Fabrication, Characterization and Biomedical Evaluation of a Statistically Optimized Gelatin Scaffold Enriched with Co-Drugs Loaded into Controlled-Release Silica Nanoparticles.

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.

Formulation development of lipid polymer hybrid nanoparticles of doxorubicin and its in-vitro, in-vivo and computational evaluation.

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.

Response Surface Methodology (RSM) Powered Formulation Development, Optimization and Evaluation of Thiolated Based Mucoadhesive Nanocrystals for Local Delivery of Simvastatin.

In oral administration systems, mucoadhesive polymers are crucial for drug localization and target-specific activities. The current work focuses on the application of thiolated xanthan gum (TXG) to develop and characterize a novel mucoadhesive nanocrystal (NC) system of simvastatin (SIM). Preparation of SIM-NC was optimized using response surface methodology (RSM) coupled with statistical applications. The concentration of Pluronic F-127 and vacuum pressure were optimized by central composite design. Based on this desirable approach, the prerequisites of the optimum formulation can be achieved by a formulation having 92.568 mg of F-127 and 77.85 mbar vacuum pressure to result in EE of 88.8747% and PS of 0.137.835 nm. An optimized formulation was prepared with the above conditions along with xanthan gum (XG) and TXG and various parameters were evaluated. A formulation containing TXG showed 98.25% of SIM at the end of 96 h. Regarding the mucoadhesion potential evaluated by measuring zeta potential, TXG-SIM-NC shoed the maximum zeta potential of 16,455.8 ± 869 mV at the end of 6 h. The cell viability percentage of TXG-SIM-NC (52.54 ± 3.4% with concentration of 50 µg/mL) was less than the plain SIM, with XG-SIM-NC showing the highest cytotoxicity on HSC-3 cells. In vivo pharmacokinetic studies confirm the enhanced bioavailability of formulated mucoadhesive systems of SIM-NC, with TXG-SIM-NC exhibiting the maximum.

In Vitro and In Vivo Evaluation of Hydroxypropyl-ß-cyclodextrin-grafted-poly(acrylic acid)/poly(vinyl pyrrolidone) Semi-Interpenetrating Matrices of Dexamethasone Sodium Phosphate.

In this paper, we fabricated semi-interpenetrating polymeric network (semi-IPN) of hydroxypropyl-ß-cyclodextrin-grafted-poly(acrylic acid)/poly(vinyl pyrrolidone) (HP-ß-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-ß-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.

Estimation of Psychological Impairment and Coping Strategies during COVID-19 Pandemic among University Students in Saudi Arabia: A Large Regional Analysis.

BACKGROUND: The COVID-19 pandemic and associated restrictive measures have substantially affected educational processes around the globe, resulting in psychological distress among students. The mental health of students in higher education is of paramount importance, and the COVID-19 pandemic has brought this vulnerable population into renewed focus. In this context, the evaluation of students' mental health at educational institutes has gained invaluable popularity during the COVID-19 pandemic. This study aimed to ascertain the psychological health and coping strategies among students from a higher education institute in Saudi Arabia. METHODS: An online study instrument was used to assess anxiety (Generalized Anxiety Disorder-7, GAD-7), depression (Patient Health Questionnaire-9, PHQ-9), post-traumatic stress disorder-PTSD (Impact of Event Scale-Revised, IES-R) and coping strategies (Brief-COPE). The severity of the psychological distress was classified as per the scoring criteria and correlated with demographics using appropriate statistical methods. RESULTS: Of 1074 students (age 21.1 ± 2.1 years), 12.9% and 9.7% had severe anxiety and depression, respectively. The mean anxiety and depression scores were 7.50 ± 5.51 and 9.31 ± 6.72, respectively. About one-third (32%) of students reported suicidal ideation, with 8.4% students having such thoughts nearly every day. The average PTSD score was 21.64 ± 17.63, where avoidance scored higher (8.10 ± 6.94) than intrusion and hyperarousal. There was no association of anxiety, depression and PTSD score with the demographics of the study participants. Religious/spiritual coping (5.43 ± 2.15) was the most adoptive coping mechanism, followed by acceptance (5.15 ± 2.10). Male students were significantly (p < 0.05) associated with active copings, instrumental support, planning, humor, acceptance and religious coping. Substance use was the least adopted coping strategy but practiced by a considerable number of students. CONCLUSIONS: The long-lasting pandemic situation, onerous protective measures and uncertainties in educational procedures have resulted in a high prevalence of psychological ailments among university students, as indicated in this study. These findings accentuate the urgent need for telepsychiatry and appropriate population-specific mental health services to assess the extent of psychological impairment and to leverage positive coping behaviors among students.

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.

Fabrication, In Vitro, and In Vivo Assessment of Eucalyptol-Loaded Nanoemulgel as a Novel Paradigm for Wound Healing.

