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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/50653
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dc.creatorBárbara Darós de Lelis Ferreirapt_BR
dc.creatorBárbara Caroline Rodrigues de Araujopt_BR
dc.creatorRita de Cássia de Oliveira Sebastiãopt_BR
dc.creatorMaria Irene Yoshidapt_BR
dc.creatorWagner da Nova Musselpt_BR
dc.creatorSílvia Ligório Fialhopt_BR
dc.creatorJamile Barbosapt_BR
dc.date.accessioned2023-03-03T19:20:31Z-
dc.date.available2023-03-03T19:20:31Z-
dc.date.issued2017-
dc.citation.volume127pt_BR
dc.citation.issue1pt_BR
dc.citation.spage577pt_BR
dc.citation.epage585pt_BR
dc.identifier.doihttps://doi.org/10.1007/s10973-016-5855-2pt_BR
dc.identifier.issn1588-2926pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/50653-
dc.description.resumoKinetic 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 10³ times lower for EFV and 10² 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.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE QUÍMICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectEfavirenzpt_BR
dc.subjectLamivudinept_BR
dc.subjectThermal decomposition analysispt_BR
dc.subjectArtificial neural network multilayerpt_BR
dc.subject.otherFarmacocinéticapt_BR
dc.subject.otherFísico-químicapt_BR
dc.subject.otherQuímica analíticapt_BR
dc.subject.otherAnálise térmicapt_BR
dc.subject.otherCalorimetriapt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherHIV (Virus)pt_BR
dc.subject.otherMedicamentospt_BR
dc.titleKinetic study of anti-HIV drugs by thermal decomposition analysis: a multilayer artificial neural network proposept_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://link.springer.com/article/10.1007/s10973-016-5855-2pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-7746-6675pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-5158-7783pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-6795-9457pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-9768-9830pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-8068-5211pt_BR
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