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Propagation of high intensity ultrasonic waves and shearing effect in formulating nanoemulsion loaded fullerene for drug delivery was investigated. Artificial neural network (ANN) was applied to optimize the emulsification process by varying the ultrasonic and homogenization parameters. Control of operating conditions, such as sonication amplitude (30–70%) and duration (60–120 s), as well as homogenization rate (4000–5000 rpm), was tested to determine the physical attributes of nanoemulsion. Ultrasonic cavitation showed far greater effects as compared to high shear homogenization in controlling the droplet size (sonication time) and viscosity (sonication amplitude) of the nanoemulsion system. Levenberg–Marquardt algorithm produced the optimum topology with network architecture of three inputs, four hidden nodes, and two outputs. Validation further confirmed the aptness of the proposed model with low root mean square error. In this study, ANN has superior predictive ability by yielding low percentage of residual standard error. An ultrasonic approach in formulating fullerene nanoemulsion system is a powerful technique in minimization of droplet size and acquisition of desirable viscosity. This serves as a platform to advance fullerene in nanomedicine field despite its hydrophobicity. Keywords: Nanoemulsion, Fullerene, Artificial neural network, Ultrasonic cavitation, High shear homogenization

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).

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.

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 27–16 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.

Meningitis is a disease caused by a bacterial infection in the brain. Streptococcus pneumonia is the most common causative agent for meningitis disease. Chloramphenicol used to be a drug used for the treatment of meningitis. Current medication in treating meningitis is using the family of cephalosporin drugs. However, due to the drug-resistant problem, this family of a drug is no longer efficient towards the meningitis bacteria. Thus, the use of chloramphenicol has caught back the attention in treating meningitis disease. Chloramphenicol is claimed to be toxic towards human cells since the higher dosage is needed per injection for every treatment. This is corresponding to the poor delivery method of this hydrophobic drug. Nanoemulsion is believed to be the best option in transporting chloramphenicol to the brain by the intravenous route. A good combination of oil mixed with a surfactant mixture led to the formation of formulation with small particle sizes with low PDI values. Optimization of nanoemulsion's composition using Response Surface Methodology (RSM) suggested that the best amount of oil, lecithin, and glycerol were 4%, 2.5%, and 2.25%, respectively. The optimized formulation was then modified due to the instability and insufficient osmolality value of the formulation. The physicochemical characteristics (particle size, PDI, zeta potential, osmolality, viscosity, and pH) of the formulation successfully fulfilled the requirement for parenteral application. Toxicity analysis showed that chloramphenicol encapsulated nanoemulsion system was much safer compared to the standard chloramphenicol. Storage of the chloramphenicol-loaded nanoemulsion at 4°C showed good stability for 3 months with no significant changes on the particle size.

Psoriasis is an autoimmune disease in which the symptoms are obviously appeared on the patient's body with thick, red/ salmon colour and dry patches. Clinically, topical treatment has been the first-line treatment for psoriasis, whether or not the systemic and phototherapy is required. Currently, cyclosporine is one of the standard oral medications used for moderate to a severe case of psoriasis. However, through oral delivery, it cannot be used for a long period and causes bad effects. Thus, a new delivery system of cyclosporine is in need to be developed to overcome its toxic effects by skipping the first pass body metabolism. A nanoemulsion colloidal system, as the main carrier for big, non-water-soluble cyclosporine, is believed could help in the delivery of activities through the stratum corneum of the skin layer. Nanoemulsion system, which mainly consists of oil phase (a mixture of virgin coconut oil, nutmeg oil, and cyclosporine), aqueous phase, and surfactant, was successfully being developed by using Mixture Experimental Design optimization tool with droplet size range between 110-160 nm. Stability studies were carried out with respect to; pH, storage at different temperatures, coalescence and Ostwald ripening mechanism, free-thaw cycle, and FTIR analysis. From the obtained results, this newly-developed nanoemulsion containing cyclosporine was proven to be stable and successfully maintained its physicochemical characteristics even after 3 months of storage at 4°C and 25°C temperature. Ostwald ripening mechanism contributed to the instability of the system at 45°C of storage. Introduction of standard cyclosporine, blank nanoemulsion, and nanoemulsion containing cyclosporine to the HaCaT cells in the treatment of MTT assay study has confirmed the safety of the nanoemulsion. Permeation study of the newly-developed nanoemulsion containing cyclosporine showed it was best fitted the Korsmeyer-Peppas kinetic model. In addition, the transepidermal water loss and hydration studies proved the efficacy of cyclosporine-loaded nanoemulsion in increasing the water storage of the volunteers’ normal skin after 3h of application.

