Prediction of blood-brain partitioning: A model based on molecular electronegativity distance vector descriptors

Journal of Molecular Graphics and Modelling

Volumn 29, Issue 2, 2010, Pages 214-220

  Zhang, Yong Hong ^a,b,c   Xia, Zhi Ning ^a   Qin, Li Tang ^c   Liu, Shu Shen ^c  

^a CHONGQING UNIVERSITY   (China)

^b CHONGQING MEDICAL UNIVERSITY   (China)

^c TONGJI UNIVERSITY   (China)

Author keywords

Blood brain barrier (BBB) permeability; Molecular electronegativity distance vector (MEDV); PLSR; QSAR; The variable importance in projection (VIP)

Indexed keywords

BLOOD-BRAIN BARRIER; MOLECULAR ELECTRONEGATIVITY DISTANCE VECTOR; PLSR; QSAR; THE VARIABLE IMPORTANCE IN PROJECTION (VIP);

BLOOD; CHEMICAL BONDS; ELECTRONEGATIVITY; FORECASTING; VECTORS;

MECHANICAL PERMEABILITY;

ARTICLE; BLOOD BRAIN BARRIER; CALCULATION; CHEMICAL STRUCTURE; CORRELATION COEFFICIENT; MOLECULAR MODEL; PARTIAL LEAST SQUARES REGRESSION; PREDICTIVE VALIDITY; PRIORITY JOURNAL; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE;

BLOOD-BRAIN BARRIER; HYDROGEN; LEAST-SQUARES ANALYSIS; MODELS, BIOLOGICAL; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS;

EID: 77956276454     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2010.06.006     Document Type: Article

References (48)

