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Herein, the antiproliferative potential of the essential oil obtained from fresh leaves of Croton campestris against human tumour cell lines was investigated for the first time. Furthermore, the essential oil obtained by hydrodistillation had the composition determined by gas chromatography-mass spectrometry (GC/MS). Ten major components were identified that comprised 91.59% of the total content, with 23.8% consisting of (Z)-caryophyllene and 16.08% consisting of γ-elemene as main components. The cytotoxic activity was observed mainly for breast (MCF-7) and colon (HT-29) human tumour cell lines, with GI (50% growth inhibition) concentration of 8.61 and 9.94 μg/mL, respectively. The results of this study showed that the essential oil obtained from Croton campestris A.St.-Hil. represents a potential source for the search of new antitumour agents.

A range of drugs used in cancer treatment comes from natural sources. However, chemotherapy has been facing a major challenge related to multidrug resistance (MDR), a mechanism that results in a decrease in the intracellular concentration of chemotherapeutic agents, resulting in reduced treatment efficacy. The protein most frequently related to this effect is P-glycoprotein (P-gp), which is responsible for promoting drug efflux into the extracellular environment. Myristicin is a natural compound isolated from nutmeg and has antiproliferative activity, which has been reported in the literature. The present study aimed to evaluate the effect of the association between myristicin and chemotherapeutic agents on the NCI/ADR-RES ovarian tumor lineage that presents a phenotype of multidrug resistance by overexpression of P-gp. It was observed that myristicin showed no cytotoxic activity for this cell line, since its IC50 was >1 mM. When myristicin was associated with the chemotherapeutic agents cisplatin and docetaxel, it potentiated their cytotoxic effects, a result evidenced by the decrease in their IC50 of 32.88% and 75.46%, respectively. Studies conducted in silico indicated that myristicin is able to bind and block the main protein responsible for MDR, P-glycoprotein. In addition, the molecule fits five of the pharmacokinetic parameters established by Lipinski, indicating good membrane permeability and bioavailability. Our hypothesis is that, by blocking the extrusion of chemotherapeutic agents, it allows these agents to freely enter cells and perform their functions, stopping the cell cycle. Considering the great impasse in the chemotherapeutic treatment of cancer that is the MDR acquired by tumor cells, investigating effective targets to circumvent this resistance remains a major challenge that needs to be addressed. Therefore, this study encourages further investigation of myristicin as a potential reverser of MDR.

RESUMO -Nesse trabalho, procurou-se estudar a influência das condições operacionais (pressão e temperatura) em duas variáveis-alvo: rendimento total mássico e composição química do extrato. Para isso, testaram-se 3 níveis para cada variável e todas as suas combinações: temperaturas de 313,15 K, 323,15 K e 333,15 K e pressões de 19,61 MPa, 29,42 MPa e 39,23 MPa, para extração com CO 2 supercrítico puro e depois, com uma mistura de CO 2 + 5 % m/m álcool etílico. Resultados mostraram que condições mais intensas favorecem maior rendimento em extrações com CO 2 puro, além de que as maiores temperaturas favorecem a extração de espilantol (composto de interesse). Para os extratos feitos com o auxílio de co-solventes, resultados mostraram que tanto o rendimento como a quantidade de espilantol são inferiores à extração com solvente puro. INTRODUÇÃOJambu (Acmella oleracea (L.) R.K. Jansen) é uma planta originária da América do Sul e muito comum em todo sudoeste asiático. No Brasil é muito utilizada na culinária (especialmente na região Norte) e também para tratamentos de dores de dente e de garganta, tuberculose, anemia e como estimulante de apetite (Di-Stasi et al., 2002; Lorenzi e Matos, 2008).O espilantol encontra-se distribuído por toda a parte aérea do jambu em concentração variável, sendo que os métodos extrativos e os solventes utilizados influenciam na seletividade de sua extração. O emprego de dióxido de carbono supercrítico apresenta grande seletividade para o espilantol, resultando em extratos concentrados, contendo até 65% de espilantol quando extraídos das flores e de 47% quando extraídos dos caules (Dias et al., 2012).Nesse contexto, a utilização de fluidos supercríticos na extração de compostos bioativos é uma alternativa promissora, pois apresenta vantagens em relação a técnicas convencionais, uma vez que não há resíduos tóxicos no extrato final e esta tecnologia permite a recuperação do solvente (Bernardo-Gil, 2013).

Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.