The field of oral drug delivery has witnessed significant advancements, with a focus on developing innovative formulations to address challenges associated with traditional dosage forms, especially for patients with difficulties in swallowing. Fast Dissolving Tablets (FDTs) have emerged as a promising class of tablets designed to rapidly disintegrate or dissolve in saliva, providing a convenient and patient-friendly alternative for various populations.

This article explores the unique properties, advantages, and potential applications of FDTs, emphasizing their role in overcoming challenges posed by conventional oral drug delivery systems. FDTs offer rapid dissolution within 15-120 seconds in the buccal cavity, facilitating direct absorption through the buccal mucosa and ensuring quick therapeutic effects. This characteristic proves particularly beneficial for individuals facing swallowing challenges, such as pediatric and geriatric patients, or those with conditions like dysphagia.

Recognizing the significance of FDTs, the European Pharmacopoeia (EP) has officially recognized them as “oral dissolving tablets,” highlighting their acceptance in both academic and industrial settings. The article delves into the anatomical and physiological characteristics of the oral cavity, shedding light on the buccal epithelium, oral mucosa vascularization, and salivary flow, which play crucial roles in drug absorption.

The ideal features of FDTs include rapid dissolution or disintegration, high drug load capacity, masking of bitter taste, positive mouth feel, ease of transport, and reduced sensitivity to environmental factors. The advantages of FDTs extend to their administration for patients unable to swallow, convenient treatment for bedridden and mobile patients, enhanced mouth feel and taste masking, ease of administration, and precise dosing.

Despite their advantages, FDTs come with limitations, including issues related to mechanical strength, hygroscopic nature, brittleness, and challenges with bitter drugs or unpleasant odors. Overcoming these challenges requires a careful formulation approach to balance rapid disintegration with mechanical strength and taste masking.

The article also discusses the salient characteristics of Fast Dissolving Dosage Forms (FDDDS) and various techniques for preparing FDTs, such as freeze-drying, tablet molding, and spray drying. Additionally, it explores the role of non-invasive drug delivery systems in addressing pharmaceutical industry needs, including improving drug half-life, solubility/stability, and bioavailability.

MAOs are flavoenzymes that aid in the oxidative deamination of neurotransmitters such as dopamine, serotonin, and epinephrine. MAO inhibitors are antidepressants that act by inhibiting neurotransmitter breakdown in the brain and controlling mood. MAO inhibitors with the chlorophenyl-chromone-carboxamide structure have been shown in investigations to be extremely effective. The current study employs in-silico screening, MD simulation, and drug kinetics evaluation, all of which are evaluated using different criteria. The study comprised 37 ligands, and three stood out as the best, with greater binding scores above the threshold value. Docking analysis found that compound 34 had the highest docking score in the series (-13.60 kcal/mol) and interacted with the important amino acids TYR 435, CYS 397, CYS 172, PHE 343, TYR 398, and LYS 296 required for MAO inhibitory activity. The ADMET study revealed that the compounds had drug-like properties. The results of this study could be used to develop chromone drugs that target the MAO inhibitor. The top three ligands with the highest force and work were then simulated using molecular dynamics. The protein-ligand complexes had steady trajectories throughout the 100 ns simulation, according to the data. Furthermore, the drug likeliness predicted by ADMET analysis findings indicated that the top three lead compounds had strong inhibitory efficiency, superior pharmacokinetics, and were non-toxic under physiological settings. As a result, these compounds have the potential to be exploited as possible treatment medications for PD.

Background: The COVID-19 pandemic significantly affects patients with RA and other rheumatic diseases. Our study aims to explore the factors associated with COVID-19-related fatality among Rheumatoid Arthritis (RA) patients, especially immunomodulatory therapies, using the international Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).

Methods: Reportes from FAERS were extracted from February 2020 to September 2022, and uesd for this cross-sectional analysis. The investigative outcome was COVID-19-related death. Age, sex, region, event date, and immunomodulatory medications classies were included as co-variates in multivariable logistic regression. In view of the different targeting and affinity of individual JAKi, Tofacitinib, Upadacitinib and Baricitinib was respectively analyzed.

Results: In all, 3808 cases (mean age 58.85 years, 82.8% female), 267 (7.0%) died. JAKi therapies (41.2%), followed by TNFi (37.7%), IL-1i (12.2%), IL-6i (4.1%) and Anti-CD20 (3%) were reported. Risk factors associated with COVID-19-related death in RA patients were age (odds ratio [OR]: 1.06; 95% confidence interval [CI]: 1.05–1.08; p < 0.01), male sex (1.71, 1.26–2.33; p = 0.01) and anti-CD20 therapies (5.05; 1.40–18.19; p = 0.013). With TNFi conference, anti-CD20 was still a risk predictor (4.29; 2.39–7.70; p < 0.01). Other DMARDs except for anti-CD20, did not confer a significant association with mortality, compared with csDMARDs or TNFi. Individual JAKi showed no obvious difference in the risk of death, compared with csDMARDs or TNFis.

Conclusion: Conclusions Using FAERS open access data for risk prediction of death, anti-CD20 therapies were recognized as a risk factor for COVID-19-related fatalities among RA patients, other immunomodulatory therapies were not associated with mortality, compared with csDMARDs or TNFis.

This study focuses on the development of a liposomal preparation for the targeted delivery of Telmisartan in the context of breast cancer treatment. Telmisartan, a pharmaceutical agent with potential anticancer properties, has been encapsulated within liposomes, lipid-based vesicles known for their capacity to enhance drug delivery and improve therapeutic outcomes. The formulation and characterization of Telmisartan-loaded liposomes were conducted, evaluating factors such as size, shape, and drug release profiles. The findings demonstrate that the liposomal preparation effectively encapsulates Telmisartan, maintaining its pharmacological properties. The development of such liposomal formulations holds promise for advancing breast cancer therapies, offering the potential for enhanced treatment efficacy and reduced side effects. This research contributes to the ongoing efforts to explore innovative drug delivery strategies in the realm of breast cancer treatment. Breast cancer is a pervasive and challenging malignancy affecting women worldwide. In the quest for more effective and targeted treatment approaches, the development of liposomal preparations for delivering therapeutic agents to breast cancer cells has emerged as a promising avenue. Telmisartan, originally recognized for its antihypertensive properties, has been increasingly investigated for its potential anticancer effects. This study delves into the design and evaluation of a liposomal formulation for Telmisartan, aiming to enhance its therapeutic potential in breast cancer. The formulation process involved the encapsulation of Telmisartan within lipid-based liposomes, which are well-known for their ability to carry a variety of drugs, protect them from degradation, and enhance their selective delivery to tumor cells.

The use of in-silico research in drug development is growing. Aspects of drug discovery and development, such as virtual ligand screening and profiling, target and lead finding, and compound library creation, are simulated by computational approaches. Databases, pharmacophores, homology models, quantitative structure–activity connections, machine learning, data mining, network analysis tools, and computer-based data analysis tools are examples of in-silico techniques. These techniques are mostly applied in conjunction with the production of in vitro data to build models that facilitate the identification and refinement of new compounds by providing insight into their features related to absorption, distribution, metabolism, and excretion.

The activity of Seborrheic dermatitis on the skin of children still remains of the dermatoses of male and female babies in the early days of their existence. Acalypha wilkesiana L have been employed by mothers to combat Seborrheic dermatitis, yet, the descriptors responsible for such activity as well as the nonbonding interactions between the selected phytochemicals and stearoyl-CoA desaturase has not been explored. The studied compounds were optimized using Spartan’14 software as well as molecular operating environments (MOE) for docking, Cytoscape software for compound-protein interaction network, Gromacs for molecular dynamic simulation as well ADMETSar for pharmacokinetics studies. The selected compounds proved to have anti-stearoyl-CoA desaturase properties via the calculated descriptors obtained from the chemical compounds obtained from Acalypha wilkesiana L as well as from the result from molecular modeling studies. The Pharmacokinetics results were observed and reported appropriately.

