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Nanoemulsions have been used as a drug carrier system, particularly for poorly water-soluble drugs. Sorafenib is a poorly soluble drug and also there is no parenteral treatment. The aim of this study is the development of nanoemulsions for intravenous administration of Sorafenib. The formulations were prepared by high energy emulsification method and optimized by using Response Surface Methodology (RSM). Here, the effect of independent composition variables of lecithin (1.16–2.84%, w/w), Medium-Chain Triglycerides (2.32–5.68%, w/ w) and polysorbate 80 (0.58–1.42%, w/w) amounts on the properties of Sorafenib-loaded nanoemulsion was investigated. The three responses variables were particle size, zeta potential, and polydispersity index. Optimization of the conditions according to the three dependent variables was performed for the preparation of the Sorafenib-loaded nanoemulsions with the minimum value of particle size, suitable rage of zeta potential, and polydispersity index. A formulation containing 0.05% of Sorafenib kept its properties in a satisfactory range over the evaluated period. The composition with 3% Medium-Chain Triglycerides, 2.5% lecithin and 1.22% polysorbate 80 exhibited the smallest particle size and polydispersity index (43.17 nm and 0.22, respectively) with the zeta potential of −38.8 mV was the optimized composition. The fabricated nanoemulsion was characterized by the transmission electron microscope (TEM), viscosity, and stability assessment study. Also, the cytotoxicity result showed that the optimum formulations had no significant effect on a normal cell in a low concentration of the drug but could eliminate the cancer cells. The dose-dependent toxicity made it a suitable candidate for parenteral applications in the treatment of breast cancer. Furthermore, the optimized formulation indicated good storage stability for 3 months at different temperatures (4 ± 2 °C, 25 ± 2 °C and 45 ± 2 °C.

type:Journal Article, In this work, lipase from Candida rugosa was immobilized onto chitosan/graphene oxide beads. This was to provide an enzyme-immobilizing carrier with excellent enzyme immobilization activity for an enzyme group requiring hydrophilicity on the immobilizing carrier. In addition, this work involved a process for the preparation of an enzymatically active product insoluble in a reaction medium consisting of lauric acid and oleyl alcohol as reactants and hexane as a solvent. This product enabled the stability of the enzyme under the working conditions and allowed the enzyme to be readily isolated from the support. In particular, this meant that an enzymatic reaction could be stopped by the simple mechanical separation of the “insoluble” enzyme from the reaction medium. Chitosan was incorporated with graphene oxide because the latter was able to enhance the physical strength of the chitosan beads by its superior mechanical integrity and low thermal conductivity. The X-ray diffraction pattern showed that the graphene oxide was successfully embedded within the structure of the chitosan. Further, the lipase incorporation on the beads was confirmed by a thermo-gravimetric analysis. The lipase immobilization on the beads involved the functionalization with coupling agents, N-hydroxysulfosuccinimide sodium (NHS) and 1-ethyl-(3-dimethylaminopropyl) carbodiimide (EDC), and it possessed a high enzyme activity of 64 U. The overall esterification conversion of the prepared product was 78% at 60°C, and it attained conversions of 98% and 88% with commercially available lipozyme and novozyme, respectively, under similar experimental conditions.

Optimization of hydrocortisone-loaded nanoemulsion formulation was achieved by low energy emulsification method. D-optimal mixture design was used with the variables parameters of Tween-20, lipoid S75, palm kernel oil ester (PKOE) and deionized water towards a response (viscosity). Hydrocortisone, ethanol and phenonip were kept constant (1.0, 24 and 0.5 wt. %). The mixing rate and time were kept constant at 400 rpm and 4 h, respectively. The optimum formulation conditions were successfully predicted. From the results obtained, it shows that the higher the concentration of palm kernel oil ester, the less the viscosity of the nanoemulsion. The optimized formulation showed good stability with the viscosity of 2.85 Pa.s. The particle size, polydispersity index and zeta potential of the optimized nanoemulsion gave a result of 172.56 nm, 0.184 and -50.03 mV, respectively. The results are in agreement with the TEM micrograph. The viscosity of the optimized nanoemulsion decreased with increased shear rate, showing a shear thinning behaviour (pseudoplastic) suitable for pharmaceutical application.

