Neural network based optimization of drug formulations

Advanced Drug Delivery Reviews

Volumn 55, Issue 9, 2003, Pages 1217-1231

  Takayama, Kozo ^a   Fujikawa, Mikito ^a   Obata, Yasuko ^a   Morishita, Mariko ^a  

^a HOSHI UNIVERSITY   (Japan)

Author keywords

Artificial neural networks; Multi objective optimization; Pharmaceutical formulation; Polynomial equation; Response surface method

Indexed keywords

NEURAL NETWORKS; OPTIMIZATION; POLYNOMIALS;

DRUG FORMULATIONS;

DRUG DOSAGE;

1 O ETHYL 3 BUTYLCYCLOHEXANOL; 2 PROPANOL; ADIPIC ACID DERIVATIVE; ADIPIC ACID DIISOPROPYL ESTER; CONTROSE; CYCLOHEXANOL DERIVATIVE; DICLOFENAC; DRUG VEHICLE; HYDROCORTISONE BUTYRATE; HYDROXYETHYLCELLULOSE; HYDROXYPROPYLCELLULOSE; INDOMETACIN; KETOPROFEN; STARCH; THEOPHYLLINE; UNCLASSIFIED DRUG;

ARTIFICIAL NEURAL NETWORK; CONTROLLED RELEASE FORMULATION; DRUG FORMULATION; HYDROGEL; MATHEMATICAL ANALYSIS; MULTI OBJECTIVE SIMULTANEOUS OPTIMIZATION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RESPONSE SURFACE METHOD; REVIEW; TABLET COMPRESSION; TECHNIQUE;

EID: 0042884371     PISSN: 0169409X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-409X(03)00120-0     Document Type: Article

References (71)

