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Prediction of Blood Brain Barrier Permeability of Ligands Using Sequential Floating Forward Selection and Support Vector Machine

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Computational Intelligence in Data Mining - Volume 1

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 31))

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Abstract

Prediction of Blood Brain Barrier (BBB) permeability index has been established as an important criterion for CNS active drug molecules. Various experimental and in silico approaches were being used for the prediction BBB permeability with accuracy level fall within 80 % on test dataset (r2 = squared correlation coefficient; 0.65–0.91 derived from training set). In this study Sequential Floating Forward Selection (SFFS) feature selection method based Support Vector Machine (SVM) classification was carried out on a set of 453 chemically diverse compounds with known BBB permeability index. The prediction efficiency for the test set was found to be r2 = 0.95 for 369 compounds (within the applicability domain after excluding four activity outliers). Classification accuracies for permeable (BBB +ve) and non-permeable (BBB −ve) were 96.84 and 98.21 % respectively.

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References

  1. Hawkins, B.T., Davis, P.: The blood-brain barrier/neurovascular unit in health and disease’. Pharmacol. Rev. 57, 173–185 (2005)

    Article  Google Scholar 

  2. Cuzner, M.L., Hayes, G.M., Newcombe, J., Woodroofe, M.N.: The nature of inflammatory components during demyelination in multiple sclerosis. J. Neuroimmunol. 20, 203–209 (1988)

    Article  Google Scholar 

  3. Esiri, M.M.: Immunoglobulin-containing cells in multiple-sclerosis plaques. Lancet 2, 478–480 (1977)

    Article  Google Scholar 

  4. Pardridge, W.M.: Isolated brain capillaries: an in vitro model of blood-brain barrier research. In: Pardridge, W.M. (ed.) Introduction to the Blood-Brain Barrier Methodology, Biology and Pathology. Cambridge University Press, Cambridge (1998)

    Chapter  Google Scholar 

  5. Pardridge, W.M.: Blood-brain barrier biology and methodology. J. Neurovirol. 5, 556–569 (1999)

    Article  Google Scholar 

  6. Lasbennes, F., Sercombe, R., Seylaz, J.: Monoamine oxidase activity in brain microvessels determined using natural and artificial substrates: relevance to the blood-brain barrier. J. Cereb. Blood Flow Metab. 3, 521–528 (1983)

    Article  Google Scholar 

  7. Takakura, Y., Audus, K.L., Borchardt, T.: Blood-brain barrier: transport studies in isolated brain capillaries and in cultured brain endothelial cells. Adv. Pharmacol. 22, 137–165 (1991)

    Article  Google Scholar 

  8. Mater, S., Maickel, R.P., Brodie, B.B.: Kinetics of penetration of drugs and other foreign compounds into cerebrospinal fluid and brain. J. Pharmacol. Exp. Ther. 127, 205–211 (1959)

    Google Scholar 

  9. Bickel, U.: How to measure drug transport across the blood-brain barrier. NeuroRx 2, 15–26 (2005)

    Article  Google Scholar 

  10. Oldendorf, W.H., Pardridge, W.M., Braun, L.D., Crane, P.D.: Measurement of cerebral glucose utilization using washout after carotid injection in the rat. J. Neurochem. 38, 1413–1418 (1982)

    Article  Google Scholar 

  11. Oldendorf, W.H.: Measurement of brain uptake of radiolabeled substances using a tritiated water internal standard. Brain Res. 24, 372–376 (1970)

    Article  Google Scholar 

  12. Takasato, Y., Rapoport, S.I., Smith, R.: An in situ brain perfusion technique to study cerebrovascular transport in the rat. Am. J. Cell Physiol. 247, 484–493 (1984)

    Google Scholar 

  13. Aasmundstad, T.A., Morland, J., Paulsen, R.E.: Distribution of morphine 6-glucuronide and morphine across the blood-brain barrier in awake, freely moving rats investigated by in vivo microdialysis sampling. J. Pharmacol. Exp. Ther. 275, 435–441 (1995)

