Efeitos de combinações entre o ácido anacárdico derivado da casca da castanha do caju (Anacardium occidentale) e o óleo de açaí (Euterpe oleracea Mart.), livres ou nanoestruturados, no tratamento de células de câncer de pele não melanoma, in vitro

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Biológicas, Pós-Graduação em Nanociência e Nanobiotecnologia, 2017.O Câncer de Pele Não Melanoma (CNPM) o tipo de câncer que possui maior incidência no Brasil e no mundo. O ácido anacárdico (AA) é um composto proveniente da casca da castanha do caju (Anacardium occidentale) que vem atraindo grande interesse nos últimos anos devido ás suas propriedades antitumorais, antibióticas, gastroprotetoras e antioxidantes. O açaí (Euterpe oleracea Mart.) também vem atraindo a atenção de pesquisadores, por ser rico em polifenóis com atividades como supressão tumoral, antiproliferativo e pró-apoptótica. Grande parte desses fitoquímicos que possuem atividades terapêuticas são pouco solúveis em soluções aquosas, o que dificulta sua administração e absorção no organismo. Desta forma, a encapsulação desses compostos em nanoestruturas se torna uma alternativa plausível para potencializar seus efeitos biológicos. Diante do exposto, o presente projeto de pesquisa tem como objetivo avaliar os efeitos de combinações entre o ácido anacárdico (AA) derivado da casca da castanha do caju (Anacardium occidentale) e o óleo de açaí (Euterpe oleracea Mart.), livres ou nanoestruturados, no tratamento de câncer de pele não melanoma in vitro. Os testes de estabilidade mostraram que a nanoemulsão à base de óleo de açaí (AçNE) apresentaram gotículas com diâmetro hidrodinâmico de ± 140 nm, com índice de polidespersão de 0,229, potencial de superfície de ± 17,6 mV e pH 7 por 120 dias. Foi possível modificar a superfície das AçNE adicionando polímeros de quitosana (CH), polietileno glicol (PEG) e fosfolipídios catiônicos DOTAP (1,2-Dioleoiloxi-3-(trimetilamónio) propano). Tais formulações não apresentaram efeito citotóxico nas linhagens A431 e HaCaT, independentemente do tipo de superfície. Os tratamentos AçNE associado ao AA provocaram uma significativa redução na viabilidade das células A431, porém não foi observado efeito de sinergismo entre os mesmos. Em contrapartida, quando ambos compostos foram adicionados na forma não-nanoestruturada, observou-se redução de 90% da viabilidade de células A431 em 24 horas. Dados de citometria de fluxo indicam que a combinação dos compostos livres resulta em morte celular por apoptose e bloqueio do ciclo celular. O presente estudo sugere que a combinação de óleo de açaí e AA é uma promissora alternativa terapêutica antitumoral a ser mais explorada em estudos futuros.Non-Melanoma Skin Cancer (CNPM) is the type of cancer that has the highest incidence in Brazil and worldwide. Anacardic acid (AA) is a compound derived from cashew nuts (Anacardium occidentale) that has attracted great interest in recent years due to its antitumor, antibiotic, gastroprotective and antioxidant properties. Açaí (Euterpe oleracea Mart.) has also attracted the attention of researchers, because it is rich in polyphenols which shows great activity as a tumor suppressor, antiproliferative and pro-apoptotic. Most of these phytochemicals that have therapeutic activities are poorly soluble in aqueous solutions, which hinders their administration and absorption in the body. In this way, the encapsulation of these compounds in nanostructures becomes a plausible alternative to enhance their biological effects. Thus, the present research project has the objective of evaluating the effects of anacardic acid (AA) derived from cashew nut shell (Anacardium occidentale) and açaí oil (Euterpe oleracea Mart.), free or nanostructured, in the treatment of non-melanoma skin cancer in vitro. The stability tests showed that the açaí oil-based nanoemulsion (AçNE) showed droplets with a hydrodynamic diameter of ± 140 nm, with a polydispersion index of 0.229, surface potential of ± 17.6 mV and pH 7 for 120 days. It was possible to modify the surface of the AçNE by adding polymers of chitosan (CH), polyethylene glycol (PEG) and cationic phospholipids DOTAP (1,2-Dioleoyloxy-3- (trimethylammonium) propane). Such formulations showed no cytotoxic effect on the A431 and HaCaT cell lines, regardless of surface type. The AçNE treatments associated with AA caused a significant reduction in the viability of A431 cells, but no synergism was observed between them. On the other hand, when both compounds were added in the non-nanostructured form, a 90% reduction in the viability of A431 cells was observed in 24 hours. Flow cytometry data indicate that the combination of the free compounds results in cell death by apoptosis and cell cycle block. The present study suggests that the combination of acai oil and AA is a promising alternative antitumor therapy to be further explored in future studies

The monthly number of imported cases for 2013, 2014, and the mean, median, and range of 2001 to 2014.

