Classification tree models for the prediction of blood-brain barrier passage of drugs

Journal of Chemical Information and Modeling

Volumn 46, Issue 3, 2006, Pages 1410-1419

  Deconinck, Eric ^a   Zhang, Menghui H ^a   Coomans, Danny ^b   Vander Heyden, Yvan ^a  

^a PHARMACEUTICAL INSTITUTE   (Belgium)

^b JAMES COOK UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; BLOOD; BRAIN; CLASSIFICATION (OF INFORMATION); DRUG PRODUCTS; MATHEMATICAL MODELS;

ADAPTIVE BOOSTING; BOOSTING ALGORITHMS; CLASSIFICATION TREE MODELS; PREDICTIVE ABILITIES;

TREES (MATHEMATICS);

ARTICLE; BLOOD BRAIN BARRIER; PHARMACOKINETICS; THEORETICAL MODEL;

BLOOD-BRAIN BARRIER; MODELS, THEORETICAL; PHARMACOKINETICS;

EID: 33745373904     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci050518s     Document Type: Article

References (31)

1. Kaliszan, R.; Markuszewski, M. Brain/blood distribution described by a combination of partition coefficient and molecular mass. Int. J. Pharm. 1996, 145, 9-16.
2. Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration. J. Pharm. Sci. 1999, 88, 815-821.
3. Platts, J. A.; Abraham, M. H.; Zhao, Y. H.; Hersey, A.; Ijaz, L.; Butina, D. Correlation and prediction of a large blood-brain distribution data set-an LFER study. Eur. J. Med. Chem. 2001, 36, 719-730.
4. Narayanan, R.; Gunturi, S. B. In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method. Bioorg. Med. Chem. 2005, 13, 3017-3028.
5. Norinder, U.; Sjöberg, P.; Österberg, T. Theoretical calculation and prediction of brain-blood partitioning of organic solutes using molsurf parametrization and PLS statistics. J. Pharm. Sci. 1998, 87, 952-958.
6. Crivori, P.; Cruciani, G.; Carrupt, P. A.; Testa, B. Predicting blood-brain barrier permeation from three-dimensional molecular structure. J. Med. Chem. 2000, 43, 2204-2216.
7. Subramanian, G.; Kitchen, D. B. Computational models to predict blood-brain barrier permeation and CNS activity. J. Comput.-Aided Mol. Des. 2003, 17, 643-664.
8. Dorronsoro, I.; Ghana, A.; Inès Abasolo, M.; Gil, C.; Stud, M.; Martinez, A. CODES/ neural network model: a useful tool for in silico prediction of oral absorption and blood-brain barrier permeability of structurally diverse drugs. QSAR Comb. Sci. 2004, 23, 89-98.
9. Hemmateenejad, B. Correlation ranking procedure for factor selection in PC-ANN modeling and application to ADMETox evaluation. Chemom. Intell. Lab. Syst. 2005, 75, 231-245.
10. Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Classification and Regression Trees; Wadsworth & Brooks: Monterey, CA, 1984.
11. Schapire, R. E. The strength of weak learnability. Mach. Learning 1990, 5, 197-227.
12. Deconinck, E.; Hancock, T.; Coomans, D.; Massart, D. L.; Vander Heyden, Y. Classification of drugs in absorption classes using classification and regression trees (CART)-methodology. J. Pharm. Biomed. Anal. 2005, 39, 91-103.
13. Bai, J. P. F.; Utis, A.; Crippen, G.; He, H.; Fischer, V.; Tullman, R.; Yin, H.; Hsu, C.; Jiang, L.; Hwang, K. Use of classification regression tree in predicting oral absorption in humans. J. Chem. Inf. Comput. Sci 2004, 44, 2061-2069.
14. Put, R.; Perrin, C.; Questier, F.; Coomans, D.; Massart, D. L.; Vander Heyden, Y. Classification and regression tree analysis for molecular descriptor selection and retention prediction in Chromatographic structure-retention relationship studies. J. Chromatogr., A 2003, 988, 261-276.
15. Caetano, S.; Aires-de-Sousa, J.; Daszykowski, M.; Vander Heyden, Y. Prediction of enantioselectivity using chirality codes and classification and regression trees. Anal. Chim. Acta 2005, 544, 315-326.
16. Zhang, M. H.; Xu, Q. S.; Daeyaert, F.; Lewi, P. J.; Massart, D. L. Application of boosting to classification problems in chemometrics. Anal. Chim. Acta 2005, 544, 167-176.
17. He, P.; Xu, C. J.; Liang, Y. Z.; Fang, K. T. Improving the classification accuracy in chemistry via boosting technique. Chemom. Intell. Lab. Syst. 2004, 70, 39-47.
18. He, P.; Fang, K. T.; Liang, Y. Z.; Li, B. Y. A generalized boosting algorithm and its application to two-class chemical classification problem. Anal. Chim. Acta 2005, 54, 181-191.
19. Friedman, J.; Hastie, T.; Tibshirani, R. Additive logistic regression: a statistical view of boosting. Ann. Stat. 2000, 28, 337-407.
20. Wehrens, R.; Putter, H.; Buydens, L. M. C. The Bootstrap: a tutorial. Chemom. Intell. Lab. Syst. 2000, 54, 35-52.
21. Vandeginste, B. G. M.; Massart, D. L.; Buydens, L. M. C.; De Jong, S.; Lewi, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics - Part B; Elsevier Science: Amsterdam, 1997.
22. Varmuza, K.; He, P.; Fang, K. T. Boosting applied to classification of mass spectral data. J. Data Sei. 2003, 1, 391-404.
23. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, Germany, 2000.
24. Todeschini, R.; Consonni, V.; Mauri, A.; Pavan, M. Dragon Professional Version, version 5.0; Milano Chemometrics and QSAR Research Group, Talete SRL: Italy, 2004.
25. Abraham, M. H.; Ibrahim, A.; Zissimos, A. M.; Zhao, Y. H.; Corner, J.; Reynolds, D. P. Application of hydrogen bonding calculations in property based drug design. Drug Discovery Today 2002, 7, 1056-1063.
26. Deconinck, E.; Xu, Q. S.; Put, R.; Coomans, D.; Massart, D. L.; Vander Heyden, Y. Prediction of gastro-intestinal membrane permeability using multivariate adaptive regression splines. J. Pharm. Biomed. Anal. 2005, 39, 1021-1030.
27. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 2001, 46, 3-26
28. Poole, S. K.; Poole, C. F. Separation methods for estimating octanolwater partition coefficients. J. Chromatogr., B 2003, 797, 3-19.
29. Yang, S.; Bumgarner, J. F.; Kruk, L. F. R.; Khaledi, M. G. Quantitative structure-activity relationships studies with micellar electrokinetic chromatography. Influence of surfactant type and mixedmicelles on estimation of hydrofobicity and bioavailability. J. Chromatogr., A 1996, 721, 323-335.
30. Abraham, M. H.; Chadha, H. S.; Leitao, R. A. E.; Mitchell, R. C.; Lambert, W. J.; Kaliszan, R.; Nasal, A.; Haber, P. Determination of solute lipophilicity, as log P(octanol) and log P(alkane) using poly(styrene- divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography. J. Chromatogr., A 1997, 766, 35-47.
31. Put, R.; Van Gyseghem, E.; Coomans, D.; Vander Heyden, Y. The selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees. J. Chromatogr., A 2005, 1096, 187-198.