The increasing quest for therapeutic alternatives in treating non-communicable chronic diseases like obesity has propelled research into bioactive peptides, with a particular focus on milk due to its rich protein composition and associated health benefits. Milk fermentation, a traditional process in dairy production, enhances the bioactivity of peptides, broadening their potential therapeutic uses. This study investigated the anti-obesity potential of peptides from bovine milk fermented by Lacticaseibacillus casei LBC 237, identifying 143 peptides, notably LGPV and EVPMP. In silico analyses revealed that LGPV and EVPMP biopeptides exhibited significant interactions with target proteins, employing various molecular interactions such as Van der Waals forces, hydrogen bonds, and electrostatic interactions. These peptides shared common binding sites in some enzymes, suggesting a similar mode of interaction between molecule and target protein, akin to key pharmaceuticals recommended for treating these pathologies. Furthermore, amino acid characteristics present in the peptides, including hydrophobic residues like Leucine, Glutamate, Valine, and Proline, proved essential for their bioactive and inhibitory activities. These findings highlight the potential of LGPV and EVPMP biopeptides as therapeutic agents in managing obesity and metabolic disorders. They provide important insights into their mechanisms of action, paving the way for future research to apply them practically in preventing and treating metabolic conditions.

Influenza virus transmission is largely mediated by its mutation and genome reassortment from distinct strains resulting in drug-resistances and pandemics. This necessitates the need for the discovery of more potential influenza inhibitors to prevent future epidemics. An in-silico approach was utilized here to design six new (21a-f) potential inhibitors of influenza neuraminidase (NA) using a hit compound 21 with good binding affinity, predicted activity, and pharmacokinetic properties in our previous work. The modeled activities (pEC₅₀) of the newly designed compounds (ranging between 8.188 and 7.600) were better than that of the hit compound 21 with predicted activity (pEC₅₀) of 6.0101 and zanamivir (pEC₅₀ of 5.6755) as the standard reference control used. The MolDock scores (ranging between -189.67 and -142.47 kcal/mol) of these newly designed compounds in the NA binding cavity were also better than the hit template 21 with a MolDock score of -125.33 kcal/mol and zanamivir standard drug (-136.36 kcal/mol). In addition, the conformational stability of the best-designed compound 21a in the NA binding cavity was further studied through the MD simulation of 100 ns. Moreover, the drug-likeness and ADMET predictions of these designed compounds showed their good oral bioavailability and pharmacokinetic profiling respectively. More so, the DFT calculations also revealed the relevance of these designed compounds in view of their smaller band energy gaps from the frontier molecular orbital calculations. This study could serve as a reliable in-silico perspective for the search and discovery of potential anti-influenza agents.

Alzheimer’s disease (AD) is a brain disorder that is known to be one of the deadliest diseases affecting humanity, especially adults from the age of sixty (60) years and above. It mostly affects thinking ability, behaviour and social skills, eventually, AD causes the brain to shrink and brain cells to die. To curb the menace of this disease, virtual screening of potent, non-toxic hybrid natural therapeutic inhibitors was performed on some inhibitors of AD. We performed simulations on the screened compounds and predicted their druggability. A model with satisfactory statistical properties was developed in this study. The ligands underwent molecular docking, C-19 exhibited the highest docked score of -12.8 kcal/mol against the target, while the referenced compound (harmine) indicated the lowest docked score of -8.2 kcal/mol. The docked complex was validated using molecular dynamic simulations. Trajectory plots of C-19 were obtained and found to be stable. C-19 was stable during the 100 ns intervals which implies that the compounds were better than the referenced compound. In addition, ADMET has demonstrated that these ligands have good pharmacokinetic properties. All the evaluations were more comprehensive and beneficial to researchers and the medical community as outstanding results were obtained.

