{Reference Type}: Journal Article
{Title}: Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors.
{Author}: Jovanović M;Radan M;Čarapić M;Filipović N;Nikolic K;Crevar M;
{Journal}: Future Med Chem
{Volume}: 16
{Issue}: 9
{Year}: 2024 Apr 19
{Factor}: 4.767
{DOI}: 10.4155/fmc-2023-0390
{Abstract}: Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.<BR>[Box: see text].