1. Tetko I.V., Bruneau P., Mewes H.W., Rohrer D.C., Poda G.I. Can we estimate the accuracy of ADME-Tox predictions?. Drug Discov. Today 2006, 11:700-707.
2. Liu R.F., Sun H.M., So S.S. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 2. Blood-brain barrier penetration. J. Chem. Inf. Comput. Sci. 2001, 41:1623-1632.
3. Golden P.L., Pollack G.M. Blood-brain barrier efflux transport. J. Pharm. Sci. 2003, 92:1739-1753.
4. Shen J., Du Y.P., Zhao Y.X., Liu G.X., Tang Y. In silico prediction of blood-brain partitioning using a chemometric method called genetic algorithm based variable selection. QSAR Comb. Sci. 2008, 27:704-717.
5. Konovalov D.A., Coomans D., Deconinck E., Vander Heyden Y. Benchmarking of QSAR models for blood-brain barrier permeation. J. Chem. Inf. Model. 2007, 47:1648-1656.
6. Levin V.A. Relationship of octanol/water partition coefficient and molecular weight to rat brain capillary permeability. J. Med. Chem. 1980, 23:682-684.
7. 2 receptor histamine antagonists. J. Med. Chem. 1988, 31:656-671.
8. Abraham M.H. Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical process. Chem. Soc. Rev. 1993, 22:73-83.
9. Abraham M.H., Chadha H.S., Mitchell R.C. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J. Pharm. Sci. 1994, 83:1257-1268.
10. Abraham M.H., Chadha H.S., Mitchell R.C. Hydrogen-bonding. Part 36. Determination of blood brain distribution using octanol-water partition coefficients. Drug Des. Discov. 1995, 13:123-131.
11. Abraham M.H., Takacs-Novak K., Mitchell R.C. On the partitioning of ampholytes: application to blood-brain distribution. J. Pharm. Sci. 1997, 86:310-315.
12. Norinder U., Sjoberg P., Osterberg T. Theoretical calculation and prediction of brain-blood partitioning of organic solutes using MolSurf parametrization and PLS statistics. J. Pharm. Sci. 1998, 87:952-959.
13. 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.
14. Luco J.M. Prediction of the brain-blood distribution of a large set of drugs from structurally derived descriptors using partial least-squares (PLS) modeling. J. Chem. Inf. Comput. Sci. 1999, 39:396-404.
15. Norinder U., Haeberlein M. Computational approaches to the prediction of the blood-brain distribution. Adv. Drug Deliv. Rev. 2002, 54:291-313.
16. Goodwin J.T., Clark D.E. In silico predictions of blood-brain barrier penetration: considerations to " Keep in mind" J. Pharmacol. Exp. Ther. 2005, 315:477-483.
17. Chen Y., Zhu Q.J., Pan J., Yang Y., Wu X.P. A prediction model for blood-brain barrier permeation and analysis on its parameter biologically. Comput. Methods Prog. Biomed. 2009, 95:280-287.
18. Todeschini R., Consonni V., Mauri A., Pavan M. DRAGON Software for the Calculation of Molecular Descriptors ver. 5.2 for Windows 2005, Talete S.r.l., Milan, Italy.
19. Accelrys TSAR 3.3 Software 2000, Oxford Molecular Limited, Oxford, England.
20. Geldenhuys W.J., Lockman P.R., Nguyen T.H., Van der Schyf C.J., Crooks P.A., Dwoskin L.P., Allen D.D. 3D-QSAR study of bis-azaaromatic quaternary ammonium analogs at the blood-brain barrier choline transporter. Bioorg. Med. Chem. 2005, 13:4253-4261.
21. Geldenhuys W.J., Lockman P.R., McAfee J.H., Fitzpatrick K.T., Van der Schyf C.J., Allen D.D. Molecular modeling studies on the active binding site of the blood-brain barrier choline transporter. Bioorg. Med. Chem. Lett. 2004, 14:3085-3092.
22. Lessigiarska I., Pajeva I., Cronin M.T., Worth A.P. 3D QSAR investigation of the blood-brain barrier penetration of chemical compounds. SAR QSAR Environ. Res. 2005, 16:79-91.
23. Ooms F., Weber P., Carrupt P.A., Testa B. A simple model to predict blood-brain barrier permeation from 3D molecular fields. BBA-Mol. Basis Dis. 2002, 1587:118-125.
24. Katritzky A.R., Kuanar M., Slavov S., Dobchev D.A., Fara D.C., Karelson M., Acree W.E., Solov'ev V.P., Varnek A. Correlation of blood-brain penetration using structural descriptors. Bioorg. Med. Chem. 2006, 14:4888-4917.
25. Obrezanova O., Csanyi G., Gola J.M.R., Segall M.D. Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model. 2007, 47:1847-1857.
26. Obrezanova O., Gola J.M.R., Champness E.J., Segall M.D. Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J. Comput. Aid Mol. Des. 2008, 22:431-440.
27. Hutter M.C. Prediction of blood-brain barrier permeation using quantum chemically derived information. J. Comput. Aid Mol. Des. 2003, 17:415-443.
28. Van Damme S., Langenaeker W., Bultinck P. Prediction of blood-brain partitioning: a model based on ab initio calculated quantum chemical descriptors. J. Mol. Graph. Model. 2008, 26:1223-1236.
29. Fu X.C., Song Z.F., Fu C.Y., Liang W.Q. A simple predictive model for blood-brain barrier penetration. Pharmazie 2005, 60:354-358.
30. Ma X.L., Chen C., Yang J. Predictive model of blood-brain barrier penetration of organic compounds. Acta Pharmacol. Sin. 2005, 26:500-512.
31. Feher M., Sourial E., Schmidt J.M. A simple model for the prediction of blood-brain partitioning. Int. J. Pharm. 2000, 201:239-247.
32. 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.
33. Zhang L., Zhu H., Oprea T.I., Golbraikh A., Tropsha A. QSAR Modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm. Res. 2008, 25:1902-1914.
34. Norinder U. Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing 2003, 55:337-346.
35. Liu S.S., Cao C.Z., Li Z.L. Approach to estimation and prediction for normal boiling point (NBP) of alkanes based on a novel molecular distance-edge (MDE) vector, λ. J. Chem. Inf. Comput. Sci. 1998, 38:387-394.
36. Liu S.S., Yin C.S., Li Z.L., Cao S.X. QSAR study of steroid benchmark and dipeptides based on MEDV-13. J. Chem. Inf. Comput. Sci. 2001, 41:321-329.
37. Liu S.S., Yin C.S., Wang L.S. Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides, and COX-2 inhibitors. J. Chem. Inf. Comput. Sci. 2002, 42:749-756.
38. Liu S.S., Cui S.H., Yin D.Q., Shi Y.Y., Wang L.S. QSAR studies on the COX-2 inhibition by 3,4-diarylcycloxazolones based on MEDV descriptor. Chin. J. Chem. 2003, 21:1510-1516.
39. Qin L.T., Liu S.S., Liu H.L., Ge H.L. A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV). Chemosphere 2008, 70:1577-1587.
40. Liu S.S., Liu Y., Yin D.Q., Wang X.D., Wang L.S. Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector. J. Sep. Sci. 2006, 29:296-301.
41. Zhang Y.H., Liu S.S., Liu H.Y. Predicting the gas chromatographic relative retention time of polybrominated diphenyl ethers by MEDV-13 descriptors. Chromatographia 2007, 65:319-324.
42. Liu S.S., Qin L.T., Liu H.L., Yin D.Q. Molecular electronegativity distance vector model for the prediction of bioconcentration factors in fish. J. Mol. Model. 2008, 14:83-92.
43. Qin L.T., Liu S.S., Liu H.L. QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors. Mol. Divers. 2010, 14:67-80.
44. Liu S.S., Liu H.L., Yin C.S., Wang L.S. VSMP: a novel variable selection and modeling method based on the prediction. J. Chem. Inf. Comput. Sci. 2003, 43:964-969.
45. Wold S., Sjostrom M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58:109-130.
46. Narayanan R., Gunturi S.B. In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method. Bioorgan. Med. Chem. 2005, 13:3017-3028.
47. Topliss J.G., Edwards R.P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 1979, 22:1238-1244.
48. Al-Fahemi J.H.A., Cooper D.L., Allan N.L. Investigating the utility of momentum-space descriptors for predicting blood-brain barrier penetration. J. Mol. Graph. Model. 2007, 26:607-612.