Study of reaction parameters and kinetics of esterification of lauric acid with butanol by immobilized Candida antarctica lipase.

Esterification of lauric acid with n-butanol, catalyzed by immobilized Candida antarctica lipase (CAL) in aqueous-organic biphasic solvent system was studied. Effects of various reaction parameters on esterification were investigated, such as type and amount of solvent, amount of buffer, pH, temperature, speed of agitation, amount of enzyme, butanol and lauric acid. The most suitable reaction conditions for esterification were observed at 50ºC and pH 7.0 using 5000 μmoles of lauric acid, 7000 μmoles of butanol, 0.25 ml phosphate buffer, 1 ml of isooctane as the solvent and 50 mg of immobilized enzyme in the reaction medium at agitation speed of 150 rpm. Maximum esterification of 96.36% was acheived in 600 min of reaction time at n-butanol to lauric acid molar ratio of 1: 0.7. Kinetic study for the esterification of lauric acid with n-butanol using immobilized CAL was carried out and the kinetic constants were estimated by using non-linear regression method. The estimated value of Michaelis kinetic constants for butanol (KmBt) and acid (KmAc) were 451.56 (M) and 4.7 × 10-7(M), respectively and the value of dissociation constant (KBt) of the butanol-lipase complex was 9.41 × 107(M). The estimated constants agreed fairly well with literature data.

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).