Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network

Journal of Pharmaceutical Sciences

Volumn 94, Issue 1, 2005, Pages 153-168

  Yap, C W ^a   Chen, Y Z ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Blood brain barrier; Computational ADME; Distribution; General regression neural network; Human serum albumin binding; Milk plasma distribution; Pharmacokinetics

Indexed keywords

CHEMICAL AGENT; HUMAN SERUM ALBUMIN;

ARTICLE; BLOOD BRAIN BARRIER; DRUG BLOOD LEVEL; DRUG DISTRIBUTION; DRUG MILK LEVEL; DRUG PENETRATION; DRUG PROTEIN BINDING; GENERAL REGRESSION NEURAL NETWORK; LINEAR REGRESSION ANALYSIS; NONLINEAR SYSTEM; PHYSICAL CHEMISTRY; QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIP; QUANTITATIVE STRUCTURE PROPERTY RELATION; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 12244273713     PISSN: 00223549     EISSN: None     Source Type: Journal    
DOI: 10.1002/jps.20232     Document Type: Article

References (84)

1. Eddershaw PJ, Beresford AP, Bayliss MK. 2000. ADME/PK as part of a rational approach to drug discovery. Drug Discov Today 5:409-414.
2. van de Waterbeemd H, Gifford E. 2003. ADMET in silico modelling: Towards prediction paradise? Nat Rev Drug Discov 2:192-204.
3. Butina D, Segall MD, Frankcombe K. 2002. Predicting ADME properties in silico: Methods and models. Drug Discov Today 7(Suppl):S83-S88.
4. Ekins S, Waller CL, Swaan PW, Cruciani G, Wrighton SA, Wikel JH. 2000. Progress in predicting human ADME parameters in silico. J Pharmacol Toxicol Methods 44:251-272.
5. Geladi P, Kowalski BR. 1986. Partial least squares regression: A tutorial. Anal Chim Acta 185:1-17.
6. Wythoff BJ. 1993. Backpropagation neural networks. A tutorial. Chemom Intell Lab Sys 18:115-155.
7. Specht DF. 1991. A general regression neural network. IEEE Trans Neural Networks 2:568-576.
8. Niwa T. 2003. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures. J Chem Inf Comput Sci 43:113-119.
9. Mosier PD, Jurs PC. 2002. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J Chem Inf Comput Sci 42:1460-1470.
10. Hardman JG, Limbird LE, Goodman Gilman A. 2002. Goodman and Gilman's The pharmacological basis of therapeutics, 10th ed. New York: McGraw-Hill.
11. Meltzer EO. 1990. Performance effects of antihistamines. J Allergy Clin Immunol 86(Part 2):613-619.
12. Kaliner MA. 1992. Nonsedating antihistamines: Pharmacology, clinical efficacy and adverse effects. Am Fam Phys 45:1337-1342.
13. Young RC, Mitchell RC, Brown TH, Ganellin CR, Griffith R, Jones M, Rana KK, Saundesr D, Smith IR, Sore NE, Wilks TJ. 1988. Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists. J Med Chem 31:656-671.
14. van de Waterbeemd H, Kansy M. 1992. Hydrogen-bonding capacity and brain penetration. Chimia 46:299-303.
15. Abraham MH, Chadha HS, Mitchell R. 1994. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J Pharm Sci 83:1257-1268.
16. Lombardo F, Blake JF, Curatolo WJ. 1996. Computation of brain-blood partitioning of organic solutes via free energy calculations. J Med Chem 39:4750-4755.
17. Norinder U, Sjöberg P, Österberg T. 1998. Theoretical calculation and prediction of brain-blood partitioning of organic solutes using Mol Surf parametrization and PLS statistics. J Pharm Sci 87:952-959.
18. Clark DE. 1999. 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.
19. Kelder J, Grootenhuis PDJ, Bayada DM, Delbressine LPC, Ploemen JP. 1999. Polar molecular surface area as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res 16:1514-1519.
20. Luco JM. 1999. 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 39:396-404.
21. Feher M, Sourial E, Schmidt JM. 2000. A simple model for the prediction of blood-brain partitioning. Int J Pharm 201:239-247.
22. Keserü GM, Molnér L. 2001. High-throughput prediction of blood-brain partitioning: A thermodynamic approach. J Chem Inf Comput Sci 41:120-128.
23. Liu R, Sun H, So SS. 2001. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 2. Blood-brain barrier penetration. J Chem Inf Comput Sci 41:1623-1632.
24. Kaznessis YN, Snow ME, Blankley CJ. 2001. Prediction of blood-brain partitioning using Monte Carlo simulations of molecules in water. J Comput Aided Mol Des 15:697-708.
