92
Views
8
CrossRef citations to date
0
Altmetric
Original Research

Computational intelligence models to predict porosity of tablets using minimum features

, , , , , & show all
Pages 193-202 | Published online: 12 Jan 2017

References

  • HattoriYOtsukaMNIR spectroscopic study of the dissolution process in pharmaceutical tabletsVib Spectrosc2011572 275 281
  • GucluyildizHBankerGSPeckGEDetermination of porosity and pore-size distribution of aspirin tablets relevant to drug stabilityJ Pharm Sci1977663 407 414191587
  • JohanssonBAlderbornGThe effect of shape and porosity on the compression behaviour and tablet forming ability of granular materials formed from microcrystalline celluloseEur J Pharm Biopharm2001523 347 35711677077
  • de OliveiraJMJrAndreo FilhoNChaudMVAngiolucciTAranhaNMartinsACPorosity measurement of solid pharmaceutical dosage forms by gamma-ray transmissionAppl Radiat Isot20106812 2223 222820580565
  • RiepmaKAVromansHZuurmanKLerkCFThe effect of dry granulation on the consolidation and compaction of crystalline lactoseInt J Pharm1993971–3 29 38
  • ZuurmanKRiepmaKABolhuisGKVromansHLerkCFThe relationship between bulk density and compactibility of lactose granulationsInt J Pharm19941021–3 1 9
  • JuppoAMRelationship between breaking force and pore structure of lactose, glucose and mannitol tabletsInt J Pharm19951271 95 102
  • WestermarckSJuppoAMKervinenLYliruusiJPore structure and surface area of mannitol powder, granules and tablets determined with mercury porosimetry and nitrogen adsorptionEur J Pharm Biopharm1998461 61 689700023
  • YassinSGoodwinDJAndersonAThe disintegration process in microcrystalline cellulose based tablets, part 1: influence of temperature, porosity and superdisintegrantsJ Pharm Sci201510410 3440 345026073446
  • ÇelikMOverview of compaction data analysis techniquesDrug Dev Ind Pharm1992186&7 767 810
  • MasteauJCThomasGModelling to understand porosity and specific surface area changes during tablettingJ Chem Phys19991013 240 248
  • HeckelRWDensity-pressure relationship in powder compactionTrans Metall Soc AIME1961221 671 675
  • GongXChangSYOsei-YeboahFDependence of tablet brittleness on tensile strength and porosityInt J Pharm20154931–2 208 21326226338
  • BourquinJSchmidliHvan HoogevestPLeuenbergerHComparison of artificial neural networks (ANN) with classsical modelling techniques using different experimental designs and data from a galenical study on a solid dosage formEur J Pharm Sci199864 287 3019795084
  • ShaoQRoweRCYorkPComparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulationEur J Pharm Sci2007312 129 13617459671
  • LandinMRoweRCYorkPAdvantages of neurofuzzy logic against conventional experimental design and statistical analysis in studying and developing direct compression formulationsEur J Pharm Sci2009384 325 33119716414
  • MendykATuszynskiPKKhalidMHJachowiczRPolakSHow-to: empirical IVIVR without intravenous dataDissolution Technol2015222 12 18
  • KhalidMHTuszyńskiPKKazemiPSzlekJJachowiczRMendykATransparent computational intelligence models for pharmaceutical tableting processComplex Adapt Syst Model20164 7
  • YuCBrianSMolecular docking and ligand specificity in fragment-based inhibitor discoveryNat Chem Biol20095 358 36419305397
  • ChanDSYangHKwanMHStructure-based optimization of FDA-approved drug methylene blue as a c-myc G-quadruplex DNA stabilizerBiochimie2011936 1055 106421377506
  • DeimelPBababrikRWangBDirect quantitative identification of the “surface trans-effect”Chem Sci20167 5647 5656
  • Perez-GandarillasLMazorASouriouDLecoqOMichrafyACompaction behaviour of dry granulated binary mixturesPowder Technol2015285 62 67
  • Perez-GandarillasLPerez-GagoAMazorAKleinebuddePLecoqOMichrafyAEffect of roll-compaction and milling conditions on granules and tablet propertiesEur J Pharm Biopharm2016106 38 4927237776
  • LachenbruchPAMickeyMREstimation of error rates in discriminant analysisTechnometrics1968101 1 12
  • QuinlanJRLearning with continuous classesProceedings of the 5th Australian Joint Conference on Artificial Intelligence1992HobartTAS: World Scientific Pub Co Inc
  • BreimanLRandom forestsMach Learn200145 5 32
  • CannonAJ webpage on the Internetmonmlp: Monotone multi-layer perceptron neural network2012 R package version 1.1.2. Available from: http://CRAN.R-project.org/package=monmlpAccessed October 27, 2016
  • Di NicolaDPierantozziMSurface tension of alcohols: a scaled equation and an artificial neural networkFluid Phase Equilib2015389 16 27
  • Rsymbolic ProjectCologne University of Applied Sciences. C2010-13 Available from: https://cran.r-project.org/web/packages/rgp/vignettes/rgp_introduction.pdfAccessed October 27, 2016
  • KozaJRGenetic programming as a means for programming computers by natural selectionStat Comput19944 87 112
  • SetteSBoullartLGenetic programming: principles and applicationsEng Appl Artif Intell200114 727 736
  • KazemiPKhalidMHSzlekJComputational intelligence modeling of granule size distribution for oscillating millingPowder Technol2016301 1252 1258
  • PoliRLangdonWBMcPheeNF webpage on the InternetA Field Guide to Genetic Programming2008 Available from: http://www.gp-field-guide.org.ukAccessed July 9, 2016
  • NashJCVaradhanRUnifying optimization algorithms to aid software system users: optmix for RJ Stat Softw2011439 1 14
  • NashJCOn best practice optimization methods in RJ Stat Softw2014602 1 14
  • SzlekJPaclawskiALauRJachowiczRMendykAHeuristic modeling of macromolecule release from PLGA microspheresInt J Nanomedicine201381 4601 461124348037
  • PacławskiASzlękJLauRJachowiczRMendykAEmpirical modeling of the fine particle fraction for carrier-based pulmonary delivery formulationsInt J Nanomedicine201510 801 81025653522
  • GonzalesGBSmaggheGCoelusSCollision cross section prediction of deprotonated phenolics in a travelling-wave ion mobility spectrometer using molecular descriptors and chemometricsAnal Chim Acta2016924 68 7627181646
  • MansaRFBridsonRHGreenwoodRWBarkerHSevilleJPKUsing intelligent software to predict the effects of formulation and processing parameters on roller compactionPowder Technol2008181 217 225
  • KachrimanisKKaramyanVMalamatarisSArtificial neural networks (ANNs) and modeling of powder flowInt J Pharm20032501 13 2312480269
  • KhalidMHTuszynskiPKKazemiPSzlekJJachowiczRMendykATransparent computational intelligence models for pharmaceutical tableting processComplex Adapt Syst Model20164 7
  • SovanyTPaposKKasaPJrIlicISrcicSPintye-HodiKApplication of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristicsAAPS PharmSciTech2013142 511 51623413109
  • EichieFEKudehinbuAOEffect of particle size of granules on some mechanical properties of paracetamol tabletsAfr J Biotechnol2009821 5913 5916
  • MartinCLBouvardDShimaSStudy of particle rearrangement during powder compaction by the discrete element methodJ Mech Phys Solids2003514 667 693