Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents

Drug Development Research

Volumn 66, Issue 4, 2005, Pages 245-259

  Li, H ^a   Yap, C W ^a   Xue, Y ^c   Li, Z R ^c   Ung, C Y ^a,b   Han, L Y ^a   Chen, Y Z ^a,d  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c SICHUAN UNIVERSITY   (China)

^d SHANGHAI CENTER FOR BIOINFORMATION TECHNOLOGY   (China)

Author keywords

Feature selection; Molecular descriptors; Pharmacodynamic; Pharmacokinetic; QSAR; Statistical learning methods; Toxicology

Indexed keywords

2 CHLORO 4 (4 METHOXYPHENYL) 3 PHENYLQUINOLINE; CANDESARTAN; CEFTIBUTEN; CYCLOPHOSPHAMIDE; DANSYLTRYPTAMINE; FLURITHROMYCIN; MEBENDAZOLE; PROPAFENONE; UNCLASSIFIED DRUG;

BLOOD BRAIN BARRIER; DRUG BIOAVAILABILITY; DRUG CLEARANCE; DRUG PENETRATION; DRUG PROTEIN BINDING; DRUG STRUCTURE; GENOTOXICITY; INTESTINE ABSORPTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; REVIEW; STATISTICAL ANALYSIS;

EID: 33646420101     PISSN: 02724391     EISSN: 10982299     Source Type: Journal    
DOI: 10.1002/ddr.20044     Document Type: Review

References (74)