25. Platts JA, Abraham MH, Zhao YH, Hersey A, Ijaz L, Butina D. 2001. Correlation and prediction of a large blood-brain distribution data set-An LFER study. Eur J Med Chem 36:719-730.
26. Iyer M, Mishru R, Han Y, Hopfinger AJ. 2002. Predicting blood-brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis. Pharm Res 19:1611-1621.
27. Hou TJ, Xu XJ. 2003. ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J Chem Inf Comput Sci 43:2137-2152.
28. Colmenarejo G. 2003. In silico prediction of drug-binding strengths to human serum albumin. Med Res Rev 23:275-301.
29. Gobburu JV, Shelver WH. 1995. Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks. J Pharm Sci 84:862-865.
30. Kratochwil NA, Huber W, Muller F, Kansy M, Gerber PR. 2002. Predicting plasma protein binding of drugs: A new approach. Biochem Pharmacol 64:1355-1374.
31. Colmenarejo G, Alvarez-Pedraglio A, Lavandera JL. 2001. Cheminformatic models to predict binding affinities to human serum albumin. J Med Chem 44:4370-4378.
32. Turner JV, Maddalena DJ, Cutler DJ, Agatonovic-Kustrin S. 2003. Multiple pharmacokinetic parameter prediction for a series of cephalosporins. J Pharm Sci 92:552-559.
33. Hall LM, Hall LH, Kier LB. 2003. QSAR modeling of beta-lactam binding to human serum proteins. J Comput Aided Mol Des 17:103-118.
34. Begg EJ, Atkinson HC. 1993. Modelling of the passage of drugs into milk. Pharmacol Ther 59:301-310.
35. Wilson JT. 1981. Drugs in breast milk. Sydney: ADIS Press.
36. Meskin MS, Lien EJ. 1985. QSAR analysis of drug excretion into human breast milk. J Clin Hosp Pharm 10:269-278.
37. Fleishaker JC, Desai N, McNamara PJ. 1987. Factors affecting the milk-to-plasma drug concentration ratio in lactating women: Physical interactions with protein and fat. J Pharm Sci 76:189-193.
38. Atkinson HC, Begg EJ. 1990. Prediction of drug distribution into human milk from physiochemical characteristics. Clin Pharmacokinet 18:151-167.
39. Agatonovic-Kustrin S, Tucker IG, Zecevic M, Zivanovic LJ. 2000. Prediction of drug transfer into human milk from theoretically derived descriptors. Anal Chim Acta 418:181-195.
40. Agatonovic-Kustrin S, Ling LH, Tham SY, Alany RG. 2002. Molecular descriptors that influence the amount of drugs transfer into human breast milk. J Pharm Biomed Anal 29:103-119.
41. Parzen E. 1962. On estimation of a probability density function and mode. Ann Math Stat 33:1065-1076.
42. Cacoullos T. 1966. Estimation of a multivariate density. Ann I Stat Math 18:179-189.
43. Masters T. 1995. Advanced algorithms for neural networks: A C++ sourcebook. New York: Wiley, p 431.
44. Erb RJ. 1995. The backpropagation neural network - A Bayesian classifier. Introduction and applicability to pharmacokinetics. Clin Pharmacokinet 29:69-79.
45. Hudson PTW, Postma EO. 1995. Choosing and using a neural net. In: Thuijsman F, Weijters AJMM, Braspenning PJ, editors. Artificial neural networks: An introduction to ANN theory and practice. Berlin: Springer-Verlag, pp 273-287.
46. Accelrys. DS ViewerPro. 5.0 ed. Accelrys.
47. Pearlman RS. CONCORD User's manual. St. Louis, MO: Tripos Inc.
48. Todeschini R, Consonni V, Mauri A, Pavan M. 2003. DRAGON Web version 3.0. Milan: Talete SRL.
49. Hemmer MC, Steinhauer V, Gasteiger J. 1999. Deriving the 3D structure of organic molecules from their infrared spectra. Vibrat Spectrosc 19:151-164.
50. Rücker G, Rücker C. 1993. Counts of all walks as atomic and molecular descriptors. J Chem Inf Comput Sci 33:683-695.
51. Schuur JH, Setzer P, Gasteiger J. 1996. The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure-spectra correlations and studies of biological activity. J Chem Inf Comput Sci 36:334-344.
52. Pearlman RS, Smith KM. 1999. Metric validation and the receptor-relevant subspace concept. J Chem Inf Comput Sci 39:28-35.
53. Bravi G, Gancia E, Mascagni P, Pegna M, Todeschini R, Zaliani A. 1997. MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids. J Comput Aided Mol Des 11:79-92.
54. Galvez J, Garcia R, Salabert MT, Soler R. 1994. Charge indexes. New topological descriptors. J Chem Inf Comput Sci 34:520-525.
