|
[1]
|
AGATONOVIC-KUSTRIN S�� BERESFORD R. Basic concepts of artificial neural network modeling and its application in pharmaceutical research[J]. J Pharm Biomed Anal�� 2000�� 22(5)��717-772.
|
|
[2]
|
YAN P F�� ZHANG C S. Artificial Neural Networks and Evolutionary Computing���˹���������ģ��������㣩[M]. Beijing��Tsinghua University Press�� 2005��19-26.
|
|
[3]
|
GUERRA A A�� CAMPILLO N E�� PAEZ J A. Neural computational prediction of oral drug absorption based on CODES 2D descriptors[J]. Eur J Med Chem�� 2010�� 45(3)��930-940.
|
|
[4]
|
SHEIKH-BAHAEI S�� ALI K A�� BERA S R�� et al. Predicting human hepatic clearance using hypernet neural networks[A]. 19th International Conference on Software Engineering and Data Engineering(SEDE-2010) [C]. San Francisco�� CA�� USA�� 2010�� 355-359.
|
|
[5]
|
SAINI S�� SINGH S K. Quantitative structure pharmacokinetic relationship(QSPkR) of cox-2 inhibitors using artifical neural network[J]. Acta Pharm Sci�� 2009�� 51��313-321.
|
|
[6]
|
PAIXA O P�� GOUVEIA L F�� MORAIS J G. Prediction of drug distribution in blood[J]. Eur J Pharm Sci�� 2009�� 36(4-5)�� 544-554.
|
|
[7]
|
AGATONOVIC-KUSTRIN S�� TURNER J V�� GLASS B D. Quantitative structure-retention - pharmacokinetic relationship studies[J]. Drug Metab Lett�� 2008�� 2(2)��130-137.
|
|
[8]
|
TURNER J V�� MADDALENA D J�� CUTLER D J. Pharmacokinetic parameter prediction from drug structure using artificial neural networks[J]. Int J Pharm�� 2004�� 270(1-2)��209-219.
|
|
[9]
|
LI Z R�� HAN L Y�� XUE Y�� et al. Model-molecular descriptor lab��a web-based server for computing structural and physicochemical features of compounds[J]. Biotechnol Bioeng�� 2007�� 97(2)��389-396.
|
|
[10]
|
HECHT D�� FOGEL G B. A novel in silico approach to drug discovery via computational intelligence[J]. J Chem Inf Model�� 2009�� 49(4)��1105-1121.
|
|
[11]
|
TURNER J V�� MADDALENA D J�� CUTLER D J�� et al. Multiple pharmacokinetic parameter prediction for a series of cephalosporins[J]. J Pharm Sci�� 2003�� 92(3)��552-559.
|
|
[12]
|
HASHEMI R R�� YOUNG J F. The prediction of methylmercury elimination half-life in humans using animal data��a neural network/rough sets analysis[J]. J Toxicol Environ Health�� 2003�� 66(23)��2227-2252.
|
|
[13]
|
VENG-PEDERSEN P�� MODI N B. Application of neural networks to pharmacodynamics[J]. J Pharm Sci�� 1993�� 82(9)��918-926.
|
|
[14]
|
CHEN Y C�� CAO W W�� CAO Y�� et al. Using neural networks to determine the contribution of danshensu to its multiple cardiovascular activities in acute myocardial infarction rats[J]. J Ethnopharmacol�� 2011�� 138��1����126-134.
|
|
[15]
|
TOGUN N�� KOSE A�� GUNAY N�� et al. Formulation of effects of atropine�� pralidoxime and magnesium sulfate on cardiac tissue levels of nitric oxide�� malondialdehyde and glutathione in organophosphate poisoning using artificial neural network[J]. Comput Biol Med�� 2010�� 40(1)��29-36.
|
|
[16]
|
CAO G�� SHAN Q Y�� ZHANG C R�� et al. Pharmacokinetic parameters of morroniside in iridoid glycosides of Fructus corni processing based on back-propagation neural network[J]. Pharm Biol�� 2011�� 49(9)��989-993.
|
|
[17]
|
SONG X L�� NIU X�� SI Y C. Study on proportion of pinellisa decoction for purging stomach-fire�� ginger rind and licorice root with BP artificial neural network[J]. Chin J Chin Pharmacol Ther(�й��ٴ�ҩ��ѧ������ѧ)�� 2005�� 10(5)��527-531.
|
|
[18]
|
NG C M. Comparison of neural network�� bayesian�� and multiple stepwise regression- based limited sampling models to estimate area under the curve[J]. Pharmacotherapy�� 2003�� 23(8)��1044-1051.
|
|
[19]
|
CHEN M Y�� WANG J�� LIANG W Q. Predition of elimination rate constant of amikacin in neonates with BP neural network[J]. Chin Pharm J(�й�ҩѧ��־)�� 2008�� 43(18)��1420-1423.
