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Abstract Using first-principles density functional theory (DFT), this study examines the improved chemical catalytic performance and biochemical sensing capabilities of iron (Fe) and gold (Au) nanoclusters decorated flawless γ-graphyne (GPN) as nanocarriers for the Ertapenem (EPM) antibiotic drug, in contrast to pristine γ-graphyne. The evaluation of binding energy analysis, it has been noted that perfect GPN (-0.96 eV), Au-decorated GPN (-1.852 eV), as well as Fe-decorated GPN (-1.520 eV), can be suitable candidates for drug delivery, as the binding energy falls in the physisorption to chemisorption range. There is a red shift in the ultraviolet-visible (UV-Vis) spectrum when EPM is adsorbed on the Fe- and Au-decorated GPN surfaces in comparison to the pristine substrates. Based on thermodynamic parameters, the values of Gibbs free energy changes (ΔG) and enthalpy change (ΔH) illustrate a strong interaction between EPM and the Au-decorated GPN (F: -1.130 and − 2.288 eV) in contrast to EPM with the Fe-decorated GPN carrier (I: -1.190 and − 2.210 eV), indicating that the interaction is stable and spontaneous. The Fe-decorated GPN improves the adsorption of EPM with a small binding energy, facilitated by a greater charge transfer from the substrate as an electron donor to the drug. This phenomenon results in a significant rise in dipole moment and a change in the energy gap. The results indicate that Fe-decorated GPN surface can serve as carriers for delivering the EPM drug.

We examined the adsorption of thiotepa (TTP) on the outer surface of B12N12, Mg12O12, and Si12C12 fullerene-like cages using density functional theory (DFT) calculations. The computations were carried out at the Perdew-Burke-Ernzerhof level with the D3 dispersion correction (PBE-D3) in a water solvent. The adsorption mechanism of TTP through N-head and S-head on the external surface of Si12C12 involves a covalent interaction (binding energy ∼ −1.31 eV) with the carrier surface. In contrast, the adsorption of the TTP molecule through N-head and S-head on the outer surfaces of B12N12 (binding energy ∼ −0.75 eV) and Mg12O12 (binding energy ∼ −0.62 eV) fullerene-like cages is driven by electrostatic interactions. Therefore, due to their low values of recovery time, both B12N12 and Mg12O12 fullerene-like cages can serve as carriers for TTP in the treatment of cancer. Thermodynamic parameters (Gibbs free energy and enthalpy energy) demonstrate that these interactions are exothermic and spontaneous. The results further reveal the significant disparity in the calculated HOMO and LUMO energies at the computational level. The formation of intricate bonds between the drug and the fullerene-like cages is attributed to the charge transfer dynamics that occur during their interactions. Through computational analysis and examination of the total density of state (TDOS) plots, it is evident that B12N12 and Si12C12 fullerene-like cages exhibit a high degree of sensitivity to the TTP drug. The adsorption process can increase the electrical conductivity of B12N12 and Si12C12, while having minimal effect on Mg12O12. This suggests that B12N12 could potentially serve as a suitable biosensor for detecting TTP.

We investigated the adsorption of ifosfamide (IFS) on the outer surface of zigzag (10, 0) carbon nanotubes (CNT), boron nitride nanotubes (BNNT), and silicon carbon nanotubes (SiCNT), using density functional theory (DFT) calculations at the PBE-D3 level in a water solvent phase. Based on zero-point corrected binding energies (Ebin), IFS exhibits chemisorption through its O-head and Cl-head on CNT (−1.05 eV) compared to BNNT (−0.93 eV), characterized by covalent interaction. In contrast, IFS undergoes physisorption via its O-head on SiCNT with binding energy of −0.68 eV as the most stable model this interaction is driven by electrostatic forces. The formation of complexes between the drug and nanotubes is influenced by charge transfer dynamics. Our thermodynamic analysis demonstrates the Gibbs free energy (ΔG) and enthalpy energy (ΔH) for all models are exothermic and spontaneous. The observed decrease in binding energy for BNNT and CNT correlates with changes in their energy gap, dipole moment, and charge transfer upon IFS adsorption. Notably, SiCNT exhibits a different response with a significant energy gap change leading to an increase in dipole moment and charge transfer. These findings suggest that these nanotubes demonstrate promising sensitivity to the presence of IFS and could be explored as potential drug delivery systems for this drug.

