1. bookVolumen 62 (2012): Edición 3 (September 2012)
Detalles de la revista
License
Formato
Revista
eISSN
1846-9558
ISSN
1330-0075
Primera edición
28 Feb 2007
Calendario de la edición
4 veces al año
Idiomas
Inglés
Acceso abierto

Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans

Publicado en línea: 06 Nov 2012
Volumen & Edición: Volumen 62 (2012) - Edición 3 (September 2012)
Páginas: 305 - 323
Detalles de la revista
License
Formato
Revista
eISSN
1846-9558
ISSN
1330-0075
Primera edición
28 Feb 2007
Calendario de la edición
4 veces al año
Idiomas
Inglés

1. T. Kennedy, Managing the drug discovery and development interface, Drug Discov. Today 2 (1997) 436-444; DOI: 10.1016/S1359-6446(97)01099-4.10.1016/S1359-6446(97)01099-4Search in Google Scholar

2. M. Brvar, A. Perdih, V. Hodnik, M. Renko, G. Anderluh, R. Jerala and T. Solmajer, In silico discovery and biophysical evaluation of novel 5-(2-hydroxybenzylidene) rhodanine inhibitors of DNA gyrase B, Bioorg. Med. Chem. 20 (2012) 2572-2580; DOI: 10.1016/j.bmc.2012.02.052.10.1016/j.bmc.2012.02.05222444877Search in Google Scholar

3. N. G. Chee, Y. Xiao, W. Putnam, B. Lum and A. Tropsha, 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 (2004) 2535-2544; DOI: 10.1002/jps.20117.10.1002/jps.2011715349962Search in Google Scholar

4. J. V. Turner, D. J. Maddalena, D. J. Cutler and S. Agatonovic-Kustrin, Multiple pharmacokinetic parameter prediction for a series of cephalosporins, J. Pharm. Sci. 92 (2003) 552-559; DOI: 10.1002/jps.10314.10.1002/jps.1031412587116Search in Google Scholar

5. R. S. Obach, F. Lombardo and N. J. Waters, Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 compounds, Drug Metab. Dispos. 36 (2008) 1385-1405; DOI: 10.1124/dmd.108.020479.10.1124/dmd.108.02047918426954Search in Google Scholar

6. World Health Organization Collaborating Centre for Drug Statistics Methodology, Guidelines for ATC classification and DDD assignment 2010, 13th ed., WHO Collaborating Centre for Drug Statistics Methodology, Oslo 2009, pp. 163-177.Search in Google Scholar

7. A. Tropsha, P. Gramatica and V. K. Gombar, The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003) 69-77; DOI: 10.1002/qsar.200390007.10.1002/qsar.200390007Search in Google Scholar

8. National Center for Biotechnology Information, PubChem Compound Database; http://pubchem.ncbi.nlm.nih.gov/access January 20, 2011.Search in Google Scholar

9. I. V. Tetko, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V. A. Palyulin, E. V. Radchenko, N. S. Zefirov, A. S. Makarenko, V. Y. Tanchuk and V. V. J. Prokopenko, Virtual computational chemistry laboratory - design and description, Comput. Aid. Mol. Des. 19 (2005) 453-463; DOI: 10.1007/s10822-005-8694-y.10.1007/s10822-005-8694-y16231203Search in Google Scholar

10. M. A. Hall and G. Holmes, Benchmarking attribute selection techniques for discrete class data mining, IEEE Trans. Knowl. Data Eng. 15 (2003) 1437-1447; DOI: 10.1109/TKDE.2003.1245283.10.1109/TKDE.2003.1245283Search in Google Scholar

11. T. Wajima, K. Fukumura, Y. Yano and T. Oguma, Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis, J. Pharm. Sci. 91 (2002) 2489-2499; DOI: 10.1002/jps.10242.10.1002/jps.1024212434392Search in Google Scholar

12. P. P. Roy, S. Paul, I. Mitra and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009) 1660-1701; DOI: 10.3390/molecules 15010604.10.3390/molecules14020738Search in Google Scholar

13. J. Zupan and J. Gasteiger, Neural Networks in Chemistry and Drug Design, Wiley-VCH, Weinheim 1999, pp. 125-154.Search in Google Scholar

14. H. Li, Y. Liang and Q. Xu, Support vector machines and its applications in chemistry, Chemometr. Intell. Lab. 95 (2009) 188-198; DOI: 10.1016/j.chemolab.2008.10.007.10.1016/j.chemolab.2008.10.007Search in Google Scholar

15. S. K. Shevade, S. S. Keerthi, C. Bhattacharyya and K. R. K. Murthy, Improvements to SMO Algorithm for SVM Regression, Technical Report CD-99-16, Control Division, Department of Mechanical and Production Engineering, National University of Singapore, Singapore 1999.Search in Google Scholar

16. J. Gasteiger, J. Sadowski, J. Schuur, P. Selzer, L. Steinhauer and V. Steinhauer, Chemical information in 3D space, J. Chem. Inf. Comput. Sci. 36 (1996) 1030-1037; DOI: 10.1021/ci960343.10.1021/ci960343+Search in Google Scholar

17. M. Petitjean, Applications of the radius-diamater diagram to the classification of topological and geometrical shapes of chemical compounds, J. Chem. Inf. Comput. Sci. 32 (1992) 331-337; DOI: 10.1021/ci00008a012.10.1021/ci00008a012Search in Google Scholar

18. R. S. Obach, J. G. Baxter, T. E. Liston, B. M. Silber, B. C. Jones, F. MacIntyre, D. J. Rance and P. Wastall, The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data, J. Pharmacol Exp. Ther. 283 (1997) 46-58.Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo