Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling
There is a growing interest in implantable drug delivery systems (DDS) in pharmaceutical science. The aim of the present study is to investigate whether it is possible to customize drug release from implantable DDSs through drug–carrier interactions. Therefore, a series of chemically similar active...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/14/2/228 |
| _version_ | 1797477285203279872 |
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| author | Ernő Benkő Ilija German Ilič Katalin Kristó Géza Regdon Ildikó Csóka Klára Pintye-Hódi Stane Srčič Tamás Sovány |
| author_facet | Ernő Benkő Ilija German Ilič Katalin Kristó Géza Regdon Ildikó Csóka Klára Pintye-Hódi Stane Srčič Tamás Sovány |
| author_sort | Ernő Benkő |
| collection | DOAJ |
| description | There is a growing interest in implantable drug delivery systems (DDS) in pharmaceutical science. The aim of the present study is to investigate whether it is possible to customize drug release from implantable DDSs through drug–carrier interactions. Therefore, a series of chemically similar active ingredients (APIs) was mixed with different matrix-forming materials and was then compressed directly. Compression and dissolution interactions were examined by FT-IR spectroscopy. Regarding the effect of the interactions on drug release kinetics, a custom-made dissolution device designed for implantable systems was used. The data obtained were used to construct models based on artificial neural networks (ANNs) to predict drug dissolution. FT-IR studies confirmed the presence of H-bond-based solid-state interactions that intensified during dissolution. These results confirmed our hypothesis that interactions could significantly affect both the release rate and the amount of the released drug. The efficiencies of the kinetic parameter-based and point-to-point ANN models were also compared, where the results showed that the point-to-point models better handled predictive inaccuracies and provided better overall predictive efficiency. |
| first_indexed | 2024-03-09T21:15:25Z |
| format | Article |
| id | doaj.art-e5681127c09c4df19f31ea8b0e58d704 |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-09T21:15:25Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-e5681127c09c4df19f31ea8b0e58d7042023-11-23T21:35:47ZengMDPI AGPharmaceutics1999-49232022-01-0114222810.3390/pharmaceutics14020228Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based ModellingErnő Benkő0Ilija German Ilič1Katalin Kristó2Géza Regdon3Ildikó Csóka4Klára Pintye-Hódi5Stane Srčič6Tamás Sovány7Institute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryDepartment of Pharmaceutical Technology, University of Ljubljana, Aškerčeva cesta 7, SI-1000 Ljubljana, SloveniaInstitute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryInstitute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryInstitute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryInstitute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryDepartment of Pharmaceutical Technology, University of Ljubljana, Aškerčeva cesta 7, SI-1000 Ljubljana, SloveniaInstitute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös u. 6, H-6720 Szeged, HungaryThere is a growing interest in implantable drug delivery systems (DDS) in pharmaceutical science. The aim of the present study is to investigate whether it is possible to customize drug release from implantable DDSs through drug–carrier interactions. Therefore, a series of chemically similar active ingredients (APIs) was mixed with different matrix-forming materials and was then compressed directly. Compression and dissolution interactions were examined by FT-IR spectroscopy. Regarding the effect of the interactions on drug release kinetics, a custom-made dissolution device designed for implantable systems was used. The data obtained were used to construct models based on artificial neural networks (ANNs) to predict drug dissolution. FT-IR studies confirmed the presence of H-bond-based solid-state interactions that intensified during dissolution. These results confirmed our hypothesis that interactions could significantly affect both the release rate and the amount of the released drug. The efficiencies of the kinetic parameter-based and point-to-point ANN models were also compared, where the results showed that the point-to-point models better handled predictive inaccuracies and provided better overall predictive efficiency.https://www.mdpi.com/1999-4923/14/2/228drug–excipient interactionpolymersnondegradablematrix tabletcontrolled releasedesign of experiments |
| spellingShingle | Ernő Benkő Ilija German Ilič Katalin Kristó Géza Regdon Ildikó Csóka Klára Pintye-Hódi Stane Srčič Tamás Sovány Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling Pharmaceutics drug–excipient interaction polymers nondegradable matrix tablet controlled release design of experiments |
| title | Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling |
| title_full | Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling |
| title_fullStr | Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling |
| title_full_unstemmed | Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling |
| title_short | Predicting Drug Release Rate of Implantable Matrices and Better Understanding of the Underlying Mechanisms through Experimental Design and Artificial Neural Network-Based Modelling |
| title_sort | predicting drug release rate of implantable matrices and better understanding of the underlying mechanisms through experimental design and artificial neural network based modelling |
| topic | drug–excipient interaction polymers nondegradable matrix tablet controlled release design of experiments |
| url | https://www.mdpi.com/1999-4923/14/2/228 |
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