1. Khuri A.I., Cornel J.A. Response Surface, Design and Analysis. 1987;Marcel Dekker, New York.
2. Lewis G.A., Mathieu D., Phan-Yan-Luu R. Pharmaceutical Experimental Design. 1999;Marcel Dekker, New York.
3. Takayama K., Nambu N., Nagai T. Computer optimization of formulation of flufenamic acid/polyvinylpolypyrrolidone/methyl cellulose solid dispersions. Chem. Pharm. Bull. 31:1983;4496-4507.
4. Takai T., Takayama K., Nambu N., Nagai T. Optimum formulation of griseofulvin/hydroxypropyl cellulose solid dispersions with desired dissolution properties. Chem. Pharm. Bull. 32:1984;1942-1947.
5. Takayama K., Imaizumi H., Nambu N., Nagai T. Mathematical optimization of formulation of indomethacin/polyvinylpolypyrrolidone/methyl cellulose solid dispersions by the sequential unconstrained minimization technique. Chem. Pharm. Bull. 33:1985;292-300.
6. Akitoshi Y., Takayama K., Machida Y., Nagai T. Computer optimization of the formulation of acrylic plaster. Chem. Pharm. Bull. 33:1985;4536-4543.
7. Privorsky J., Takayama K., Nagai T., Waitzova D., Elis J. Combination effect of penetration enhancers and propylene glycol on in vitro transdermal absorption of insulin. Drug Design and Delivery. 2:1987;91-97.
8. Arai K., Takayama K., Nambu Y., Nambu N., Ueda H., Machida Y., Nagai T. Preparation of polylactic acid-polylipoic acid nanospheres as drug targeting carriers. Drug Design and Delivery. 2:1987;109-120.
9. Takayama K., Nagai T. Novel computer optimization methodology for pharmaceutical formulations investigated by using sustained-release granules of indomethacin. Chem. Pharm. Bull. 37:1989;160-167.
10. Takayama K., Okabe H., Obata Y., Nagai T. Formulation design of indomethacin gel ointment containing D-limonene using computer optimization methodology. Int. J. Pharm. 61:1990;225-234.
11. Takayama K., Nagai T. Simultaneous optimization for several characteristics concerning percutaneous absorption and skin damage of ketoprofen hydrogels containing D-limonene. Int. J. Pharm. 74:1991;115-126.
12. Hirata M., Takayama K., Nagai T. Formulation optimization of sustained-release tablet of chlorpheniramine maleate by means of extreme vertices design and simultaneous optimization technique. Chem. Pharm. Bull. 74:1992;741-746.
13. Imasaka K., Takayama K., Yoshida M., Omichi H., Nagai T. A new attempt to design erodible terpolymers using computer-optimization methodology. Eur. Polym. J. 7:1992;709-712.
14. Ogawa S., Kamijima T., Miyamoto Y., Miyajima M., Sato H., Takayama K., Nagai T. A new attempt to solve the scale-up problem for granulation using response surface methodology. J. Pharm. Sci. 83:1994;439-443.
15. Matsumura M., Nakagami H., Yamao T., Takayama K., Nagai T. Computer optimization for the formulation of controlled-release theophylline tablet made of micronized low-substituted hydroxypropylcellulose and methylcellulose. Chem. Pharm. Bull. 42:1994;1902-1908.
16. Levison K.K., Takayama K., Isowa K., Okabe K., Nagai T. Formulation optimization of indomethacin gels containing a combination of three kinds of cyclic monoterpenes as percutaneous penetration enhancers. J. Pharm. Sci. 83:1994;1367-1372.
17. Miyamoto Y., Ogawa S., Miyajima M., Matsui M., Sato H., Takayama K., Nagai T. An application of the computer optimization technique to wet granulation process involving explosive growth of particles. Int. J. Pharm. 149:1997;25-36.
18. Fonner D.E. Jr., Buck J.R., Banker G.S. Mathematical optimization techniques in drug product design and process analysis. J. Pharm. Sci. 59:1970;1587-1596.
19. Schwartz J.B., Flamholz J.R., Press R.H. Computer optimization of pharmaceutical formulations I. General procedure. J. Pharm. Sci. 62:1973;1165-1170.
20. Derringer G., Suich R. Simultaneous optimization of several response variables. J. Quality Tech. 12:1980;214-219.
21. Khuri A.I., Conlon M. Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics. 23:1981;363-375.
22. McLeod A.D., Lam F.C., Gupta P.K., Hung C.T. Optimized synthesis of polyglutaraldehyde nanoparticles using central composite design. J. Pharm. Sci. 77:1988;704-710.
