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 p...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024003359 |
| _version_ | 1797328641693057024 |
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| author | Yongfen Ma Mengrong Jiang Huma Javeria Dingwei Tian Zhenxia Du |
| author_facet | Yongfen Ma Mengrong Jiang Huma Javeria Dingwei Tian Zhenxia Du |
| author_sort | Yongfen Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T06:55:36Z |
| format | Article |
| id | doaj.art-177c7dbe8c364ad6a53d9eecc6ed7e8f |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T06:55:36Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-177c7dbe8c364ad6a53d9eecc6ed7e8f2024-02-03T06:36:53ZengElsevierHeliyon2405-84402024-01-01102e24304Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discoveryYongfen Ma0Mengrong Jiang1Huma Javeria2Dingwei Tian3Zhenxia Du4College of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China; DMPK Department, Sironax (Beijing) Co., Ltd, Beijing, 102206, ChinaDMPK Department, Sironax (Beijing) Co., Ltd, Beijing, 102206, ChinaCollege of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, ChinaCollege of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, ChinaCollege of Chemistry, Beijing Key Laboratory of Environmentally Harmful Chemical Analysis, Beijing University of Chemical Technology, Beijing, 100029, China; Corresponding author. College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, China.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.http://www.sciencedirect.com/science/article/pii/S2405844024003359Central nervous system (CNS)Physicochemical parametersBrain-to-plasma unbound fraction ratio (fu,b/fu,p)Unbound brain-to-plasma concentration ratio (Kp,uu,brain)Quantitative structure-activity relationship (QSAR) |
| spellingShingle | Yongfen Ma Mengrong Jiang Huma Javeria Dingwei Tian Zhenxia Du Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery Heliyon 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) |
| title | Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery |
| title_full | Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery |
| title_fullStr | Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery |
| title_full_unstemmed | Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery |
| title_short | Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery |
| title_sort | accurate prediction of kp uu brain based on experimental measurement of kp brain and computed physicochemical properties of candidate compounds in cns drug discovery |
| topic | 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) |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024003359 |
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