A MODELING BASED PREDICTIVE TOOL FOR CENTRAL NERVOUS SYSTEM DRUGS USING CHROMATOGRAPHY
Journal Title: World Journal of Pharmaceutical and life sciences - Year 2017, Vol 3, Issue 3
Abstract
51 central nervous system (CNS) drugs, clinically established to enter blood brain barrier (BBB), were characterized in terms of their chromatographic behavior in order to develop a predictive model for candidate drugs in drug design methodology. A lipophilic column was used to simulate the behavior of a drug towards the BBB and a zwitterionic-HILIC column to simulate blood. The Partial Least Squares (PLS) models compiled proved that the pharmacokinetic properties of a drug along with specific physicochemical properties are crucial for BBB penetration. These properties are all correlated in individual molecules while there are cases that high values in one descriptor could counteract a substandard score in another. To overcome such misunderstandings and avoid needless animal experiments, as well as time and material consuming syntheses, a new parameter was inserted. The accordance of the new factor with the calculated logBB values, along with the reliability of the models developed (R2≥0.90, Q2≥0.83) showed that it is possible to utilize this parameter as a tool to predict the logBB for candidate drugs. The use of more sophisticated in silico methods has been the object of study of several researches but until today, a descriptor to mirror this behavior has not been found.
Authors and Affiliations
Maria G. Kouskoura
Keywords
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