Siti Hajar Musa,1 Mahiran Basri,1 Hamid Reza Fard Masoumi,1 Norashikin Shamsudin,2 Norazlinaliza Salim1 1Department of Chemistry, Faculty of Science, 2Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia Abstract: Psoriasis is a chronic autoimmune disease that cannot be cured. It can however be controlled by various forms of treatment, including topical, systemic agents, and phototherapy. Topical treatment is the first-line treatment and favored by most physicians, as this form of therapy has more patient compliance. Introducing a nanoemulsion for transporting cyclosporine as an anti-inflammatory drug to an itchy site of skin disease would enhance the effectiveness of topical treatment for psoriasis. The addition of nutmeg and virgin coconut-oil mixture, with their unique properties, could improve cyclosporine loading and solubility. A high-shear homogenizer was used in formulating a cyclosporine-loaded nanoemulsion. A D-optimal mixture experimental design was used in the optimization of nanoemulsion compositions, in order to understand the relationships behind the effect of independent variables (oil, surfactant, xanthan gum, and water content) on physicochemical response (particle size and polydispersity index) and rheological response (viscosity and k-value). Investigation of these variables suggests two optimized formulations with specific oil (15% and 20%), surfactant (15%), xanthan gum (0.75%), and water content (67.55% and 62.55%), which possessed intended responses and good stability against separation over 3 months’ storage at different temperatures. Optimized nanoemulsions of pH 4.5 were further studied with all types of stability analysis: physical stability, coalescence-rate analysis, Ostwald ripening, and freeze–thaw cycles. In vitro release proved the efficacy of nanosize emulsions in carrying cyclosporine across rat skin and a synthetic membrane that best fit the Korsmeyer&am

The recovery of waste as feedstock away from organizational limitations corresponds to a prospective supplementary revenue stream for the organization. A novel waste eggshell of Gallus domesticus derived superbasic nanocatalyst was synthesized through betaine amphoteric surfactant-assisted decomposition, adsorption and precipitation processes. By varied the duration synthesis of gel mixture, the morphology transformation from liquid-solid interconnected macro-size particles to regular spheroidal nanoassemblies particles is detected. The surfactant at the liquid-solid interface facilitates the mono dispersion of nanoparticles by hindering growth of crystals. The average particle diameter of the produced superbasic nanocatalyst was in the range of 27e16 nm. The synthesized nanoparticle formation mechanism in the presence surfactant has also been addressed in this study. The catalytic activity of superbasic nanocatalyst was investigated for biodiesel production from crude Jatropha curcas oil (JCO) via glycerolysis and transesterification with methanol at atmospheric pressure. Artificial neural network (ANN) based on the genetic algorithm (GA) was applied for optimization of varied reaction parameters. It was observed that the reduction of acidity varied with varying reaction conditions. The highest fatty acid methyl ester (FAME) yield (97%) was obtained when the reaction was allowed to run at 60 C for 300 min, while at 90 C the maximal FAME yield of 98% was achieved after 120 min. The kinetic parameters of nanocatalyst were determined, and the reaction system followed pseudo first order kinetics. The results suggest that this two steps process using superbasic nanocatalyst affords a promising method to convert oils with high FFA level to biodiesel.

Psoriasis is a chronic autoimmune disease that cannot be cured. It can however be controlled by various forms of treatment, including topical, systemic agents, and phototherapy. Topical treatment is the first-line treatment and favored by most physicians, as this form of therapy has more patient compliance. Introducing a nanoemulsion for transporting cyclosporine as an anti-inflammatory drug to an itchy site of skin disease would enhance the effectiveness of topical treatment for psoriasis. The addition of nutmeg and virgin coconut-oil mixture, with their unique properties, could improve cyclosporine loading and solubility. A high-shear homogenizer was used in formulating a cyclosporine-loaded nanoemulsion. A D-optimal mixture experimental design was used in the optimization of nanoemulsion compositions, in order to understand the relationships behind the effect of independent variables (oil, surfactant, xanthan gum, and water content) on physicochemical response (particle size and polydispersity index) and rheological response (viscosity and k-value). Investigation of these variables suggests two optimized formulations with specific oil (15% and 20%), surfactant (15%), xanthan gum (0.75%), and water content (67.55% and 62.55%), which possessed intended responses and good stability against separation over 3 months' storage at different temperatures. Optimized nanoemulsions of pH 4.5 were further studied with all types of stability analysis: physical stability, coalescence-rate analysis, Ostwald ripening, and freeze-thaw cycles. In vitro release proved the efficacy of nanosize emulsions in carrying cyclosporine across rat skin and a synthetic membrane that best fit the Korsmeyer-Peppas kinetic model. In vivo skin analysis towards healthy volunteers showed a significant improvement in the stratum corneum in skin hydration.

