Multivariate optimization in the biosynthesis of a triethanolamine (TEA)-based esterquat cationic surfactant using an artificial neural network

Microbe cultivation guidelines to optimize rhamnolipid applications

In the growing landscape of interest in natural surfactants, selecting the appropriate one for specific applications remains challenging. The extensive, yet often unsystematized, knowledge of microbial surfactants, predominantly represented by rhamnolipids (RLs), typically does not translate beyond the conditions presented in scientific publications. This limitation stems from the numerous variables and their interdependencies that characterize microbial surfactant production. We hypothesized that a computational recipe for biosynthesizing RLs with targeted applicational properties could be developed from existing literature and experimental data. We amassed literature data on RL biosynthesis and micellar solubilization and augmented it with our experimental results on the solubilization of triglycerides (TGs), a topic underrepresented in current literature. Utilizing this data, we constructed mathematical models that can predict RL characteristics and solubilization efficiency, represented as logPRL = f(carbon and nitrogen source, parameters of biosynthesis) and logMSR = f(solubilizate, rhamnolipid (e.g. logPRL), parameters of solubilization), respectively. The models, characterized by robust R2 values of respectively 0.581-0.997 and 0.804, enabled the ranking of descriptors based on their significance and impact-positive or negative-on the predicted values. These models have been translated into ready-to-use calculators, tools designed to streamline the selection process for identifying a biosurfactant optimally suited for intended applications.

Enhancing the Bioconversion of Azelaic Acid to Its Derivatives by Response Surface Methodology

Azelaic acid (AzA) and its derivatives have been known to be effective in the treatment of acne and various cutaneous hyperpigmentary disorders. The esterification of azelaic acid with lauryl alcohol (LA) to produce dilaurylazelate using immobilized lipase B from Candida antarctica (Novozym 435) is reported. Response surface methodology was selected to optimize the reaction conditions. A well-fitting quadratic polynomial regression model for the acid conversion was established with regards to several parameters, including reaction time and temperature, enzyme amount, and substrate molar ratios. The regression equation obtained by the central composite design of RSM predicted that the optimal reaction conditions included a reaction time of 360 min, 0.14 g of enzyme, a reaction temperature of 46 °C, and a molar ratio of substrates of 1:4.1. The results from the model were in good agreement with the experimental data and were within the experimental range (R² of 0.9732).The inhibition zone can be seen at dilaurylazelate ester with diameter 9.0±0.1 mm activities against Staphylococcus epidermidis S273. The normal fibroblasts cell line (3T3) was used to assess the cytotoxicity activity of AzA and AzA derivative, which is dilaurylazelate ester. The comparison of the IC₅₀ (50% inhibition of cell viability) value for AzA and AzA derivative was demonstrated. The IC₅₀ value for AzA was 85.28 μg/mL, whereas the IC₅₀ value for AzA derivative was more than 100 μg/mL. The 3T3 cell was still able to survive without any sign of toxicity from the AzA derivative; thus, it was proven to be non-toxic in this MTT assay when compared with AzA.

Synthesis and Characterization of Biodegradable Cationic Esterquat Surfactants and the Evaluation of its Physico-Chemical Properties

Three biodegradable cationic esterquat surfactants such as 2-(dodecanoyloxy)-N,N-bis(2-hydroxyethyl)-N-(oxiran-2-ylmethyl)ethane-1-ammonium chloride (II-a), 2-hydroxy-N-(2-hydroxyethyl)-N-(oxiran-2-ylmethyl)-N-(2-(tetradecanoyloxy) ethyl) ethane-1-ammonium chloride (II-b) and 2-hydroxy-N-(2-hydroxyethyl)-N-(oxiran-2-ylmethyl)-N-palmitoyloxy) ethyl) ethane-1-ammonium chloride (II-c) have been synthesized. The synthesis involves the esterification of three different fatty acids (lauric, myristic and palmitic) with triethanolamine followed by the quaternization with epichlorohydrin. The synthesized surfactants have been characterized by FT-IR, 1H NMR, 13C NMR, and elementary analysis. Physico-chemical properties such as surface tension and interfacial tension, Critical Micelle Concentration (CMC), wetting time, foam stability, and emulsion stability have been evaluated. Based on the results of the physico-chemical properties, the efficacy of the surfactants is in the following order II-c > II-b > II-a.

The prediction of the optimum compositions of a parenteral nanoemulsion system loaded with a low water solubility drug for the treatment of schizophrenia by artificial neural networks

Aripiprazole was encapsulated in a palm kernel oil esters nanoemulsion for the purpose of brain deliveryviaintravenous administration.

Modeling of a natural lipstick formulation using an artificial neural network

An artificial neural network (ANN) was applied in conjunction with experimental data from a mixture of experimental designs to predict the melting point of a lipstick formulation.

Artificial neural network analysis in preclinical breast cancer

OBJECTIVE

In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated.

MATERIALS AND METHODS

In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used to train ANN.

RESULTS

The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R(2)) between the actual and predicted values was determined as 0.897118 for all data.

CONCLUSION

It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week).

Statistical optimization of process parameters for lipase-catalyzed synthesis of triethanolamine-based esterquats using response surface methodology in 2-liter bioreactor

Lipase-catalyzed production of triethanolamine-based esterquat by esterification of oleic acid (OA) with triethanolamine (TEA) in n-hexane was performed in 2 L stirred-tank reactor. A set of experiments was designed by central composite design to process modeling and statistically evaluate the findings. Five independent process variables, including enzyme amount, reaction time, reaction temperature, substrates molar ratio of OA to TEA, and agitation speed, were studied under the given conditions designed by Design Expert software. Experimental data were examined for normality test before data processing stage and skewness and kurtosis indices were determined. The mathematical model developed was found to be adequate and statistically accurate to predict the optimum conversion of product. Response surface methodology with central composite design gave the best performance in this study, and the methodology as a whole has been proven to be adequate for the design and optimization of the enzymatic process.

Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.