23. Muller B.G., Leuenberger H., Kissel T. Albumin nanospheres as carriers for passive drug targeting: An optimized manufacturing technique. Pharm. Res. 13:1996;32-37.
24. Achanta A.S., Kowalski J.G., Rhodes C.T. Artificial neural networks: Implications for pharmaceutical sciences. Drug Dev. Ind. Pharm. 21:1995;119-155.
25. Hussain A.S., Yu X., Jornson R.D. Application of neural computing in pharmaceutical product development. Pharm. Res. 8:1991;1248-1252.
26. Takahara J., Takayama K., Nagai T. Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations. J. Control. Release. 49:1997;11-20.
27. Takahara J., Takayama K., Isowa K., Nagai T. Multi-objective simultaneous optimization based on artificial neural network in a ketoprofen hydrogel formula containing O-ethylmenthol as a percutaneous absorption enhancer. Int. J. Pharm. 158:1997;203-210.
28. Takayama K., Takahara J., Fujikawa M., Ichikawa H., Nagai T. Formula optimization based on artificial neural networks in transdermal drug delivery. J. Control. Release. 62:1999;161-170.
29. Takayama K., Fujikawa M., Nagai T. Artificial neural network as a novel method to optimize pharmaceutical formulations. Pharm. Res. 16:1999;1-6.
30. Takayama K., Morva A., Fujikawa M., Hattori Y., Obata Y., Nagai T. Formula optimization of theophylline controlled-release tablet based on artificial neural networks. J. Control. Release. 68:2000;175-186.
31. Wu P.C., Obata Y., Fujikawa M., Li C.J., Higashiyama K., Takayama K. Simultaneous optimization based on artificial neural networks in ketoprofen hydrogel formula containing O-ethyl-3-butylcyclohexanol as percutaneous absorption enhancer. J. Pharm. Sci. 90:2001;1004-1014.
32. Lim C.P., Quek S.S., Peh K.K. Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis. J. Pharm. Biomed. Anal. 31:2003;159-168.
33. Kachrimanis K., Karamyan V., Malamataris S. Artificial neural networks (ANNs) and modeling of powder flow. Int. J. Pharm. 250:2003;13-23.
34. Plumb A.P., Rowe R.C., York P., Doherty C. The effect of experimental design on the modeling of a tablet coating formulation using artificial neural networks. Eur. J. Pharm. Sci. 16:2002;281-288.
35. Ibric S., Jovanovic M., Djuric Z., Parojcic J., Solomun L. The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit RS PO as matrix substance. J. Control. Release. 82:2002;213-222.
36. Vaithiyalingam S., Khan M.A. Optimization and characterization of controlled release multi-particulate beads formulated with a customized cellulose acetate butyrate dispersion. Int. J. Pharm. 234:2002;179-193.
37. Aoyama T., Suzuki Y., Ichikawa H. Neural networks applied to quantitative structure-activity relationship analysis. J. Med. Chem. 33:1990;2583-2590.
38. Huuskonen J. QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals. Chemosphere. 50:2003;949-953.
39. Agrafiotis D.K., Cedeno W., Lobanov V.S. On the use of neural network ensembles in QSAR and QSPR. J. Chem. Inf. Comput. Sci. 42:2002;903-911.
40. Hussain A.S., Jornson R.D., Vachhrajani N., Ritschel W.A. Feasibility of developing a neural network for prediction of human pharmacokinetic parameters from animal data. Pharm. Res. 10:1993;466-469.
41. Brier E., Zurada J.M., Aronoff G.R. Neural network predicted peak and trough gentamicin concentrations. Pharm. Res. 12:1995;406-412.
42. Gobburu J.V.S., Shelver W.H. Quantitative structure-pharmacokinetic relationship (QSPR) of beta blockers derived using neural networks. J. Pharm. Sci. 84:1995;862-865.
43. Smith B.P., Brier M.E. Statistical approach to neural network model building for gentamicin peak predictions. J. Pharm. Sci. 85:1995;65-69.
44. Gao H., Shanmugasundaram V., Lee P. Estimation of aqueous solubility of organic compounds with QSPR approach. Pharm. Res. 19:2002;497-503.
45. Nestorov I.S., Hadjitodorov S.T., Petrov I., Rowland M. Empirical versus mechanistic modeling: comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationship of a homologues serious of barbiturates. AAPS PharmSci. 1:1999;E17.
46. Jha B.K., Tambe S.S., Kulkarni B.D. Estimating diffusion coefficients of a micellar system using an artificial neural network. J. Coll. Interf. Sci. 170:1995;392-398.
