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Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. Methods: In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. Results: By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. Conclusions: The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models. [ABSTRACT FROM AUTHOR]

Abstract Recently, increased attention has been paid to the raw materials of plants as a source of biologically active substances. Black rosehip (Rosa pimpinellifolia L.) fruits could be a good resource for potential functional components in the food, cosmetic and pharmaceutical industries. Also, drying can influence the composition of heat-sensitive phytochemicals. However, less attention is given to comparing black rosehip bioactive compounds particularly compositions of fatty acid, amino acids, and phenolic content in fresh and dried fruits. So in this study, the amino acid constituents (by amino acid analyzer), fatty acids (by GC–MS), mineral elements (by atomic absorption spectrometer), antioxidant (by DPPH) and phenolic compounds (by HPLC) present in fresh and dried fruits of black rosehip naturally grown in Iran were comprehensively investigated. The results showed that dried fruits had a lower level moisture by 51.55%, and a higher level of total phenolic compounds and total sugar by 786.20 mg GAE/100 g and 15.77 g/100 g, respectively. Chlorogenic acid and gallic acid were the major phenolic compounds (109.3 mg/g). Whilst, linoleic acid, oleic acid, and arachidic acid (85.34%) were the most dominant fatty acids. The most dominant amino acids were glutamic acid, phenylalanine, and arginine (29.41 g/100 g). Also, Fe and Mn as micro-elements were the most dominant elements. In general, the results illustrated the potentials and differences of black rosehip fruits grown in the Arasbaran region as promising resources for food sources, pharmaceutics, cosmetics, and breeding programs. Also, these findings confirm that black rosehip fruits contain significant amounts of secondary metabolites that may positively affect human health.

Abstract Antibiotics, improper food, and stress have created a dysbiotic state in the gut and almost 81% of the world’s population has been affected due to the pandemic of COVID-19 and the prevalence of dengue virus in the past few years. The main intent of this study is to synthesize nanosynbiotics as nu traceuticals by combining probiotics, and prebiotics with nanoformulation. The effectiveness of the nanosynbiotics was evaluated using a variety of Nutra-pharmacogenetic assays leading to an AI-integrated formulation profiling was assessed by using machine learning methods. Consequently, Acetobacter oryzoeni as a probiotic and inulin as a prebiotic has been chosen and iron-mediated nanoformulation of symbiotic is achieved. Nanosynbiotics possessed 89.4, 96.7, 93.57, 83.53, 88.53% potential powers of Nutra-pharmacogenetic assays. Artificial intelligent solid dispersion formulation of nanosynbiotics has high dissolution, absorption, distribution, and synergism, in addition, they are non-tox, non-allergen and have a docking score of − 10.83 kcal/mol, implying the best interaction with Pregnane X receptor involved in dysbiosis. The potential of nanosynbiotics to revolutionize treatment strategies through precise targeting and modulation of the gut microbiome for improved health outcomes and disease management is promising. Their transformational influence is projected to be powered by integration with modern technology and customized formulas. Further in-vivo studies are required for the validation of nanosynbiotics as nutraceuticals.

Recently, increased attention has been paid to the raw materials of plants as a source of biologically active substances. Black rosehip (Rosa pimpinellifolia L.) fruits could be a good resource for potential functional components in the food, cosmetic and pharmaceutical industries. Also, drying can influence the composition of heat-sensitive phytochemicals. However, less attention is given to comparing black rosehip bioactive compounds particularly compositions of fatty acid, amino acids, and phenolic content in fresh and dried fruits. So in this study, the amino acid constituents (by amino acid analyzer), fatty acids (by GC–MS), mineral elements (by atomic absorption spectrometer), antioxidant (by DPPH) and phenolic compounds (by HPLC) present in fresh and dried fruits of black rosehip naturally grown in Iran were comprehensively investigated. The results showed that dried fruits had a lower level moisture by 51.55%, and a higher level of total phenolic compounds and total sugar by 786.20 mg GAE/100 g and 15.77 g/100 g, respectively. Chlorogenic acid and gallic acid were the major phenolic compounds (109.3 mg/g). Whilst, linoleic acid, oleic acid, and arachidic acid (85.34%) were the most dominant fatty acids. The most dominant amino acids were glutamic acid, phenylalanine, and arginine (29.41 g/100 g). Also, Fe and Mn as micro-elements were the most dominant elements. In general, the results illustrated the potentials and differences of black rosehip fruits grown in the Arasbaran region as promising resources for food sources, pharmaceutics, cosmetics, and breeding programs. Also, these findings confirm that black rosehip fruits contain significant amounts of secondary metabolites that may positively affect human health. [ABSTRACT FROM AUTHOR]

