Abstract
In the present work, 79 structurally unrelated analytes were taken into account and their chromatographic retention coefficients, measured by immobilized artificial membrane liquid chromatography (IAM-LC) and by micellar liquid chromatography (MLC) employing sodium dodecyl sulfate (SDS) as surfactant, were determined. Such indexes, along with topological and physicochemical parameters calculated in silico, were subsequently used for the development of blood-brain barrier passage-predictive statistical models using partial least-squares (PLS) regression. Highly significant relationships were observed either using IAM (r2 (n - 1) = 0.78) or MLC (r2 (n - 1) = 0.83) derived indexes along with in silico descriptors. This hybrid approach proved fast and effective in the development of highly predictive BBB passage oriented models, and therefore, it can be of interest for pharmaceutical industries as a high-throughput BBB penetration oriented screening method. Finally, it shed new light into the molecular mechanism involved in the BBB uptake of therapeutics.
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