55. Consonni V, Todeschini R, Pavan M. 2002. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682-692.
56. Randic M. 1975. Graph theoretical approach to local and overall aromaticity of benzenoid hydrocarbons. Tetrahedron 31:1477-1481.
57. Randic M. 1995. Molecular profiles. Novel geometry-dependent molecular descriptors. New J Chem 19:781-791.
58. Kennard RW, Stone L. 1969. Computer aided design of experiments. Technometrics 11:137-148.
59. Wu W, Walczaka B, Massarta DL, Heuerdingb S, Ernib F, Laste IR, Prebble KA. 1996. Artificial neural networks in classification of NIR spectral data: Design of the training set. Chemom Intell Lab Sys 33:35-46.
60. Zuegge J, Fechner U, Roche O, Parrott NJ, Engkvist O, Schneider G. 2002. A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries. Quantum Struct Act Relat 21:249-256.
61. Rajer-Kanduc K, Zupan JMN. 2003. Separation of data on the training and test set for modelling: A case study for modelling of five colour properties of a white pigment. Chemom Intell Lab Sys 65:221-229.
62. Lucasius CB, Kateman G. 1993. Understanding and using genetic algorithms Part 1. Concepts, properties and context. Chemom Intell Lab Sys 19:1-33.
63. Livingstone DJ. 1995. Data pre-treatment. Data analysis for chemists: Applications to QSAR and chemical product design. Oxford: Oxford University Press, pp 48-64.
64. Wall M, GAlib. A C++ library of genetic algorithm components. Massachusetts Institute of Technology.
65. Wessel MD, Jurs PC, Tolan JW, Muskal SM. 1998. Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comput Sci 38:726-735.
66. Manly BFJ. 1997. Randomization bootstrap and Monte Carlo methods in biology, 2nd ed. London: Chapman and Hall.
67. Leardia R, González AL. 1998. Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemom Intell Lab Sys 41:195-207.
68. Golbraikh A, Tropsha A. 2002. Beware of q2! J Mol Graph Mod 20:269-276.
69. Kozak A, Kozak R. 2003. Does cross validation provide additional information in the evaluation of regression models? Can J For Res 33:976-987.
70. Reunanen J. 2003. Overfitting in making comparisons between variable selection methods. J Mach Learn Res 3:1371-1382.
71. Olsson I-M, Gottfries J, Wold S. 2004. D-optimal onion designs in statistical molecular design. Chemom Intell Lab Sys 73:37-46.
72. Hawkins DM, Basak SC, Mills D. 2004. Assessing model fit by cross-validation. J Chem Inf Comput Sci 43:579-586.
73. Eriksson L, Johansson E, Kettaneh-Wold N, Wade KM. 2001. PCA. Multi- and megavariate data analysis - Principles and applications. Umea, Sweden: Umetrics AB, pp 43-70.
74. Todeschini R, Consonni V. 2000. Handbook of molecular descriptors. Weinheim: Wiley-VCH.
75. Pardridge WM. 1998. CNS drug design based on principles of blood-brain barrier transport. J Neurochem 70:1781-1792.
76. Ooms F, Weber P, Carrupt PA, Testa B. 2002. A simple model to predict blood-brain barrier permeation from 3D molecular fields. Biochim Biophys Acta 1587:118-125.
77. Lobell M, Molnár L, Keserü GM. 2003. Recent advances in the prediction of blood-brain partitioning from molecular structure. J Pharm Sci 92:360-370.
78. Abraham MH. 2004. The factors that influence permeation across the blood-brain barrier. Eur J Med Chem 39:235-240.
79. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple K. 2002. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615-2623.
80. Begg EJ, Atkinson HC, Duffull SB. 1992. Prospective evaluation of a model for the prediction of milk:plasma drug concentrations from physicochemical characteristics. Br J Clin Pharmacol 33:501-505.
81. Schultz TW, Netzeva TI, Cronin MTD. 2003. Selection of data sets for QSARs: Analyses of Tetrahymena toxicity from aromatic compounds. SAR QSAR Environ Res 14:59-81.
82. Willett P, Barnard JM, Downs GM. 1998. Chemical similarity searching. J Chem Inf Comput Sci 38:983-996.
83. Mosier PD, Jurs PC, Custer LL, Durham SK, Pearl GM. 2003. Predicting the genotoxicity of thiophene derivatives from molecular structure. Chem Res Toxicol 16:721-732.
84. Currit N. 2002. Inductive regression: Overcoming OLS limitations with the general regression neural network. Comput Environ Urban Syst 26:335-353.