Antibiotics, improper food, and stress have created a dysbiotic state in the gut and almost 81% of the world's population has been affected due to the pandemic of COVID-19 and the prevalence of dengue virus in the past few years. The main intent of this study is to synthesize nanosynbiotics as nu traceuticals by combining probiotics, and prebiotics with nanoformulation. The effectiveness of the nanosynbiotics was evaluated using a variety of Nutra-pharmacogenetic assays leading to an AI-integrated formulation profiling was assessed by using machine learning methods. Consequently, Acetobacter oryzoeni as a probiotic and inulin as a prebiotic has been chosen and iron-mediated nanoformulation of symbiotic is achieved. Nanosynbiotics possessed 89.4, 96.7, 93.57, 83.53, 88.53% potential powers of Nutra-pharmacogenetic assays. Artificial intelligent solid dispersion formulation of nanosynbiotics has high dissolution, absorption, distribution, and synergism, in addition, they are non-tox, non-allergen and have a docking score of − 10.83 kcal/mol, implying the best interaction with Pregnane X receptor involved in dysbiosis. The potential of nanosynbiotics to revolutionize treatment strategies through precise targeting and modulation of the gut microbiome for improved health outcomes and disease management is promising. Their transformational influence is projected to be powered by integration with modern technology and customized formulas. Further in-vivo studies are required for the validation of nanosynbiotics as nutraceuticals. [ABSTRACT FROM AUTHOR]

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Artificial intelligence has brought crucial changes to the whole field of natural sciences. Myriads of machine learning algorithms have been developed to facilitate the work of experimental scientists. Molecular property prediction and drug synthesis planning become routine tasks. Moreover, inverse design of compounds with tunable properties as well as on-the-fly autonomous process optimization and chemical space exploration became possible in silico. Affordable robotic platforms exist able to perform thousands of experiments every day, analyzing the results and tuning the protocols. Despite this, most of these developments get trapped at the stage of code or overlooked, limiting their use by experimental scientists. Meanwhile, visibility and the number of user-friendly tools and technologies available to date is too low to compensate for this fact, rendering the development of novel therapeutic compounds inefficient. In this Review, we set the goal to bridge the gap between modern technologies and experimental scientists to improve drug development efficacy. Here we survey advanced and easy-to-use technologies able to help medical chemists at every stage of their research, including those integrated in technological processes during COVID-19 pandemic motivated by the need for fast yet precise solutions. Moreover, we review how these technologies are integrated by industry and clinics to streamline drug development and production. These technologies already transform the current paradigm of scientific thinking and revolutionize not only medicinal chemistry, but the whole field of natural sciences.

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. Methods: In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. Results: By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. Conclusions: The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models.

Background: External parasites, particularly ticks and fleas, are among the most common problems affecting dogs. Chemical medicines are commonly used to prevent and eliminate such external parasites, but their improper use can cause adverse reactions, and the toxins they contain may remain in the environment. Objectives: The objective of this study was to investigate the in vitro efficacy of Zanthoxylum limonella, citronella, clove, peppermint, and ginger essential oils against dog ticks and fleas and to test the sensitivity of dogs' skin to these essential oils. Methods: The five essential oils were tested for in vitro efficacy against ticks and fleas, and the two most effective essential oils were then tested on the dogs' skin. Results: The results revealed that these five essential oils at 16% concentrations effectively inhibited the spawning of female engorged ticks. In addition, all five essential oils had a strong ability to kill tick larvae at concentrations of 2% upward. Furthermore, 4% concentrations of the five essential oils quickly eliminated fleas, especially clove oil, which killed 100% of fleas within 1 h. A 50%, 90%, and 99% lethal concentration (LC50, LC90, and LC99) for the essential oils on tick larvae in 24 h were found to be low values. LC50, LC90, and LC99 for the essential oils on flea in 1 h was lowest values. Clove oil at 16% concentration was the most satisfactory essential oil for application on dogs' skin, with a low percentage of adverse effects. Conclusions: This study confirmed the effectiveness of essential oils for practical use as tick and flea repellents and eliminators. Essential-oil-based pharmaceutical can replace chemical pesticides and provide benefits for both consumers and the environment. [ABSTRACT FROM AUTHOR]