Abstract In this research, the generation of nanobubbles was carried out using a structure of vortex pump based on the relative blockage of flow (without the use of venturi and orifices, which consume a lot of energy to generate nanobubbles), which has made this process economical and commercial. In addition, the use of advanced synthesized nanoreactors with the Yolk@Shell structure, which forms a photoanode by coating the anode electrode and can operate in the visible light range, has highlighted this research work. An in-depth study of the synergistic effect of advanced photoelectrofenton oxidation methods in addition to the hydrodynamic reactor has shown that the intelligent selection of these three types of advanced oxidation methods together has improved the performance of each other and solved their negative aspects, including the use of hydrogen peroxide, divalent iron ion, and the removal of sludge generated by the electrofenton method. The use of hollow cylindrical electrodes allowed adequate loading of the advanced synthesized nanoreactors with Yolk@Shell structure. The investigation of the effects of micro (advanced synthesized nanoreactors with Yolk@Shell structure) and macro (vortex structure based on relative blockage of the flow) processes on the degradation of pharmaceutical pollutants, both separately and in combination, is a focus of this work. At the end, the energy consumption for each of these processes and this system in general was studied, which showed that the operating cost of this combined system according to the energy consumption requirements for the almost complete removal of the pollutant naproxen and the 90% reduction of its chemical oxygen demand is 6530 Rials/L.h (or 0.15525 USD/L.h), which presents this system as an economical method with industrialization capability. The degradability index (DI) of the introduced system under optimal operating conditions was 3.38, which shows that the development of the system based on the combination of advanced oxidation methods is a suitable method used in this research work due to its environmental friendliness, absence of side effluent production, efficiency and high degradation performance, ability to recover the nanocatalyst and consequently economic efficiency.

IntroductionThe study aimed to systematically enhance the fabrication process of flurbiprofen-loaded bilosomes (FSB) using Quality by Design (QbD) principles and Design of Experiments (DOE). The objective was to develop an optimized formulation with improved entrapment efficiency and targeted drug delivery capabilities.MethodsThe optimization process involved applying QbD principles and DOE to achieve the desired formulation characteristics. Superparamagnetic iron oxide nanoparticles (SPIONs) were incorporated to impart magnetic responsiveness. The size, entrapment efficiency, morphology, and in vitro release patterns of the FSB formulation were evaluated. Additionally, an in situ forming hydrogel incorporating FSB was developed, with its gelation time and drug release kinetics assessed. In vivo studies were conducted on osteoarthritic rats to evaluate the efficacy of the FSB-loaded hydrogel.ResultsThe optimized FSB formulation yielded particles with a size of 453.60 nm and an entrapment efficiency of 91.57%. The incorporation of SPIONs enhanced magnetic responsiveness. Morphological evaluations and in vitro release studies confirmed the structural integrity and sustained release characteristics of the FSB formulation. The in situ forming hydrogel exhibited a rapid gelation time of approximately 40 ± 1.8 s and controlled drug release kinetics. In vivo studies demonstrated a 27.83% reduction in joint inflammation and an 85% improvement in locomotor activity in osteoarthritic rats treated with FSB-loaded hydrogel.DiscussionThis comprehensive investigation highlights the potential of FSB as a promising targeted drug delivery system for the effective management of osteoarthritis. The use of QbD and DOE in the formulation process, along with the integration of SPIONs, resulted in an optimized FSB formulation with enhanced entrapment efficiency and targeted delivery capabilities. The in situ forming hydrogel further supported the formulation’s applicability for injectable applications, providing rapid gelation and sustained drug release. The in vivo results corroborate the formulation’s efficacy, underscoring its potential for improving the treatment of osteoarthritis.

This research is an analysis of multiple regression models developed for predicting ketoprofen solubility in supercritical carbon dioxide under different levels of T(K) and P(bar) as input features. Solubility of the drug was correlated to pressure and temperature as major operational variables. Selected models for this study are Piecewise Polynomial Regression (PPR), Kernel Ridge Regression (KRR), and Tweedie Regression (TDR). In order to improve the performance of the models, hyperparameter tuning is executed utilizing the Water Cycle Algorithm (WCA). Among, the PPR model obtained the best performance, with an R2 score of 0.97111, alongside an MSE of 1.6867E-09 and an MAE of 3.01040E-05. Following closely, the KRR model demonstrated a good performance with an R2 score of 0.95044, an MSE of 2.5499E-09, and an MAE of 3.49707E-05. In contrast, the TDR model produces a lower R2 score of 0.84413 together with an MSE of 7.4249E-09 and an MAE of 5.69159E-05.

