133 related articles for article (PubMed ID: 28138223)
1. Computational intelligence models to predict porosity of tablets using minimum features.
Khalid MH; Kazemi P; Perez-Gandarillas L; Michrafy A; Szlęk J; Jachowicz R; Mendyk A
Drug Des Devel Ther; 2017; 11():193-202. PubMed ID: 28138223
[TBL] [Abstract][Full Text] [Related]
2. Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures: computational intelligence modeling and parametric analysis.
Kazemi P; Khalid MH; Pérez Gago A; Kleinebudde P; Jachowicz R; Szlęk J; Mendyk A
Drug Des Devel Ther; 2017; 11():241-251. PubMed ID: 28176905
[TBL] [Abstract][Full Text] [Related]
3. Combining experimental design and orthogonal projections to latent structures to study the influence of microcrystalline cellulose properties on roll compaction.
Dumarey M; Wikström H; Fransson M; Sparén A; Tajarobi P; Josefson M; Trygg J
Int J Pharm; 2011 Sep; 416(1):110-9. PubMed ID: 21708239
[TBL] [Abstract][Full Text] [Related]
4. The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis.
Boersen N; Carvajal MT; Morris KR; Peck GE; Pinal R
Drug Dev Ind Pharm; 2015; 41(9):1470-8. PubMed ID: 25212638
[TBL] [Abstract][Full Text] [Related]
5. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.
Aksu B; Paradkar A; de Matas M; Ozer O; Güneri T; York P
AAPS PharmSciTech; 2012 Dec; 13(4):1138-46. PubMed ID: 22956056
[TBL] [Abstract][Full Text] [Related]
6. The effects of screw-to-roll speed ratio on ribbon porosity during roll compaction.
Olaleye B; Wu CY; Liu LX
Int J Pharm; 2020 Oct; 588():119770. PubMed ID: 32805384
[TBL] [Abstract][Full Text] [Related]
7. Near-infrared chemical imaging (NIR-CI) as a process monitoring solution for a production line of roll compaction and tableting.
Khorasani M; Amigo JM; Sun CC; Bertelsen P; Rantanen J
Eur J Pharm Biopharm; 2015 Jun; 93():293-302. PubMed ID: 25917640
[TBL] [Abstract][Full Text] [Related]
8. Impact of feed material properties on the milling of pharmaceutical ribbons: A PBM analysis.
Olaleye B; Wu CY; Liu LX
Int J Pharm; 2020 Nov; 590():119954. PubMed ID: 33039493
[TBL] [Abstract][Full Text] [Related]
9. Influence of the porosity of cushioning excipients on the compaction of coated multi-particulates.
Elsergany RN; Chan LW; Heng PWS
Eur J Pharm Biopharm; 2020 Jul; 152():218-228. PubMed ID: 32445966
[TBL] [Abstract][Full Text] [Related]
10. A quantitative correlation of the effect of density distributions in roller-compacted ribbons on the mechanical properties of tablets using ultrasonics and X-ray tomography.
Akseli I; Iyer S; Lee HP; Cuitiño AM
AAPS PharmSciTech; 2011 Sep; 12(3):834-53. PubMed ID: 21710336
[TBL] [Abstract][Full Text] [Related]
11. Predictions of tensile strength of binary tablets using linear and power law mixing rules.
Michrafy A; Michrafy M; Kadiri MS; Dodds JA
Int J Pharm; 2007 Mar; 333(1-2):118-26. PubMed ID: 17097245
[TBL] [Abstract][Full Text] [Related]
12. Population balance modelling of ribbon milling with a new mass-based breakage function.
Olaleye B; Pozza F; Wu CY; Liu LX
Int J Pharm; 2019 Nov; 571():118765. PubMed ID: 31610282
[TBL] [Abstract][Full Text] [Related]
13. Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome.
Simões MF; Silva G; Pinto AC; Fonseca M; Silva NE; Pinto RMA; Simões S
Eur J Pharm Biopharm; 2020 Jul; 152():282-295. PubMed ID: 32442736
[TBL] [Abstract][Full Text] [Related]
14. Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset.
Nagy B; Szabados-Nacsa Á; Fülöp G; Turák Nagyné A; Galata DL; Farkas A; Mészáros LA; Nagy ZK; Marosi G
Int J Pharm; 2023 Feb; 633():122620. PubMed ID: 36669581
[TBL] [Abstract][Full Text] [Related]
15. Unified compaction curve model for tensile strength of tablets made by roller compaction and direct compression.
Farber L; Hapgood KP; Michaels JN; Fu XY; Meyer R; Johnson MA; Li F
Int J Pharm; 2008 Jan; 346(1-2):17-24. PubMed ID: 17689211
[TBL] [Abstract][Full Text] [Related]
16. Prediction of mechanical properties of compacted binary mixtures containing high-dose poorly compressible drug.
Patel S; Bansal AK
Int J Pharm; 2011 Jan; 403(1-2):109-14. PubMed ID: 21034802
[TBL] [Abstract][Full Text] [Related]
17. Terahertz time-domain spectroscopy for the investigation of tablets prepared from roller compacted granules.
Anuschek M; Skelbæk-Pedersen AL; Kvistgaard Vilhelmsen T; Skibsted E; Zeitler JA; Rantanen J
Int J Pharm; 2023 Jul; 642():123165. PubMed ID: 37356510
[TBL] [Abstract][Full Text] [Related]
18. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.
Petrović J; Ibrić S; Betz G; Đurić Z
Int J Pharm; 2012 May; 428(1-2):57-67. PubMed ID: 22402474
[TBL] [Abstract][Full Text] [Related]
19. Roll compaction/dry granulation: effect of raw material particle size on granule and tablet properties.
Herting MG; Kleinebudde P
Int J Pharm; 2007 Jun; 338(1-2):110-8. PubMed ID: 17324537
[TBL] [Abstract][Full Text] [Related]
20. A methodological evaluation and predictive in silico investigation into the multi-functionality of arginine in directly compressed tablets.
ElShaer A; Kaialy W; Akhtar N; Iyire A; Hussain T; Alany R; Mohammed AR
Eur J Pharm Biopharm; 2015 Oct; 96():272-81. PubMed ID: 26255158
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]