1. Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery
- Author
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Yongfen Ma, Mengrong Jiang, Huma Javeria, Dingwei Tian, and Zhenxia Du
- Subjects
Central nervous system (CNS) ,Physicochemical parameters ,Brain-to-plasma unbound fraction ratio (fu,b/fu,p) ,Unbound brain-to-plasma concentration ratio (Kp,uu,brain) ,Quantitative structure-activity relationship (QSAR) ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
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.
- Published
- 2024
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