A nanoemulsion-based formulation containing leaf extracts of Clinacanthus nutans Lindau (C. nutans) was prepared for therapeutic use and optimized by artificial neural network (ANN). The model was applied to optimize the particle size of the transdermal nanoemulsion system containing an extract of C. nutans leaves for skin antiaging. Five universal learning algorithms—incremental back propagation, batch back propagation, quick propagation, genetic algorithm, and Levenberg-Marquardt—were used in the ANN to achieve the optimum topologies. The optimum topologies were selected among the learning algorithms trained with lowest root mean square values. Genetic Algorithm-5-13-1 was found to be the optimum topology for the final model to predict the optimum particle size. Under these conditions, the actual particle size of the optimum nanoemulsion compared well with the predicted values with the residual standard error of less than 1.5%. The final formulation was observed to be stable against phase separation under an accelerated stability study and during storage at room temperature and 45°C for 90 days. The dermal irritation assay demonstrated that the developed nanoemulsion was found to be compatible with human skin and could be classified as nonirritant. In vivo ultrasound attributes of the skin study showed that the collagen content increased significantly with the application of the C. nutans nanoemulsion among all 21 volunteers during the 21 days of the treatment period. Hence, ANN was found to be an effective tool for optimizing the nanoemulsion containing C. nutans leaves extract with efficacy to protect collagen from breakdown.

A predictive model of a virgin coconut oil (VCO) nanoemulsion system for the topical delivery of copper peptide (an anti-aging compound) was developed using an artificial neural network (ANN) to investigate the factors that influence particle size. Four independent variables including the amount of VCO, Tween 80: Pluronic F68 (T80:PF68), xanthan gum and water were the inputs whereas particle size was taken as the response for the trained network. Genetic algorithms (GA) were used to model the data which were divided into training sets, testing sets and validation sets. The model obtained indicated the high quality performance of the neural network and its capability to identify the critical composition factors for the VCO nanoemulsion. The main factor controlling the particle size was found out to be xanthan gum (28.56%) followed by T80:PF68 (26.9%), VCO (22.8%) and water (21.74%). The formulation containing copper peptide was then successfully prepared using optimum conditions and particle sizes of 120.7 nm were obtained. The final formulation exhibited a zeta potential lower than -25 mV and showed good physical stability towards centrifugation test, freeze-thaw cycle test and storage at temperature 25°C and 45°C.

The artificial neural network (ANN) modeling of adsorption of Pb(II) and Cu(II) was carried out for determination of the optimum values of the variables to get the maximum removal efficiency. The input variables were initial ion concentration, adsorbent dosage, and removal time, while the removal efficiency was considered as output. The performed experiments were designed into two data sets including training, and testing sets. To acquire the optimum topologies, ANN was trained by quick propagation (QP), Batch Back Propagation (BBP), Incremental Back Propagation (IBP), genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were defined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the IBP-3-9-2 was selected as the optimized topologies for heavy metal removal, due to the minimum RMSE and maximum R-squared.

Aripiprazole was encapsulated in the palm kernel oil esters nanoemulsion for the purpose of brain delivery via intravenous administration. High shear and high pressure homogenizers were applied for formulating low solubility drug in the nanoemulsion system and stabilized by different emulsifiers; lecithin, Tween 80 and glycerol. The artificial neural networks (ANNs) modeling of nanoemulsion formulation was carried out to achieve the minimum particle size. The effects of palm kernel oil ester (PKOE) (3-6%, w/w), lecithin (2-3%, w/w), Tween 80 (0.5-1%, w/w), glycerol (1.5-3%, w/w), and water (87-93%, w/w) amounts on the particle size were considered as inputs of the network trained. The particle size of samples in various compositions was measured as output. To obtain the optimum topologies, ANNs were trained by Incremental Back Propagation (IBP), Genetic Algorithm (GA), Batch Back Propagation (BBP), Quick Propagation (QP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the results, the QP-5-4-1, GA-5-12-1, IBP-5-11-1, BBP-5-10-1, and LM-5-9-1 were selected as the optimized topologies. It was found that the optimal algorithm and topology were the quick propagation and the configuration with 5 inputs, 4 hidden and 1 output nodes, respectively. Conclusively, ANN models were developed for the prediction of particle size of nanoemulsions loaded with aripiprazole and stable nanoemulsion system which could be used effectively for intravenous administration.

