Abstract
Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.
Highlights
The distribution of many drugs to the brain is significantly different from that occurring in other organs, owingThe blood-brain barrier (BBB) plays an important role in maintaining homeostasis, separating the brain from systemic circulation.M
The present study employed a set of 21 molecules to build 2D-QSPR models in order to predict BBB
The contribution to the biological property (ClogP) contribution to the models was positive in all the cases, suggesting that lipophilic moieties, that increase this parameter, facilitate passive translocation
Summary
The distribution of many drugs to the brain is significantly different from that occurring in other organs, owing. A literature review reported several studies, carried out on a number of different chemical structures, and in which 2D and 3D QSPR models have been proposed to predict logBB values (Katritzky et al, 2006; Van Damme et al, 2008; Iyer et al, 2002; Konovalov et al, 2007; Subramanian et al, 2003; Zhao et al, 2007; Zhang et al, 2008; Narayanan et al, 2005). Easy interpretable mathematical models, with good internal and external predictability, are still needed for some specific drug classes Against this background, in the present study a two-dimensional (2D) QSPR approach was applied to a set of 28 structurally similar molecules including benzodiazepines, tricyclic compounds and their metabolites, with CNS activity as antidepressant and neuroleptic, in order to build a QSPR model able to predict logBB values and provide relevant findings about the BBB crossing ability of other compounds structurally related to the investigated set
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