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Doctoral Thesis
DOI
https://doi.org/10.11606/T.75.2019.tde-16102019-101438
Document
Author
Full name
Estevão Bombonato Pereira
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2019
Supervisor
Committee
Silva, Albérico Borges Ferreira da (President)
Ferreira, Márcia Miguel Castro
Honorio, Káthia Maria
Leitão, Andrei
Malpass, Ana Claudia Granato
Title in Portuguese
Estudos de QSAR de derivados de benzilfenildiaminoeter, promissores para o tratamento da Tripanossomíase Humana Africana causada pelo Tripanossoma Brucei Rhondesiense
Keywords in Portuguese
HCA
KNN
modelagem molecular
PCA
PLS
QSAR
quimiometria
redes neurais artificiais
SAR
SIMCA
tripanossomíase humana africana
Abstract in Portuguese

A Tripanossomíase Humana Africana, ou doença do sono, é uma doença negligenciada que assola alguns países da África, principalmente na região centro-sul do continente. Essa doença é causada pelo tripanossoma brucei rhondesiense, que é transmitido pela mosca silvestre Tsé-Tsé. Há projetos para combate à mosca para evitar a disseminação da doença, mas ainda se faz necessário o estudo de fármacos para combate ao tripanossoma. Com vistas a ajudar nesta busca por fármacos que sejam uteis no combate ao tripanossoma, estudamos um conjunto de derivados de benzofenildiaminoeteres desenvolvidos e testados por Patrick e colaboradores para avaliar quais seriam as características mais importantes para que as moléculas tenham uma melhor atividade contra o tripanossoma. Ao avaliarmos as propriedades das moléculas e a relação com a atividade biológica, selecionamos como descritores a energia do orbital HOMO, o número de aminas primárias alifáticas (nNH2), fator hidrofílico (Hy), o índice de primeira ordem da média de carga topológica (JGI1), a autocorrelação de Moran na lag-1 ponderada pela eletronegatividade de Sanderson (MATS1e), e autocorrelação de Geary na lag-7 ponderada pela eletronegatividade de Sanderson (GATS7e). Para o estudo SAR utilizamos os métodos de classificação análise hierárquica de grupos (HCA), análise de componentes principais (PCA), análise dos k-ésimos vizinhos mais próximos (KNN) e SIMCA. Para o estudo QSAR, utilizamos o método de análise de mínimos quadrados parciais (PLS) e redes neurais artificiais (ANN). Todos os métodos utilizados foram testados e validados, quando cabível. Os resultados obtidos em cada método apresentam uma boa relação entre si, mostrando que o uso de vários métodos em conjunto torna o resultado mais confiável.

Title in English
QSAR studies of benzil phenyl ether diamine derivatives, promising for the treatment of Human African trypanosomiasis caused by trypanosoma brucei rhondesiense
Keywords in English
artificial neural network
chemometrics
HCA
human african trypanosomiasis
KNN
molecular modeling
PCA
PLS
QSAR
SAR
SIMCA
Abstract in English

African Human Trypanosomiasis, or sleeping sickness, is a neglected disease that plagues some African countries, especially in the center-south region of the continent. This disease is caused by the Trypanosoma Brucei Rhodesiense, which is transmitted by the wild fly Tse-Tsé. There are projects to combat the fly to avoid the spread of the disease, but it still requires the study of drugs to combat the trypanosoma. In order to aid in this search for drugs that are useful in the fight against trypanosome, we have studied a set of benzyl phenyl ether diamine derivatives developed and tested by Patrick and his group to evaluate the most important characteristics for the molecules to have a better activity against the trypanosome. When evaluating the properties of the molecules and the relation with the biological activity, we selected the HOMO orbital energy, the number of aliphatic primary amines (nNH2), hydrophilic factor (Hy), the first order index of the topological load average JGI1), the Moran autocorrelation at lag-1 weighted by Sanderson's electronegativity (MATS1e), and Geary's autocorrelation at lag-7 weighted by Sanderson's electronegativity (GATS7e). For the SAR study we used the methods of classification hierarchical cluster analysis (HCA), principal component analysis (PCA), analysis of k-nearest neighbor (KNN) and SIMCA. For the QSAR study we used the method of analysis of partial least squares (PLS) and artificial neural networks (ANN). All methods used were tested and validated when appropriate. The results obtained in each method show a good relation between them, showing that the use of several methods together makes the result more reliable.

 
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Publishing Date
2019-10-18
 
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