Kinetic study of anti-HIV drugs by thermal decomposition analysis
Abstract Kinetic study by thermal decomposition of antiretroviral drugs, efavirenz (EFV) and lamivudine (3TC), usually present in the HIV cocktail, can be done by individual adjustment of the solid decomposition models. However, in some cases, unacceptable errors are found using this methodology. To circumvent this problem, here is proposed to use a multilayer perceptron neural network, with an appropriate algorithm, which constitutes a linearization of the network by setting weights between the input layer and the intermediate one and the use of kinetic models as activation functions of neurons in the hidden layer. The interconnection weights between that intermediate layer and output layer determine the contribution of each model in the overall fit of the experimental data. Thus, the decomposition is assumed to be a phenomenon that can occur following different kinetic processes. In investigated data, the kinetic thermal decomposition process was best described by R1 and D4 models for all temperatures to EFV and 3TC, respectively. The residual error of adjustment over the network is on average $ 10^{3} $ times lower for EFV and $ 10^{2} $ times lower for 3TC compared to the best individual kinetic model. These improvements in physical adjustment allow detailed study of the process and therefore a more accurate calculation of the kinetic parameters such as the activation energy and frequency factor. It was found $$E_{\text{a}} = 75.230\,{\text{kJ}}\,{\text{mol}}^{ - 1}$$ and $$\ln \left( A \right) = 3.2190 \times 10^{ - 16} \,{\text{s}}^{ - 1}$$ for EFV and $$E_{\text{a}} = 103.25\,{\text{kJ}}\,{\text{mol}}^{ - 1}$$ and $$\ln \left( A \right) = 2.5587 \times 10^{ - 3} \,{\text{s}}^{ - 1}$$ for 3TC..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2016 |
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| Erschienen: |
2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:127 |
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| Enthalten in: |
Journal of thermal analysis and calorimetry - 127(2016), 1 vom: 26. Sept., Seite 577-585 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ferreira, B. D. L. [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Artificial neural network multilayer |
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| Anmerkungen: |
© Akadémiai Kiadó, Budapest, Hungary 2016 |
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| doi: |
10.1007/s10973-016-5855-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC204985000X |
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| 520 | |a Abstract Kinetic study by thermal decomposition of antiretroviral drugs, efavirenz (EFV) and lamivudine (3TC), usually present in the HIV cocktail, can be done by individual adjustment of the solid decomposition models. However, in some cases, unacceptable errors are found using this methodology. To circumvent this problem, here is proposed to use a multilayer perceptron neural network, with an appropriate algorithm, which constitutes a linearization of the network by setting weights between the input layer and the intermediate one and the use of kinetic models as activation functions of neurons in the hidden layer. The interconnection weights between that intermediate layer and output layer determine the contribution of each model in the overall fit of the experimental data. Thus, the decomposition is assumed to be a phenomenon that can occur following different kinetic processes. In investigated data, the kinetic thermal decomposition process was best described by R1 and D4 models for all temperatures to EFV and 3TC, respectively. The residual error of adjustment over the network is on average $ 10^{3} $ times lower for EFV and $ 10^{2} $ times lower for 3TC compared to the best individual kinetic model. These improvements in physical adjustment allow detailed study of the process and therefore a more accurate calculation of the kinetic parameters such as the activation energy and frequency factor. It was found $$E_{\text{a}} = 75.230\,{\text{kJ}}\,{\text{mol}}^{ - 1}$$ and $$\ln \left( A \right) = 3.2190 \times 10^{ - 16} \,{\text{s}}^{ - 1}$$ for EFV and $$E_{\text{a}} = 103.25\,{\text{kJ}}\,{\text{mol}}^{ - 1}$$ and $$\ln \left( A \right) = 2.5587 \times 10^{ - 3} \,{\text{s}}^{ - 1}$$ for 3TC. | ||
| 650 | 4 | |a Thermal decomposition analysis | |
| 650 | 4 | |a Artificial neural network multilayer | |
| 650 | 4 | |a Efavirenz | |
| 650 | 4 | |a Lamivudine | |
| 700 | 1 | |a Araujo, B. C. R. |4 aut | |
| 700 | 1 | |a Sebastião, R. C. O. |4 aut | |
| 700 | 1 | |a Yoshida, M. I. |4 aut | |
| 700 | 1 | |a Mussel, W. N. |4 aut | |
| 700 | 1 | |a Fialho, S. L. |4 aut | |
| 700 | 1 | |a Barbosa, J. |4 aut | |
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