The murrcidae gastropod operculum has many therapeutic uses in ayurveda, including treating cancer, gastric, hepatic, cardiovascular, and immunological disorders. Antibacterial, cell reinforcement, FTIR, and mass spectrum datas were used to identify important functional groups and chemical constituents in Chicoreus ramosus operculum concentrate. At 100 mg/L, the operculum extract showed stronger inhibitory movement (125 mm) against Bacillus subtilis and less (08 mm) against Staphylococcus aureus. Operculum extract’s biochemical composition, total antioxidant properties, protein denaturation, metal chelation movement, all-out cell reinforcement action, and anti-diabetic action were 85.71%, 80.98%, 32.03%, and 76.47% at 1000 µg/mL concentration. The operculum remove FTIR showed nine significant groups, including amines, esters, and fragrant mixtures. 11 dynamic mixtures from GC–MS analysis of operculum rough concentrate. These bioactive fractions interacted with IL 23 in molecular docking experiments. Androst-1-en-3-one, Bis (2-ethylhexyl) phthalate, and 3-Methoxy-2,4,5-trifluorobenzoic acid had the highest docking scores and target protein receptor interactions. -11.9 kcal/mol, -08.6 kcal/mol and -7.7 kcal/mol are the maximum scores. These compounds are therapeutic and antimicrobial. These bioactive compounds in operculum extracts allow C. ramosus to be used in conventional medicine and may lead to the development of new drugs.

A number of dengue viruses can seriously impact public health, and their spread has long been a concern. The development and administration of antiviral drugs have played a crucial role in combating viral infections in recent years. These drugs have shown that they can effectively inhibit viral replication and alleviate associated viral complications. The aim of this article is to provide an overview of current evidence on the effectiveness of administered antiviral drugs in controlling viral replication and treating viral problems. In the present study, the PyRx tool was used to docked proteins and ligands. In summary, the present study shows that rosmarinic acid has remarkable docking values against various dengue viral targets. Specifically, it shows a docking value of -8.0 for DENV1-E111, -8.1 for the RNA-dependent RNA polymerase (NS5), -8.2 for the non-structural A chain protein 1 (NS1), and -8.6 for the RNA helicase. These results suggest that rosmarinic acid may have an antiviral effect against the virus's target proteins. Further research is needed to investigate the therapeutic effects of rosmarinic acid in fighting viral infections. In addition, many enzymatic activities of rosmarinic acid have been reported by the PASS (Prediction of Activity Spectra for Substances) tool. The present investigation led to the definitive conclusion that rosmarinic acid possesses remarkable antiviral properties. The present study is promising for future applications, particularly in the search for a drug molecule that can effectively combat a variety of viral infections.

A variety of medicinal compounds, including 4,6-di(1H-indol-3-yl)-1,6-dihydropyrimidin-2-amine, were synthesized through a single-step, multicomponent, stepwise reaction. In this reaction, a mixture of 1H-indole-3-Carbaldehyde, 1-(1H-indol-3-yl) ethanone and guanidine nitrate in ethanol was refluxed. The synthesized compounds were characterized using ¹H NMR and ¹³C NMR studies and their antimicrobial activities against Escherichia coli, Staphylococcus aureus, Aspergillus niger and Aspergillus flavus were evaluated. Molecular docking analysis revealed specific amino acid residues (LEU704, GLY708, LEU707, GLN711, MET749, PHE764, VAL746, MET787, MET745, LEU873, HIS874, VA; 903, MET742, ILE898, MET895, ILE899, TRP741, THR877, P HE 876, LEU701, MET780) are involved in the interaction between androgen receptor and ligand. The optimal interaction and docking score were observed (7.0 kcal/mol).

