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ABSTRACT
Assessment of the influence of six physicochemical properties used in the multiparameter optimization (MPO) approach for chemical penetration of the blood–brain barrier was carried out by means of application of logistic regression and multiple linear regression, using a data set of 578 diverse chemicals. It was found that use of an aggregation MPO-score descriptor did not give satisfactory results with central nervous system (CNS)/non-CNS classification. Thus an application of the MPO approach for CNS penetration is ambiguous. An alternative to the MPO approach in this work contains detailed (quantitative) structure–activity relationship analysis using a number of methods (linear discriminant analysis, random forest, support vector machine, Gaussian process). Three properties (molecular weight, number of H-bond donors and octanol–water partition coefficient) yielded optimal categorical models with modest statistical parameters (accuracy 0.730–0.765 for CNS/non-CNS classification). The poor statistics of regression models for the common data set suggested the presence of subsets with different mechanisms of penetrations. Based on graphic comparison of experimental and calculated Cu,b values, subset clusters have satisfactory statistics. The regression models obtained allowed the estimation of descriptor contributions in log Cu,b. This means that medicinal chemists now have a simple additive scheme for at least preliminary quantitative assessment of this important pharmacokinetic parameter.
Acknowledgements
This research was supported by state program (topic number 0090-2017-0020)
Supplemental material
Supplementary material is available at: https://doi.org/10.1080/1062936X.2018.1514652 and 10.6084/m9.figshare.6974369
Disclosure of interest
There were no potential conflicts of interest disclosed by the authors in this work.