Copper peptide is an important compound used to prevent ageing; a process that leads to several unwanted aging signs such as wrinkles, sagging, pigmented spots and dryness. The D-optimal mixture design was used for optimizing the particle size of Virgin Coconut Oil (VCO) nanoemulsions for topical delivery of copper peptide. Effects of formulation variables including VCO (10–20%, w/w), Tween 80 : Pluronic PF68 (10–15%, w/w), xanthan gum (0.5–1.0%, w/w) and water (64.008–79.291), towards particle size as a response were studied. D-optimal mixture design analysis demonstrated that the variation in particle size as a response could be expressed as a quadratic function of the main components of the emulsion. Analysis of variance (ANOVA) showed that the quadratic polynomial model sufficiently fits the experimental data. The best combination of the composition factors that gave the optimum particle size was found to be VCO (10%, w/w), T80 : PF68 (15%, w/w), xanthan gum (0.867%, w/w) and water (74.133%, w/w). Under these optimum compositions, the actual particle size showed good agreement with predicted particle size with a residual standard error (RSE) less than 2%. The formulation containing copper peptide was successfully prepared with a particle size value of 120.7 nm, which was consistent with the result obtained by TEM. The final formulation also showed good conductivity and pH values as well as stability against phase separation. A rheology study demonstrates that the VCO nanoemulsion containing copper peptide exhibited shear thinning and pseudoplastic properties and showed more elastic rather than viscous characteristics due to the presence of a large linear viscoelastic region. Permeability test results showed that copper peptide in aqueous solution with and without penetration enhancer cannot be transported through the cellulose acetate membrane after 8 h application. The nanoemulsion increased the permeability of copper peptide with 21.89 ± 0.53% active matter re

This paper deals with the comparative study on glycerolysis of palm fatty acid distillate (PFAD) in a solvent free system at different reaction conditions in an attempt to get maximum degree of FFA% reduction for biodiesel feedstock. Initially, optimization of varied reaction parameters was performed under all the different reaction conditions using artificial neural network (ANN) based on the genetic algorithm (GA). It has been found that the reduction of acidity varies with varying reaction conditions with maximum reaction rate observed in case of reaction carried-out in open reactor system with inert gas flow, followed by the reaction in open reactor system without inert gas flow and then in case of reaction under the close reactor system. In the most favorable case, 1.5 mgKOH/gPFAD of FFA (free fatty acid) was achieved after 90 min of reaction time with an excess glycerol of 4% at 220 °C. The results from the ANN model show good agreement with experimental results. Thus, the glycerolysis in open reactor system with inert gas flow (N2) option is much-preferred option compared to acid esterification for the same biodiesel plant capacity, particularly for high-FFA feedstocks.

Hamid Reza Fard Masoumi, Mahiran Basri, Wan Sarah Samiun, Zahra Izadiyan, Chaw Jiang Lim Nanodelivery Group, Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Abstract: Aripiprazole is considered as a third-generation antipsychotic drug with excellent therapeutic efficacy in controlling schizophrenia symptoms and was the first atypical anti­psychotic agent to be approved by the US Food and Drug Administration. Formulation of nanoemulsion-containing aripiprazole was carried out using high shear and high pressure homo­genizers. Mixture experimental design was selected to optimize the composition of nanoemulsion. A very small droplet size of emulsion can provide an effective encapsulation for delivery system in the body. The effects of palm kernel oil ester (3–6 wt%), lecithin (2–3 wt%), Tween 80 (0.5–1 wt%), glycerol (1.5–3 wt%), and water (87–93 wt%) on the droplet size of aripiprazole nanoemulsions were investigated. The mathematical model showed that the optimum formulation for preparation of aripiprazole nanoemulsion having the desirable criteria was 3.00% of palm kernel oil ester, 2.00% of lecithin, 1.00% of Tween 80, 2.25% of glycerol, and 91.75% of water. Under optimum formulation, the corresponding predicted response value for droplet size was 64.24 nm, which showed an excellent agreement with the actual value (62.23 nm) with residual standard error <3.2%. Keywords: schizoaffective disorder, antipsychotic drug, bipolar I disorder, D-optimal mixture design, optimization formulation

