Research on the use of biodegradable polymers for drug delivery has been ongoing since they were first used as bioresorbable surgical devices in the 1980s. For tissue engineering and drug delivery, biodegradable polymer poly-lactic-co-glycolic acid (PLGA) has shown enormous promise among all biomaterials. PLGA are a family of FDA-approved biodegradable polymers that are physically strong and highly biocompatible and have been extensively studied as delivery vehicles of drugs, proteins, and macromolecules such as DNA and RNA. PLGA has a wide range of erosion times and mechanical properties that can be modified. Many innovative platforms have been widely studied and created for the development of methods for the controlled delivery of PLGA. In this paper, the various manufacturing processes and characteristics that impact their breakdown and drug release are explored in depth. Besides different PLGA-based nanoparticles, preclinical and clinical applications for different diseases and the PLGA platform types and their scale-up issues will be discussed.

Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead‐of‐time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine‐learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.

Allium sativum L., also known as garlic, is a perennial plant widely used as a spice and also considered a medicinal herb since antiquity. The aim of this study was to determine by gas chromatography–mass spectrometry (GC–MS) the chemical profile fingerprint of the essential oil (EO) of one accession of Peruvian A. sativum (garlic), to evaluate its antioxidant activity and an in- silico study on NADPH oxidase activity of the volatile phytoconstituents. The antioxidant activity was tested using DPPH and β-carotene assays. An in-silico study was carried out on NADPH oxidase (PDB ID: 2CDU), as was ADMET prediction. The results indicated that diallyl trisulfide (44.21%) is the major component of the EO, followed by diallyl disulfide (22.08%), allyl methyl trisulfide (9.72%), 2-vinyl-4H-1,3-dithiine (4.78%), and α-bisabolol (3.32%). Furthermore, the EO showed antioxidant activity against DPPH radical (IC₅₀ = 124.60 ± 2.3 µg/mL) and β-carotene bleaching (IC₅₀ = 328.51 ± 2.0). The best docking score on NADPH oxidase corresponds to α-bisabolol (ΔG = −10.62 kcal/mol), followed by 5-methyl-1,2,3,4-tetrathiane (ΔG = −9.33 kcal/mol). Additionally, the volatile components could be linked to the observed antioxidant activity, leading to potential inhibitors of NADPH oxidase.

Glucokinase activators are considered as new therapeutic arsenals that bind to the allosteric activator sites of glucokinase enzymes, thereby maximizing its catalytic rate and increasing its affinity to glucose. This study was designed to identify potent glucokinase activators from prenylated flavonoids isolated from medicinal plants using molecular docking, molecular dynamics simulation, density functional theory, and ADMET analysis. Virtual screening was carried out on glucokinase enzymes using 221 naturally occurring prenylated flavonoids, followed by molecular dynamics simulation (100 ns), density functional theory (B3LYP model), and ADMET (admeSar 2 online server) studies. The result obtained from the virtual screening with the glucokinase revealed arcommunol B (−10.1 kcal/mol), kuwanon S (−9.6 kcal/mol), manuifolin H (−9.5 kcal/mol), and kuwanon F (−9.4 kcal/mol) as the top-ranked molecules. Additionally, the molecular dynamics simulation and MM/GBSA calculations showed that the hit molecules were stable at the active site of the glucokinase enzyme. Furthermore, the DFT and ADMET studies revealed the hit molecules as potential glucokinase activators and drug-like candidates. Our findings suggested further evaluation of the top-ranked prenylated flavonoids for their in vitro and in vivo glucokinase activating potentials.

(1) Objectives: Clinical pharmacists are now playing a significant role in hospitals aiming to reduce medication errors, adverse drug reactions, and healthcare costs. Therefore, the main objective of this study was to assess the interventions provided by a clinical pharmacist in the intensive care unit at the King Faisal Hospital in Taif city. (2) Methods: For this single-center retrospective study, patients were included from December 2021 to May 2022. In the present study, all the interventions of clinical pharmacists made over six months were included. The Intensive care unit (ICU) ward was covered by three clinical pharmacists, and the interventions made were categorized into four groups: (1) interventions related to indications; (2) interventions regarding safety; (3) interventions regarding dosing, and (4) miscellaneous. Descriptive statistics was applied to evaluate the results in the form of frequencies and percentages. Analysis was performed using the statistical package SPSS 20.0. (3) Results: Overall, a total of 404 interventions were recommended for 165 patients during the six- month period of study. Among them, 370 interventions (91.5%) were accepted by physicians. Among all the interventions, the majority were suggested regarding ‘indication’ (45.7%), including the addition of drugs, drugs with no indications, and duplication. The acceptance rate of clinical pharmacist intervention was 98.5%. (4) Conclusions: This retrospective study shows that clinical pharmacists played a critical role in optimizing drug therapy which could subsequently help to prevent drug-related issues and lower drug costs. More research is needed to do a thorough cost-benefit analysis.