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.

Since Reactive Blue 21 (RB21) is one of the dye compounds which is harmful to human life, a simple and sensitive method to remove this pollutant from wastewater is using Nano Zero-Valent Iron (NZVI) catalyst. In this paper, a Central Composite Rotatable Design (CCRD) was employed for response surface modeling to optimize experimental conditions of the RB21 removal from aqueous solution. The significance and adequacy of the model were analyzed using analysis of variance (ANOVA). Four independent variables—including catalyst amount (0.1–0.9 g), pH (3.5–9.5), removal time (30–150 s) and dye concentration (10–50 mg/L)—were transformed to coded values and consequently second order quadratic model was built to predict the responses. The result showed that under optimized experimental conditions the removal of RB21 was over 95%.

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.

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.

Response surface methodology (RSM) was used to optimize the formulation of a nanoemulsion for central delivery following parenteral administration. A mixture of medium-chain triglyceride (MCT) and safflower seed oil (SSO) was determined as a sole phase from the emulsification properties. Similarly, a natural surfactant (lecithin) and non-ionic surfactant (Tween 80) (ratio 1:2) were used in the formulation. A central composite design (CCD) with three-factor at five-levels was used to optimize the processing method of high energy ultrasonicator. Effects of pre-sonication ultrasonic intensity (A), sonication time (B), and temperature (C) were studied on the preparation of nanoemulsion loaded with valproic acid. Influence of the aforementioned specifically the effects of the ultrasonic processing parameters on droplet size and polydispersity index were investigated. From the analysis, it was found that the interaction between ultrasonic intensity and sonication time was the most influential factor on the droplet size of nanoemulsion formulated. Ultrasonic intensity (A) significantly affects the polydispersity index value. With this optimization method, a favorable droplet size of a nanoemulsion with reasonable polydispersity index was able to be formulated within a short sonication time. A valproic acid loaded nanoemulsion can be obtained with 60% power intensity for 15 min at 60 °C. Droplet size of 43.21±0.11 nm with polydispersity index of 0.211 were produced. The drug content was then increased to 1.5%. Stability study of nanoemulsion containing 1.5% of valproic acid had a good stability as there are no significant changes in physicochemical aspects such as droplet size and polydispersity index. With the characteristisation study of pH, viscosity, transmission electron microscope (TEM) and stability assessment study the formulated nanoemulsion has the potential to penetrate blood-brain barrier in the treatment of epilepsy.

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 release after 8 h application.

Clinacanthus nutans Lindau (C. nutans) is a well-known medicinal plant in South-East Asia that recently has attracted attention due to its therapeutic characteristics and cosmeceutical applications. However, delivering the beneficial attributes of the bioactive ingredients into a formulation is challenging due to poor solubility and low bioavailability of bioactive substances, which may hinder their effective transdermal delivery. Therefore, a nanoemulsion has been chosen to be a carrier in the encapsulation of bioactive ingredients of C. nutans extract for pharmaceutical and cosmeceutical formulations. In this work, a D-optimal mixture design was used to determine the optimal composition of a nanoemulsion-based system loaded with C. nutans leaves extract. The ultimate goal of the present work was to determine the optimum level of five independent variables (surfactant, oil, xanthan gum, bioactive extract, and water) in the optimal C. nutans nanoemulsion composition with minimum average droplet size. Under the optimal conditions, the predicted average droplet size was 99.99 nm. The actual response showed that the model was in good agreement with the predicted value with residual standard error (RSE) of 2.61%. The optimal nanoemulsion composition was observed to be stable under an accelerated stability study during storage at 25 and 45 °C for 90 days, centrifugal force and freeze–thaw cycles. Physicochemical characterization of the optimal nanoemulsion showed its suitability for transdermal application due to its stability against phase separation.

Aripiprazole was encapsulated in a palm kernel oil esters nanoemulsion for the purpose of brain delivery via intravenous administration. High shear and high pressure homogenizers were applied for formulating the low water solubility drug in the nanoemulsion system, which was stabilized using different emulsifiers; lecithin, Tween 80 and glycerol. The artificial neural networks (ANNs) modeling of the nanoemulsion formulation was carried out to achieve the minimum particle size. The effects of the amount 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) on the particle size were considered as inputs to the network. The particle size of the samples with various compositions was measured as an output. To obtain the optimum topologies, the ANNs were trained using Incremental Back Propagation (IBP), Genetic Algorithm (GA), Batch Back Propagation (BBP), Quick Propagation (QP), and Levenberg–Marquardt (LM) algorithms for the testing data set. The topologies were determined by the indicator of the minimized root mean squared error (RMSE) for each algorithm. According to the results, 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 input, 4 hidden and 1 output nodes. Conclusively, ANN models were developed for the prediction of the particle size of nanoemulsions loaded with aripiprazole and its stable nanoemulsion system, which could be used effectively for intravenous administration.

Bioavailability of quercetin, a flavonoid potentially known to combat cancer, is challenging due to hydrophobic nature. Oil-in-water (O/W) nanoemulsion system could be used as nanocarrier for quercertin to be delivered to lung via pulmonary delivery. The novelty of this nanoformulation was introduced by using palm oil ester/ricinoleic acid as oil phase which formed spherical shape nanoemulsion as measured by transmission electron microscopy and Zetasizer analyses. High energy emulsification method and D-optimal mixture design were used to optimize the composition towards the volume median diameter. The droplet size, polydispersity index, and zeta potential of the optimized formulation were 131.4 nm, 0.257, and 51.1 mV, respectively. The formulation exhibited high drug entrapment efficiency and good stability against phase separation and storage at temperature 4 °C for 3 months. It was discovered that the system had an acceptable median mass aerodynamic diameter (3.09 ± 0.05 μm) and geometric standard deviation (1.77 ± 0.03) with high fine particle fraction (90.52 ± 0.10%), percent dispersed (83.12 ± 1.29%), and percent inhaled (81.26 ± 1.28%) for deposition in deep lung. The in vitro release study demonstrated that the sustained release pattern of quercetin from naneomulsion formulation up to 48 h of about 26.75% release and it was in adherence to Korsmeyer’s Peppas mechanism. The cytotoxicity study demonstrated that the optimized nanoemulsion can potentially induce cyctotoxicity towards A549 lung cancer cells without affecting the normal cells. These results of the study suggest that nanoemulsion is a potential carrier system for pulmonary delivery of molecules with low water solubility like quercetin.

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.

Palm-based nanoemulsion has played a significant role as a biocompatible delivery system in various industries, but has yet to achieve its full potential since little information about formulation with palm oil is known. Such a nanoemulsion can be developed and optimized systematically to attain excellent physicochemical properties, such as low viscosity, high kinetic stability, and large interfacial area, while being safe for human use. Most poorly water-soluble drugs could be loaded in this nanoemulsion and delivered efficiently to the target sites. This chapter summarizes recent studies conducted on the development of palm-based nanoemulsion, physicochemical characterization in contributing to drug efficacy, and in vitro permeation studies. Computational simulation study was used as a tool to investigate the self-assembly process and physicochemical properties at atomic details, based on experimentally phase diagrams obtained. Deeper understanding and research on nanodelivery via palm-based nanoemulsion will give positive impact, especially on the pharmaceutical industries.

An increasing demand for natural additives has drawn more interest from consumers due to their relatively safe and wide acceptance. The present work examines the potential of different solvent system of Clinacanthus nutans Lindau leaves as a source of natural antioxidant. We also screened for its phytochemical constituents and cytotoxicity toward breast cancer cell. In vitro antioxidant activity of the n-hexane, dichloromethane, ethyl acetate, and ethanol extracts were assessed via 1,1-diphenyl-picrylhydrazyl (DPPH) radical scavenging activity, oxygen radical absorbance capacity and s-carotene bleaching activity assays, whereas the cytotoxic effect was tested on tumorigenic breast cancer estrogen positive cell (MCF-7) and normal fibroblast cell (3T3) using the tetrazolium assay. Liquid chromatography mass spectrometry equipped with an electrospray ionization source liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI/MS) was used to analyze the amount of targeted phenolic and fatty acids in ethanol and ethyl acetate extracts. Several phenolic compounds and fatty acids were identified and quantified from ethanol and ethyl acetate extracts of this plant. Ethanol and ethyl acetate extracts demonstrated stronger antioxidant activity than n-hexane and dichloromethane extracts (p

Artificial neural networks have been widely used to solve problems because of their reliable, robust, and salient characteristics in capturing nonlinear relationships between variables in complex systems. In this study, a wavelet neural network (WNN) based on the incremental backpropagation (IBP) algorithm was used in conjunction with an experimental design. To optimize the network, independent variables including ion concentration, adsorbent dose, and removal time were used as input parameters, while the removal percentage of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite were selected as outputs. The network was trained by the IBP and four other algorithms as a model. To determine the number of hidden-layer nodes in the model, the root-mean-square error of a testing set was minimized. After minimizing this error, the topologies of the algorithms were compared based on the coefficient of determination and absolute average deviation. This comparison indicated that the IBP algorithm had the minimum root-mean-square error and absolute average deviation, and maximum coefficient of determination, for the test dataset. The importance values included 35.16 % for initial ion concentration, 32.74 % for adsorbent dose, and 32.11 % for removal time, showing that none of these were negligible. These results show that the WNN has great potential ability for prediction of removal of heavy-metal ions from aqueous solution with residual standard error less than 1.2 %.

Fe3O4/montmorillonite nanocomposite (Fe3O4/MMT NC) was synthesized for removal of Pb2+, Cu2+ and Ni2+ ions from aqueous systems. The nanoadsorbent was characterized by X-ray diffraction and transmission electron microscopy and mean diameter of magnetic nanoparticles was about 8.24 nm. The experiments were designed by response surface methodology and quadratic model was used to prediction of the variables. The adsorption parameters of adsorbent dosage, removal time, and initial heavy metal ions concentration were used as the independent variables and their effects were investigated on the heavy metal ions removal. Variance analysis was utilized to judge the adequacy of the chosen models. Optimum conditions with initial heavy metal ions concentration of 510.16, 182.94, and 111.90 mg/L, 120 s of removal time and 0.06 g/0.025 L, 0.08 g/0.025 L, and 0.08 g/0.025 L of adsorbent amount were given 89.72%, 94.89%, and 76.15% of removal efficiency Pb2+, Cu2+ and Ni2+ ions, respectively. Prediction of models was in good agreement with experimental results and Fe3O4/MMT NC was found successful in removing heavy metals from their aqueous solutions.

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.

An application of artificial neural networks (ANNs) to predict the performance of a lipase-catalyzed synthesis for esterification of dilauryl azelate ester was carried out. The central composite rotatable design (CCRD) experimental data were utilized for training and testing of the proposed ANN model. The model was applied to predict various performance parameters of the enzymatic reaction conditions, namely enzyme amount (0.05–0.45 g), reaction time (90–450 min), reaction temperature (40–64 °C) and molar ratio of substrates (AzA:LA, 1:3–1:9 mol). The incremental back propagation (IBP), batch back propagation (BBP), quick propagation (QP), genetic algorithm (GA), and the Levenberg–Marguardt (LM) algorithms were used in the network. It was found that the optimal algorithm and topology were the incremental back propagation (IBP) and the configuration with 4 inputs, 14 hidden, and 1 output nodes, respectively.

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 aim of this study is the development of nanoemulsions for intravenous administration of Sorafenib, which is a poorly soluble drug with no parenteral treatment. The formulation was prepared by a high energy emulsification method and optimized by response surface methodology. The effects of overhead stirring time, high shear rate, high shear time, and cycles of high-pressure homogenizer were studied in the preparation of nanoemulsion loaded with Sorafenib. Most of the particles in nanoemulsion are spherical in shape, the smallest particle size being 82.14 nm. The results of the 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, a tetrazole reveal that the optimum formulation does not affect normal cells significantly in low drug concentrations but could remove the cancer cells. Finally, a formulation containing Sorafenib retained its properties over a period of 90 days. With characterization, the study of the formulated nanoemulsion has the potential to be used as a parenteral nanoemulsion in the treatment of cancer. Graphical abstract Schematic figure of high pressure homogenizer device.

The D-optimal mixture experimental design was employed to optimize the melting point of natural lipstick based on pitaya (Hylocereus polyrhizus) seed oil. The influence of the main lipstick components—pitaya seed oil (10%–25% w/w), virgin coconut oil (25%–45% w/w), beeswax (5%–25% w/w), candelilla wax (1%–5% w/w) and carnauba wax (1%–5% w/w)—were investigated with respect to the melting point properties of the lipstick formulation. The D-optimal mixture experimental design was applied to optimize the properties of lipstick by focusing on the melting point with respect to the above influencing components. The D-optimal mixture design analysis showed that the variation in the response (melting point) could be depicted as a quadratic function of the main components of the lipstick. The best combination of each significant factor determined by the D-optimal mixture design was established 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). With respect to these factors, the 46.0 °C melting point property was observed experimentally, similar to the theoretical prediction of 46.5 °C. Carnauba wax is the most influential factor on this response (melting point) with its function being with respect to heat endurance. The quadratic polynomial model sufficiently fit the experimental data.

Box–Behnken (BBD) and central composite rotatable designs (CCRD) were used as statistical multivariate methods in the formulation optimization of fullerene loaded nano-emulsions. Effect of palm kernel oil ester (10–20%, w/w), emulsifier (5–10%, w/w) and xanthan gum (0.6–1.0%, w/w) as formulation variables on the particle size, ζ-potential and viscosity of the nano-emulsions were investigated. Under the optimum conditions, CCRD model predicted the response values for particle size, ζ-potential and viscosity were 153.6 nm, −53.4 mV and 42.1 Pa s, respectively. Nonetheless, BBD model suggested that the optimum conditions for a fullerene loaded nano-emulsion would gave particle size, ζ-potential and viscosity of 151.6 nm, −53.8 mV and 43.1 Pa s, respectively. The actual response according to suggested compositions for both models showed excellent agreement with the predicted value with residual standard error (RSE) of less than 4%. Optimum nano-emulsions were stable during storage at 25 and 45 °C for 90 days and freeze–thaw cycle.

Fe3O4/talc nanocomposite was used for removal of Cu(II), Ni(II), and Pb(II) ions from aqueous solutions. Experiments were designed by response surface methodology (RSM) and a quadratic model was used to predict the variables. The adsorption parameters such as adsorbent dosage, removal time, and initial ion concentration were used as the independent variables and their effects on heavy metal ion removal were investigated. Analysis of variance was incorporated to judge the adequacy of the models. Optimal conditions with initial heavy metal ion concentration of 100, 92 and 270 mg/L, 120 s of removal time and 0.12 g of adsorbent amount resulted in 72.15%, 50.23%, and 91.35% removal efficiency for Cu(II), Ni(II), and Pb(II), respectively. The predictions of the model were in good agreement with experimental results and the Fe3O4/talc nanocomposite was successfully used to remove heavy metals from aqueous solutions.

A wavelet neural network (WNN) based on the genetic algorithm (GA) was used in conjunction with an experimental design to optimize the enzymatic reaction conditions for the preparation of esteramines-based esterquats. 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 and agitation speed were studied under the given conditions designed by Design Expert software. All these show that the WNN has great potential ability in prediction of reaction conversion in lipase-catalyzed synthesis of products.

Zero-valent iron nanoparticles were synthesized, for removal of Direct Yellow 12 dye. The experiments were designed by response surface methodology. Quadratic model was used to predict the variables. Analysis of variance was used for investigation of variables and interaction between them. High F-value (10.01), very low P-value (

An artificial neural network (ANN) was used to develop predictive models for studying and identifying factors that influence particle size and viscosity of sodium diclofenac-loaded palm kernel oil esters-nanomeulsions. The effect of four independent variables namely water content, oil and surfactant (O/S) ratio, mixing rate and mixing time were considered as inputs of the network trained. The particle size and viscosity of samples in various compositions prepared under different rate and time of high shear emulsification, were measured as output. Data, split into training, testing and validating sets, were modeled by incremental back propagation (IBP) algorithm. The developed model represents high quality performance of the neural network and its capability in modeling and identifying the critical factors that control preparation of the nanoemulsions. Water content with 30.82% importance was found to be the main parameter controlling the particle size and viscosity in the system, followed by O/S ratio, mixing rate and mixing time, with 27.28, 22.06 and 19.84% importance, respectively. The model was then employed to investigate the effect of composition and processing factors on particle size and viscosity of the nanoemulsions.

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.

An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.

While the oil palm industry has been recognized for its contribution towards economic growth and rapid development, it has also contributed to environmental pollution due to the production of huge quantities of by-products from the oil extraction process. A phytoremediation technique (floating Vetiver system) was used to treat Palm Oil Mill Secondary Effluent (POMSE). A batch study using 40 L treatment tanks was carried out under different conditions and Response Surface Methodology (RSM) was applied to optimize the treatment process. A three factor central composite design (CCD) was used to predict the experimental variables (POMSE concentration, Vetiver plant density and time). An extraordinary decrease in organic matter as measured by BOD and COD (96% and 94% respectively) was recorded during the experimental duration of 4 weeks using a density of 30 Vetiver plants. The best and lowest final BOD of 2 mg/L was obtained when using 15 Vetiver plants after 13 days for low concentration POMSE (initial BOD = 50 mg/L). The next best result of BOD at 32 mg/L was obtained when using 30 Vetiver plants after 24 days for medium concentration POMSE (initial BOD = 175 mg/L). These results confirmed the validity of the model, and the experimental value was determined to be quite close to the predicted value, implying that the empirical model derived from RSM experimental design can be used to adequately describe the relationship between the independent variables and response. The study showed that the Vetiver system is an effective method of treating POMSE.

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 (3–6wt%), lecithin (2–3wt%), Tween 80 (0.5–1wt%), glycerol (1.5–3wt%), and water (87–93wt%) 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 64.24nm, which showed an excellent agreement with the actual value (62.23nm) with residual standard error

Kojic acid is widely used to inhibit the browning effect of tyrosinase in cosmetic and food industries. In this work, synthesis of kojic monooleate ester (KMO) was carried out using lipase-catalysed esterification of kojic acid and oleic acid in a solvent-free system. Response Surface Methodology (RSM) based on central composite rotatable design (CCRD) was used to optimise the main important reaction variables, such as enzyme amount, reaction temperature, substrate molar ratio, and reaction time along with immobilised lipase from Candida Antarctica (Novozym 435) as a biocatalyst. The RSM data indicated that the reaction temperature was less significant in comparison to other factors for the production of a KMO ester. By using this statistical analysis, a quadratic model was developed in order to correlate the preparation variable to the response (reaction yield). The optimum conditions for the enzymatic synthesis of KMO were as follows: an enzyme amount of 2.0 wt%, reaction temperature of 83.69°C, substrate molar ratio of 1:2.37 (mmole kojic acid:oleic acid) and a reaction time of 300.0 min. Under these conditions, the actual yield percentage obtained was 42.09%, which is comparably well with the maximum predicted value of 44.46%. Under the optimal conditions, Novozym 435 could be reused for 5 cycles for KMO production percentage yield of at least 40%. The results demonstrated that statistical analysis using RSM can be used efficiently to optimise the production of a KMO ester. Moreover, the optimum conditions obtained can be applied to scale-up the process and minimise the cost.