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AAPS PharmSciTech. 2005 September; 6(3): E449–E457.
Published online 2005 October 22. doi:  10.1208/pt060356
PMCID: PMC2750390

Generalization of a prototype intelligent hybrid system for hard gelatin capsule formulation development

Abstract

The aim of this project was to expand a previously developed prototype expert network for use in the analysis of multiple biopharmaceutics classification system (BCS) class II drugs. The model drugs used were carbamazepine, chlorpropamide, diazepam, ibuprofen, ketoprofen, naproxen, and piroxicam. Recommended formulations were manufactured and tested for dissolution performance. A comprehensive training data set for the model drugs was developed and used to retrain the artificial neural network. The training and the system were validated based on the comparison of predicted and observed performance of the recommended formulations. The initial test of the system resulted in high error values, indicating poor prediction capabilities for drugs other than piroxicam. A new data set, containing 182 batches, was used for retraining. The percent of the test batches were used for cross-validation, resulting in models with R2≥70%. The comparison of observed performance to the predicted performance found that the system predicted succcessfully. The hybrid network was generally able to predict the amount of drug dissolved within 5% for the model drugs. Through validation, the system was proven to be capable of designing formulations that met specific drug performance criteria. By including parameters to address wettability and the intrinsic dissolution characteristics of the drugs, the hybrid system was shown to be suitable for analysis of multiple BCS class II drugs.

Keywords: in silico modeling, capsule formulation, artificial neural networks, expert systems, low solubility drugs

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
1. Bourquin J, Schmidli H, Hoogevest P, Leuenberger H. Application of artificial neural networks (ANN) in the development of solid dosage forms. Pharm Dev Tech. 1997;2:111–121. doi: 10.3109/10837459709022616. [PubMed] [Cross Ref]
2. Achanta AS, Kowalski JG, Rhodes CT. Artificial neural networks: implications for pharmaceutical sciences. Drug Dev Ind Pharm. 1995;21:119–155. doi: 10.3109/03639049509048099. [Cross Ref]
3. Rowe RC. Expert systems in solid dosage development. Pharm Ind. 1993;55:1040–1045.
4. Ebube NK, McCall T, Chen Y, Meyer MC. Relating formulation variables toin vitro dissolution using artificial neural network. Pharm Dev Tech. 1997;2:225–232. doi: 10.3109/10837459709031442. [PubMed] [Cross Ref]
5. Peh KK, Lim CP, Quek SS, Khoh KH. Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor. Pharm Res. 2000;17:1384–1388. doi: 10.1023/A:1007578321803. [PubMed] [Cross Ref]
6. Kandimalla KK, Kanikkannan N, Singh M. Optimization of a vehicle mixture for the transdermal delivery of melatonin using artificial neural networks and response surface method. J Control Release. 1999;61:71–82. doi: 10.1016/S0168-3659(99)00107-8. [PubMed] [Cross Ref]
7. Takayama K, Fujikawa M, Nagai T. Artificial neural networks as a novel method to optimize pharmaceutical formulations. Pharm Res. 1999;16:1–6. [PubMed]
8. Takayama K, Takahara J, Fujikawa M, Ichikawa H, Nagai T. Formula optimization based on artificial neural networks in transdermal drug delivery. J Control Release. 1999;62:161–170. doi: 10.1016/S0168-3659(99)00033-4. [PubMed] [Cross Ref]
9. Leane MM, Cumming I, Corrigan OI. The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict thein vitro dissolution of sustained release minitablets. Pharm Sci Tech. 2003;4:218–229. [PMC free article] [PubMed]
10. 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. 2000;68:175–186. doi: 10.1016/S0168-3659(00)00248-0. [PubMed] [Cross Ref]
11. Kesavan JG, Peck GE. Pharmaceutical granulation and tablet formulation using neural networks. Pharm Dev Tech. 1996;1:391–404. doi: 10.3109/10837459609031434. [PubMed] [Cross Ref]
12. Huuskonen J, Salo M, Taskinen J. Neural network modeling for estimation of the aqueous solubility of structurally related drugs. J Pharm Sci. 1996;86:450–454. doi: 10.1021/js960358m. [PubMed] [Cross Ref]
13. Dowell JA, Hussain A, Devane J, Young D. Artificial neural networks applied to thein vitro-in vivo correlation of an extended release formulation: initial trials and experience. J Pharm Sci. 1998;88:154–160. doi: 10.1021/js970148p. [PubMed] [Cross Ref]
14. Rocksloh K, Rapp FR, Abu Abed S, et al. Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks. Drug Dev Ind Pharm. 1999;25:1015–1025. doi: 10.1081/DDC-100102264. [PubMed] [Cross Ref]
15. Ebube NK, Owusu-Ababio G, Moji Adeyeye C. Preformulation studies and characterization of the physiochemical properties of amorphous polymers using artificial neural networks. Int J Pharm. 2000;196:27–35. doi: 10.1016/S0378-5173(99)00405-6. [PubMed] [Cross Ref]
16. Hussain AS, Yu X, Johnson RD. Application of neural computing in pharmaceutical product development. Pharm Res. 1991;8:1248–1252. doi: 10.1023/A:1015843527138. [PubMed] [Cross Ref]
17. Murray FJ. The application of expert systems to pharmaceutical processing equipment. Pharm Technol. 1989;13:100–110.
18. Greer ML. RXPERT: A prototype expert system for formulary decision making. Ann Pharmacother. 1992;26:244–250. [PubMed]
19. Guo M, Kalra G, Wilson W, Peng Y, Augsburger LL. A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharm Technol N Am. 2002;26:44–60.
20. Lai FKY. A prototype expert, system for selecting pharmaceutical powder mixers. Pharm. Technol. 1998;12:22–31.
21. Expert System for Formulation Support. 1996.
22. Amidon GL, Lennernas H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation ofin vitro drug product dissolution andin vivo bioavailability. Pharm Res. 1995;12:413–420. doi: 10.1023/A:1016212804288. [PubMed] [Cross Ref]
23. In Vivo Bioavailability and Bioequivalence Studies for Immediate Release Solid Oral Dosage Forms Based on a Biopharmaceutical Classification System. Rockville, MD: Food and Drug Administration; 2001.
24. Dissolution Testing of Immediate Release Oral Solid Dosage Forms. Rockville, MD: Food and Drug Administration; 1997.

Articles from AAPS PharmSciTech are provided here courtesy of American Association of Pharmaceutical Scientists