This research paper presents a comprehensive thermodynamic and heat transfer study on predicting the ternary solubility of Nystatin in SC-CO2-Ethanol (supercritical CO2 and ethanol). The employed process is a thermal-based green processing for preparation of solid nanoparticles. The data collection, consisting of temperature and pressure as input features and ternary solubility as the target variable, was used to train and evaluate four different machine learning algorithms: Random Forest (RF), Extra Trees (ET), NU-SVR, and EPSILON-SVR. The hyper-parameter tuning process employed the Bat Optimization Algorithm (BA), a nature-inspired optimization technique to fine-tune the models and enhance their predictive capabilities. The ET model had a notable R2 score of 0.98526, RMSE of 2.48774E-02, and MAE of 2.13417E-02. The RF model also yielded strong performance, achieving an R2 score of 0.98436, RMSE of 2.55130E-02, and MAE of 2.06314E-02. However, the NU-SVR model exhibited superior performance compared to other models, as evidenced by its remarkable R2 score of 0.99943, thereby showcasing its exceptional precision. The RMSE and MAE for NU-SVR were 4.92372E-03 and 3.94943E-03, respectively, underscoring its precision in predicting ternary solubility. The EPSILON-SVR model, while still respectable, obtained a score of 0.93574 in terms of R2, RMSE of 4.37434E-02, and MAE of 3.79800E-02.

Based on the administration convenience, transmucosal buccal drug delivery allows special strength points over peroral routes for systemic delivery. It could achieve local or systemic effect and boost drugs’ bioavailability for agents with first pass metabolism. The current study aimed to manufacture and optimize a lavender oil–based nanoemulsion loaded with zaleplon and incorporate it into fast-disintegrating tablets to promote its dissolution and oral bioavailability via oral mucosa. Zaleplon-loaded nanoemulsions were devised with various levels of lavender oil (10% to 25%), the surfactant Sorbeth-20 (35% to 65%), and the co-surfactant HCO-60 (20% to 40%); the extreme vertices mixture statistical design was adopted. The droplet size and drug-loading efficiency were the evaluated. The optimal formulation was transformed into self-nanoemulsified lyophilized tablets (ZP-LV-SNELTs), which were tested for their uniformity of content, friability, and disintegration time with in-vitro release. Finally, the pharmacokinetic parameters of the ZP-LV-SNELTs were determined and compared with those of marketed formulations. The optimal nanoemulsion had a droplet size of 87 nm and drug-loading capacity of 185 mg/mL. ZP-LV-SNELTs exhibited acceptable friability and weight uniformity and a short disintegration time. The in-vitro release of ZP-LV-SNELTs was 17 times faster than that of the marketed tablet. Moreover, the optimal ZP-LV-SNELTs increased the bioavailability of zaleplon in rabbits by 1.6-fold compared with the commercial tablets. Hence, this investigation revealed that ZP-LV-SNELTs delivered zaleplon with enhanced solubility, a fast release, and boosted bioavailability thru oral mucosa which provided a favorable route for drug administration which is suggested to be clinically investigated in future studies

Objectives: Non melanoma skin cancers are common neoplasms worldwide. In India, squamous cell carcinoma (SCC), is the most prevalent skin disorder and its incidence rises quickly with cumulative exposure to sun. Numerous techniques are available for SCC but reversion and metastasis are common concern that needs effective and safe strategies for its control. With this in view, the study was planned to investigate the activity of Bakuchiol (Bak), traditionally used in various countries for curing skin ailments but its mechanism of action is unexplored. In our study, we explored anti-proliferative, pro-apoptotic and anti-inflammatory potential of Bak toward human squamous carcinoma (A431) cell line. Methods: The pure compound Bak was isolated from the plant Psoralea corylifolia and characterized using NMR, HRMS and FTIR. To explore their bioefficacy, different in vitro assays were performed against A431 cell line. To have molecular insights, RT-qPCR investigation was done to analyzed the expression level of inflammatory markers (TLR 9, IFN β, IL 23, JAK 3 and STAT 3). Results: The results showed the growth inhibitory effect on A431 cells after Bak treatment in dose-dependent way. To understand mode of cell death, cells were initially analyzed under phase-contrast, fluorescence and scanning electron microscope that showed characteristics of apoptosis. Furthermore, cell cycle studies with a flow cytometer were carried out which showed increased level of ROS, reduced MMP and cells arrested at G0/G1 phase in Bak treated cells further strengthening the induction of apoptosis. Moreover, RT-qPCR analysis indicated the downregulation of inflammatory markers in Bak-treated A431 cells that further confirmed its therapeutic role. The molecular docking study also confirmed that Bak has perfect scaffold that can complete the pharmacophoric need for JAK3 kinase inhibition. Conclusion: A critical analysis of results points towards the role of Bak in ameliorating inflammatory markers along with apoptosis induction in A431 cells by regulating the expression level of variable markers.

