These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

133 related articles for article (PubMed ID: 16306006)

  • 1. Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis.
    Değim Z
    Drug Dev Ind Pharm; 2005 Oct; 31(9):935-42. PubMed ID: 16306006
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of skin penetration using artificial neural network (ANN) modeling.
    Değím T; Hadgraft J; Ilbasmiş S; Ozkan Y
    J Pharm Sci; 2003 Mar; 92(3):656-64. PubMed ID: 12587127
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Modeling Caco-2 permeability of drugs using immobilized artificial membrane chromatography and physicochemical descriptors.
    Chan EC; Tan WL; Ho PC; Fang LJ
    J Chromatogr A; 2005 Apr; 1072(2):159-68. PubMed ID: 15887485
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of human skin permeability using artificial neural network (ANN) modeling.
    Chen LJ; Lian GP; Han LJ
    Acta Pharmacol Sin; 2007 Apr; 28(4):591-600. PubMed ID: 17376301
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach.
    Di Fenza A; Alagona G; Ghio C; Leonardi R; Giolitti A; Madami A
    J Comput Aided Mol Des; 2007 Apr; 21(4):207-21. PubMed ID: 17265097
    [TBL] [Abstract][Full Text] [Related]  

  • 6. ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.
    Agatonovic-Kustrin S; Beresford R; Yusof AP
    J Pharm Biomed Anal; 2001 Sep; 26(2):241-54. PubMed ID: 11470201
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting Caco-2 permeability using support vector machine and chemistry development kit.
    Guangli M; Yiyu C
    J Pharm Pharm Sci; 2006; 9(2):210-21. PubMed ID: 16959190
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of permeability of FD-4 through porous poly (2-hydroxyethyl methacrylate) membrane by multiple linear regression and artificial neural network.
    Yanagawa F; Onuki Y; Morishita M; Takayama K
    Pharmazie; 2009 May; 64(5):311-5. PubMed ID: 19530441
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties.
    Atobe T; Mori M; Yamashita F; Hashida M; Kouzuki H
    J Toxicol Sci; 2015 Apr; 40(2):277-94. PubMed ID: 25786531
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Permeability profiles of M-alkoxysubstituted pyrrolidinoethylesters of phenylcarbamic acid across caco-2 monolayers and human skin.
    Gyürösiová L; Laitinen L; Raiman J; Cizmárik J; Sedlárová E; Hirvonen J
    Pharm Res; 2002 Feb; 19(2):162-8. PubMed ID: 11883643
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Estimation of permeability by passive diffusion through Caco-2 cell monolayers using the drugs' lipophilicity and molecular weight.
    Camenisch G; Alsenz J; van de Waterbeemd H; Folkers G
    Eur J Pharm Sci; 1998 Oct; 6(4):317-24. PubMed ID: 9795088
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A new topological descriptors based model for predicting intestinal epithelial transport of drugs in Caco-2 cell culture.
    Marrero Ponce Y; Cabrera Pérez MA; Romero Zaldivar V; González Díaz H; Torrens F
    J Pharm Pharm Sci; 2004 Jun; 7(2):186-99. PubMed ID: 15367375
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Relationships between structure and high-throughput screening permeability of peptide derivatives and related compounds with artificial membranes: application to prediction of Caco-2 cell permeability.
    Ano R; Kimura Y; Shima M; Matsuno R; Ueno T; Akamatsu M
    Bioorg Med Chem; 2004 Jan; 12(1):257-64. PubMed ID: 14697791
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network.
    Fujiwara S; Yamashita F; Hashida M
    Int J Pharm; 2002 Apr; 237(1-2):95-105. PubMed ID: 11955808
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of corneal permeability using artificial neural networks.
    Agatonovic-Kustrin S; Evans A; Alany RG
    Pharmazie; 2003 Oct; 58(10):725-9. PubMed ID: 14609285
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Secretory transport of ranitidine and famotidine across Caco-2 cell monolayers.
    Lee K; Ng C; Brouwer KL; Thakker DR
    J Pharmacol Exp Ther; 2002 Nov; 303(2):574-80. PubMed ID: 12388638
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of the affinity of the newly synthesised azapirone derivatives for 5-HT1A receptors based on artificial neural network analysis of chromatographic retention data and calculation chemistry parameters.
    Nasal A; Bucinski A; Baczek T; Wojdelko A
    Comb Chem High Throughput Screen; 2004 Jun; 7(4):313-25. PubMed ID: 15200379
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.
    Agatonovic-Kustrin S; Beresford R
    J Pharm Biomed Anal; 2000 Jun; 22(5):717-27. PubMed ID: 10815714
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Combined application of parallel artificial membrane permeability assay and Caco-2 permeability assays in drug discovery.
    Kerns EH; Di L; Petusky S; Farris M; Ley R; Jupp P
    J Pharm Sci; 2004 Jun; 93(6):1440-53. PubMed ID: 15124203
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Caco-2 permeability of weakly basic drugs predicted with the double-sink PAMPA pKa(flux) method.
    Avdeef A; Artursson P; Neuhoff S; Lazorova L; Gråsjö J; Tavelin S
    Eur J Pharm Sci; 2005 Mar; 24(4):333-49. PubMed ID: 15734300
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.