Breast cancer remains a significant global health concern, necessitating the exploration of novel preventive and therapeutic strategies. Dietary interventions have gained substantial attention due to their potential to modulate cancer risk and progression. Millets, a group of small-seeded grasses, have emerged as promising candidates in this regard, owing to their rich nutritional composition and diverse bioactive compounds. Among these bioactive compounds, phytate antinutrients have garnered considerable interest for their potential health benefits. This review aims to unravel the intricacies of phytate antinutrients in millets and their therapeutic implications in breast cancer. Phytates are naturally occurring compounds present in various plant-based foods, including millets, and are known for their ability to chelate minerals and inhibit their bioavailability. However, recent research has shed light on the multifaceted properties of phytates, highlighting their potential as functional bioactive molecules. Phytates exhibit various anticancer properties, including “antioxidant, anti-inflammatory, and anti-proliferative effects”, which have been shown to inhibit the growth and progression of breast cancer cells. Additionally, phytates have been reported to modulate key signaling pathways involved in cancer development, such as PI3K/Akt, MAPK, and NF-κB, thereby exerting their anticancer effects. Moreover, phytates demonstrate the potential to enhance the efficacy of conventional breast cancer treatments, such as chemotherapy and radiation therapy, while mitigating their adverse effects. Furthermore, the bioavailability and metabolism of phytates are complex processes influenced by factors such as food processing, gut microbiota composition, and genetic variations. Understanding these intricacies is crucial for harnessing the full potential of phytates in breast cancer prevention and treatment. In conclusion, this review provides a comprehensive overview of the intricate roles of phytate antinutrients in millets and their therapeutic implications in breast cancer. The findings suggest that millets, as a rich source of phytates, could be incorporated into dietary strategies to reduce breast cancer risk and complement existing therapeutic approaches. However, further research is warranted to elucidate the precise mechanisms of action, optimal dosage, and potential synergistic effects with other bioactive compounds. The information that is given here is supported by accurate facts and arguments that have undergone rigorous scrutiny.

The procedure for learning and creating a new medicine is widely seen as a drawn-out and costly endeavor. Different rational strategies are considered, depending on their requirements, as potential ways; nevertheless, techniques to designing drugs based on structure and ligands are well acknowledged as very practical and potent tactics in drug discovery. Computational approaches help decrease the need for Medicinal research with animals, helping to develop fresh, safe therapeutic concepts via rational design and positioning of existing products and supporting pharmaceutical scientists and medicinal chemists during the medication development process. Computer-aided drug discovery (CADD) methods are useful for reducing the time and cost of drug discovery and development and understanding the molecular mechanisms of drug action and toxicity. Molecular docking is a technique that predicts a ligand’s binding mode and affinity to a target protein. At the same time, QSAR is a technique that establishes mathematical relationships between the structural features and biological activities of a series of compounds. This study reviews the current state and applications of CADD methods, focusing on molecular docking and quantitative structure–activity relationship (QSAR) techniques. This study reviews the principles, advantages, limitations, and challenges of these methods, as well as some recent advances and examples of their applications in drug discovery for various diseases. The study also discusses the future prospects and directions of CADD methods in the era of big data and artificial intelligence.

Sea buckthorn (Hippophae rhamnoides L.) is a plant with various nutritional and medicinal properties. It contains vitamins, carotenoids, polyphenols, fatty acids, and phytosterols, which have antioxidant, anticancer, anti-hyperlipidemic, anti-obesity, anti-inflammatory, antimicrobial, antiviral, dermatological, neuroprotective, and hepatoprotective effects. Sea buckthorn can be used as a functional food or a dietary supplement for the prevention and treatment of chronic diseases. This review summarizes the phytochemistry, health benefits, and food applications of sea buckthorn, and highlights the potential and challenges for its further development and utilization. Sea buckthorn not only has greatmedicinal and therapeutic potential, but also is a promising economic plant. The potential of seabuckthorn in the human food industry has attracted the research interest of researchers and pro- ducers. The present review mainly summarizes the phytochemistry, nutrients, health benefits, andfood applications of sea buckthorn. Overall, sea buckthorn is a dietary source of bioactive ingredi- ents with the potential to be developed into functional foods or dietary supplements for the pre- vention and treatment of certain chronic diseases, which deserves further research.