    Google Scholar 

  14. Westergren, I., Nystrom, B., Hamberger, A., Johansson, B.B.: Intracerebral dialysis and the blood-brain barrier. J. Neurochem. 64, 229–234 (1995)

    Article  Google Scholar 

  15. Webb, S., Ott, R.J., Cherry, S.R., Quantization of blood-brain barrier permeability by positron emission tomography. Phys Med. Biol. 34, 1767–171

    Google Scholar 

  16. Goodwin, J.T., Clark, E.: In silico predictions of blood-brain barrier penetration: considerations to “keep in mind”. J. Pharmacol. Exp. Ther. 315, 477–483 (2005)

    Article  Google Scholar 

  17. Clark, D.E.: In-silico prediction of blood-brain barrier permeation. Drug Discov. Today 8, 927–933 (2003)

    Article  Google Scholar 

  18. 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. 83, 1257–1268 (1994)

    Article  Google Scholar 

  19. Abraham, M.H., Chadha, H.C., Mitchell, R.C.: Hydrogen-bonding. Part 36. Determination of blood brain distribution using octanol-water partition coefficients. Drug Des. Discov. 13, 123–131 (1995)

    Google Scholar 

  20. Lombardo, F., Blake, J.F., Curatolo, W.J.: Computation of brain–blood partitioning of organic solutes via free energy calculations. J. Med. Chem. 39, 4750–4755 (2003)

    Article  Google Scholar 

  21. Subramanian, G., Kitchen, D.B.: Computational models to predict blood–brain barrier permeation and CNS activity. J. Comput. Aided Mol. Des. 17, 643–664 (2003)

    Article  Google Scholar 

  22. 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. 14, 4888–4917 (2006)

    Article  Google Scholar 

  23. Hou, T.J., Xu, X.J.: ADME evaluation in drug discovery. 3. Modeling blood–brain barrier partitioning using simple molecular descriptors. J. Chem. Inf. Comput. Sci. 43, 2137–2152 (2003)

    Article  Google Scholar 

  24. Iyer, M., Mishru, R., Han, Y., Hopfinger, A.J.: Predicting blood–brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis. Pharm. Res. 19, 1611–1621 (2002)

    Article  Google Scholar 

  25. Pan, D., Iyer, M., Liu, J., Li, Y., Hopfinger, A.J.: Constructing optimum blood brain barrier QSAR models using a combination of 4D-molecular similarity measures and cluster analysis. J. Chem. Inf. Comput. Sci. 44, 2083–2098 (2004)

    Article  Google Scholar 

  26. Ma, X.L., Chen, C., Yang, J.: Predictive model of blood–brain barrier penetration of organic compounds. Acta Pharmacol. Sin. 26, 500–512 (2005)

    Article  Google Scholar 

  27. Norinder, U., Haeberlein, H.: Computational approaches to the prediction of the blood–brain distribution. Adv. Drug Deliv. Rev. 54, 291–313 (2002)

    Article  Google Scholar 

  28. 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. 36, 719–730 (2001)

    Article  Google Scholar 

  29. Hemmateenejad, B., Miri, R., Safarpour, M.A., Mehdipour, A.R.: Accurate prediction of the blood–brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. J. Comput. Chem. 27, 1125–1135 (2006)

    Article  Google Scholar 

  30. 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. 25, 1902–1914 (2008)

    Article  Google Scholar 

  31. Kortagere, S., Chekmarev, D., Welsh, W.J., Ekins, S.: New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm. Res. 25, 1836–1845 (2008)

    Article  Google Scholar 

  32. Dureja, H., Madan, A.K.: Validation of topochemical models for the prediction of permeability through the blood-brain barrier. Acta Pharm. 57, 451–467 (2007)

    Article  Google Scholar 

  33. Doniger, S., Hofmann, T., Yeh, J.: Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms. J. Comput. Biol. 9, 849–864 (2002)

    Article  Google Scholar 

  34. Guangli, M., Yiyu, C.: Predicting Caco-2 permeability using support vector machine and chemistry development kit. J. Pharm. Pharm. Sci. 9, 210–221 (2006)

