Numerical Optimization of Prednisolone-Tacrolimus Loaded Ultraflexible Transethosomes for Transdermal Delivery Enhancement; Box-Behnken Design, Evaluation, Optimization, and Pharmacokinetic Study.

The aim of the present study is to formulate highly permeable carriers (i.e., transethosomes) for enhancing the delivery of prednisolone combined with tacrolimus for both topical and systemic pathological conditions. A Box-Behnken experimental design was implemented in this research. Three independent variables: surfactant concentration (X1), ethanol concentration (X2), and tacrolimus concentration (X3) were adopted in the design while three responses: entrapment efficiency (Y1), vesicle size (Y2), and zeta potential (Y3) were investigated. By applying design analysis, one optimum formulation was chosen to be incorporated into topical gel formulation. The optimized transethosomal gel formula was characterized in terms of pH, drug content, and spreadability. The gel formula was challenged in terms of its anti-inflammatory effect and pharmacokinetics against oral prednisolone suspension and topical prednisolone-tacrolimus gel. The optimized transethosomal gel achieved the highest rate of rat hind paw edema reduction (98.34%) and highest pharmacokinetics parameters (Cmax 133.266 ± 6.469 µg/mL; AUC0-∞ 538.922 ± 49.052 µg·h/mL), which indicated better performance of the formulated gel.

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models.

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10-8, 2.173 × 10-9, and 1.372 × 10-8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10-3 as an output.

Central Composite Optimization of Glycerosomes for the Enhanced Oral Bioavailability and Brain Delivery of Quetiapine Fumarate.

This study aimed to formulate and statistically optimize glycerosomal formulations of Quetiapine fumarate (QTF) to increase its oral bioavailability and enhance its brain delivery. The study was designed using a Central composite rotatable design using Design-Expert® software. The independent variables in the study were glycerol % w/v and cholesterol % w/v, while the dependent variables were vesicle size (VS), zeta potential (ZP), and entrapment efficiency percent (EE%). The numerical optimization process resulted in an optimum formula composed of 29.645 (w/v%) glycerol, 0.8 (w/v%) cholesterol, and 5 (w/v%) lecithin. It showed a vesicle size of 290.4 nm, zeta potential of -34.58, and entrapment efficiency of 80.85%. The optimum formula was further characterized for DSC, XRD, TEM, in-vitro release, the effect of aging, and pharmacokinetic study. DSC thermogram confirmed the compatibility of the drug with the ingredients. XRD revealed the encapsulation of the drug in the glycerosomal nanovesicles. TEM image revealed spherical vesicles with no aggregates. Additionally, it showed enhanced drug release when compared to a drug suspension and also exhibited good stability for one month. Moreover, it showed higher brain Cmax, AUC0-24, and AUC0-∞ and plasma AUC0-24 and AUC0-∞ in comparison to drug suspension. It showed brain and plasma bioavailability enhancement of 153.15 and 179.85%, respectively, compared to the drug suspension. So, the optimum glycerosomal formula may be regarded as a promising carrier to enhance the oral bioavailability and brain delivery of Quetiapine fumarate.

Amphotericin B-PEG Conjugates of ZnO Nanoparticles: Enhancement Antifungal Activity with Minimal Toxicity.

Amphotericin B (AMB) is commonly used to treat life-threatening systemic fungal infections. AMB formulations that are more efficient and less nephrotoxic are currently unmet needs. In the current study, new ZnO-PEGylated AMB (ZnO-AMB-PEG) nanoparticles (NPs) were synthesized and their antifungal effects on the Candida spp. were investigated. The size and zeta potential values of AMB-PEG and ZnO-AMB-PEG NPs were 216.2 ± 26.9 to 662.3 ± 24.7 nm and -11.8 ± 2.02 to -14.2 ± 0.94 mV, respectively. The FTIR, XRD, and EDX spectra indicated that the PEG-enclosed AMB was capped by ZnO, and SEM images revealed the ZnO distribution on the surface NPs. In comparison to ZnO-AMB NPs and free AMB against C.albicans and C.neoformans, ZnO-AMB-PEG NPs significantly reduced the MIC and MFC. After a week of single and multiple dosage, the toxicity was investigated utilizing in vitro blood hemolysis, in vivo nephrotoxicity, and hepatic functions. ZnO-AMB-PEG significantly lowered WBC count and hematocrit concentrations when compared to AMB and ZnO-AMB. RBC count and hemoglobulin content, on the other hand, were unaltered. ZnO-AMB-PEG considerably lowered creatinine and blood urea nitrogen (BUN) levels when compared to AMB and ZnO-AMB. The difference in liver function indicators was determined to be minor by all formulae. These findings imply that ZnO-AMB-PEG could be utilized in the clinic with little nephrotoxicity, although more research is needed to determine the formulation's in vivo efficacy.

Formulation of Chitosan-Coated Brigatinib Nanospanlastics: Optimization, Characterization, Stability Assessment and In-Vitro Cytotoxicity Activity against H-1975 Cell Lines.

The purpose of the current study was to develop Brigatinib (BGT)-loaded nanospanlastics (BGT-loaded NSPs) (S1-S13) containing Span 60 with different edge activators (Tween 80 and Pluronic F127) and optimized based on the vesicle size, zeta potential (ZP), and percent entrapment efficiency (%EE) using Design-Expert® software. The optimum formula was recommended with desirability of 0.819 and composed of Span-60:Tween 80 at a ratio of 4:1 and 10 min as a sonication time (S13). It showed predicted EE% (81.58%), vesicle size (386.55 nm), and ZP (-29.51 mv). The optimized nanospanlastics (S13) was further coated with chitosan and further evaluated for Differential Scanning Calorimetry (DSC), X-ray Diffraction (XRD), in vitro release, Transmission Electron Microscopy (TEM), stability and in-vitro cytotoxicity studies against H-1975 lung cancer cell lines. The DSC and XRD revealed complete encapsulation of the drug. TEM imagery revealed spherical nanovesicles with a smooth surface. Also, the coated formula showed high stability for three months in two different conditions. Moreover, it resulted in improved and sustained drug release than free BGT suspension and exhibited Higuchi kinetic release mechanism. The cytotoxic activity of BGT-loaded SPs (S13) was enhanced three times in comparison to free the BGT drug against the H-1975 cell lines. Overall, these results confirmed that BGT-loaded SPs could be a promising nanocarrier to improve the anticancer efficacy of BGT.