Cowhide collagen hydrolysates (CCHs) are peptides and amino acids obtained from the partial hydrolysis of collagen. These have numerous potential applications in the food, biomedical, and pharmaceutical industries. The study analyzed the physicochemical, antioxidant, and anti-atherosclerosis properties of collagen hydrolysates (CCHs) from cowhide using in silico methods. Proteins were identified in silico based on their molecular weights and origin from the protein database (UniProtKB). Using bioinformatics tools, numerous physicochemical properties (toxicity and amino acid composition) were determined. The identified proteins were subsequently subjected to an in silico enzymatic hydrolysis using pepsin, thermolysin, and proteinase K. The peptides obtained were characterized. Molecular docking was conducted between the peptides generated in silico and the three target enzymes (3-Hydroxy-3-Methylglutaryl-CoA (HMG-CoA) reductase, cyclooxygenase-2, and Nicotinamide Adenine Dinucleotide Phosphate (NADPH) oxidase). Two cowhide collagens were identified, F1MJQ6 and G3MZI7, with molecular weights of 172,076 and 184,867 Da, respectively. A compositional analysis of F1MJQ6 and G3MZI7 revealed the significant presence of glycine residues at 25% and 23%, and proline residues at 16% and 18%, respectively. The G3MZI7 and F1MJQ6 proteins exhibited a high concentration of both essential and semiessential amino acids. The molecular docking results indicate that the antioxidant peptides ADF, PHF, and LW (novel potential anti-atherosclerosis peptides released by enzymatic hydrolysis with pepsin, thermolysin, and proteinase K) are the most promising candidates for further development as inhibitors of HMG-CoA reductase, cyclo-oxygenase-2, and NADPH oxidase. In silico analysis revealed that cowhide collagen hydrolysates exhibited particularly significant antioxidant and anti-atherosclerosis properties.

This paper examines the transformative impact of artificial intelligence (AI) on pharmacogenomics, signaling a paradigm shift in personalized medicine. With a focus on enhancing drug response prediction and treatment optimization, AI, particularly machine learning and deep learning algorithms, navigates the complexity of genomic data. By elucidating intricate relationships between genetic factors and drug responses, AI augments the identification of genetic markers and contributes to the development of comprehensive models. The review emphasizes AI’s role in guiding treatment decisions, minimizing adverse reactions, and optimizing drug dosages in clinical settings. Ethical considerations, challenges, and future directions are also discussed. This work underscores the synergy of AI and pharmacogenomics, offering a more effective and patient-centric approach to drug therapy, marking a significant advancement in the field of personalized medicine.

Vernonia cinerea L., also known as purple fleabane, is a plant with medicinal properties that have been traditionally used to treat respiratory infections, digestive disorders, and skin conditions. Its antimicrobial and anti-inflammatory properties make it a potential candidate for treating various infections and inflammatory diseases. The plant contains alkaloids, flavonoids, and essential oils, which possess antimicrobial properties, making it a promising candidate for treating bacterial and fungal infections. Its anti-inflammatory properties have shown potential for managing inflammatory diseases like arthritis and dermatitis. Additionally, the plant's analgesic and antipyretic effects suggest its potential for pain management and fever reduction. Its antioxidant properties make it a potential candidate for preventing and treating oxidative stress-related diseases like cardiovascular disorders and neurodegenerative conditions. However, more studies are needed to determine its optimal dosage, safety profile, and potential drug interactions before widespread use in medical practice.

The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure–activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet.

This overview examines the latest developments and applications of 3D and 4D printing in the health care and pharmaceutical field. The technique of creating a 3D object from a 3D model in the required size and shape is known as 3D printing. Whereas, 4D printing is the establishment of intricate three-dimensional formations that can change form in response to various external inputs. Applying 3D printing technology 3D printing, the healthcare industry has produced significant strides toward a patient-centred approach. The future of biomedical science and patient-centered care could be completely transformed by 3D printing with further advancements in research and development. 3D, 4D Technology used in printing is one of the most advanced industrial technologies available worldwide. The 3D and 4D printing pharmaceutical enterprises have completed the shift from centralised to distributed systems for the purpose of creating dosage forms. The goal of the study is to support the research goal of determining the degree to which patient-specific treatment is enhanced and healthcare outcomes are improved through the use of printing technologies in pharmaceuticals. Beyond this thorough analysis, the study highlights potential and problems from several angles and comparative aspects between 3D and 4D printing.