Eculizumab, a monoclonal antibody against complement C5, is a novel therapy to treat refractory myasthenia gravis (MG). The present review was undertaken to study the role of eculizumab in MG. This includes the drug's mechanism, pharmacokinetics, clinical trial findings, tolerability, side effects, safety, dosage, administration, and cost. An English-language search for relevant items was undertaken using Embase and PubMed from 1946 to present. Clinical trial registries/databases and websites were also searched for relevant data. Keywords were eculizumab and MG. The present review found 103 articles after initial screening. Current data support eculizumab as an effective, safe, and tolerable drug in cases of refractory MG. However, its cost can prevent it from being widely accessible to a majority of the general population. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human struggle against the deadly disease conditions is continued since ages. The contribution of science and technology in fighting against these diseases cannot be ignored exclusively due to the invention of novel procedure and products, extending their size ranges from micro to nano. Recently nanotechnology has been gaining more consideration for its ability to diagnose and treat different cancers. Different nanoparticles have been used to evade the issues related with conservative anticancer delivery systems, including their nonspecificity, adverse effects and burst release. These nanocarriers including, solid lipid nanoparticles (SLNs), liposomes, nano lipid carriers (NLCs), nano micelles, nanocomposites, polymeric and magnetic nanocarriers, have brought revolutions in antitumor drug delivery. Nanocarriers improved the therapeutic efficacy of anticancer drugs with better accumulation at the specific site with sustained release, improved bioavailability and apoptosis of the cancer cells while bypassing the normal cells. In this review, the cancer targeting techniques and surface modification on nanoparticles are discussed briefly with possible challenges and opportunities. It can be concluded that understanding the role of nanomedicine in tumor treatment is significant, and therefore, the modern progressions in this arena is essential to be considered for a prosperous today and an affluent future of tumor patients. [ABSTRACT FROM AUTHOR]

AbstractHuman struggle against the deadly disease conditions is continued since ages. The contribution of science and technology in fighting against these diseases cannot be ignored exclusively due to the invention of novel procedure and products, extending their size ranges from micro to nano. Recently nanotechnology has been gaining more consideration for its ability to diagnose and treat different cancers. Different nanoparticles have been used to evade the issues related with conservative anticancer delivery systems, including their nonspecificity, adverse effects and burst release. These nanocarriers including, solid lipid nanoparticles (SLNs), liposomes, nano lipid carriers (NLCs), nano micelles, nanocomposites, polymeric and magnetic nanocarriers, have brought revolutions in antitumor drug delivery. Nanocarriers improved the therapeutic efficacy of anticancer drugs with better accumulation at the specific site with sustained release, improved bioavailability and apoptosis of the cancer cells while bypassing the normal cells. In this review, the cancer targeting techniques and surface modification on nanoparticles are discussed briefly with possible challenges and opportunities. It can be concluded that understanding the role of nanomedicine in tumor treatment is significant, and therefore, the modern progressions in this arena is essential to be considered for a prosperous today and an affluent future of tumor patients.

Natural ingredients have been used for centuries for skin treatment and care. Interest in the health effects of plants has recently increased due to their safety and applicability in the formulation of pharmaceuticals and cosmetics. Long-known plant materials as well as newly discovered ones are increasingly being used in natural products of plant origin. This review highlights the beneficial effects of plants and plant constituents on the skin, including moisturizing (e.g., Cannabis sativa, Hydrangea serrata, Pradosia mutisii and Carthamus tinctorius), anti-aging (e.g., Aegopodium podagraria, Euphorbia characias, Premna odorata and Warburgia salutaris), antimicrobial (e.g., Betula pendula and Epilobium angustifolium), antioxidant (e.g., Kadsura coccinea, Rosmarinus officinalis, Rubus idaeus and Spatholobus suberectus), anti-inflammatory (e.g., Antidesma thwaitesianum, Helianthus annuus, Oenanthe javanica, Penthorum chinense, Ranunculus bulumei and Zanthoxylum bungeanum), regenerative (e.g., Aloe vera, Angelica polymorpha, Digitaria ciliaris, Glycyrrihza glabra and Marantodes pumilum), wound healing (e.g., Agrimonia eupatoria, Astragalus floccosus, Bursera morelensis, Jatropha neopauciflora and Sapindus mukorossi), photoprotective (e.g., Astragalus gombiformis, Calea fruticose, Euphorbia characias and Posoqueria latifolia) and anti-tyrosinase activity (e.g., Aerva lanata, Bruguiera gymnorhiza, Dodonaea viscosa, Lonicera japonica and Schisandra chinensis), as well as their role as excipients in cosmetics (coloring (e.g., Beta vulgaris, Centaurea cyanus, Hibiscus sabdariffa and Rubia tinctiorum), protective and aromatic agents (e.g., Hyssopus officinalis, Melaleuca alternifolia, Pelargonium graveolens and Verbena officinalis)). [ABSTRACT FROM AUTHOR]

