Toxicity of clove essential oil and its ester eugenyl acetate against Artemia salina / Toxicidade do óleo essencial de cravo e seu éster acetato de eugenila contra Artemia salina

Abstract The production of compounds via enzymatic esterification has great scientific and technological interest due to the several inconveniences related to acid catalysis, mainly by these systems do not fit to the concept of “green chemistry”. Besides, natural products as clove oil present compounds with excellent biological potential. Bioactives compounds are often toxic at high doses. The evaluation of lethality in a less complex animal organism can be used to a monitoring simple and rapid, helping the identification of compounds with potential insecticide activity against larvae of insect vector of diseases. In this sense, the toxicity against Artemia salina of clove essential oil and its derivative eugenyl acetate obtained by enzymatic esterification using Novozym 435 as biocatalyst was evaluated. The conversion of eugenyl acetate synthesis was 95.6%. The results about the evaluation of toxicity against the microcrustacean Artemia salina demonstrated that both oil (LC50= 0.5993 µg.mL–1) and ester (LC50= 0.1178 µg.mL–1) presented high toxic potential, being the eugenyl acetate almost 5 times more toxic than clove essential oil. The results reported here shows the potential of employing clove oil and eugenyl acetate in insecticide formulations.

Resumo A produção de compostos via esterificação enzimática possui grande interesse científico e tecnológico devido às inúmeras inconveniências relacionadas com a catálise ácida, principalmente por estes sitemas não se adequarem ao atual termo “tecnologias limpas”. Além disso, produtos naturais como o óleo de cravo, apresentam compostos com excelentes potenciais biológicos. Compostos bioativos são quase sempre tóxicos em altas doses. A avaliação da letalidade em um organismo animal menos complexo pode ser usada para um monitoramento simples e rápido, servindo também para a identificação de compostos com potencial atividade inseticida contra larvas de insetos vetores de doenças. Neste sentido, foi determinada a toxicidade frente a Artemia salina do óleo essencial de cravo e do seu derivado acetato de eugenila obtido por esterificação enzimática com lipase Novozym 435. A conversão da reação de síntese de acetato de eugenila foi de 95,6%. Os resultados referentes à avaliação da toxicidade frente ao microcrustáceo Artemia salina demonstraram que tanto o óleo (LC50= 0,5993 µg.mL–1) quanto o éster (LC50= 0,1178 µg.mL–1) apresentam elevado potencial toxicológico, sendo que o éster apresenta aproximadamente 5 vezes mais toxicidade em relação ao óleo. Estes resultados demonstram o potencial emprego do óleo de cravo e de acetato de eugenila em formulações de inseticidas.

Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester

3 beta-O-phthalic ester of betulinic acid was synthesized from reaction of betulinic acid and phthalic anhydride using lipase as biocatalyst. This ester has clinical potential as an anticancer agent. In this study, artificial neural network (ANN) analysis of Candida antarctica lipase (Novozym 435) -catalyzed esterification of betulinic acid with phthalic anhydride was carried out. A multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model. The input parameters of the model are reaction time, reaction temperature, enzyme amount and substrate molar ratio while the percentage isolated yield of ester is the output. Four different training algorithms, belonging to two classes, namely gradient descent and Levenberg-Marquardt (LM), were used to train ANN. The paper makes a robust comparison of the performances of the above four algorithms employing standard statistical indices. The results showed that the quick propagation algorithm (QP) with 4-9-1 arrangement gave the best performances. The root mean squared error (RMSE), coefficient of determination (R²) and absolute average deviation (AAD) between the actual and predicted yields were determined as 0.0335, 0.9999 and 0.0647 for training set, 0.6279, 0.9961 and 1.4478 for testing set and 0.6626, 0.9488 and 1.0205 for validation set using quick propagation algorithm (QP).