Search

Abstract This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML). The ML models explored include the Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Fully Connected Neural Network (FCNN), and Deep Neural Network (DNN). Model optimization is achieved through the Fireworks Algorithm (FWA). Results reveal promising performance across all models, with the MLP demonstrating the highest accuracy on both test and training datasets, achieving an R2 score of 0.99713 and 0.99717 respectively. The SLP also exhibits strong performance, with an R2 of 0.88903 on the test dataset. The FCNN and DNN models also perform admirably, achieving R2 scores of 0.99158 and 0.99639 on the test dataset respectively. These results highlight the efficiency of neural network-driven models, specifically the MLP, in precisely forecasting temperature values based on spatial coordinates. Additionally, the integration of the Fireworks Algorithm for model refinement yields advantages in improving the predictive performance of these models.

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

Hesperidin (Hes) protects different organs from damage by acting as a potent antioxidant and anti-inflammatory. This study aims to evaluate the gastroprotective effects of free hesperidin and its chitosan nanoparticles (HNPs) against ethanol-induced gastric ulcers in rats, hypothesizing that HNPs will enhance bioavailability and therapeutic efficacy due to improved solubility and targeted delivery. HNPs were synthesized via ion gelation and characterized using TEM, SEM, and zeta potential analyses. Key assessments included gastric acidity, histological analysis, and markers of inflammation, oxidative stress, and apoptosis. HNPs significantly decreased gastric acidity, reduced inflammatory and apoptotic markers, and enhanced antioxidant enzyme activities compared to free hesperidin and esomeprazole. Furthermore, Sirt-1, PGC-1α, HO-1, and FOXO1 gene expression were also evaluated. HNPs raised Sirt-1, PGC-1α, HO-1, and downregulated FOXO1, and they suppressed the activities of NF-κB p65, COX-2, IL-1β, CD86, FOXO1 P53, and caspase-3 and increased Sirt-1 activity. HNPs treatment notably restored antioxidant enzyme activity, reduced oxidative stress and inflammatory markers, and improved histological outcomes more effectively than free hesperidin and esomeprazole. These results indicate that chitosan nanoparticles significantly enhance the gastroprotective effects of hesperidin against ethanol-induced gastric ulcers, potentially offering a more effective therapeutic strategy. Further research should explore the clinical applications of HNPs in human subjects.

For cancer therapy, the focus is now on targeting the chemotherapy drugs to cancer cells without damaging other normal cells. The new materials based on bio-compatible magnetic carriers would be useful for targeted cancer therapy, however understanding their effectiveness should be done. This paper presents a comprehensive analysis of a dataset containing variables x(m), y(m), and U(m/s), where U represents velocity of blood through vessel containing ferrofluid. The effect of external magnetic field on the fluid flow is investigated using a hybrid modeling. The primary aim of this research endeavor was to construct precise and dependable predictive models for velocity, utilizing the provided input variables. Several base models, including K-nearest neighbors (KNN), decision tree (DT), and multilayer perceptron (MLP), were trained and evaluated. Additionally, an ensemble model called AdaBoost was implemented to further enhance the predictive performance. The hyper-parameter optimization technique, specifically the BAT optimization algorithm, was employed to fine-tune the models. The results obtained from the experiments demonstrated the effectiveness of the proposed approach. The combination of the AdaBoost algorithm and the decision tree model yielded a highly impressive score of 0.99783 in terms of R2, indicating a strong predictive performance. Additionally, the model exhibited a low error rate, as evidenced by the root mean square error (RMSE) of 5.2893 × 10−3. Similarly, the AdaBoost-KNN model exhibited a high score of 0.98524 using R2 metric, with an RMSE of 1.3291 × 10−2. Furthermore, the AdaBoost-MLP model obtained a satisfactory R2 score of 0.99603, accompanied by an RMSE of 7.1369 × 10−3.

AbstractIn order to load metformin in a nano formula and evaluate the produced nano form towards cancer cells, metformin was loaded on natural carrier coconut oil. The formed metformin-loaded coconut oil nanoemulsion was characterized by Zeta potential, particle size, drug content, drug release, and drug stability. The formed nanoemulsion was evaluated towards MCF-7, HepG2, and HCT-116 cell lines. Cell cycle analysis and apoptosis mechanism were studied. The nanoemulsion was created using deionized water, 1.5% Span 20, 1.5% Tween 80, 1.5% coconut oil, and 0.5% Metformin in an ultrasonicator to produce a homogenous solution. The anticancer activities of the metformin-loaded coconut nanoemulsion were highly improved compared to non-formulated metformin with IC50s of 8.3 ± 0.1 µg/ml, 12 ± 1.5 µg/ml, 2.685 ± 0.3 µg/ml for MCF-7, HepG2, and HCT-116 cell lines, respectively. There was a 76.5 ± 2.3 and 78.3 ± 3.2% increase in the number of apoptotic cells of MCF-7 and HepG2 cells after nanoemulsion treatment. This formula may be considered a new anticancer medication.

This research focused on the development of films based on pectin and xanthan gum composite loaded with different concentrations of grapefruit essential oil (GFO). The fabricated films were characterized to assess the effect of GFO on the structural, mechanical, barrier, chemical, and antioxidant properties. The addition of GFO enhanced the functional properties of the films, as confirmed by FTIR analysis showing molecular interactions within the film matrix. SEM observations revealed that films with higher GFO content had a smoother, more compact structure with uniform oil distribution. Films loaded with oil demonstrated enhanced water resistance, as their decreased permeability ranged from 0.733 ± 0.009 to 0.561 ± 0.020 (g mm)/(m2.h.kPa). Additionally, these films showed a notable increase in tensile strength, ranging from 2.91 ± 0.19 to 8.55 ± 0.62 MPa. However, the addition of oil led to a reduction in the elongation at break of the films, which decreased from 52.84 ± 3.41 % to 12.68 ± 1.52 %, and a decline in transparency from 87.57 ± 0.65 % to 76.18 ± 1.12 %. Fabricated films exhibited enhanced antioxidant properties, as evidenced by increased DPPH• and ABTS•+ radical scavenging activities with the addition of GFO. The findings of the current study suggest that GFO is an effective natural additive for enhancing the physiochemical properties of pectin and xanthan gum-based films, making them more suitable for food packaging applications.

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.

Warfarin is favored over newer direct oral anticoagulants (DOACs) for many older adults. However, its use necessitates rigorous monitoring due to the fine line between toxic and therapeutic doses. Few studies have evaluated the anticoagulation quality of warfarin among elderly patients in Saudi Arabia. This study aimed to assess and identify factors affecting the anticoagulation quality of warfarin using the time in the therapeutic range (TTR) among older adults attending two hospitals in Saudi Arabia. Additionally, we aimed to evaluate differences in the anticoagulation quality of warfarin when managed by pharmacists or physicians. This cross-sectional study was conducted at King Abdullah bin Abdulaziz University Hospital (KAAUH) and King Fahad Medical City (KFMC) in Riyadh, Saudi Arabia. After calculating the TTR of each patient, the anticoagulation control level was determined using these values: a) good control: >70 %; b) intermediate control: 50–70 %; c) poor control:

Curcumin (CR) is a natural product that has great interest in biopharmaceutical applications. It has several biological effects that antagonizes inflammation, oxidative stress, and cancer. On the other hand, low aqueous solubility, stability, and dissolution rate are the main challenges to using curcumin. In the present investigation, curcumin was formulated as a nanosystem known as nanocurcumin using a combination of polyvinyl pyrrolidone (PVP) with different polymers as stabilizing agents. The nanocurcumin formulations were prepared and evaluated by measuring the particle size, surface charge, drug content, and stability. The selected nanocurcumin formulation prepared using PVPK30-Poloxamer 188 demonstrated a significantly smaller particle size (272.1 ± 15.32 nm) and a higher settlement volume ration as compared with other formulations. Also, the selected formulation showed higher solubility and dissolution rates as compared with other stabilizer combinations. Chitosan was added to the selected nanocurcumin formulation as a coating polymer to enhance short-term storage colloidal stability. The result of stability proved that chitosan (0.05%)-coated nanocurcumin dispersion showed good stability during storage as compared with non-coated nanocurcumin. Non-coated nanocurcumin and chitosan-coated curcumin were loaded into the Carbopol934 hydrogel matrix to investigate the effect on curcumin permeability across the skin. The results revealed that nanocurcumin hydrogel formulations showed a significantly higher amount of curcumin permeated across the skin.

AbstractLow bone mass, degeneration of bone tissue, and disruption of bone microarchitecture are all symptoms of the disease osteoporosis, which can decrease bone strength and increase the risk of fractures. The main objective of the current study was to use a phospholipid-based phase separation in-situ gel (PPSG) in combination with an alendronate sodium nanoemulsion (ALS-NE) to help prevent bone resorption in rats. The effect of factors such as concentrations of the ALS aqueous solution, surfactant Plurol Oleique CC 497, and Maisine CC oil on nanoemulsion characteristics such as stability index and globular size was investigated using an l-optimal coordinate exchange statistical design. Injectable PPSG with the best nanoemulsion formulation was tested for viscosity, gel strength, water absorption, and in-vitro ALS release. ALS retention in the rats’ muscles was measured after 30 days. The droplet size and stability index of the optimal nanoemulsion were 90 ± 2.0 nm and 85 ± 1.9%, respectively. When mixed with water, the optimal ALS-NE–loaded PPSG became viscous and achieved 36 seconds of gel strength, which was adequate for an injectable in-situ formulation. In comparison with the ALS solution–loaded in-situ gel, the newly created optimal ALS-NE–loaded PPSG produced the sustained and regulated release of ALS; hence, a higher percentage of ALS remained in rats’ muscles after 30 days. PPSG that has been loaded with an ALS-NE may therefore be a more auspicious, productive, and effective platform for osteoporosis treatment than conventional oral forms.

This research focuses on investigating the solubility of tolfenamic acid in SC-CO2 (supercritical carbon dioxide) and the density of SC-CO2 solvent via theoretical artificial intelligence method. The study involves analyzing the relationship between temperature, pressure, and the corresponding mentioned outputs for the process. Three different predictive models, namely Multi-layer Perceptron (MLP), Polynomial Regression (PR), and Extra Trees (ET) are utilized to forecast the solubility of drug and the density of solvent (SC–CO2). The models are fine-tuned with hyper-parameters using the Dragonfly Algorithm (DA) to ensure accurate predictions. The solubility prediction is remarkably accurate using the MLP model, showing a high score of 0.98329 in terms of R-squared. The maximum error is 0.2474, and the MAE is 0.1095, demonstrating the model's high precision in estimating tolfenamic acid's solubility in SC-CO2. The PR model demonstrates exceptional accuracy, yielding a score of 0.99844 by R-squared metric, a maximum error of 0.068, and an MAE of 0.0314. The ET model also performs well, with an R-squared score of 0.90977, a maximum error of 0.445, and an MAE of 0.1665. Regarding density prediction, MLP outperforms the other techniques, achieving a significant R2 parameter of 0.99919, an MSE of 12.663, and a mean MAPE of 0.0037.

Data-driven models were employed for the solubility correlation, while the focus was on modeling the solubility of raloxifene drug and density of carbon dioxide based on temperature (T) and pressure (P) inputs. Three Machine Learning models, namely Multilayer Perceptron (MLP), Bayesian Ridge Regression (BRR), and LASSO regression, were employed and optimized using the MPHPT method for hyper-parameter tuning. The dataset consisted of experimental measurements of solubility (y), and CO2 density. For the prediction of density, the MLP model exhibited excellent performance with an R2 score of 0.99726, demonstrating a significant level of association between the anticipated and observed values. The mean squared error (MSE) was 9.8721E+01, the mean absolute percentage error (MAPE) was 1.78565E-02, and the maximum error was 1.86395E+01. The LASSO and BRR models achieved slightly lower accuracy, with R2 scores of 0.83317 and 0.83001, respectively. Regarding the solubility of the raloxifene drug, the MLP model demonstrated a strong predictive capability with an R2 score of 0.99343. The MSE was 3.0869E-02, MAPE was 4.02666E-02, and the maximum error was 3.01133E-01. The LASSO and BRR models also provided reasonable predictions, with R2 scores of 0.90955 and 0.8891, respectively. However, they exhibited higher MSEs and MAPEs compared to the MLP model.

Background: Orlistat (ORL) is an effective irreversible inhibitor of the lipase enzyme, and it possesses anticancer effects and limited aqueous solubility. This study was designed to improve the aqueous solubility, oral absorption, and tissue distribution of ORL via the formulation of nanocrystals (NCs). Methods: ORL-NC was prepared using the liquid antisolvent precipitation method (bottom-up technology), and it demonstrated significantly improved solubility compared with that of the blank crystals (ORL-BCs) and untreated ORL powder. The biodistribution and relative bioavailability of ORL-NC were investigated via the radiolabeling technique using Technetium-99m (99mTc). Female Swiss albino mice were used to examine the antitumor activity of ORL-NC against solid Ehrlich carcinoma (SEC)-induced hepatic damage in mice. Results: The prepared NCs improved ORL’s solubility, bioavailability, and tissue distribution, with evidence of 258.70% relative bioavailability. In the in vivo study, the ORL-NC treatment caused a reduction in all tested liver functions (total and direct bilirubin, AST, ALT, and ALP) and improved modifications in liver sections that were marked using hematoxylin and eosin staining (H&E) and immunohistochemical staining (Ki-67 and ER-α) compared with untreated SEC mice. Conclusions: The developed ORL-NC could be considered a promising formulation approach to enhance the oral absorption tissue distribution of ORL and suppress the liver damage caused by SEC.

Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of cholestatic liver disease based on urinary BA indices, liquid chromatography-tandem mass spectrometry was used to analyze urine samples from 257 patients with cholestatic liver diseases during a 7-year follow-up period. The urinary BA profile and non-BA parameters were monitored, and logistic regression models were used to predict the prognosis of hepatobiliary disease-related complications. Urinary BA indices were applied to quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. We have developed and validated the bile-acid liver disease complication (BALDC) model based on BA indices using logistic regression model, to predict the prognosis of cholestatic liver disease complications including ascites. The mixed BA and non-BA model was the most accurate and provided higher area under the receiver operating characteristic (ROC) and smaller akaike information criterion (AIC) values compared to both non-BA and MELD (models for end stage liver disease) models. Therefore, the mixed BA and non-BA model could be used to predict the development of ascites in patients diagnosed with liver disease at early stages of intervention. This will help physicians to make a better decision when treating hepatobiliary disease-related ascites.

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.

Leishmaniasis, remains a serious health problem in many developing countries with thousands of new cases recorded annually. Novel therapies are required as existing treatment regimens are limited by their high cost, high toxicity, increased parasite resistance, patient’s intolerance, and invasive means of long-duration administration. With several studies reporting the anti-leishmaniasis promise of medicinal plants, interest in plants and herbal drugs is attracting much attention worldwide. In this pilot study, we analysed extracts of Linum usitatissimum seeds (LU) to identify essential phytochemicals and test their activity against cutaneous leishmaniasis both in-vitro and in-vivo. We performed phytochemical screening of LU seeds extract as well as its in-vitro leishmanicidal and anti-amastigote assays. Water-in-oil cream containing 10% LU crude extract (10 mg/mL) was then prepared. The stability of the cream was evaluated for 28 days at 8 °C, 25 °C and 40 °C. In-vivo efficacy and safety of the cream was performed in 26 patients with cutaneous leishmaniasis who agreed to participate voluntarily in the study. The active treatment period lasted for 3 weeks, while the follow-up period was extended to 4 months. During the active study period, images of skin lesions were taken before and after treatment. Analyses of LU seeds extract confirmed the presence of terpenoids, flavonoids, tannins, alkaloids, and polyphenols. In-vitro studies showed significant activity against promastigote and intracellular amastigote forms of Leishmaniamajor. The cream was pharmaceutically stable, although some minor changes were noticed in relation to its physical characteristics. In-vivo assessment of the cream showed a 69.23% cure rate with no side effects, allergy, or irritation. We conclude that our newly developed water in oil cream containing 10% LU seeds extract could be an effective and safe topical anti-leishmanial medication for patients with CL.