Modelagem do impacto da adesão à terapia anti-retroviral na dinâmica populacional de variantes de HIV-1

• Ano de defesa: 2008
• Autor(a) principal: Carvalho, Chandra Mara
• Orientador(a): Codeço, Cláudia
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Laboratório Nacional de Computação Científica
• Programa de Pós-Graduação: Programa de Pós-Graduação em Modelagem Computacional
• Departamento: Serviço de Análise e Apoio a Formação de Recursos Humanos
• País: BR
• Palavras-chave em Português: HIV (Vírus) > Modelos matemáticos; HIV (Vírus) > Prevenção; Epidemiologia matemática
• Área do conhecimento CNPq: CNPQ::CIENCIAS BIOLOGICAS::MICROBIOLOGIA::BIOLOGIA E FISIOLOGIA DOS MICROORGANISMOS::VIROLOGIA
• Link de acesso: https://tede.lncc.br/handle/tede/90

In Brazil, since 1996, the antiretroviral therapy - HAART, also known as ARV - has been gratuitously distributed and granted by law. Therefore, anyone who takes the therapy, can desenvolve resistance acording to the amount of adherence they had. Drug resistance is known to have mutations increases linearly with elevated adherence in viremic patients, and some might say that resistance was more common in the most adherent patients with dectable viremia. Likewise, mathematical models are used to adresss some questions regarding the ARV, as to focus the epidemiological consequences of HAART interaction with behavioural changes or interventions, and the likely course of drug resistance evolution within the individual and also between individuals. In this study, we designed a host-parasit HIV model, that put together the intra and inter-host viral dynamics to test the adherence impact and the ARV access on the amount of resistant virus presents in the population. The basic model, model I, describes the interaction between cells susceptible to infection, target cells (T), wild infected cells (Is) and resistant infected cells (Ir), wild virus (Vs) and resistent virus (Vr). The target cells are created at rate λ, die at rate d per cell, and are infected with rate Ks and Kr. Infected cells, die at rate constant δ per cell and produce virus at rate fs e and fr per cell. We set the model I with literature, from the two patients data experimentaly colected, the grafics gerated by Model-Builder program were similar to the literature. After this, equations from previous model, model I, were modified to analyse the effects of giving an antiretroviral drug. Reverse transcriptase (RT) inhibitors are used to block the ability of the virus to infect a cell, while protease inhibitors (IP) cause the liberation of non-infectious viral particles. Based on that, model II, describes the interaction between susceptible cells to infection (T), four types of infected cells - the double wild RT and IP inhibitors (Iss), the wild for RT and resistant for IP (Isr), the resistant for RT and wild for IP (Irs) and the double resistant one (Irr), and four types of free virus, Vss, Vsr, Vrs, Vrr, according to the previous denomination.The target cells also are created at rate λ, die at rate d per cell, and are infected with rate k. Infected cells, die at rate constant δ per cell and produce virus at rate fss, frs, fsr and frr per cell, and free virus are cleared at rate c per virion, where εrt and εip are the efficacies of RT and PI, regarding the antiretroviral treatment (ε= 1 being a perfect drug). Model III was designed to combine the efficacy parameters and the resistance and suscetible virus, and to fit the intra-host dinamic in the inter-host populacional model, created in python. The inter-host model was designed to demonstrate the virus behavior in a 200 suscetible persons fictional sexual network, in 100 days of infection. First of all one randomic person was inoculated with a mixed infection, with the model III intra-host, and after that this person transmited the HIV infection to the rest of population in the network. It's known that the simulation of viral dynamics in social networks tries to identify the resistant possibilities of virus spreading over the population. Therefore our inter-host model suggest that in a mixed infection, with wild and resistant type of HIV, wild type virus can win the competition in intermediate and low adhesion cenarios, but with high adhesion in a populacion, resistant and wild type virus can coexist even in ARV presence. Hence, with this epidemiological model, we can rearange the strategies and politics of the HAART use for a better result in prolonging the HIV-1 infected ones.