Abstract These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E−09, 9.71E−09, and 3.78E−11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

A highly efficient in silico pharmacokinetics was studied for some newly synthesized 1, 5- benzothiazepine derivatives. A total of fourteen title moieties were prepared by the condensation of substituted chalcones and 2- aminothiophenol that led to ring expansion in the presence of CuO nanocatalytic framework. The reported synthetic route is advantageous due to shorter reaction time and enhanced yield. The drug-target binding analysis revealed the potential therapeutic targets against 1,5- benzothiazepine derivatives using auto in silico consensus inverse docking (ACID) server. Among the seven selected 1,5- benzothiazepine derivatives, consensus inverse docking (CID) against PDB-binding database identified potential therapeutic targets using structure-based screening. The comparative analysis predicted 4-hydroxyphenylpyruvate dioxygenase (HPPD) (Uniprot ID: P32754), involved in the regulation of blood tyrosine levels by catalyzing the reaction of HPPD to homogentisic acid in tyrosine catabolism pathway. Dimethylglycine dehydrogenase (DMGDH) (Uniprot ID: Q9AGP8), a flavin adenine dinucleotide (FAD)‐ and tetrahydrofolate (THF)‐dependent, mitochondrial matrix enzyme that degrades choline and transfers electrons to the respiratory chain, one‐carbon metabolism, and folate receptor beta (Uniprot ID: P14207), which is a glycosylphosphatidylinositol-linked protein capturing ligands from the extracellular matrix and passages them inside the cell through endosomal pathway and recognized as an emerging biomarker for cancer and chronic inflammatory diseases, as potential targets for the selected derivatives. These findings warrant for in vitro cell-based experimental validation of the currently identified actives.

The purpose of this study was to assess the parameters of doxorubicin (DOX) loaded lipid polymer hybrid nanoparticles (LPHNs) formulation development, and then the bioavailability of DOX were determined in the rabbit model, in order to evaluate the intrinsic outcome of dosage form improvement after the oral administration. LPHNs were prepared by combine approach, using both magnetic stirring and probe sonication followed by its characterization in terms of size-distribution (Zeta Size), entrapment efficiency (EE), loading capacity, and the kinetics of DOX. LPHNPs were further characterized by using scanning electron microscopy (SEM), powder X-Ray diffractometry (P-XRD), Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), in vitro and in vivo studies. The molecular modeling was determined through the density functional theory (DFT) simulations and interactions. DOX loaded and unloaded LPHNs were administered orally to the rabbits for bioavailability and pharmacokinetic parameters determinations. The plasma concentration of DOX was determined through high performance liquid chromatography (HPLC). The average size of DOX-loaded LPHNs was 121.90 ± 3.0 nm. The drug loading of DOX was 0.391% ± 0.01 of aqueous dispersion, where its encapsulation efficiency was 95.5% ± 1.39. After oral administration of the DOX-LPHNs, the area under the plasma drug concentration-time curve (AUC) improved about 2-folds comparatively (p < 0.05). DFT simulations were used to understand the interactions of polymers with different sites of DOX molecule. The larger negative binding energies (−9.33 to −18.53 kcal/mol) of the different complexes evince that the polymers have stronger affinity to bind with the DOX molecule while the negative values shows that the process is spontaneous, and the synthesis of DOX-LPHNs is energetically favorable. It was concluded that DOX-LPHNs provides a promising new formulation that can enhance the oral bioavailability, which have optimized compatibilities and improve the pharmacokinetic of DOX after oral administration.