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.

Tailoring of Rosuvastatin Calcium and Atenolol Bilayer Tablets for the Management of Hyperlipidemia Associated with Hypertension: A Preclinical Study.

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.

Cordycepin Attenuates Testosterone-Induced Benign Prostatic Hyperplasia in Rats via Modulation of AMPK and AKT Activation.

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.

Evaluation of Impact of a Pharmacist-Led Educational Campaign on Disease Knowledge, Practices and Medication Adherence for Type-2 Diabetic Patients: A Prospective Pre- and Post-Analysis.

Type 2 Diabetes mellitus is a major public health concern with an alarming global growth rate. According to the World Health Organization (WHO), Saudi Arabia ranks seventh in the world and second in the Middle East for the largest estimated burden of diabetic cases. Evidence shows that pharmacist-led care programs can be beneficial for the effective treatment of diabetes mellitus. Current study was aimed to evaluate the impact of Pharmacist-Based Diabetic Intervention (PDIM) for Type 2 Diabetes patients on knowledge of the disease, adherence to medications and self-care practices during the first wave of COVID-19. A multi-arm pre-post study was conducted among type 2 diabetic patients from April to October 2021 in Sakaka, Saudi Arabia. Patients were randomly divided into an intervention and a control group. The intervention group received the PDIM, whereas the control group only received the usual care. The pharmacist-based diabetes intervention model consisted of a diabetic educational module and medication improvement strategies. Furthermore, the intervention group also received specific telepharmacy services (calls, messages or emails) to address their medication-related problems, inquire about medication adherence and follow-up. At the end of six months, disease knowledge, self-care practices, and medication adherence score were analyzed. Furthermore, HbA1c and lipid profile were also compared. A total of 109 patients were included in the study. A significant difference was observed in the knowledge score between the intervention and control group (16.89 ± 2.01 versus 15.24 ± 2.03, p-value < 0.001). Similarly, self-care practices also improved in the intervention group as compared to the control group (4.39 ± 1.10 versus 3.16 ± 0.97, p-value < 0.001). Furthermore, the medication adherence and HbA1c significantly improved during between the group analysis (p < 0.05). Our study demonstrates that pharmacist-based diabetes intervention model is effective in improving patients' knowledge of diabetes, self-care practices, medication adherence and glycemic control.

In Vitro and In Vivo Evaluation of DTPA-HPMA Copolymers as Potential Decorporating Agents for Prophylactic Therapy of Actinide Contamination.

The release of actinides into the environment represents a significant potential public health concern. Chelation therapy utilizing diethylenetriamine pentaacetate (DTPA) is a U.S. Food and Drug Administration (FDA)-approved therapy capable of mitigating the deposition of some absorbed actinides in the body. However, the pharmacokinetic profile of DTPA is not ideal for prophylactic applications. In this study, we examine the incorporation of DTPA into a HPMA copolymer (P-DTPA) to investigate if the enhanced blood circulation time can offer superior prophylactic protection and of improving in vivo radiometal decorporation. Utilizing lutetium-177 (177Lu) as an actinide model, the performance of P-DTPA and DTPA (control) were evaluated using selectivity studies in the presence of competing biological metals, chelation and stability assays in human serum and cytotoxicity studies using human umbilical vein endothelial cells (HUVEC). The in vivo decorporation efficiency of P-DTPA relative to DTPA and untreated controls was also evaluated over two weeks in CF-1 mice. In the experimental groups, the mice were prophylactically treated with P-DTPA or DTPA (30 µmol/kg) 6 or 24 h prior to 177LuCl3 administration. The in vitro results reveal that P-DTPA gives efficient complexation yields relative to DTPA with a tolerable cytotoxicity profile and good serum stability. The in vivo decorporation studies demonstrated enhanced total excretion of the 177Lu using P-DTPA compared to DTPA in both the 6 and 24 h prophylactic treatment study arms. This enhanced decorporation effect is certainly attributable to the expected prolonged biological half-life of DTPA when grafted to the HPMA polymer.

Rapid oral transmucosal delivery of zaleplon-lavender oil utilizing self-nanoemulsifying lyophilized tablets technology: development, optimization and pharmacokinetic evaluation.

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.

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.

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug.

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.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning.

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).

Examination of Charge Modifications of an Endolysosomal Trapping Inhibitor in an Antagonistic NTSR1-Targeted Construct for Colon Cancer.