Airway mucus is a complex viscoelastic gel composed mainly of water, glycoproteins, lipids, enzymes, minerals, etc. Among them, glycoproteins are the main factors determining mucus's gel-like rheology. Airway mucus forms a protective barrier by secreting mucin, which represents a barrier for absorption, especially for more lipophilic drugs. It rapidly removes drugs from the airway through the physiological mucus clearance mechanism so drugs cannot remain in the lungs or reach the airway epithelial tissue for a long time. Significant progress has been made in enhancing drug lung deposition recently, but strategies are still needed to help drugs break through the lung mucosal barrier. Based on the physiopathological mechanisms of airway mucus, this paper reviews and summarizes strategies to enhance drug penetration and retention in the airway mucosa mediated by nano-delivery systems, including mucosal permeation systems, mucosal adhesion systems, and enzyme-modified delivery systems. On this basis, the potential and challenges of nano-delivery systems for improving airway mucus clearance are revealed. New ideas and approaches are provided for designing novel nano-delivery systems that effectively improve drug retention and penetration in the airway mucus layer. [ABSTRACT FROM AUTHOR]

Field-flow fractionation (FFF) is a family of single-phase separative techniques exploited to gently separate and characterize nano- and microsystems in suspension. These techniques cover an extremely wide dynamic range and are able to separate analytes in an interval between a few nm to 100 µm size-wise (over 15 orders of magnitude mass-wise). They are flexible in terms of mobile phase and can separate the analytes in native conditions, preserving their original structures/properties as much as possible. Molecular biology is the branch of biology that studies the molecular basis of biological activity, while biotechnology deals with the technological applications of biology. The areas where biotechnologies are required include industrial, agri-food, environmental, and pharmaceutical. Many species of biological interest belong to the operational range of FFF techniques, and their application to the analysis of such samples has steadily grown in the last 30 years. This work aims to summarize the main features, milestones, and results provided by the application of FFF in the field of molecular biology and biotechnology, with a focus on the years from 2000 to 2022. After a theoretical background overview of FFF and its methodologies, the results are reported based on the nature of the samples analyzed. [ABSTRACT FROM AUTHOR]

Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need to restrain the growth of health care costs. The search for computer technologies to stop or slow down the growth of costs acquires a new very important and significant meaning. We discussed the two information technologies in pharmacotherapy and the possibility of combining and sharing them, namely the combination of risk-sharing agreements and Machine Learning, which was made possible by the development of Artificial Intelligence (AI). Neural networks could be used to predict the outcome to reduce the risk factors for treatment. AI-based data processing automation technologies could be also used for risk-sharing agreements automation. [ABSTRACT FROM AUTHOR]

Platelets are blood components traditionally believed to have fundamental roles in vascular hemostasis and thrombosis. In recent years, platelets have received new attention for their roles in tumorigenesis and progression. On the one hand, platelets are actively recruited by various tumors and comprise a crucial part of the tumor microenvironment (TME), thus inspiring the use of platelets for tumor-targeted drug delivery. To this end, various platelet-based devices have been proposed, such as natural platelets, engineered platelets, platelet membranes, and platelet-derived microparticles. On the other hand, platelets are involved in tumor immunosuppression mechanisms, by directing and/or assisting various tumor-associated immune cells. However, in the context of inflammation and autoimmune diseases, platelets can amplify immune responses by promoting immune cell mobilization and activation, thereby exacerbating tissue damage. Thus, interest is growing in the use of tumor-associated platelets as targets for therapeutic modulation of the TME and augmenting anti-tumor immune responses. In this review, we summarize current advances in exploiting platelets for both antitumor drug delivery and immune modulation of the TME.