The pollution due to dyes from textile sector is one of the major issues faced worldwide. This study was focused on the removal of the reactive dye, Drimaren Turquoise CL-B using Schizophyllum commune, a white rot fungus (WRF) keeping in mind the current environmental conditions. Different parameters like pH, sources of carbon & nitrogen, temperature, concentration of dye and C/N ratio were used to investigate their effect on the process. Maximum dye removal of 95.45% was obtained at pH 4.5, temperature 35°C, inoculum size 3 mL, veratryl alcohol (mediator), glucose (carbon source) and ammonium nitrate (nitrogen source). The enzyme activity was determined by employing enzyme assay. Laccase and Lignin peroxidase (LiP) activity was low while Manganese peroxidase (MnP) activity was highest. Maximum bio-sorption was achieved at pH 1 and 313 K. The pseudo-2nd-order kinetic model & Freundlich isotherm was best suited for the process of removal of dye. From these data, it is concluded that white rot fungus could possibly be the excellent biomaterial for elimination of synthetic dyes from wastewater.

The presence of heavy metals in wastewater, such as Ni, Pb, Cd, V, Cr, and Cu, is a serious environmental problem. This kind of inorganic pollutant is not biodegradable for several years, and its harmful effect is cumulative. Recently, semiconductor nanomaterials based on metal oxides have gained interest due to their efficiency in the removal of heavy metals from contaminated water, by inducing photocatalytic ion reduction when they absorb light of the appropriate wavelength. The most commonly applied semiconductor oxides for these purposes are titanium oxide (TiO2), zinc oxide (ZnO), and binary nanomaterials composed of both types of oxides. The main purpose of this work is to critically analyse the existent literature concerning this topic focusing specially in the most important factors affecting the adsorption or photocatalytic capacities of this type of nanomaterials. In particular, photocatalytic activity is altered by various factors, such as proportion of polymorphs, synthesis method, surface area, concentration of defects and particle size, among others. After a survey of the actual literature, it was found that, although these metal oxides have low absorption capacity for visible light, it is possible to obtain an acceptable heavy metal reduction performance by sensitization with dyes, doping with metallic or nonmetallic atoms, introduction of defects, or the coupling of two or more semiconductors.

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R2 for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)

This study explored on the adsorption properties and electronic structure of SCN− 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 SCN− through nitrogen atom on the C60 fullerene is electrostatic (−48.02 kJ mol−1) in comparison with the C59Al fullerene that shows covalently attached to fullerene surface (−389.10 kJ mol−1). Our calculations demonstrate that the SCN− adsorption on the pristine and Al-doped single-walled CNTs are −173.13 and −334.43 kJ mol−1, indicating that the SCN− can be chemically bonded on the surface of Al-doped CNTs. Moreover, the adsorption of SCN− 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 SCN− sensor and C59Al can be used for SCN− removal applications.

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 such as 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 × 10−5, 4.66 × 10−5, and 8.35 × 10−5, 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 × 10−3 mol fraction) using this model.

Indisputable importance of drug solubility in various industrial perspectives has motivated the scientists to evaluate different techniques to improve it. Fenoprofen is a significant nonsteroidal anti-inflammatory drug (NSAID), that is the orally administered to relieve mild to moderate pain and the unfavorable symptoms of osteoarthritis and rheumatoid arthritis (i.e., inflammation and stiffness). Supercritical fluids (SCFs) belong to a certain type of fluids, in which their temperature and pressure are higher than the critical point. This property allows the CO2SCF to simultaneously possess the characteristics of both a liquid and a gas. The prominent target of this paper is to mathematically develop three predictive models via machine learning (ML) technique to optimize the solubility of Fenoprofen in CO2SCF. In this study, we have 32 data vectors in each dataset, including two input features of pressure and temperature. The output target is solubility, which we are going to model and analyze. Models are constructed through the use of Modular ANN (MANN), Gaussian processes regression (GPR), and the K-Nearest Neighbor technique (KNN) in this body of work. The glowworm swarm optimization (GSO) swarm-based method is utilized in order to carry out the process of model optimization. The root mean squared error (RMSE) rates for GSO-KNN, GSO-MANN, and GSO-GPR are respectively 5.25E-04, 5.46E-04, and 3.01E-05. The aforementioned models were also judged according to a number of other criteria, and since the GSO-GPR model was found to be the most effective according to all of these standards, it is being treated as the conclusive model of this investigation. In addition, the maximum error has been brought down to 5.02E-05 with the help of this model, which has an R2-score of 0.999.