Many low-molecular weight targeted radiotherapeutics (TRTs) are capable of rapidly achieving exceptional tumor to non-target ratios shortly after administration. However, the low tumor residence time of many TRTs limits therapeutic dose delivery and has become the Achilles heel to their clinical translation. To combat the tumor efflux of these otherwise promising agents, we have previously presented a strategy of equipping low-molecular weight TRTs with irreversible cysteine cathepsin inhibitors (e.g., E-64 analogues). These inhibitors are capable of forming irreversible adducts with cysteine proteases within the endolysosomal compartments of cells. Using these endolysosomal trapping agents (ETs), the receptor-targeted constructs are able to increase tumor retention and, thus, deliverable therapeutic doses. In this study, we examine this approach in the development of agents targeting the neurotensin receptor subtype 1 (NTSR1), a receptor overexpressed in numerous cancers. Using an antagonistic NTSR1-targeting vector, we explore the impact of charge modification of the ETs on the in vitro and in vivo biological performance of the constructs using HT-29 colon cancer models. Four ETs (based on the epoxysuccinyl peptide E-64) with various charge states were synthesized and incorporated into the structures of the NTSR1-targeted antagonist. These four 177Lu-labeled, ET-enhanced, NTSR1-targeted agents (177Lu-NA-ET1-4), along with the structurally analogous 177Lu-3BP-227, currently in clinical trials, underwent a battery of in vitro assays using HT-29 xenograft colon cancer cells to examine their NTSR1 binding, internalization and efflux, inhibition, and adduct formation properties. The biodistribution profile of these constructs was studied in an HT-29 mouse model. Charge modification of the terminal carboxylic acid and arginine of the ETs had deleterious effects on inhibition kinetics and in vitro adduct formation. Contrastingly, deletion of the arginine resulted in a modest increase in inhibition kinetics. Incorporation of ETs into the NTSR1-targeted agents was well-tolerated with minimal impact on the in vivo NTSR1 targeting but resulted in increased renal uptake. This study demonstrates that the ETs can be successfully incorporated into antagonistic NTSR1-targeted constructs without compromising their adduct formation capabilities. Based on these results, further exploration of the endolysosomal trapping approach is warranted in NTSR1- and other receptor-targeted antagonistic constructs.

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.

Biocompatibility and Hemolytic Activity Studies of Synthesized Alginate-Based Polyurethanes.

Many investigators have focused on the development of biocompatible polyurethanes by chemical reaction of functional groups contained in a spacer and introduced in the PU backbone or by a grafting method on graft polymerization of functional groups. In this study, alginate-based polyurethane (PU) composites were synthesized via step-growth polymerization by the reaction of hydroxyl-terminated polybutadiene (HTPB) and hexamethylene diisocyanate (HMDI). The polymer chains were further extended with blends of 1,4-butanediol (1,4-BDO) and alginate (ALG) with different mole ratios. The structures of the prepared PU samples were elucidated with FTIR and 1H NMR spectroscopy. The crystallinity of the prepared samples was evaluated with the help of X-ray diffraction (XRD). The XRD results reveal that the crystallinity of the PU samples increases when the concentration of alginate increases. Thermogravimetric (TGA) results show that samples containing a higher amount of alginate possess higher thermal stability. ALG-based PU composite samples show more biocompatibility and less hemolytic activity. Mechanical properties, contact angle, and water absorption (%) were also greatly affected.

Examination of the impact molecular charge has on NTSR1-targeted agents incorporated with cysteine protease inhibitors.

Our laboratory has previously reported a strategy of employing cysteine cathepsin (CC) inhibitors as adduct forming, trapping agents to extend the tumor residence time of neurotensin receptor subtype 1 (NTSR1)-targeted radiopharmaceuticals. As a follow-up, we herein report a small library of CC trapping agent (CCTA)-incorporated, NTSR1-targeted conjugates with structural modifications that reduce the number of charged functional groups for both the CCTA and the peptide targeting sequence. These modifications were pursued to reduce the renal uptake and increase the translational potential of the CCTA-incorporated, NTSR1-targeted agents as radiotherapeutics. The biological performance of these constructs was examined using a battery of in vitro and in vivo studies employing the NTSR1-positive HT-29 human colon cancer cell line as our model. In vitro studies confirmed the ability of these constructs to target the NTSR1 and efficiently form intracellular adducts with cysteine proteases. Biodistribution studies using an HT-29 xenograft mouse model revealed that truncation (removal of Lys6-Pro7) of the NTSR1-targeted peptide (177Lu-NE2a) had the greatest (3.7-fold) effect at lowering renal recognition/uptake relative to our previously reported construct. Other charge-reducing modifications to the CCTA resulted in unexpected increases in renal uptake. All of the constructs demonstrated similar levels of in vivo NTSR1-positive tumor targeting with the highest tumor residualization resulting from the construct containing the zwitterionic CCTA (177Lu-NE2a). In vivo adduct formation of the conjugates was confirmed using autoradiographic SDS-PAGE analysis.

Application of Three Ecological Assessment Tools in Examining Chromatographic Methods for the Green Analysis of a Mixture of Dopamine, Serotonin, Glutamate and GABA: A Comparative Study.

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).