    Google Scholar 

  35. Yanga, S.Y., Huanga, Q., Lib, L.L., Maa, C.Y., Zhanga, H., Baia, R., Tenga, Q.Z., Xianga, M.L., Weia, Y.Q.: An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs. Artif. Intell. Med. 46, 155–163 (2009)

    Article  Google Scholar 

  36. 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. 88, 815–821 (1999)

    Article  Google Scholar 

  37. Feher, M., Sourial, E., Schmidt, J.M.: A simple model for the prediction of blood–brain partitioning. Int. J. Pharm. 201, 239–247 (2000)

    Article  Google Scholar 

  38. Brewster, M.E., Pop, E., Huang, M.J., Bodor, N.: AM1-based model system for estimation of brain/blood concentration ratios. Int. J. Quantum Chem. 60, 51–63 (1996)

    Article  Google Scholar 

  39. Burns, J., Weaver, D.F.: A mathematical model for prediction of drug molecule diffusion across the blood-brain barrier. Can. J. Neurol. Sci. 31, 520–527 (2004)

    Google Scholar 

  40. Li, H., Yap, C.W., Ung, C.Y., Xue, Y., Cao, Z.W., Chen, Y.Z.: Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and non-penetrating agents by statistical learning methods. J. Chem. Inf. Model. 45, 1376–1384 (2005)

    Article  Google Scholar 

  41. Zhou, X., Mao, K.Z.: LS bound based gene selection for DNA microarray data. Bioinformatics 21, 1559–1564 (2005). Oxford university press

    Article  Google Scholar 

  42. Stein, W.D.: The Movement of Molecules across Cell Membranes, p. 120. Academic Press, New York (1967)

    Google Scholar 

  43. Zhang, L.Z., Lu, M., Tian, F.: Maximum Randi´c index on trees with k-pendant vertices. J. Math. Chem. 41, 161–171 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  44. Wiener, H.: Structural determination of paraffin boiling points. J. Am. Chem. Soc. 69, 17–20 (1947)

    Article  Google Scholar 

  45. Balaban, A.T.: Distance connectivity index. Chem. Phys. Lett. 89, 399–404 (1982)

    Article  MathSciNet  Google Scholar 

  46. Gutman, I., Ruscic, B., Trinajstic, N.S., Wilcox, C.F.: Graph theory and molecular orbitals XII acyclic polyenes. J. Chem. Phys. 62, 3399–3405 (1975)

    Article  Google Scholar 

  47. Wiener, H.: Correlation of heats of isomerization and differences in heats of vaporization of isomers among the paraffinic hydrocarbons. J. Am. Chem. Soc. 69, 2636–2638 (1947)

    Article  Google Scholar 

  48. Platt, J.R.: Prediction of isomeric differences in paraffin properties. J. Phys. Chem. 56, 328–336 (1952)

    Article  Google Scholar 

  49. Garg, P., Verma, J., Roy, N.: In Silico modeling for blood-brain barrier permeability predictions. Drug Absorption Stud. 8, 289–297 (2008)

    Google Scholar 

  50. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  51. Varadwaj, V., Purohit, N., Arora, B.: Detection of splice sites using support vector machine. Contemp. Comput. 40, 493–502 (2009)

    Article  Google Scholar 

  52. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software available at (2001) http://www.csie.ntu.edu.tw/~cjlin/libsvm

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Authors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Pooja Gupta, Utkarsh Raj & Pritish K. Varadwaj

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  1. Pooja Gupta
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  2. Utkarsh Raj
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  3. Pritish K. Varadwaj
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Correspondence to Pritish K. Varadwaj .

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Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Gupta, P., Raj, U., Varadwaj, P.K. (2015). Prediction of Blood Brain Barrier Permeability of Ligands Using Sequential Floating Forward Selection and Support Vector Machine. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_42

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  • DOI: https://doi.org/10.1007/978-81-322-2205-7_42

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  • Publisher Name: Springer, New Delhi

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  • Online ISBN: 978-81-322-2205-7

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