Objective: To assess student perceptions regarding: 1) exercises designed to enhance student comprehension of the roles of pharmaceutical excipients in dosage forms and 2) the use of resources to identify the roles of excipients. Description: In-class exercises regarding the roles of excipients were implemented in a foundational pharmaceutics course. The exercises covered the topics of liquid single-phase systems, liquid multiphase systems, drug delivery to the skin, and parenteral, ophthalmic, and nasal dosage forms. Students were introduced to resources to identify the roles of excipients. The exercises included the presentation of pharmaceutical preparations with various fundamental excipients, followed by student polling and class discussion. A survey was administered to evaluate student perceptions regarding the exercises about the roles of excipients and the use of resources to identify the roles of excipients. Findings: Eighty students participated in the study (response rate = 99%). Student perceptions indicated that the exercises helped them understand the material better and enhanced their performance in the non-sterile compounding course taught concurrently with the pharmaceutics course. Students indicated that the resources they used during the exercises were lecture notes from the course (95%), lists with excipients that the instructor provided (24%), a Web search, e.g., Google, Bing (20%), the sixth edition of the Handbook of Pharmaceutical Excipients (18%), Micromedex/Martindale (16%), and the International Journal of Pharmaceutical Compounding (6%). Conclusion. Targeted excipient exercises are a practical approach to enhance student understanding and can be utilized in pharmaceutics and non-sterile compounding courses across various pharmacy curricula.

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.

Preparation of small-molecule API (Active Pharmaceutical Ingredient) at submicron size would be of great benefit for pharmaceutical engineering, as the drug particles at submicron size possess higher solubility in water. Indeed, the drug bioavailability can be enhanced when the nanomedicine is prepared. In this study, the solubility of the drug desoxycorticosterone acetate (DA) is being examined to assess its viability of nanonization using supercritical operation. Two inputs are temperature and pressure which were considered for machine learning modeling in this study. The drug's solubility is the only output to be estimated by the optimized models. This dataset has 45 rows of data that were gathered at 5 different pressure and temperature levels. Support vector machine (SVM) is used as the core of the models built in this research. Epsilon-SVR and nu-SVR are models based on this concept, which together with two different polynomial and RBF kernels form the four models built in this research for estimation of DA drug solubility. The models are also optimized with the help of a new TLCO method. All four final models have an R2 score higher than 0.9, and among them, the Epsilon-SVR model with RBF kernel has the best performance with 0.967.

This paper investigates the application of Support Vector Regression with Quadratic Kernel (QSVR) for modeling the solubility of lornoxicam in supercritical carbon dioxide. The dataset comprises temperature (T) and pressure (P) as input variables, while the solubility (Y) of lornoxicam serves as the output variable entire the modeling. The temperature is measured in Kelvin (K), and the pressure is measured in bar as the inputs of models. To improve the predictive performance of the QSVR model, three distinct hyper-parameter optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), and Bayesian Hyperparameter Optimization (BHO) are employed. These optimization methods are utilized to fine-tune the hyper-parameters of the QSVR model and enhance its predictive accuracy. The BHO-QSVR model achieved an impressive R2 score of 0.96725, indicating a strong fit between the predicted and actual solubility values. Additionally, it exhibited a Mean Absolute Error (MAE) of 1.75666E-05 and a maximum error of 3.02849E-05. Comparatively, the GA-QSVR and TS-QSVR models also performed well, achieving R2 scores of 0.95346 and 0.95882, respectively. The GA-QSVR model achieved an MAE of 1.56725E-05 and a maximum error of 4.92382E-05, while the TS-QSVR model exhibited an MAE of 1.84075E-05 and a maximum error of 5.02443E-05.

The latest approach to pharmaceutical quality is Quality by Design. This article introduces Pharmaceutical Quality by Design (QbD) and shows how Quality by Design is used to assure pharmaceutical quality. A description of Quality by Design is given, as well as some of its components. Each unit operation's process parameters and quality characteristics are identified. The advantages, possibilities, and steps involved in Pharmaceutical Quality by Design are outlined. The goal of pharmaceutical development is to create a high-quality product and manufacturing method that consistently delivers the product's expected performance. Quality cannot be tested into items; instead, it should be designed into them. The Quality target product profile, crucial quality traits, and major features of Quality by Design are all included. It also compares product quality based on end-of-life testing with product quality based on Quality by Design. The ICH Guidelines are the cornerstone of Quality by Design. It is based on the International Conference on Harmonization (ICH) Guidelines Q8 for pharmaceutical development, Q9 for quality risk management, and Q10 for pharmaceutical quality systems. It also discusses how Quality by Design might be used in product development and manufacturing. [ABSTRACT FROM AUTHOR]

