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 *

157 related articles for article (PubMed ID: 12229268)

  • 1. Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets.
    Chen Y; Jiao T; McCall TW; Baichwal AR; Meyer MC
    Pharm Dev Technol; 2002; 7(3):373-9. PubMed ID: 12229268
    [TBL] [Abstract][Full Text] [Related]  

  • 2. The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms.
    Chen Y; McCall TW; Baichwal AR; Meyer MC
    J Control Release; 1999 May; 59(1):33-41. PubMed ID: 10210720
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [Application of an artificial neural network in the design of sustained-release dosage forms].
    Wei XH; Wu JJ; Liang WQ
    Yao Xue Xue Bao; 2001 Sep; 36(9):690-4. PubMed ID: 12580110
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of dissolution profiles of acetaminophen beads using artificial neural networks.
    Peng Y; Geraldrajan M; Chen Q; Sun Y; Johnson JR; Shukla AJ
    Pharm Dev Technol; 2006; 11(3):337-49. PubMed ID: 16895844
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm.
    Plumb AP; Rowe RC; York P; Brown M
    Eur J Pharm Sci; 2005; 25(4-5):395-405. PubMed ID: 15893460
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Optimization of metformin HCl 500 mg sustained release matrix tablets using Artificial Neural Network (ANN) based on Multilayer Perceptrons (MLP) model.
    Mandal U; Gowda V; Ghosh A; Bose A; Bhaumik U; Chatterjee B; Pal TK
    Chem Pharm Bull (Tokyo); 2008 Feb; 56(2):150-5. PubMed ID: 18239298
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Design porosity osmotic tablet for delivering low and pH-dependent soluble drug using an artificial neural network.
    Patel A; Mehta T; Patel M; Patel K; Patel N
    Curr Drug Deliv; 2012 Sep; 9(5):459-67. PubMed ID: 22746271
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy.
    Chen Y; Thosar SS; Forbess RA; Kemper MS; Rubinovitz RL; Shukla AJ
    Drug Dev Ind Pharm; 2001 Aug; 27(7):623-31. PubMed ID: 11694009
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor.
    Peh KK; Lim CP; Quek SS; Khoh KH
    Pharm Res; 2000 Nov; 17(11):1384-8. PubMed ID: 11205731
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Development of a Physiologically Relevant Population Pharmacokinetic in Vitro-in Vivo Correlation Approach for Designing Extended-Release Oral Dosage Formulation.
    Kim TH; Shin S; Bulitta JB; Youn YS; Yoo SD; Shin BS
    Mol Pharm; 2017 Jan; 14(1):53-65. PubMed ID: 27809538
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Relating formulation variables to in vitro dissolution using an artificial neural network.
    Ebube NK; McCall T; Chen Y; Meyer MC
    Pharm Dev Technol; 1997 Aug; 2(3):225-32. PubMed ID: 9552450
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Systematic development of pH-independent controlled release tablets of carvedilol using central composite design and artificial neural networks.
    Aktas E; Eroglu H; Kockan U; Oner L
    Drug Dev Ind Pharm; 2013 Aug; 39(8):1207-16. PubMed ID: 22804226
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Effects of manufacturing process variables on in vitro dissolution characteristics of extended-release tablets formulated with hydroxypropyl methylcellulose.
    Huang Y; Khanvilkar KH; Moore AD; Hilliard-Lott M
    Drug Dev Ind Pharm; 2003 Jan; 29(1):79-88. PubMed ID: 12602495
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Generalized regression neural networks in prediction of drug stability.
    Ibrić S; Jovanović M; Djurić Z; Parojcić J; Solomun L; Lucić B
    J Pharm Pharmacol; 2007 May; 59(5):745-50. PubMed ID: 17524242
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation.
    Barmpalexis P; Kanaze FI; Kachrimanis K; Georgarakis E
    Eur J Pharm Biopharm; 2010 Feb; 74(2):316-23. PubMed ID: 19815063
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Influence of hydroxypropyl methylcellulose mixture, apparent viscosity, and tablet hardness on drug release using a 2(3) full factorial design.
    Khanvilkar KH; Huang Y; Moore AD
    Drug Dev Ind Pharm; 2002 May; 28(5):601-8. PubMed ID: 12098849
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance.
    Ibrić S; Jovanović M; Djurić Z; Parojcić J; Petrović SD; Solomun L; Stupar B
    AAPS PharmSciTech; 2003; 4(1):E9. PubMed ID: 12916918
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Artificial neural networks applied to the in vitro-in vivo correlation of an extended-release formulation: initial trials and experience.
    Dowell JA; Hussain A; Devane J; Young D
    J Pharm Sci; 1999 Jan; 88(1):154-60. PubMed ID: 9874718
    [TBL] [Abstract][Full Text] [Related]  

  • 19. In vitro release of ketoprofen from hydrophilic matrix tablets containing cellulose polymer mixtures.
    Vueba ML; Batista de Carvalho LA; Veiga F; Sousa JJ; Pina ME
    Drug Dev Ind Pharm; 2013 Nov; 39(11):1651-62. PubMed ID: 23094867
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Pharmaceutical granulation and tablet formulation using neural networks.
    Kesavan JG; Peck GE
    Pharm Dev Technol; 1996 Dec; 1(4):391-404. PubMed ID: 9552323
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.