Fong Yen Woo,1 Mahiran Basri,1,2 Hamid Reza Fard Masoumi,1 Mansor B Ahmad,1 Maznah Ismail2 1Department of Chemistry, Faculty of Science, 2Laboratory of Molecular Biomedicine, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Abstract: Galantamine hydrobromide (GH) is an effective drug for Alzheimer’s disease. It is currently delivered via the oral route, and this might cause nausea, vomiting, and gastrointestinal disturbance. In the present work, GH was formulated in a gel-type drug reservoir and then optimized by using response surface methodology (RSM) based on central composite design. This optimization study involved three independent variables (carbopol amount, triethanolamine amount, and GH amount) and two dependent variables (cumulative drug release amount at 8 hours and the permeation flux of drug). Two models using expert design software were fitted into a quadratic polynomial model. The optimized gel was formulated with 0.89% w/w carbopol, 1.16% w/w triethanolamine, and 4.19% w/w GH. Optimization analysis revealed that the proposed formulation has the predicted cumulative drug release amount at 8 hours of 17.80 mg·cm-2 and the predicted permeation flux of 2.27 mg·cm-2/h. These predicted values have good agreement to actual cumulative drug release amount at 8 hours (16.93±0.08 mg·cm-2) and actual permeation flux (2.32±0.02 mg·cm-2/h). This optimized reservoir formulation was then fabricated in the transdermal patch system. This patch system has moderate pH, high drug content, and controlled drug-release pattern. Thus, this patch system has the potential to be used as the drug carrier for the treatment of Alzheimer’s disease. Keywords: response surface methodology, central composite design, analysis of variance, Franz diffusion cell

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. The experimental data were utilized for training and testing the suggested model. By using the ANN performance results, the optimum parameters were found to be pitaya seed oil 25% w/w, virgin coconut oil 37% w/w, beeswax 17% w/w, candelilla wax 2% w/w, and carnauba wax 2% w/w. The relative standard error under these parameters was only 0.8772%. It was found that batch back-propagation (BBP) gave the optimal algorithm and topology with a configuration of five inputs, two hidden nodes and one output node; the most important parameter was the carnauba wax content with a relative importance of 24.5%.

The usage of soy is increasing year by year. It increases the problem of financial crisis due to the limited sources of soybeans. Therefore, production of oral tablets containing the nutritious leftover of soymilk production, called okara, as the main ingredient was investigated. The okara tablets were produced using the direct compression method. The percentage of okara, guar gum, microcrystalline cellulose (Avicel PH-101), and maltodextrin influenced tablets’ hardness and friability which are analyzed using a D-optimal mixture design. Composition of Avicel PH-101 had positive effects for both hardness and friability tests of the tablets. Maltodextrin and okara composition had a significant positive effect on tablets’ hardness, but not on percentage of friability of tablets. However, guar gum had a negative effect on both physical tests. The optimum tablet formulation was obtained: 47.0% of okara, 2.0% of guar gum, 35.0% of Avicel PH-101, and 14.0% of maltodextrin.

Rice straw/magnetic nanocomposites (RS/Fe3O4-NCs) were prepared via co-precipitation method for removal of Pb(II) and Cu(II) from aqueous solutions. Response surface methodology (RSM) was utilized to find the optimum conditions for removal of ions. The effects of three independent variables including initial ion concentration, removal time, and adsorbent dosage were investigated on the maximum adsorption of Pb (II) and Cu (II). The optimum conditions for the adsorption of Pb(II) and Cu(II) were obtained (100 and 60 mg/L) of initial ion concentration, (41.96 and 59.35 s) of removal time and 0.13 g of adsorbent for both ions, respectively. The maximum removal efficiencies of Pb(II) and Cu(II) were obtained 96.25% and 75.54%, respectively. In the equilibrium isotherm study, the adsorption data fitted well with the Langmuir isotherm model. The adsorption kinetics was best depicted by the pseudo-second order model. Desorption experiments showed adsorbent can be reused successfully for three adsorption-desorption cycles.

For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique. The input effective variables for nanoemulsion formulation were drug loading, surfactant and co-surfactant content, concentration of glycerol, and concentration of vitamin E; the particle size was the output response, because size reduction will improve the stability of the nanoemulsion and the biological efficacy of the drug in vivo after parenteral administration. To achieve the optimum topologies, the ANN was trained by Incremental Back-Propagation (IBP), Batch Back-Propagation (BBP), Quick Propagation (QP), and Levenberg–Marquardt (LM) algorithms for testing data set. The topologies were confirmed by the indicator of minimized root mean squared error (RMSE) for each. Based on this indicator, BBP-5-14-1 was selected as the optimum topology to be used as a final model to predict the desirable particle size and relative importance of the effective variables of the formulation. The ANN analysis showed that the actual particle size (54.7 nm ± 0.8) of the formulated nanoemulsion was quite close to the predicted value (53.9 nm) obtained from the batch back propagation-ANN model, which supports the conclusion that the ANN model has the potential to predict a stable nanoemulsion system that could be used efficiently for the parenteral administration of azithromycin antibiotic..