Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery.

A mathematical equation model was developed by building the relationship between the fu,b/fu,p ratio and the computed physicochemical properties of candidate compounds, thereby predicting Kp,uu,brain based on a single experimentally measured Kp,brain value. A total of 256 compounds and 36 marketed published drugs including acidic, basic, neutral, zwitterionic, CNS-penetrant, and non-CNS penetrant compounds with diverse structures and physicochemical properties were involved in this study. A strong correlation was demonstrated between the fu,b/fu,p ratio and physicochemical parameters (CLogP and ionized fraction). The model showed good performance in both internal and external validations. The percentages of compounds with Kp,uu,brain predictions within 2-fold variability were 80.0 %-83.3 %, and more than 90 % were within a 3-fold variability. Meanwhile, "black box" QSAR models constructed by machine learning approaches for predicting fu,b/fu,p ratio based on the chemical descriptors are also presented, and the ANN model displayed the highest accuracy with an RMSE value of 0.27 and 86.7 % of the test set drugs fell within a 2-fold window of linear regression. These models demonstrated strong predictive power and could be helpful tools for evaluating the Kp,uu,brain by a single measurement parameter of Kp,brain during lead optimization for CNS penetration evaluation and ranking CNS drug candidate molecules in the early stages of CNS drug discovery.

High-throughput screening of LogD by using a sample pooling approach based on the traditional shake flask method.

A new approach for screening LogD is presented. The method is based on the shake flask method combined with rapid generic LC-MS/MS bioanalysis by using a sample pooling approach that enables high-throughput screening of LogD or LogP in the drug discovery stage. The method is evaluated by a comparison of measured LogD between single and pooled compounds for a test set of structurally diverse compounds with a wide range of LogD values (from -0.04 to 6.01). Test compounds include 10 commercially available drug standards along with 27 new chemical entities. A good correlation (RMSE = 0.21, R2 = 0.9879) of LogD between the single and pooled compounds was obtained, suggesting that at least 37 compounds can be simultaneously measured with acceptable accuracy. The sample pooling method significantly reduced the number of bioanalysis samples as compared to the single compound measurement by the conventional shake flask method. The impact of DMSO content on LogD measurement was also investigated and the result demonstrated that at least 0.5% DMSO was tolerated in this method. The current new development will facilitate the drug discovery process by more rapidly assessing the LogD or LogP of drug candidates.