Mimusops elengi, a widely distributed tree species in tropical regions, has been used for centuries in traditional medicine due to its numerous therapeutic properties. In this study, we conducted a comprehensive analysis of the phytocompounds present in various parts of Mimusops elengi, including leaves, bark, and flowers using advanced analytical techniques. The objective was to identify and quantify the bioactive compounds responsible for the plant’s medicinal attributes. Our findings revealed a diverse range of phytochemicals in Mimusops elengi. The analysis detected various classes of compounds, including alkaloids, flavonoids, tannins, phenolic compounds, terpenoids and glycosides. Notably, the leaves contained a significant concentration of alkaloids, particularly elengioside, which has demonstrated potential anti-inflammatory and analgesic properties. Additionally, the flowers were rich in flavonoids and tannins, such as quercetin and catechin, known for their antioxidant and anticancer activities. Furthermore, the bark of Mimusops elengi exhibited the presence of terpenoids, including lupeol and betulinic acid, which have been associated with anti-inflammatory and antiviral properties. These compounds may contribute to the plant’s traditional use for treating various ailments, including skin disorders and respiratory issues. This review provides valuable insights into the phytochemical composition of Mimusops elengi, shedding light on its potential therapeutic applications. Mimusops elengi holds promise as a valuable source of natural compounds with health-promoting benefits.

Current clinical research suggests that inhibitors of protein arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) which are of biological and therapeutic interest, may show potential in treating rheumatoid arthritis, type 1 diabetes, Graves’ disease and other autoimmune disorder. In the present study, a comprehensive analysis was conducted on a collection of 32 compounds concerning their anti-rheumatoid arthritis activity as inhibitors of PAD4. This analysis represents the first instance in which these compounds were computationally examined, employing an in-silico approach that considered 2D-3D QSAR modeling, and molecular docking and was further validated through molecular dynamics and ADMET properties assessment. A credible 2D QSAR (Q_LOO^2 = 0.6611 and R^2 = 0.7535) model was constructed and verified using an external validation test set, Y-randomization, variance inflation factor (VIF), mean effect (MF), and William’s plot applicability domain (AD). Ligand-based alignment was implemented in the 3D-QSAR examination. The outcomes demonstrated that CoMFA (uvepls) (Q²LOO = 0.5877; R² = 0.9983) possess remarkable stability and foresight. The internal validation indicated that CoMFA (uvepls) MIFs display superior predictive capability compared to COMFA (ffdsel). Structural criteria determined by the contour maps of the model and molecular docking simulations were strategically employed to computationally develop 10 new, non-toxic autoimmune disease inhibitors with increased efficacy. Docking tests were done on the newly developed compounds to illustrate their binding mechanism and to identify critical interaction residues inside the active region of rheumatoid arthritis (PDB id: 3BLU). In addition, docking results of the selected designed compounds inside the active sites of type 1 diabetes receptor (6DFX), and Graves’ disease receptor (4QT5) demonstrated their rheumatoid arthritis (PDB id: 3BLU) selectivity. A molecular dynamics simulation and binding free energy calculations using the MM/GBSA technique confirmed the stability of the proposed compound D4 inside the rheumatoid arthritis (3BLU) receptor active site. In summary, the results of our investigation might give considerable insight into the future design and development of new autoimmune disease inhibitors.

In a recent article in Intelligent Pharmacy, a portion of the text appears to have been generated by a generative artificial intelligence (AI) system. The usage of AI is not documented in the article. If AI was used, therefore, the article is in violation of the journal’s policy on generative AI use and declaration.

Dengue fever is a mosquito-borne illness that affects millions of people worldwide. Artificial intelligence (AI) is being employed in the battle against it. AI is being used to analyse dengue immune repertoires, which may yield hitherto unheard-of insights into the complexities of dengue adaptive immunity, help in the development of novel treatments, and influence the creation of vaccines. This AI-driven strategy may contribute to the creation of dengue fever therapies that are more potent.

Employing the Internet of Things (IoT) in healthcare provides many advantages for patient monitoring and analysis of the patient’s health with the help of generated data. The basic role of IoT in healthcare is to ease the patient’s life by giving them a monitor over their medical condition. The use of IoT in medical devices requires a focus on the end-user. Medical devices such as glucose meters are designed to record the data of the patient and their vital signs. The generated information can be used to enhance decision-making for the physician. The collected information about the patient can be at risk due to certain security issues during the transferring of information can compromise the identity and social life of the patient. This review explores the IoT regarding its structural requirement and its role in various fields with special emphasis on healthcare. The security and privacy issues than can hinder the utilization of IoT at its potential and ways to overcome these issues are being addressed.