47. Lim C.W., Fujiwara S., Yamashita F., Hashida M. Prediction of human skin permeability using a combination of molecular orbital calculations and artificial neural network. Biol. Pharm. Bull. 25:2002;361-366.
48. Degim T., Hadgraft J., Ilbasmis S., Ozkan Y. Prediction of skin penetration using artificial neural network (ANN) modeling. J. Pharm. Sci. 92:2003;656-664.
49. Fu X.C., Ma X.W., Liang W.Q. Prediction of skin permeability using an artificial neural network. Pharmazie. 57:2002;655-656.
50. Fujiwara S., Yamashita F., Hashida M. Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network. Int. J. Pharm. 237:2002;95-105.
51. Weinstein J.N., Kohn K.W., Grever M.R., Viswanadhan V.N., Rubinstein L.V., Monks A.P., Scudiero D.A., Welch L., Koutsoukos A.D., Chiausa A.J., Paull K.D. Neural computing in cancer drug development: predicting mechanism of action. Science. 258:1992;447-451.
52. McClelland J.L., Rumelhart D.E. Explorations in Parallel Distributed Processing. 1988;MIT Press, Cambridge, MA.
53. Erb R.J. Introduction to backpropagation neural network computation. Pharm. Res. 10:1993;165-170.
54. Murase H., Koyama S., Honami N., Kuwabara T. Kalman filter neuron training. Bull. Univ. Osaka Pref., Ser. B. 43:1991;91-101.
55. Blank T.B., Brown S.D. Adaptive, global, extended Kalman filters for training feedforward neural networks. J. Chemometr. 8:1994;391-407.
56. Simutis R., Havlik I., Dors M., Luebbert A. Training of artificial neural networks extended by linear dynamic subsystems. Process Control Qual. 4:1993;211-220.
57. Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science. 200:1983;671-680.
58. Lippman R.P. An introduction to computing with neural nets. IEEE ASSP Mag. April:1987;4-22.
59. Bounds D.G., Lloyd P.J. A multilayer perceptron network for the diagnosis of low back pain. Proceedings of the 2nd IEEE International Conference on Neural Networks, San Diego, CA: 24-27 July 1988;II481-489.
60. Cybenko G. Approximations by superpositions of a sigmoidal function. Math. Control Signals Syst. 2:1989;303-314.
61. Carpenter W.C., Hoffman M.E. Understanding neural network approximations and polynomial approximations helps neural network performance. AI Expert. March:1995;31-33.
62. Fiacco A.V., McCormik G.P. Nonlinear Programming: Sequential Unconstrained Minimization Techniques. 1968;Wiley, New York.
63. Kowalik J., Osborne M.R. Methods for Unconstrained Optimization Problems. 1968;Elsevier, New York.
64. Obata Y., Sato H., Li C.J., Takayama K., Higashiyama K., Nagai T., Isowa K. Effect of synthesized cyclohexanol derivatives using L-menthol as a lead compound on the percutaneous absorption of ketoprofen. Int. J. Pharm. 198:2000;191-200.
65. Obata Y., Li C.J., Fujikawa M., Takayama K., Sato H., Higashiyama K., Isowa K., Nagai T. Evaluation and strucure-activity relationship of synthesized cyclohexanol derivatives on percutaneous absorption of ketoprofen using artificial neural network. Int. J. Pharm. 212:2001;223-231.
66. Okuyama H., Ikeda Y., Kasai S., Inamori K., Takayama K., Nagai T. Influence of diisopropyl adipate on percutaneous absorption and subcutaneous tissue penetration of diclofenac from alcoholic gel ointment. Yakuzaigaku. 59:1999;75-83.
67. Inagi T., Muramatsu T., Terada H. Interaction of indomethacin with the vehicle component diisopropyl adipate. Chem. Pharm. Bull. 34:1986;1228-1234.
68. Ozawa Y., Yamahira T., Sunada H., Nadai T. Influence of fatty acid-alcohol esters on percutaneous absorption of hydrocortizone butyrate propionate. Chem. Pharm. Bull. 36:1988;2145-2151.
69. Goodman M., Barry B.W. Lipid-protein-partitioning (LPP) theory of skin enhancer activity: finite dose technique. Int. J. Pharm. 57:1989;29-40.
70. Arellano A., Santoyo S., Martin C., Ygartua O. Influence of propylene glycol and isopropyl myristate on the in vitro percutaneous penetration of diclofenac sodium from carbopol gels. Eur. J. Pharm. Sci. 7:1998;129-135.
71. Goldberg-Cettina M., Liu P., Nightingale J., Kurihara-Bergstrom T. Enhanced transdermal delivery of estradiol in vitro using binary vesicles of isopropyl myristate and short-chain alkanols. Int. J. Pharm. 114:1995;237-245.