Novel and accurate mathematical simulation of various models for accurate prediction of surface tension parameters through ionic liquids

Ionic Liquids (ILs) as a novel class of liquid solvent simultaneously carry the positive characteristics of both molten salts and organic liquids. Remarkable positive properties of ILs have such as low vapor pressure and excellent permittivity have encouraged the motivation of researchers to use them in various applications over the last decade. Surface tension is an important physicochemical property of ILs, which its experimental-based measurement has been done by various researchers. Despite great precision, some major shortcomings such as high cost and health related problems caused the researchers to develop mathematical models based on artificial intelligence (AI) approach to predict surface tension theoretically. In this research, the surface tension of two novel ILs (bis [(trifluoromethyl) sulfonyl] imide and 1,3-nonylimidazolium bis [(trifluoromethyl) sulfonyl] imide) were predicted using three predictive models. The available dataset contains 45 input features, which is relatively high in dimension. We decided to use AdaBoost with different base models, including Gaussian Process Regression (GPR), support vector regression (SVR), and decision tree (DT). Also, for feature selection and hyper-parameter tuning, a genetic algorithm (GA) search is used. The final R2 -score for boosted DT, boosted GPR, and boosted SVR is 0.849, 0.981, and 0.944, respectively. Also, with the MAPE metric, boosted GPR has an error rate of 1.73E-02, boosted SVR has an error rate of 2.35E-02, and it is 3.36E-02 for boosted DT. So, the ADABOOST-GPR model was considered as the primary model for the research

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

A robust computational investigation on C₆₀ fullerene nanostructure as a novel sensor to detect SCNˉ

This study explored on the adsorption properties and electronic structure of SCNˉ via density functional theory analysis on the exterior surfaces of C₆₀ and CNTs using B3LYP functional and 6-31G** standard basis set. Then adsorption of SCNˉ through nitrogen atom on the C60 fullerene is electrostatic (₋48.02 kJ molˉ1) in comparison with the C₅₉Al fullerene that shows covalently attached to fullerene surface (₋389.10 kJ mol̄ˉ1). Our calculations demonstrate that the SCNˉ adsorption on the pristine and Al-doped single-walled CNTs are ₋173.13 and ₋334.43 kJ molˉ1, indicating that the SCNˉ can be chemically bonded on the surface of Al-doped CNTs. Moreover, the adsorption of SCNˉ on the C₆₀ surface is weaker in comparison with C₅₉B, C₅₉Al, and C₅₉Ga systems but its electronic sensitivity improved in comparison with those of C₅₉B, C₅₉Al, and C₅₉Ga fullerenes. The evaluation of adsorption energy, energy gap, and dipole moment demonstrates that the pure fullerene can be exploited in the design practice as an SCNˉ sensor and C₅₉Al can be used for SCNˉ removal application

Intranasal Delivery of Granisetron to the Brain via Nanostructured Cubosomes-Based In Situ Gel for Improved Management of Chemotherapy-Induced Emesis

This research aimed to boost granisetron (GS) delivery to the brain via the intranasal route to better manage chemotherapy-induced emesis. Glycerol monooleate (GMO), Poloxamer 407 (P 407) and Tween 80 (T 80) were used to formulate GS-loaded cubosomes (GS-CBS) utilizing a melt dispersion-emulsification technique. GS-CBS were characterized by testing particle diameter, surface charge and entrapment efficiency. The formulations were optimized using a Box–Behnken statistical design, and the optimum formula (including GMO with a concentration of 4.9%, P 407 with a concentration of 10%, and T 80 with a concentration of 1%) was investigated for morphology, release behavior, ex vivo permeation through the nasal mucosa, and physical stability. Moreover, the optimal formula was incorporated into a thermosensitive gel and subjected to histopathological and in vivo biodistribution experiments. It demonstrated sustained release characteristics, increased ex vivo permeability and improved physical stability. Moreover, the cubosomal in situ gel was safe and biocompatible when applied to the nasal mucosa. Furthermore, compared to a drug solution, the nose-to-brain pathway enhanced bioavailability and brain distribution. Finally, the cubosomal in situ gel may be a potential nanocarrier for GS delivery to the brain through nose-to-brain pathway

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

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

MODULATING THE DRUG SOLUBILITY OF ACECLOFENAC BY DESIGN AS SOLID LIPID PARTICLES: IN VITRO/ IN VIVO CORRELATION

Objective: The main objective of the study was to enhance the dissolution and hence the oral bioavailability of Aceclofenac (ACF).   Methods: ACF was formulated as solid lipid particles (SLPs), which compressed into a tablet form for immediate release purpose and certain formulations were then coated by Eudragit RS100 polymer for sustained release action. SLPs of ACF were prepared by melt fusion method under the optimum conditions, using Compritol ATO 888 (Cr), Precirol ATO 5 (Pr), glyceryl monstearate, polyethylene glycols 4000, and Poloxamer 188 at different ratios SLP formulations were characterized for particle size, flow characteristics. The compressed tablets were identified in term of hardness, friability, content, moisture uptake, and in vitro release. Oral pharmacokinetics of the optimum tablet formulation and marketed tablet as reference were studied in rabbits. Results: SLP of acecloenac (ACF) showed accepted flowing properties, and the dissolution rate of the ACF from tablets was significantly enhanced compared to unprocessed drug. The results showed that about 45.5±2.5% of AC was released within 30 minutes from F1 while 12.7±4.5% was released from commercial AC tablets. The in vivo studies verified that the Cmax was 1.98± 0.29, 2.10±0.33, and 4.83± 86 µg/µL for the optimized immediate, sustained formula and commercial tablet respectively. While the area under the curve from zero time to 24 h for the immediate and sustained release formula was 1.79, and 2.41 fold greater than the marketed formulation. Conclusion: The results showed that solid lipid particles under optimized conditions might be an efficient method for improving the solubility and hence the bioavailability of poorly soluble drugs likes ACF. The proper coating of the formula helps to achieve a convenient release of the drug

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with myelodysplastic syndrome (MDS). In recent years, the prediction of drug solubility by applying mathematical models through artificial intelligence (AI) has become known as an interesting topic due to the high cost of experimental investigations. The purpose of this study is to develop various machine-learning-based models to estimate the optimum solubility of the anti-cancer drug decitabine, to evaluate the effects of pressure and temperature on it. To make models on a small dataset in this research, we used three ensemble methods, Random Forest (RFR), Extra Tree (ETR), and Gradient Boosted Regression Trees (GBRT). Different configurations were tested, and optimal hyper-parameters were found. Then, the final models were assessed using standard metrics. RFR, ETR, and GBRT had R2 scores of 0.925, 0.999, and 0.999, respectively. Furthermore, the MAPE metric error rates were 1.423 × 10−1 7.573 × 10−2, and 7.119 × 10−2, respectively. According to these facts, GBRT was considered as the primary model in this paper. Using this method, the optimal amounts are calculated as: P = 380.88 bar, T = 333.01 K, Y = 0.001073