1. Bethesda. 2001. AHFS drug information. American Society of Health-System Pharmacists, Inc.
2. 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.
3. Bugrim A, Nikolskaya T, Nikolsky Y. 2004. Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov Today 9:127-135.
4. Burbidge R, Trotter M, Buxton B, Holden S. 2001. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26:5-14.
5. Caldwell J, Gardner I, Swales N. 1995. An introduction to drug disposition: the basic principles of absorption, distribution, metabolism, and excretion. Toxicol Pathol 23:102-114.
6. Chen X, Ji ZL, Zhi DG, Chen YZ. 2002. CLiBE: a database of computed ligand binding energy for ligand-receptor complexes. Comput Chem 26:661-666.
7. 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.
8. Cristianini N, Shawe-Taylor J. 2000. An introduction to Support Vector Machines: and other kernel-based learning methods. New York: Cambridge University Press.
9. Cruciani G, Pastor M, Guba W. 2000. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci 11:S29-S39.
10. Doniger S, Hofman T, Yeh J. 2002. Predicting CNS permeability of drug molecules: Comparison of neural network and Support Vector Machine algorithms. J Comput Biol 9:849-864.
11. Drews J. 2000. Drug discovery: a historical perspective. Science 287:1960-1964.
12. Ekins S, Ring BJ, Grace J, McRobie-Belle DJ, Wrighton SA. 2000. Present and future in vitro approaches for drug metabolism. J Pharmacol Toxicol Methods 44:313-324.
13. Fix E, Hodges J. 1951. Discriminatory analysis: Non-parametric discrimination: consistency properties. Texas: USAF School of Aviation Medicine, Randolph Field. p 261-279.
14. Furlanello C, Serafini M, Merler S, Jurman G. 2003. An accelerated procedure for recursive feature ranking on microarray data. Neural Netw 16:641-648.
15. Galvez J, Garcia R, Salabert MT, Soler R. 1994. Charge indexes. New topological descriptors. J Chem Inf Comput Sci 34:520-525.
16. Gramatica P, Pilutti P, Papa E. 2004. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling. J Chem Inf Comput Sci 44: 1794-1802.
17. Grover II, Singh II, Bakshi II. 2000. Quantitative structure-property relationships in pharmaceutical research-Part 2. Pharm Sci Technol Today 3:50-57.
18. Guyon I, Weston J, Barnhill S, Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Mach Learn 46:389-422.
19. Hall LH, Kellogg GE, Haney DN. 2002. Molconn-Z. Version 4.05+: eduSoft, LC.
20. Hallstrom C, Lader M. 1980. Diazepam and N-desmethyldiazepam concentrations in saliva, plasma and CSF. Br J Clin Pharmacol 9:333-339.
21. Hansch C, Leo A, Mekapati SB, Kurup A. 2004. QSAR and ADME. Bioorg Med Chem 12:3391-3400.
22. Hemmer MC, Steinhauer V, Gasteiger J. 1999. Deriving the 3D structure of organic molecules from their infrared spectra. Vib Spectrosc 19:151-164.
23. Hopfinger A. 1980. A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis. J Am Chem Soc 102:7196-7206.
24. Hosmer D, Lemeshow S. 1989. Applied logistic regression. New York: Wiley.
25. Hou T, Xu X. 2003. ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J Chem Inf Comput Sci 43:2137-2152.
26. Huberty C. 1994. Applied discriminant analysis. New York: John Wiley & Sons.
27. 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.
28. Izrailev S, Agrafiotis DK. 2004. A method for quantifying and visualizing the diversity of QSAR models. J Mol Graph Model 22:275-284.
29. Johnson R, Wichern D. 1982. Applied multivariate statistical analysis. Englewood Cliffs, NJ: Prentice Hall.
30. Katritzky AR, Karelson M, Lobanov V. 1997. QSPR as a means of predicting and understanding chemical and physical properties in terms of structure. Pure Appl Chem 69:245-248.
31. Kier LB, Hall LH. 1999. Molecular structure description: The electrotopological state. San Diego: Academic Press.
32. Kohavi R, John G. 1997. Wrappers for feature subset selection. Artif Intell Med 97:273-324.
33. Kubinyi H. 1997. QSAR and 3D QSAR in drug design. Part 2: applications and problems. Drug Discov Today 2:538-546.
34. Li H, Ung C, Yap C, Xue Y, Li Z, Cao Z, Chen Y. 2005a. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem Res Toxicol 18:1071-1080.
35. Li H, Yap C, Ung C, Xue Y, Cao Z, Chen Y. 2005b. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45:1376-1384.
36. Liu Y. 2004. A comparative study on feature selection methods for drug discovery. J Chem Inf Comput Sci 44:1823-1828.
37. Lobell M, Sivarajah V. 2003. In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values. Mol Divers 7:69-87.
38. Lucasius CB, Kateman G. 1993. Understanding and using genetic algorithms. Part 1. Concepts, properties and context. Chemometr Intell Lab 19:1-33.
39. Manallack DT, Livingstone DJ. 1999. Neural networks in drug discovery: have they lived up to their promise? Eur J Med Chem 34:195-208.
40. MICROMEDEX. 2003. MICROMEDEX. Greenwood Village, Colorado: MICROMEDEX.
41. Ng C, Xiao Y, Putnam W, Lum B, Tropsha A. 2004. Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods. J Pharm Sci 93:2535-2544.
42. Park BK, Kitteringham NR, Powell H, Pirmohamed M. 2000. Advances in molecular toxicology-towards understanding idiosyncratic drug toxicity. Toxicology 153:39-60.
43. Pearlman RS, Smith KM. 1999. Metric validation and the receptor-relevant subspace concept. J Chem Inf Comput Sci 39:28-35.
44. Platts JA, Butina D, Abraham MH, Hersey A. 1999. Estimation of molecular free energy relation descriptors using a group contribution approach. J Chem Inf Comput Sci 39:835-845.
45. Prentis RA, Lis Y, Walker SR. 1988. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). Br J Pharmacol 25:387-396.
46. PubChem. 2004. http://pubchem.ncbi.nlm.nih.gov/. National Institutes of Health (NIH).
47. Quinlan J. 1993. C4.5: programs for machine learning. San Mateo, California: Morgan Kaufmann.
48. Randic M. 1975. Graph theoretical approach to local and overall aromaticity of benzenoid hydrocarbons. Tetrahedron 31:1477-1481.
49. Randic M. 1995. Molecular profiles. Novel geometry-dependent molecular descriptors. New J Chem 19:781-791.
50. Roth BL, Kroeze WK, Patel S, Lopez E. 2000. The multiplicity of serotonin receptors: Uselessly diverse molecules or an embarrassment of riches? Neuroscientist 6:252-262.
51. Ruecker G, Ruecker C. 1993. Counts of all walks as atomic and molecular descriptors. J Chem Inf Comput Sci 33:683-695.
52. Schnur 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.
53. Serra J, Thompson E, Jurs P. 2003. Development of binary classification of structural chromosome aberrations for a diverse set of organic compounds from molecular structure. Chem Res Toxicol 16:153-163.
54. Spalding DJ, Harker AJ, Bayliss MK. 2000. Combining high-throughput pharmacokinetic screens at the hits-to-leads stage of drug discovery. Drug Discov Today 5(Suppl 11):70-76.
55. Specht D. 1990. Probabilistic neural networks. Neural Netw 3: 109-118.
56. Stanton DT. 2003. On the physical interpretation of QSAR models. J Chem Inf Comput Sci 43:1423-1433.
57. Sutter JM, Kalivas JH. 1993. Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem J 47:60-66.
58. Todeschini R, Consonni V, Mauri A, Pavan M. 2005. DRAGON. Version 5.3.
59. Trotter M, Holden S. 2003. Support vector machines for ADME property classification. QSAR & Comb Sci 22:533-548.
60. Turner JV, Maddalena DJ, Agatonovic-Kustrin S. 2004. Bioavailability prediction based on molecular structure for a diverse series of drugs. Pharm Res 21:68-82.
61. van de Waterbeemd H, Gifford E. 2003. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2: 192-204.
62. Vapnik V. 1995. The nature of statistical learning theory. New York: Springer.
63. Venkatraman V, Dalby AR, Yang ZR. 2004. Evaluation of mutual information and genetic programming for feature selection in QSAR. J Chem Inf Comput Sci 44:1686-1692.
64. Walters WP, Goldman BB. 2005. Feature selection in quantitative structure-activity relationships. Curr Opin Drug Discov Devel 8:329-333.
65. Wegner JK. 2005. JOELib/JOELib2: http://www-ra.informatik. uni-tuebingen.de/software/joelib/index.html
66. Weston J, Perez-Cruz F, Bousquet O, Chapelle O, Elisseeff A, Scholkopf B. 2003. Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics 19:764-771.
67. White RE. 2000. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu Rev Pharmacol Toxicol 40:133-157.
68. Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ. 2004a. Effect of molecular descriptor feature selection in Support Vector Machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 44: 1630-1638.
69. Xue Y, Yap C, Sun L, Cao Z, Wang J, Chen Y. 2004b. Prediction of p-glycoprotein substrates by support vector machine approach. J Chem Inf Comput Sci 44:1497-1505.
70. Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, Doucet JP, Fan B. 2005. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2:348-356.
71. Yap C, Chen Y. 2004. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94:153-168.
72. Yap CW, Chen YZ. 2005. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 45:982-992.
73. Yu H, Yang J, Wang W, Han J. Discovering compact and highly discriminative features or feature combinations of drug activities using Support Vector Machines. 2003 August 11-14, Stanford, California. p 220-228.
74. Zhang J, Aizawa M, Amari S, Iwasawa Y, Nakano T, Nakata K. 2004. Development of KiBank, a database supporting structure-based drug design. Comput Biol Chem 28:401-407.