Enzymatic Synthesis of 3-O-Acylbetulinic Acid Derivatives and Prediction of Acylation Using Response Surface Methodology and Artificial Neural Network Analyses

In this study, 3-O-acyl-betulinic acid derivatives were synthesized by the reaction of betulinic acid with various anhydrides using lipase as a biocatalyst in organic solvents. The reaction between betulinic acid and phthalic anhydride was chosen as the model reaction for optimization studies. The immobilized lipase from Candida antarctica (Novozym 435) was selected as a biocatalyst. The effects of different reaction parameters were investigated and optimized in the model reaction using one-variable-ata- time technique for the first time. Optimum conditions to produce 3-O-phthalylbetulinic acid up to 61.8% were observed at a reaction time of 24 hours; amount of enzyme, 176 mg; betulinic acid to phthalic anhydride molar ratio of 1:1; amount of celite, 170 mg and 6 mg of K2CO3 in a mixture of n-hexane-chloroform (1:1, v/v) as organic solvent at 55'C. The response surface methodology (RSM), based on a five-level, four-variable central composite rotatable design (CCRD), was employed to evaluate the effects of synthesis parameters of the model reaction. Using the RSM analysis, it was observed that the maximum yield of 3-O-phthalyl-betulinic acid (65.8%) could be obtained using 145.6 mg of enzyme, reaction temperature of 53.9°C, reaction time of 20.3 hours and betulinic acid to phthalic anhydride molar ratio of 1:1.11. The actual experimental value obtained was at 64.7%. Artificial neural network (ANN) was successfully developed to model and predict the enzymatic synthesis of 3-O-phthalyl-betulinic acid. The network consists of an input layer, a hidden layer and an output layer. Inputs for the network were reaction time, reaction temperature, enzyme amount and substrate molar ratio, while the output was percentage isolated yield of ester. Four different training algorithms, belonging to two classes, namely gradient descent and Levenberg-Marquardt, were used to train ANN. The best results were obtained from the quick propagation algorithm (QP) with 4-9-1 topology. Based on the ANN analysis, the optimal conditions to obtain the highest yield were 148.3 mg enzyme, reaction temperature of 53.1°C, reaction time of 20.3 hours and betulinic acid to phthalic anhydride molar ratio of 1:1.24. The predicted and actual yields were 64.9 and 64.3%, respectively. In this work, the ANN and RSM analysis were investigated on the enzymatic synthesis of 3-O-phthalyl-betulinic acid for the first time. Finally, several betulinic acid esters (compounds 57-66) were synthesized using the optimal operation conditions which were obtained by the RSM technique. Esterification of betulinic acid with various anhydrides was performed at 54ºC in a mixture of n-hexane- chloroform (1:1, v/v) for 20.3 hours, catalyzed by Novozym 435, gave 24.7 to 79.3% yield. Five new compounds (58, 62, 64, 65 and 66) were synthesized for the first time in the present study. In brief, the anti-cancer activity of betulinic acid (1) and its 3-O-acylated derivatives (compounds 57-66) were evaluated against human lung carcinoma (A549) and human ovarian (CAOV3) cancer cell lines. In particular, compounds (59), (61) and (63) were found to show IC50 < 10 μg/ml against A549 cancer cell line tested and showed better cytotoxicity than betulinic acid. In the ovarian cancer cell line, all betulinic acid derivatives prepared revealed weaker cytotoxicity than betulinic acid

Spectroscopic data of 3-O-acetyl-betulinic acid: an antitumor reagent

In this paper, the spectroscopic data of the 3-O-acetyl betulinic acid is reported. This compound was prepared by enzymatic reaction of betulinic acid and acetic anhydride in the presence of lipase from Candida antarctica (Novozem® 435) at 54oC for 20 h in 79.3 % yield

Enzymatic synthesis of betulinic acid ester as an anticancer agent: optimization study

Immobilized Candida antarctica lipase, Novozym 435, was used to catalyze the esterification reaction between betulinic acid and phthalic anhydride to synthesize 3-O-phthalyl betulinic acid in n-hexane/chloroform. Response surface methodology based on a five-level, four-variable central composite rotatable design was employed to evaluate the effects of synthesis parameters such as reaction time, reaction temperature, enzyme amount and substrate molar ratio on the yield of ester. Based on the response surface model, the optimal enzymatic synthesis conditions were predicted to be: reaction time 20.3 h, reaction temperature 53.9°C, enzyme amount 145.6 mg, betulinic acid to phthalic anhydride molar ratio 1:1.11. The predicted yield was 65.8% and the actual yield was 64.7%

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

3β-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)

Anticancer activity of 3-O-acylated betulinic acid derivative obtained by enzymatic synthesis