<p>The monthly number of imported cases for 2013, 2014, and the mean, median, and range of 2001 to 2014.</p

Model structure.

<p>Blue and black rectangles represent mosquito and human subpopulations, respectively. Greek letters stand for constant rates, among which the black letters can be estimated from statistical yearbooks, while the blue letters are unknown and need to be estimated in the deterministic model. English letters stand for transition functions, among which the red letters only depend on temperature, while the yellow letters depend on both temperature and water level. If a heavy rainfall occurs when the current water level is close to its maximum value, a fraction of the immature stage mosquitos will be washed out (spillover effect) [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005701#pntd.0005701.ref022" target="_blank">22</a>]. Numbers are the IDs for different events. Only events with underlined IDs are modelled stochastically. More information can be found in [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005701#pntd.0005701.ref007" target="_blank">7</a>] and <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005701#pntd.0005701.s001" target="_blank">S1 File</a>.</p

The epidemic curve and passing criteria for Guangzhou Dengue outbreaks in 2013 and 2014.

<p>Black dots represent the amount of daily new cases, and the red shaded rectangles show the time and amount window for the eight criteria (See the detailed descriptions for these criteria in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004417#pntd.0004417.s001" target="_blank">S1 File</a>).</p

Climate and the Timing of Imported Cases as Determinants of the Dengue Outbreak in Guangzhou, 2014: Evidence from a Mathematical Model

<div><p>As the world’s fastest spreading vector-borne disease, dengue was estimated to infect more than 390 million people in 2010, a 30-fold increase in the past half century. Although considered to be a non-endemic country, mainland China had 55,114 reported dengue cases from 2005 to 2014, of which 47,056 occurred in 2014. Furthermore, 94% of the indigenous cases in this time period were reported in Guangdong Province, 83% of which were in Guangzhou City. In order to determine the possible determinants of the unprecedented outbreak in 2014, a population-based deterministic model was developed to describe dengue transmission dynamics in Guangzhou. Regional sensitivity analysis (RSA) was adopted to calibrate the model and entomological surveillance data was used to validate the mosquito submodel. Different scenarios were created to investigate the roles of the timing of an imported case, climate, vertical transmission from mosquitoes to their offspring, and intervention. The results suggested that an early imported case was the most important factor in determining the 2014 outbreak characteristics. Precipitation and temperature can also change the transmission dynamics. Extraordinary high precipitation in May and August, 2014 appears to have increased vector abundance. Considering the relatively small number of cases in 2013, the effect of vertical transmission was less important. The earlier and more frequent intervention in 2014 also appeared to be effective. If the intervention in 2014 was the same as that in 2013, the outbreak size may have been over an order of magnitude higher than the observed number of new cases in 2014.The early date of the first imported and locally transmitted case was largely responsible for the outbreak in 2014, but it was influenced by intervention, climate and vertical transmission. Early detection and response to imported cases in the spring and early summer is crucial to avoid large outbreaks in the future.</p></div

Climate file and intervention used in each scenario.

<p>Climate file and intervention used in each scenario.</p

Mosquito submodel patterns.

<p>The scaled 637 simulated results and field data for (a) larva and (b) adults. Gray lines show model output, red lines median output, and dark blue points show mosquito surveillance data acquired from Guangzhou CDC.</p

Trajectories for daily new cases of the 637 passing parameter sets in Cycle 5.

<p>Black dots indicate the number of daily new cases from Guangzhou CDC, while gray lines are model outputs and red line is the median for all outputs. Blue and red vertical dash lines stand for washout and intervention days, respectively. Blue shaded area for the 90 percent interval for all 637 simulations.</p

Number of travelers and incidence in surrounding dengue endemic areas.

<p>(A) the number of direct air travelers collected from ICAO DATA+; (B) number of tourists from Guangzhou by tour group, and number of foreigners staying overnight in Guangzhou from the Tourism Administration of Guangzhou; (C) reported dengue incidences in the surrounding countries from WHO dengue situation updates and local health departments. Data for Bangladesh and Myanmar is not available for (B) and for Bangladesh, India, and Myanmar is not available for (C).</p

Trajectories of daily new cases under different scenarios.

<p>(A) Postponing the date of imported case in 2014; (B) advancing the date of imported case in 2013; (C) setting the intervention in 2014 to the same as that in 2013; (D) removing all the infected eggs at the beginning of 2014; (E) advancing the date of imported case in 2013 and removing all the infected eggs at the beginning of 2014; and (F) trajectories of the final epidemic size for 2014 after changing the date of imported case between March 1^st and November 30^th. Black dots indicate for the daily reported case in 2013 and 2014. Gray lines indicate the trajectories for each simulation. Red lines indicate for the median and blue shaded area for the 90 percent interval for all 637 simulations.</p