The moisture content of pharmaceutical powder is a key parameter contributing to tablet sticking during the tableting process. This study investigates powder moisture behavior during the compaction phase of the tableting process. Finite element analysis software COMSOL Multiphysics® 5.6 was used to simulate the compaction microcrystalline cellulose (VIVAPUR PH101) powder and predict temperature and moisture content distributions, as well as their evolution over time, during a single compaction. To validate the simulation, a near-infrared sensor and a thermal infrared camera were used to measure tablet surface temperature and surface moisture, respectively, just after ejection. The partial least squares regression (PLS) method was used to predict the surface moisture content of the ejected tablet. Thermal infrared camera images of the ejected tablet showed powder bed temperature increasing during compaction and a gradual rise in tablet temperature along with tableting runs. Simulation results showed that moisture evaporate from the compacted powder bed to the surrounding environment. The predicted surface moisture content of ejected tablets after compaction was higher compared to that of loose powder and decreased gradually as tableting runs increased. These observations suggest that the moisture evaporating from the powder bed accumulates at the interface between the punch and tablet surface. Evaporated water molecules can be physiosorbed on the punch surface and cause a capillary condensation locally at the punch and tablet interface during dwell time. Locally formed capillary bridge may induce a capillary force between tablet surface particles and the punch surface and cause the sticking. [ABSTRACT FROM AUTHOR]

In order to improve the clarity of the development of the markers in the digestive tract，the formulation and preparation process of high drug loading pellets of barium sulfate were explored and optimized. Firstly, extrusion-spheronization process was used to prepare the barium sulfate-loaded pellet cores with barium sulfate as a model drug. Secondly, the powder layering technique was applied to prepare the barium sulfate pellets by coating the core with barium slfate drug layer. Finally, the influencing factors of formulation and process on the properties of the pellet cores or pellets were investigated, and the process and formulation parameters were optimized by single factor test and orthogonal test based on the index of roundness, friability, and yield. The results show that the obtained barium sulfate-loaded pellet cores have possessed high roundness, the preparation method of the pellet cores is stable, the drug-loading rate is up to 80%, and the yield of 550~700 μm (mesh of 24～30) is 8533%.The content of barium sulfate in the powder of powder layering method is about 90%, and the yield of 830~1 180 μm (mesh of 14～20) is 9033%. The prepared barium sulfate pellets are white spheres with uniform particle size distribution, low friability, high density, smooth surface, and better tensile strength, which can act as intermediate products to be coated in the following step. The barium sulfate pellets prepared by this method have the characteristic of high drug-loaded and achieve the desired experimental purpose, which can provide useful information for further study and utilization of barium sulfate pellets and a new choice for the design of radioopaque markers.

This article provides a brief outline of the history and teaching of the discipline "Toxicological Chemistry" on the chair of pharmaceutical and toxicological chemistry of the First MSMU named after M.I. Sechenov.

Dihydromyricetin (DHM) is an important natural flavonoid that has attracted much attention because of its various functions such as protecting the cardiovascular system and liver, treating cancer and neurodegenerative diseases, and anti-inflammation effect, etc. Despite its great development potential in pharmacy, DHM has some problems in pharmaceutical applications such as low solubility, permeability, and stability. To settle these issues, extensive research has been carried out on its physicochemical properties and dosage forms to produce all kinds of DHM preparations in the past ten years. In addition, the combined use of DHM with other drugs is a promising strategy to expand the application of DHM. However, although invention patents for DHM preparations have been issued in several countries, the current transformation of DHM research results into market products is insufficient. To date, there is still a lack of deep research into the pharmacokinetics, pharmacodynamics, toxicology, and action mechanism of DHM preparations. Besides, preparations for combined therapy of DHM with other drugs are scarcely reported, which necessitates the development of dosage forms for this application. Apart from medicine, the development of DHM in the food industry is also of great potential. Due to its multiple effects and excellent safety, DHM preparations can be developed for functional drinks and foods. Through this review, we hope to draw more attention to the development potential of DHM and the above challenges and provide valuable references for the research and development of other natural products with a similar structure-activity relationship to this drug.