An easy and efficient strategy to prepare betulinic acid esters with various anhydrides was used by the enzymatic synthesis method. It involves lipase-catalyzed acylation of betulinic acid with anhydrides as acylating agents in organic solvent. Lipase from Candida antarctica immobilized on an acrylic resin (Novozym 435) was employed as a biocatalyst. Several 3-O-acyl-betulinic acid derivatives were successfully obtained by this procedure. The anticancer activity of betulinic acid and its 3-O-acylated derivatives were then evaluated in vitro against human lung carcinoma (A549) and human ovarian (CAOV3) cancer cell lines. 3-O-glutaryl-betulinic acid, 3-O-acetyl-betulinic acid, and 3-O-succinyl-betulinic acid showed IC(50)<10 microg/ml against A549 cancer cell line tested and showed better cytotoxicity than betulinic acid. In an ovarian cancer cell line, all betulinic acid derivatives prepared showed weaker cytotoxicity than betulinic acid

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

3\u3b2-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)

Synthesis and characterization of silver/montmorillonite/chitosan bionanocomposites by chemical reduction method and their antibacterial activity

Silver nanoparticles (AgNPs) of a small size were successfully synthesized using the wet chemical reduction method into the lamellar space layer of montmorillonite/chitosan (MMT/Cts) as an organomodified mineral solid support in the absence of any heat treatment. AgNO3, MMT, Cts, and NaBH4 were used as the silver precursor, the solid support, the natural polymeric stabilizer, and the chemical reduction agent, respectively. MMT was suspended in aqueous AgNO3/Cts solution. The interlamellar space limits were changed (d-spacing = 1.24–1.54 nm); therefore, AgNPs formed on the interlayer and external surface of MMT/Cts with d-average = 6.28–9.84 nm diameter. Characterizations were done using different methods, ie, ultraviolet-visible spectroscopy, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy dispersive X-ray fluorescence spectrometry, and Fourier transform infrared spectroscopy. Silver/montmorillonite/chitosan bionanocomposite (Ag/MMT/Cts BNC) systems were examined. The antibacterial activity of AgNPs in MMT/Cts was investigated against Gram-positive bacteria, ie, Staphylococcus aureus and methicillin-resistant S. aureus and Gram-negative bacteria, ie, Escherichia coli, E. coli O157:H7, and Pseudomonas aeruginosa by the disc diffusion method using Mueller Hinton agar at different sizes of AgNPs. All of the synthesized Ag/MMT/Cts BNCs were found to have high antibacterial activity. These results show that Ag/MMT/Cts BNCs can be useful in different biological research and biomedical applications, including surgical devices and drug delivery vehicles

A Pyridyltriazol Functionalized Zirconium Metal-Organic Framework for Selective and Highly Efficient Adsorption of Palladium

[EN] This work reports the synthesis of pyridyltriazol-functionalized UiO-66 (UiO stands for University of Oslo), namely, UiO-66-Pyta, from UiO-66-NH2 through three postsynthetic modification (PSM) steps. The good performance of the material derives from the observation that partial formylation (similar to 21% of -NHCHO groups) of H2BDC-NH2 by DMF, as persistent impurity, takes place during the synthesis of the UiO-66-NH2. Thus, to enhance material performance, first, the as-synthesized UiO-66-NH2 was deformylated to give pure UiO-66-NH2. Subsequently, the pure UiO-66-NH2 was converted to UiO-66-N-3 with a nearly complete conversion (similar to 95%). Finally, the azide-alkyne[3+2]-cycloaddition reaction of 2-ethynylpyridine with the UiO-66-N-3 gave the UiO-66-Pyta. The porous MOF was then applied for the solid-phase extraction of palladium ions from an aqueous medium. Affecting parameters on extraction efficiency of Pd(II) ions were also investigated and optimized. Interestingly, UiO-66-Pyta exhibited selective and superior adsorption capacity for Pd(II) with a maximum sorption capacity of 294.1 mg.g(-1) at acidic pH (4.5). The limit of detection (LOD) was found to be 1.9 mu g L-1. The estimated intra- and interday precisions are 3.6 and 1.7%, respectively. Moreover, the adsorbent was regenerated and reused for five cycles without any significant change in the capacity and repeatability. The adsorption mechanism was described based on various techniques such as FT-IR, PXRD, SEM/EDS, ICP-AES, and XPS analyses as well as density functional theory (DFT) calculations. Notably, as a case study, the obtained UiO-66-Pyta after palladium adsorption, UiO-66-Pyta-Pd, was used as an efficient catalyst for the Suzuki-Miyaura cross-coupling reaction.Authors gratefully acknowledge the financial support for this work from the Politecnica de Valencia, Valencia, Spain. Also, financial support by the University of Zabol is gratefully acknowledged (grant nos. 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The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe