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.
A Taguchi robust design method with an L₉ orthogonal array was implemented to optimize experimental conditions for the biosynthesis of triethanolamine (TEA)-based esterquat cationic surfactants using an enzymatic reaction method. The esterification reaction conversion% was considered as the response. Enzyme amount, reaction time, reaction temperature and molar ratio of substrates, [oleic acid: triethanolamine (OA:TEA)] were chosen as main parameters. As a result of the Taguchi analysis in this study, the molar ratio of substrates was found to be the most influential parameter on the esterification reaction conversion%. The amount of enzyme in the reaction had also a significant effect on reaction conversion%.
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.
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software's option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), 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 indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
This research aims to formulate and to optimize a nanoemulsion-based formulation containing fullerene, an antioxidant, stabilized by a low amount of mixed surfactants using high shear and the ultrasonic emulsification method for transdermal delivery. Process parameters optimization of fullerene nanoemulsions was done by employing response surface methodology, which involved statistical multivariate analysis. Optimization of independent variables was investigated using experimental design based on Box-Behnken design and central composite rotatable design. An investigation on the effect of the homogenization rate (4,000-5,000 rpm), sonication amplitude (20%-60%), and sonication time (30-150 seconds) on the particle size, ζ-potential, and viscosity of the colloidal systems was conducted. Under the optimum conditions, the central composite rotatable design model suggested the response variables for particle size, ζ-potential, and viscosity of the fullerene nanoemulsion were 152.5 nm, -52.6 mV, and 44.6 pascal seconds, respectively. In contrast, the Box-Behnken design model proposed that preparation under the optimum condition would produce nanoemulsion with particle size, ζ-potential, and viscosity of 148.5 nm, -55.2 mV, and 39.9 pascal seconds, respectively. The suggested process parameters to obtain optimum formulation by both models yielded actual response values similar to the predicted values with residual standard error of <2%. The optimum formulation showed more elastic and solid-like characteristics due to the existence of a large linear viscoelastic region.
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.
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.
Palm kernel oil esters nanoemulsion-loaded with chloramphenicol was optimized using response surface methodology (RSM), a multivariate statistical technique. Effect of independent variables (oil amount, lecithin amount and glycerol amount) toward response variables (particle size, polydispersity index, zeta potential and osmolality) were studied using central composite design (CCD). RSM analysis showed that the experimental data could be fitted into a second-order polynomial model. Chloramphenicol-loaded nanoemulsion was formulated by using high pressure homogenizer. The optimized chloramphenicol-loaded nanoemulsion response values for particle size, PDI, zeta potential and osmolality were 95.33nm, 0.238, -36.91mV, and 200mOsm/kg, respectively. The actual values of the formulated nanoemulsion were in good agreement with the predicted values obtained from RSM. The results showed that the optimized compositions have the potential to be used as a parenteral emulsion to cross blood-brain barrier (BBB) for meningitis treatment.
The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation.
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.
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.
Aripiprazole is considered as a third-generation antipsychotic drug with excellent therapeutic efficacy in controlling schizophrenia symptoms and was the first atypical anti-psychotic 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%.
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.
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.
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.
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 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 abstractSchematic figure of high pressure homogenizer device.
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.
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%.
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).