Qualitative prediction of blood-brain barrier permeability on a large and refined dataset

Abstract

The prediction of blood-brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood-brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood-brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (n(trees) = 5) based on only four descriptors yields a validated accuracy of 88%.

Fig. 1

The cross-sectional area (CSA) has been introduced as a measure for the area occupied by a compound after insertion into a lipid membrane. Local polarity of the membrane determines the orientation of the ligand

Fig. 2

Comparison of two different strategies to calculate the hydrophobic center (red sphere) for compounds with halogen atoms (like perphenazine). On the left side, the hydrophobic center is calculated weighting atom positions by their contribution to logP prediction; on the right side the calculation is done with modifications presented in this study

Fig. 3

Amitriptyline with hydrophilic center (yellow sphere), hydrophobic center (red sphere), amphiphilic axis (green line) and CSA (green dotted area). This BBB-permeable compound illustrates the role of the amphiphilic axis and the CSA

Fig. 4

a Experimental logBB plotted against 11 experimental and 362 calculated CSA show no correlation. Blue dots represent experimental CSA values, whereas grey dots are based on calculated CSA values. b Colour coded scatterplot of CSA versus LogD (at pH = 7.4), where green dots represent BBB permeable, red dots represent non-BBB permeable and gray dots represent unclassified compounds

Fig. 5

CSA plotted against the number of atoms (a_count) reveals a remarkably high correlation (r = 0.959)

Fig. 6

Different training sets with 50–350 examples all selected from our dataset (n = 362) show that the size of the training set highly influences the performance given by squared correlation, even when constructed with exactly the same descriptor (TPSA) and the same procedure