In pharmaceutical industry, finding promising ways to enhance the solubility of disparate types of drugs is an important challenge for the orally administered drug delivery system. Disparate techniques based on drug characteristics, nature of dosage form and properties of excipients have recently been under extensive evaluation all over the world to improve the solubility of poorly water-soluble drugs. Among them, supercritical fluid carbon dioxide (SC-CO2) has received paramount attentions due to having considerable advantages like cost-effectiveness and low flammability. Lenalidomide belongs is an orally administered anti-cancer agent, which has recently received indication for the treatment of adult patients with different bone marrow-related malignancies such as multiple myeloma, mantle cell lymphoma and follicular lymphoma. Predicting the optimized value of Lenalidomide inside the SC-CO2 in a wide range of pressure and temperature via developing mathematical models based on artificial intelligence (AI) is the main objective of this paper. In this study, three different machine learning based models are selected to predict and optimized the drug solubility. The available data includes 28 rows of data with two inputs including temperature and pressure and two outputs including density and solubility. Selected models are Kernel Ridge Regression (KRR), least angle regression (LAR), and Multilayer Perceptron (MLP). After optimizing models and comparing the results, the MLP was selected as the primary model of this research. The models illustrated R-squared scores of 0.999 and 0.994 for density and solubility. The maximum errors are also 2.92 and 6.44 × 10-2 for these outputs, which shows the accuracy and significant generality of the model.

The current study focused on the fabrication of a well-designed, biocompatible, physically stable, non-irritating and highly porous gelatin scaffold loaded with controlled-release triamcinolone acetonide (TA) and econazole nitrate (EN) co-loaded into mesoporous silica nanoparticles (EN-TA-loaded MSNs) to provide a better long-lasting antifungal therapeutic effect with minimal unfavorable effects. Optimization of the MSNs-loaded scaffold was performed using central composite rotatable design (CCRD), where the effect of gelatin concentration (X1), plasticizer (X2) and freezing time (X3) on the entrapment of EN (Y1) and TA (Y2) and on the release of EN (Y3) and TA (Y4) from the scaffold were studied. The significant compatibility of all formulation ingredients with both drugs was established from XRD, DSC and FT-IR spectra analyses while SEM and zeta studies represented a very precise unvarying distribution of the loaded MSNs in the porous structure of the scaffold. The stability of the optimized scaffold was confirmed from zeta potential analysis (−16.20 mV), and it exhibited higher entrapment efficiency (94%) and the slower (34%) release of both drugs. During in vitro and in vivo antifungal studies against Candida albicans, the MSNs-loaded scaffold was comparatively superior in the eradication of fungal infections as a greater zone of inhibition was observed for the optimized scaffold (16.91 mm) as compared to the pure drugs suspension (14.10 mm). Similarly, the MSNs-loaded scaffold showed a decreased cytotoxicity because the cell survival rate in the scaffold presence was 89% while the cell survival rate was 85% in the case of the pure drugs, and the MSNs-loaded scaffold did not indicate any grade of erythema on the skin in comparison to the pure medicinal agents. Conclusively, the scaffold-loaded nanoparticles containing the combined therapy appear to possess a strong prospective for enhancing patients’ adherence and therapy tolerance by yielding improved synergistic antifungal efficacy at a low dose with abridged toxicity and augmented wound-healing impact.

Alumina nanoparticles were prepared by sol-gel method, where the obtained nanosize was 35 nm, and the nanomaterial was coated with PVP polymer, where the nanomaterial was dispersed by ultrasonic waves for half an hour, and then, the polymer was added, and under high magnetic stirring for 24 hours, it was dried at a temperature of 60°C for 24 hours. Cadmium salt solutions were prepared with different concentrations of 10, 30, and 60 ppm, and the nanomaterial was immersed in the prepared solutions at different times of 10, 30, and 60 minutes, and the measurement was done by an atomic absorption device. By means of the electronic scanner, a difference appeared in the nanosize, and this indicates that the packaging has completely occurred.

The stability constants of 1,2-bis(2,5-dimethoxybenzylidene) hydrazine (DMBH) were determined using the modified Job’s method with the addition of the UV-Vis approach using salts of copper (II) (chloride and acetate) and salts of cobalt (II) (chloride and nitrate). Copper (II) chloride with a ligand at 1L:2M (stoichiometric ratio) was discovered to have a higher stability constant log value (6.995) than other metal salts. The stability constant log value (5.811) for cobalt (II) nitrate at 1L:2M (stoichiometric ratio) was found to be less stable than the other stability constants evaluated. Antimicrobial properties of the ligand were tested against Entamoeba coli, Candida albicans, and Staphylococcus aureus. The ligand was found to be effective against these strains, with positive results.

