Doctoral Thesis
DOI
https://doi.org/10.11606/T.75.2011.tde-16092011-160536
Document
Author
Full name
Luciana Luzia de Carvalho
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2011
Supervisor
Committee
Silva, Albérico Borges Ferreira da (President)
Honorio, Káthia Maria
Canduri, Fernanda
Leitão, Andrei
Weber, Karen Cacilda
Title in Portuguese
Modelagem molecular de uma série de compostos inibidores da enzima integrase do vírus HIV-1
Keywords in Portuguese
CoMFA
Docking molecular
HIV-I
HQSAR
Integrase
QSAR
QSAR-3D
Abstract in Portuguese
Uma etapa essencial no ciclo de vida do vírus HIV é a integração do DNA viral no cromossomo hospedeiro. Essa etapa é catalisada pela enzima integrase (IN) de 32-kDa. HIV-1 IN é um importante e validado alvo, e as drogas que inibem seletivamente a enzima, quando utilizadas em combinação com os inibidores da transcriptase reversa (RT) e protease (PR), são consideradas altamente eficazes em suprimir a replicação viral. IN catalisa dois processos enzimáticos designados por 3' processamento e transferência de DNA. Agentes ativos contra integrase, inibindo a etapa de transferência da vertente já estão em fase clínica. O fármaco Raltegravir® é o primeiro nesta nova classe. Os ensaios clínicos no tratamento em novos pacientes têm uma atividade anti-retroviral potente e bem tolerado. Dada a sua potência, segurança e novo mecanismo de ação, os inibidores da integrase representam um importante avanço terapêutico contra o HIV-1. Na presente tese de doutorado, foram realizados estudos quimiométricos utilizando descritores teóricos e QSAR bi- (2D) e tridimensionais (3D) empregando, respectivamente, as técnicas holograma QSAR (HQSAR) e a análise comparativa dos campos moleculares (CoMFA), visando à geração de modelos preditivos para um conjunto de inibidores da integrase do vírus HIV-1. Modelos de QSAR com boa consistência interna, habilidade preditiva e estabilidade foram obtidos em todos os casos. Os modelos gerados, associados às informações obtidas pelos mapas de contribuição 2D e de contorno 3D, são guias químico-medicinais úteis no planejamento de novos inibidores mais potentes e seletivos da integrase do HIV-1.
Title in English
Molecular modelling for a series of integrase HIV-I inhibitors
Keywords in English
CoMFA
HIV-I
HQSAR
Integrase HIV-I
Molecular Docking
QSAR
QSAR-3D
Abstract in English
An essential step in the HIV life cycle is integration of the viral DNA into the host chromosome. This step is catalyzed by a 32-kDa viral enzyme HIV integrase (IN). HIV-1 IN is an important and validated target, and the drugs that selectively inhibit this enzyme, when used in combination with reverse transcriptase (RT) and protease (PR) inhibitors, are believed to be highly effective in suppressing the viral replication. IN catalyzes two discrete enzymatic processes referred as 3' processing and DNA strand transfer. Agents active against HIV-1, which target the viral integrase by inhibiting the strand transfer step of integration, have now initialized the clinical trials. The Raltegravir® is the first drug in this new class. Clinical trials in treatment-experienced and in treatment-naive patients have shown that raltegravir-containing regimens have potent antiretroviral activity and are well tolerated. Given their potency, safety and novel mechanism of action, integrase inhibitors represent an important advance in HIV-1 therapy. In the present thesis, Bi- and Tridimensional Quantitative Structure-Activity Relationship (QSAR) studies were performed applying chemometric methods based on theoretical descriptors, Comparative Molecular Field Analysis (CoMFA) and Holograma QSAR (HQSAR) techniques, aiming to generate predictive models for a large set of HIV-1 IN inhibitors. QSAR models presenting good internal consistency, predictive power and stability were obtained in all cases. The final models along with the information resulted by 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selective within the chemical diversity of the data.
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Publishing Date
2011-11-01