Predicting oral disintegrating tablet formulations by neural network techniques
Tài liệu tham khảo
Bhowmik, 2009, Fast dissolving tablet: an overview, J Chem Pharm Res, 1, 163
Bandari, 2014, Orodispersible tablets: an overview, Asian J Pharm, 2
Lindgren, 1993, Dysphagia: prevalence of swallowing complaints and clinical finding, Med Clin North Am, 77, 3
Dutta, 2011, Formulation of fast disintegrating tablets, Int J Drug Formul Res, 201, 1
Fu, 2004, Orally fast disintegrating tablets: developments, technologies, taste-masking and clinical studies, Crit Rev Ther Drug Carrier Syst, 21, 10.1615/CritRevTherDrugCarrierSyst.v21.i6.10
Prateek, 2012, Fast dissolving tablets: a new venture in drug delivery, Am J PharmTech Res, 2, 252
Kaur, 2011, Mouth dissolving tablets: a novel approach to drug delivery, Int J Curr Pharm Res, 3, 1
Douroumis, 2011, Orally disintegrating dosage forms and taste-masking technologies; 2010, Expert Opin Drug Deliv, 8, 665, 10.1517/17425247.2011.566553
Shukla, 2009, Mouth dissolving tablets I: an overview of formulation technology, Sci Pharm, 77, 309, 10.3797/scipharm.0811-09-01
Siddiqui, 2010, Fast dissolving tablets: preparation, characterization and evaluation: an overview, Int J Pharm Rev Res, 4, 87
Al-Khattawi, 2013, Compressed orally disintegrating tablets: excipients evolution and formulation strategies, Expert Opin Drug Deliv, 10, 651, 10.1517/17425247.2013.769955
Aguilar-Díaz, 2009, The use of the SeDeM Diagram expert system to determine the suitability of diluents–disintegrants for direct compression and their use in formulation of ODT, Eur J Pharm Biopharm, 73, 414, 10.1016/j.ejpb.2009.07.001
Pérez, 2006, A new expert systems (SeDeM Diagram) for control batch powder formulation and preformulation drug products, Eur J Pharm Biopharm, 64, 351, 10.1016/j.ejpb.2006.06.008
Suñé-Negre, 2005, Nueva metodología de preformulaciÓn galénica para la caracterizaclÓn de sustancias en relaciÓn a su viabilidad oara la comoresiÓn: Diaqrama SeDeM, Cienc Tecnol Pharm, 15, 125
Suñé-Negre, 2008, Application of the SeDeM diagram and a new mathematical equation in the design of direct compression tablet formulation, Eur J Pharm Biopharm, 69, 1029, 10.1016/j.ejpb.2008.01.020
Aguilar-Díaz, 2012, Predicting orally disintegrating tablets formulations of ibuprophen tablets: an application of the new SeDeM-ODT expert system, Eur J Pharm Biopharm, 80, 638, 10.1016/j.ejpb.2011.12.012
Aguilar-Díaz, 2014, SeDeM expert system a new innovator tool to develop pharmaceutical forms, Drug Dev Ind Pharm, 40, 222, 10.3109/03639045.2012.756007
Hopfield, 1987, Neural networks and physical systems with emergent collective computational abilities, 411
Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Netw, 61, 85, 10.1016/j.neunet.2014.09.003
Rost, 1994, Combining evolutionary information and neural networks to predict protein secondary structure, Proteins, 19, 55, 10.1002/prot.340190108
Akseli, 2017, A practical framework toward prediction of breaking force and disintegration of tablet formulations using machine learning tools, J Pharm Sci, 106, 234, 10.1016/j.xphs.2016.08.026
Dudek, 2006, Computational methods in developing quantitative structure-activity relationships (QSAR): a review, Comb Chem High Throughput Screen, 9, 213, 10.2174/138620706776055539
Murcia-Soler, 2004, Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds, J Chem Inf Comp Sci, 44, 1031, 10.1021/ci030340e
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Xu, 2015, Deep learning for drug-induced liver injury, J Chem Inf Model, 55, 2085, 10.1021/acs.jcim.5b00238
Baskin, 2016, A renaissance of neural networks in drug discovery, Expert Opin Drug Discov, 11, 785, 10.1080/17460441.2016.1201262
Ma, 2015, Deep neural nets as a method for quantitative structure–activity relationships, J Chem Inf Model, 55, 263, 10.1021/ci500747n
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Proc Mag, 29, 82, 10.1109/MSP.2012.2205597
Bengio, 1994, Learning long-term dependencies with gradient descent is difficult, IEEE Trans Neural Netw, 5, 157, 10.1109/72.279181