In this paper, we fabricated semi-interpenetrating polymeric network (semi-IPN) of hydroxypropyl-β-cyclodextrin-grafted-poly(acrylic acid)/poly(vinyl pyrrolidone) (HP-β-CD-g-poly(AA)/PVP) by the free radical polymerization technique, intended for colon specific release of dexamethasone sodium phosphate (DSP). Different proportions of polyvinyl pyrrolidone (PVP), acrylic acid (AA), and hydroxypropyl-beta-cyclodextrin (HP-β-CD) were reacted along with ammonium persulphate (APS) as initiator and methylene-bis-acrylamide (MBA) as crosslinker to develop a hydrogel system with optimum swelling at distal intestinal pH. Initially, all formulations were screened for swelling behavior and AP-8 was chosen as optimum formulation. This formulation was capable of releasing a small amount of drug at acidic pH (1.2), while a maximum amount of drug was released at colonic pH (7.4) by the non-Fickian diffusion mechanism. Fourier transformed infrared spectroscopy (FTIR) revealed successful grafting of components and development of semi-IPN structure without any interaction with DSP. Thermogravimetric analysis (TGA) confirmed the thermal stability of developed semi-IPN. X-ray diffraction (XRD) revealed reduction in crystallinity of DSP upon loading in the hydrogel. The scanning electron microscopic (SEM) images revealed a rough and porous hydrogel surface. The toxicological evaluation of semi-IPN hydrogels confirmed their bio-safety and hemocompatibility. Therefore, the prepared hydrogels were pH sensitive, biocompatible, showed good swelling, mechanical properties, and were efficient in releasing the drug in the colonic environment. Therefore, AP-8 can be deemed as a potential carrier for targeted delivery of DSP to treat inflammatory bowel diseases.

Wounds are the most common causes of mortality all over the world. Topical drug delivery systems are more efficient in treating wounds as compared to oral delivery systems because they bypass the disadvantages of the oral route. The aim of the present study was to formulate and evaluate in vitro in vivo nanoemulgels loaded with eucalyptol for wound healing. Nanoemulsions were prepared using the solvent emulsification diffusion method by mixing an aqueous phase and an oil phase, and a nanoemulgel was then fabricated by mixing nanoemulsions with a gelling agent (Carbopol 940) in a 1:1 ratio. The nanoemulgels were evaluated regarding stability, homogeneity, pH, viscosity, Fourier-transform infrared spectroscopy (FTIR), droplet size, zeta potential, polydispersity index (PDI), spreadability, drug content, in vitro drug release, and in vivo study. The optimized formulation, F5, exhibited pH values between 5 and 6, with no significant variations at different temperatures, and acceptable homogeneity and spreadability. F5 had a droplet size of 139 ± 5.8 nm, with a low polydispersity index. FTIR studies showed the compatibility of the drug with the excipients. The drug content of F5 was 94.81%. The percentage of wound contraction of the experimental, standard, and control groups were 100% ± 0.015, 98.170% ± 0.749, and 70.846% ± 0.830, respectively. Statistically, the experimental group showed a significant difference (p < 0.03) from the other two groups. The results suggest that the formulated optimized dosage showed optimum stability, and it can be considered an effective wound healing alternative.

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.

Hyperlipidemia is still the leading cause of heart disease in patients with hypertension. The purpose of this study is to make rosuvastatin calcium (ROS) and atenolol (AT) bilayer tablets to treat coexisting dyslipidemia and hypertension with a single product. ROS was chosen for the immediate-release layer of the constructed tablets, whereas AT was chosen for the sustained-release layer. The solid dispersion of ROS with sorbitol (1:3 w/w) was utilized in the immediate-release layer while hydroxypropyl methylcellulose (HPMC), ethylcellulose (EC), and sodium bicarbonate were incorporated into the floating sustained-release layer. The concentrations of HPMC and EC were optimized by employing 32 full factorial designs to sustain AT release. The bilayer tablets were prepared by the direct compression method. The immediate-release layer revealed that 92.34 ± 2.27% of ROS was released within 60 min at a pH of 1.2. The second sustained-release layer of the bilayer tablets exhibited delayed release of AT (96.65 ± 3.36% within 12 h) under the same conditions. The release of ROS and AT from the prepared tablets was found to obey the non-Fickian diffusion and mixed models (zero-order, Higuchi and Korsmeyer–Peppas), respectively. Preclinical studies using rabbit models investigated the impact of ROS/AT tablets on lipid profiles and blood pressure. A high-fat diet was used to induce obesity in rabbits. Bilayer ROS/AT tablets had a remarkable effect on decreasing the lipid profiles, slowing weight gain, and lowering blood pressure to normal levels when compared to the control group.

