Revisiting blood-brain barrier: a chromatographic approach

Data de publicació

2017-06-23T08:22:25Z

2019-06-17T05:10:10Z

2017-06-17

2017-06-23T08:22:25Z

Resum

Drugs designed to reach a pharmacological CNS target must be effectively transported across the blood-brain barrier (BBB), a thin monolayer of endothelial cells tightly attached together between the blood and the brain parenchyma. Because of the lipidic nature of the BBB, several physicochemical partition models have been studied as surrogates for the passive permeation of potential drug candidates across the BBB (octanol-water, alkane-water, PAMPA...). In the last years, biopartition chromatography is gaining importance as a noncellular system for the estimation of biological properties in early stages of drug development. Microemulsions (ME) are suitable mobile phases, because of their ease of formulation, stability and adjustability to a large number of compositions mimicking biological structures. In the present work, several microemulsion liquid chromatographic (MELC) systems have been characterized by means of the Abraham's solvation parameter model, in order to assess their suitability as BBB distribution or permeability surrogates. In terms of similarity between BBB and MELC systems (dispersion forces arising from solute non-bonded electrons, dipolarity/polarizability, hydrogen-bond acidity and basicity, and molecular volume), the passive permeability surface area product (log PS) for neutral (including zwitterions), fully and partially ionized drugs was found to be well correlated with the ME made of 3.3% SDS (w/v; surfactant) 0.8% heptane (w/v; oil phase) and 6.6% 1-butanol (w/v; co-surfactant) in 50 mM aqueous phosphate buffer, pH 7.4.

Tipus de document

Article


Versió acceptada

Llengua

Anglès

Publicat per

Elsevier B.V.

Documents relacionats

Versió postprint del document publicat a: https://doi.org/10.1016/j.jpba.2017.06.027

Journal of Pharmaceutical and Biomedical Analysis, 2017

https://doi.org/10.1016/j.jpba.2017.06.027

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cc-by-nc-nd (c) Elsevier B.V., 2017

http://creativecommons.org/licenses/by-nc-nd/3.0/es

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