QSAR Studies on Blood-Brain Barrier Permeation

Abstract

This review focuses on both physicochemical and theoretical QSAR methods for the prediction of drug transport across the blood-brain barrier (BBB). Special emphasis is given to the recent progress that has been made in the modeling of BBB penetration, with a particular focus on the models based on kinetic parameters of BBB permeability dataset. Physicochemical models based on partition coefficients and chromatographic capacity factors, as well as computerized parameters such as polar surface area and hydrogenbonding descriptors are described and their success and limitations are discussed. Theoretical models based on topological or molecular orbital calculations are summarized and assessed in terms of descriptors, model type, predictive performance and interpretability. Strengths and weaknesses of the various methods are described. Related issues that are mentioned include the transporter-mediated permeation of drugs across the BBB and its implications on the stability and predictive quality of QSAR models.

Ionization-Specific Prediction of Blood–Brain Permeability

“…Other parameters deal with statistical corrections of H‐bonding and size effects rather than hydrophobic entrapment or ionization. In a recent review Luco and Marchevsky25 revised previously published QSAR studies on BBB transport of compounds and reanalyzed the provided data sets proposing new predictive models of log PS constants. These models were based on a similar approach like most of the earlier works (log P and various functions of molecular weight (MW) used as descriptors).…”

Permeability Data of Organosulfur Garlic Compounds Estimated by Immobilized Artificial Membrane Chromatography: Correlation Across Several Biological Barriers

“…Given that IAM chromatography has been successfully applied to drug permeability predictions across the blood–brain barrier ( Luco and Marchevsky, 2006 ), a QSAR model based on IAM phase parameters was built to assess the BB barrier permeation of the target OSCs. Acknowledging that there is no BBB-empirical data reported for OSCs, the BBB-QSAR model was built using estimated data from the SwissADME web tool.…”

Prediction of protein 13Cα NMR chemical shifts using a combination scheme of statistical modeling and quantum-mechanical analysis

“…PLS is a widely used latent linear multivariate regression method that relates the information in the response matrix Y to the systematic variation in the descriptor matrix X. PLS method has been successfully applied to many quantitative structure-activity and structure-property studies [68,69]. In this research, the determination of the significant number of PLS components was carried out by cross-validation, and the term was excluded if the increase of the cumulative q 2 of cross-validation was below 0.097 when a latent variable was added into the PLS model [70].…”