Benign prostatic hyperplasia (BPH) is a disease that commonly affects elderly men. Cordycepin is an adenosine analog with a wide range of pharmacological activities including antiproliferative and prostatic smooth muscle relaxant effects. This study was designed to assess the actions of cordycepin in testosterone-induced BPH in rats. Animals were divided into six treatment groups: control, cordycepin-alone (10 mg/kg), testosterone-alone (3 mg/kg), cordycepin (5 mg/kg) + testosterone, cordycepin (10 mg/kg) + testosterone, and finasteride (0.5 mg/kg) + testosterone. Treatments were continued daily, 5 days a week, for 4 weeks. Cordycepin significantly prevented the increase in prostate weight and prostate index induced by testosterone. This was confirmed by histopathological examinations. Cordycepin antiproliferative activity was further defined by its ability to inhibit cyclin-D1 and proliferating cell nuclear antigen (PCNA) expression. In addition, cordycepin exhibited significant antioxidant properties as proven by the prevention of lipid peroxidation, reduced glutathione diminution, and superoxide dismutase exhaustion. This was paralleled by anti-inflammatory activity as shown by the inhibition of interleukin-6, tumor necrosis factor-α, and nuclear factor-κB expression in prostatic tissues. It also enhanced apoptosis as demonstrated by its ability to enhance and inhibit mRNA expression of Bax and Bcl2, respectively. Western blot analysis indicated that cordycepin augmented phospho-AMP-activated protein kinase (p-AMPK) and inhibited p-AKT expression. Collectively, cordycepin has the ability to prevent testosterone-induced BPH in rats. This is mediated, at least partially, by its antiproliferative, antioxidant, anti-inflammatory, and pro-apoptotic actions in addition to its modulation of AMPK and AKT activation.

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.

The present work aimed to re-assess the bioavailability enhancement potential of fulvic acid (FA). Carbamazepine (CBZ) and peat were used as a model drug and FA source, respectively. Our group has already evaluated the bioavailability enhancement potential of a less commercially viable source of FA, i.e., shilajit. In the present work, the phase solubility of CBZ was analyzed with varying concentrations of peat-sourced FA (2–12% w/v). The prepared complex (CBZ-FA) was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). Dissolution, pharmacokinetic, and pharmacodynamic studies were also carried out. The results showed the presence of an interaction between the drug and FA within the complex, which led to 98.99 ± 2.0% enhancement in drug solubility. The results also showed 79.23 ± 2.1% dissolution of the complexed drug over 60 min and 69.32 ± 2.2% permeation from the intestinal gut sac over 90 min, which led to a significant enhancement of bioavailability and a reduction in the duration of epileptic seizures. Thus, this study re-authenticates our earlier results and suggests switching the FA source (shilajit to peat) for commercial product development.

The assessment of greenness of analytical protocols is of great importance now to preserve the environment. Some studies have analyzed either only the neurotransmitters, dopamine, serotonin, glutamate, and gamma-aminobutyric acid (GABA), together or with other neurotransmitters and biomarkers. However, these methods have not been investigated for their greenness and were not compared with each other to find the optimum one. Therefore, this study aims to compare seven published chromatographic methods that analyzed the four neurotransmitters and their mixtures using the National Environmental Method Index, Analytical Eco-Scale Assessment (ESA), and Green Analytical Procedure Index (GAPI). As these methods cover both qualitative and quantitative aspects, they offer better transparency. Overall, GAPI showed maximum greenness throughout the analysis. Method 6 was proven to be the method of choice for analyzing the mixture, owing to its greenness, according to NEMI, ESA, and GAPI. Additionally, method 6 has a wide scope of application (13 components can be analyzed), high sensitivity (low LOQ values), and fast analysis (low retention times, especially for glutamate and GABA).