title: Potential of biopartitioning micellar chromatography as an in vitro technique for predicting drug penetration across the blood-brain barrier.
abstract: The blood-brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. The ease with which any particular drug diffuses across the BBB is determined largely by the molecular features of drugs, and it is therefore possible to predict the BBB permeability of a drug from its molecular structure. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, can be useful in mimicking the drug partitioning process into biological systems. Retention in BMC depends on the hydrophobicity, electronic and steric properties of drugs. In this paper, the usefulness of BMC for predicting the BBB penetration ability of drugs expressed as the brain/blood distribution coefficient (BB) is demonstrated. A multiple linear regression (MLR) model that relates the BB distribution coefficients data with BMC retention data and total molar charge is proposed. The model is obtained using 44 heterogeneous drugs including, neutral, anionic, and cationic compounds. A comparison with other reported methodologies to predict the BBB permeability is also presented.
authors.affiliation: Departamento de Química Analítica, Universidad de Valencia, C/Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain.
authors.forename: L
authors.initials: L
authors.lastname: Escuder-Gilabert
authors.forename: M
authors.initials: M
authors.lastname: Molero-Monfort
authors.forename: R M
authors.initials: RM
authors.lastname: Villanueva-Camañas
authors.forename: S
authors.initials: S
authors.lastname: Sagrado
authors.forename: M J
authors.initials: MJ
authors.lastname: Medina-Hernández
mesh_terms: D001812:Blood-Brain Barrier; D002853:Chromatography, Liquid / Q000379:methods*; D066298:In Vitro Techniques; D008962:Models, Theoretical; D012044:Regression Analysis