fischer et al 1998;Fischer et al 1998;Gerebtzoff and Seelig 2006;Golin et al 2003;Pauletti et al 1997;Varma et al 2005). For an understanding at the molecular level, the lipid bilayer, the efflux transporters, and the permeating drugs have to be characterized (Seelig and Gatlik-Landwojtowicz 2005).…”

Ameliorating Huntington's Disease by Targeting Huntingtin mRNA

“…The first one is simple diffusion. Small lipophilic substances which have a hydrogen bond are more likely to pass the BBB (Gerebtzoff & Seelig, 2006). The second mechanism is via active transport mediated by transporter molecules.…”

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

“…[19][20][21] Nonetheless, the complexity, costs, resources and time involved in these assays have increased the importance of in silico approaches to predict BBB permeability of lead compounds that selectively target the CNS. 7,8,22,23 In the present work, robust QSPR models were developed for the consensus prediction of BBB permeation using the fragment-based hologram QSAR (HQSAR) approach. 10,14 To the best of our knowledge, the majority of the models reported in the literature are associated with qualitative data (cross/not cross BBB) that offers imprecise values of BBB permeability, thus, the quantitative nature of the models generated in this work is of considerable importance in medicinal chemistry and drug design.…”

“…However, animal models and cell-based assays are typically time consuming and expensive, and thus not applicable to the early screening of large libraries of compounds. [7][8][9] In recent years, the appearance and consolidation of in silico ADME models have provided useful tools for a faster, simpler, and more cost-effective evaluation of pharmacokinetic properties. [10][11][12] Quantitative structure-activity and structure-property relationships (QSAR/QSPR) are powerful technologies that correlate descriptors based on molecular structures and use computational algorithms to relate the key descriptors to relevant ADME properties.…”