The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance
Само за регистроване кориснике
2002
Чланак у часопису (Објављена верзија)
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The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) fac...tors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.
Извор:
Journal of Controlled Release, 2002, 82, 2-3, 213-222Издавач:
- Elsevier Science BV, Amsterdam
DOI: 10.1016/S0168-3659(02)00044-5
ISSN: 0168-3659
PubMed: 12175738
WoS: 000177945700003
Scopus: 2-s2.0-0037151316
Институција/група
PharmacyTY - JOUR AU - Ibrić, Svetlana AU - Jovanović, M AU - Đurić, Zorica AU - Parojčić, Jelena AU - Solomun, Ljiljana PY - 2002 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/321 AB - The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) factors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile. PB - Elsevier Science BV, Amsterdam T2 - Journal of Controlled Release T1 - The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance VL - 82 IS - 2-3 SP - 213 EP - 222 DO - 10.1016/S0168-3659(02)00044-5 ER -
@article{ author = "Ibrić, Svetlana and Jovanović, M and Đurić, Zorica and Parojčić, Jelena and Solomun, Ljiljana", year = "2002", abstract = "The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) factors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.", publisher = "Elsevier Science BV, Amsterdam", journal = "Journal of Controlled Release", title = "The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance", volume = "82", number = "2-3", pages = "213-222", doi = "10.1016/S0168-3659(02)00044-5" }
Ibrić, S., Jovanović, M., Đurić, Z., Parojčić, J.,& Solomun, L.. (2002). The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance. in Journal of Controlled Release Elsevier Science BV, Amsterdam., 82(2-3), 213-222. https://doi.org/10.1016/S0168-3659(02)00044-5
Ibrić S, Jovanović M, Đurić Z, Parojčić J, Solomun L. The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance. in Journal of Controlled Release. 2002;82(2-3):213-222. doi:10.1016/S0168-3659(02)00044-5 .
Ibrić, Svetlana, Jovanović, M, Đurić, Zorica, Parojčić, Jelena, Solomun, Ljiljana, "The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance" in Journal of Controlled Release, 82, no. 2-3 (2002):213-222, https://doi.org/10.1016/S0168-3659(02)00044-5 . .