Chromatographic Data in Statistical Analysis of BBB Permeability Indices

Blood-brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/- groups). A number of regression models were constructed in order to observe the connections between the APIs' physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R-2 = 0.87. Models for Kp,uu,brain resulted in lower statistics: R-2 = 0.56 for the group of 23 APIs with the participation of k IAM.

Automated summary

This study investigates the relationship between a group of active pharmaceutical ingredients and their blood-brain barrier permeability data through statistical analysis and correlation assessment. The results reveal that molecular descriptors such as hydrogen bond acceptors and donors, physiological charge, and energy of the lowest unoccupied molecular orbital play an important role in blood-brain barrier permeability modeling. A log BB regression model is constructed using TLC data through multiple linear regression, which shows good predictive value.