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%.

01 Dec 2010·Brazilian Journal of Pharmaceutical Sciences

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Author: Sá, Matheus Malta de ; Pasqualoto, Kerly Fernanda Mesquita ; Rangel-Yagui, Carlota de Oliveira

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.

01 Oct 2008·European Journal of Pharmaceutics and BiopharmaceuticsQ2 · MEDICINE

Predicting blood–brain barrier penetration from molecular weight and number of polar atoms

Q2 · MEDICINE

Article

Author: Wen-Quan Liang ; Jian-Qing Gao ; Xu-Chun Fu ; Guo-Ping Wang ; Hai-Li Shan

A simple three-descriptor model to predict blood-brain barrier is derived from a training set of 78 compounds: logBB=-9.880 x 10(-6)M(W)(2)+7.339 x 10(-3)M(W)-0.2268n(pol)-0.1143 (n=78, r(2)=0.74), where logBB is the logarithm of the ratio of the steady-state concentration of the compound in the brain to concentration in the blood, M(W) is the molecular weight, n(pol) is the number of polar atoms (oxygen, nitrogen, and attached hydrogen), n is the number of compounds, and r is the correlation coefficient. The model is validated through use of leave-one-out procedure and an external test set (25 compounds). The model is suitable for the rapid prediction of the blood-brain barrier penetration of drug candidates because of its predictive ability and simplicity.

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