Since the dawn of time, people have used herbal medications to both prevent and treat a variety of illnesses. Numerous illnesses, including diabetes, skin conditions, and most seriously cancer, have been claimed to be successfully treated by Acacia Arabica. According to Indian traditional medicine, the fresh sections of Acacia Arabica have beneficial nutritional properties as well as astringent, demulcent, aphrodisiac, anthelmintic, antibacterial, and antidiarrheal properties. The ethnobotanical and therapeutic benefits of Acacia Arabica are briefly reviewed in this article along with information about the plants. This is an attempt to gather and record data regarding many facets of Acacia Arabica and its prospective applications. Before using Acacia Arabica’s therapeutic potential, more research is required.

One form of uncommon cancer that develops in the smooth muscles is called leiomyosarcoma, or LMS. The body’s hollow organs, such as the stomach, bladder, intestines, and blood vessels, contain smooth muscles. The three main components of conventional therapy are surgery, chemotherapy, and radiotherapy. These treatments have numerous drawbacks, including insufficient surgical resection, drug resistance to chemotherapy, radiotherapy insensitivity, and postoperative bone defects. Because of their easy modification, targeting, and other characteristics, along with their unique physicochemical properties, nanoparticles have been used extensively in research on anti-leiomyosarcoma treatment. Inhibiting the onset and progression of leiomyosarcoma, nanoparticles can have a variety of effects on the growth of leiomyosarcoma cells. We provide a brief overview of the development of nanoparticle research closely associated with leiomyosarcoma treatment in this review.

Cancer drug resistance is a serious issue in Bangladesh that must be addressed with effective solutions. The growth of resistant bacterial strains, inappropriate use of antimicrobials, and inadequate healthcare standards in Bangladesh have resulted in a severe problem with cancer medication resistance. A comprehensive strategy will be needed to address these problems, one that includes expanding knowledge of antibiotic resistance, bettering healthcare system regulation, and developing more potent cancer therapies.

Respiratory diseases (RD) are a major healthcare issue and is predicted to be leading cause of mortality by 2030. Artificial intelligence (AI) has been recently gained interest -in scientific fields. Among a variety of application, it has made a marked embrace in medical domain where it is applied for diagnosis and disease progression by clinicians. Further, in particular, AI based Machine learning (ML) and Deep Learning (DL) algorithms have come up as an effective emerging trend in diagnosis of respiratory diseases. These algorithms are trained to classify the respiratory diseases such as pneumonia, fibrosis, cancer, tuberculosis emphysema, asthma based on radiographs, CT scans etc. images. The AI enabled diagnosis can facilitate precise diagnosis and differentiation among over-lapping characteristics bearing lung diseases. This review focuses on the AI-based algorithms assisted, improved diagnosis of three respiratory diseases specifically COPD (Chronic obstructive pulmonary disease), asthma and lung fibrosis. Further, AI is expected to play a crucial role in facilitating diagnosis aiding clinicians in predicting and management of lung diseases taking it towards a promising tool for everyday clinical practice soon. It is written with a hope that this brief review of emphasizing utilization of AI in medical field will be helpful to clinicians.

This comprehensive review explores modern granulation techniques in pharmaceutical dosage forms along with conventional methods, focusing on dry granulation and wet granulation. Dry granulation techniques, including slugging, roller compaction, and pneumatic dry granulation, are dissected with thorough analyses of their processing methods, advantages, disadvantages, and diverse applications. The article delves into eleven wet granulation techniques, offering insights into high-shear granulation, low-shear granulation, fluidized bed granulation, reverse wet granulation, steam granulation, moisture-activated dry granulation, melt granulation, freeze-dry granulation, foam granulation, thermal adhesion, and twin screw wet granulation. Each method is scrutinized, providing a comprehensive understanding of its processing steps, merits, drawbacks, and practical applications in pharmaceutical manufacturing. The article serves as a valuable resource for researchers, pharmaceutical professionals, and students, offering a nuanced exploration of diverse granulation techniques vital in drug formulation. This synthesis of information aims to enhance the understanding of granulation processes, facilitating informed decision-making in pharmaceutical development and manufacturing.