|
|
[20]
|
PAPPADA S M�� CAMERON B D�� ROSMAN P M�� et al. Neural network- based real-time prediction of glucose in patients with insulin-dependent diabetes[J]. Diabetes Technol Ther�� 2011�� 13(2)��135-141.
|
|
[21]
|
MATAS M D�� SHAO Q�� BIDDISCOMBE M F�� et al. Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks[J]. Eur J Pharm Sci�� 2010�� 41(5)��707-715.
|
|
[22]
|
MAGER D E�� SHIREY J D�� COX D�� et al. Mapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network[J]. J Pharm Sci�� 2005�� 94(11)��2475-2486.
|
|
[23]
|
BERNO E�� BRAMBILLA L�� CANAPARO R�� et al. Application of probabilistic neural networks to population pharmacokinetics[J]. Neural Networks�� 2003�� 7(20-24)��2637-2642.
|
|
[24]
|
GAWEDA A E�� JACOBS A A�� BRIER M E�� et al. Pharmacodynamic population analysis in chronic renal failure using artificial neural networks-a comparative study[J]. Neural Networks�� 2003�� 16(5-6)��841-845.
|
|
[25]
|
KANG S H�� POYNTON M R�� KIM K M�� et al. Population pharmacokinetic and pharmacodynamic models of remifentanil in healthy volunteers using artificial neural network analysis[J]. Br J Clin Pharmacol�� 2007�� 64(1)��3-13.
|
|
[26]
|
HAIDAR S H�� JOHNSON S B�� FOSSLER M J�� et al. Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent using neural networks[J]. Pharm Res�� 2002�� 19(1)��87-91.
|
|
[27]
|
MOUGIAKAKOU S G�� PROUNTZOU A�� ILIOPOULOU D�� et al. Neural network based glucose-insulin metabolism models for children with type 1 diabetes[A]. Proceedings of the 28th IEEE EMBS annual international conference[C]. New York City�� USA�� 2006��3545-3548.
|
|
[28]
|
YU J X�� SHI L M�� WANG R L�� et al. Individual administration model for cyclosporine A established using artificial neural network[J]. Chin Pharm J (�й�ҩѧ��־)�� 2010�� 45(12)��927-930.
|
|
[29]
|
YAMAMURA S�� TAKEHIRA R�� KAWADA K�� et al. Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations[J]. J Clin Pharm Ther�� 2003�� 28(5)��425-432.
|
|
[30]
|
FDA. Guidance for industry population pharmacokinetics. http��//www. fda. gov/downloads/ Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM072137. pdf
|
|
[31]
|
WANG G J�� LIU X D�� LIU X Q. Pharmacokinetics (ҩ���л����ѧ)[M]. Beijing��Chemical Industry Press�� 2005: 141-142.
|
|
[32]
|
LIU C X. Difficulty and hot-points on pharmacokinetics studies of traditional Chinese medicine[J]. Acta Pharm Sin (ҩѧѧ��)�� 2005�� 40(5)��395-401.
|
|
[33]
|
HAO H P�� ZHEN C N�� WANG G J. Thoughts and experimental exploration on pharmacokinetic study of herbal medicines with multiple-components and targets[J]. Acta Pharm Sin (ҩѧѧ��)�� 2009�� 44(3)��270-275.
|
|
[34]
|
MANDAL U�� GOWDA V�� GHOSH A�� et al. Optimization of metformin HCl 500 mg sustained release matrix tablets using artificial neural network based on multilayer perceptrons model[J]. Chem Pharm Bull�� 2008�� 56(2)��150-155.
|
|
[35]
|
BARMPALEXIS P�� KANAZE F I�� KACHRIMANIS K�� et al. Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation[J]. Eur J Pharm Biopharm�� 2010�� 74(2)��316-323.
|
|
[36]
|
MATAS M D�� SHAO Q�� RICHARDSON C H�� et al. Evaluation of in vitro in vivo correlations for dry powder inhaler delivery using artificial neural networks[J]. Eur J Pharm Sci�� 2008�� 33(1)��80-90.
|
|
[37]
|
BERTRAM J P�� RAUCH M F�� CHANG K�� et al. Factors affecting the stability of nanoemulsions use of artificial neural networks[J]. Pharm Res�� 2010�� 27(1)��82-91.
|
|
[38]
|
HADDAD W M�� BAILEY J M�� HAYAKAWA T�� et al. Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia [J]. IEEE Trans Neural Netw�� 2007�� 18(4)��1049-1066.
|
|
[39]
|
YAN L�� SHEIHK-BAHAEI S�� PARK S�� et al. Preditions of hepatic disposition properties using a mechanistically realistic�� physiologically based mode[J]. Drug Metab Dispos�� 2008�� 36(4)��759-768.
|
|
[40]
|
PAIX�� O P�� GOUVEIA L F�� MORAIS J A G. Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks[J]. Eur J Pharm Sci�� 2010�� 39(5)��310-321.
|
2012, Vol. 47 
