Improved Mucoadhesive Properties of Repaglinide-Loaded Nanoparticles: Mathematical Modelling through Machine Learning-Based Approach

This research work aims to develop a modified repaglinide-loaded chitosan-ethyl cellulose nanoparticles (RPG-ECSNPs) as a novel sustained-release dosage form with improved mucoadhesive properties using an emulsification solvent-evaporation technique. The RPG-ECSNPs with different particle sizes were prepared from various polymers containing ethyl cellulose (EC) as the internal phase and chitosan (CS) as the external phase, and the use of surfactants, including Tween 80 and poloxamer 188 as emulsifiers. In vitro drug release, drug loading amount, and entrapment efficiency have been influenced by changes in the concentrations of CS and EC. The mean droplet size and zeta potential of RPG-ECSNPs were 213 ± 8.5 nm and 16.4 ± 2.4 mV, respectively. The optimized formulation's entrapment efficiency was 66 ± 2.3%, and drug loading was 7.9 ± 1.65%. The release profile was significantly higher in PBS (90%) than in diluted hydrochloric acid (30%) during 24 h of the study. The mucoadhesive function of the particles was examined in vitro using part of rat intestines. The highest adhesive % was observed for the chitosan-coated NPs. No adhesive properties were noticed for chitosan-free NPs (P-value > 0.05). This indicated that ECSNPs can be successfully utilized for sustained and controlled drug delivery of RPG through the GIT

Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

Nowadays, supercritical fluid technology (SFT) has been an interesting scientific subject in disparate industrial-based activities such as drug delivery, chromatography, and purification. In this technology, solubility plays an incontrovertible role. Therefore, achieving more knowledge about the development of promising numerical/computational methods of solubility prediction to validate the experimental data may be advantageous for increasing the quality of research and therefore, the efficacy of novel drugs. Decitabine with the chemical formula C₈H₁₂N₄O₄ is a chemotherapeutic agent applied for the treatment of disparate bone-marrow-related malignancies such as acute myeloid leukemia (AML) by preventing DNA methyltransferase and activation of silent genes. This study aims to predict the optimum value of decitabine solubility in CO₂SCF by employing different machine learning-based mathematical models. In this investigation, we used AdaBoost (Adaptive Boosting) to boost three base models including Linear Regression (LR), Decision Tree (DT), and GRNN. We used a dataset that has 32 sample points to make solubility models. One of the two input features is P (bar) and the other is T (k). ADA-DT (Adaboost Algorithm Decision Tree), ADA-LR (Adaboost Algorithm-Linear Regresion), and ADA-GRNN (Generative Regression Neural Network) models showed MAE of 6.54 ˣ 10ˉ⁵, 4.66 10 ˉ⁵, and 8.35 10 ˉ⁵, respectively. Also, in terms of R-squared score, these models have 0.986, 0.983, and 0.911 scores, respectively. ADA-LR was selected as the primary model according to numerical and visual analysis. Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10ˉ³ mol fraction) using this model

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10−4, 7.814 × 10−5, and 1.664 × 10−4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10−4, 6.197 × 10−5, and 8.777 × 10−5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10−3) vector