Nano-drug delivery strategies have been highlighted in cancer treatment, and much effort has been made in the optimization of bioavailability, biocompatibility, pharmacokinetics profiles, and in vivo distributions of anticancer nano-drug delivery systems. However, problems still exist in the delicate balance between improved anticancer efficacy and reduced toxicity to normal tissues, and opportunities arise along with the development of smart stimuli-responsive delivery strategies. By on-demand responsiveness towards exogenous or endogenous stimulus, these smart delivery systems hold promise for advanced tumor-specificity as well as controllable release behavior in a spatial-temporal manner. Meanwhile, the blossom of nanotechnology, material sciences, and biomedical sciences has shed light on the diverse modern drug delivery systems with smart characteristics, versatile functions, and modification possibilities. This review summarizes the current progress in various strategies for smart drug delivery systems against malignancies and introduces the representative endogenous and exogenous stimuli-responsive smart delivery systems. It may provide references for researchers in the fields of drug delivery, biomaterials, and nanotechnology.

Periodontitis is an inflammatory disease of the gums characterized by the degeneration of periodontal ligaments, the formation of periodontal pockets, and the resorption of the alveolar bone, which results in the destruction of the teeth's supporting structure. Periodontitis is caused by the growth of diverse microflora (particularly anaerobes) in the pockets, releasing toxins and enzymes and stimulating the immune system. Various approaches, both local and systemic, have been used to treat periodontitis effectively. Successful treatment depends on reducing bacterial biofilm, bleeding on probing (BOP), and reducing or eliminating pockets. Currently, the use of local drug delivery systems (LDDSs) as an adjunctive therapy to scaling and root planing (SRP) in periodontitis is a promising strategy, resulting in greater efficacy and fewer adverse effects by controlling drug release. Selecting an appropriate bioactive agent and route of administration is the cornerstone of a successful periodontitis treatment plan. In this context, this review focuses on applications of LDDSs with varying properties in treating periodontitis with or without systemic diseases to identify current challenges and future research directions. [ABSTRACT FROM AUTHOR]

Modified polymeric gels, including nanogels, which play not only the role of a bioinert matrix, but also perform regulatory, catalytic, and transport functions due to the active fragments introduced into them, can significantly advance the solution to the problem of targeted drug delivery in an organism. This will significantly reduce the toxicity of used pharmaceuticals and expand the range of their therapeutic, diagnostic, and medical application. This review presents a comparative description of gels based on synthetic and natural polymers intended for pharmaceutical-targeted drug delivery in the field of therapy of inflammatory and infectious diseases, dentistry, ophthalmology, oncology, dermatology, rheumatology, neurology, and the treatment of intestinal diseases. An analysis was made of most actual sources published for 2021–2022. The review is focused on the comparative characteristics of polymer gels in terms of their toxicity to cells and the release rate of drugs from nano-sized hydrogel systems, which are crucial initial features for their further possible application in mentioned areas of biomedicine. Different proposed mechanisms of drug release from gels depending on their structure, composition, and application are summarized and presented. The review may be useful for medical professionals, and pharmacologists dealing with the development of novel drug delivery vehicles. [ABSTRACT FROM AUTHOR]

Background: Fast disintegrating tablet (FDT) of ondansetron has better patient compliance as it is easy to swallow for the treatment of nausea and vomiting. Moreover, fast absorption, rapid onset, reduced loss of drugs, and enhanced bioavailability are making oral dispersible systems a better drug delivery system. This research was conducted with the objective to formulate and evaluate the oral FDT of ondansetron HCl using natural polymers. Methods: The tablets were formulated employing the direct compression technique using different ratios of natural super disintegrants such as spray-dried banana powder, ispaghula husk powder, and combination of both polymers. The developed powders were subjected to the pre-compression parameters and the powders were punched using a single-head rotatory compression machine. The formulated 200 mg tablets were subjected to post-compression evaluation. Results: The Fourier-transform infrared spectroscopy studies showed no chemical interaction between pure drugs, natural super disintegrants, and other excipients. Formulation F6 containing ispaghula husk powder in a 1:4 ratio demonstrated 98.60±0.003% of drug release within 10 minutes indicating the best formulation. Conclusion: FDT of ondansetron was considered the best formulation in the drug delivery system which followed the Higuchi model with the Fickian release.

Modeling and simulations based on machine learning techniques were conducted in this research for determination of pharmaceutical solubility in supercritical solvent for the sake of green processing development in the area of pharmaceutics. By this method, drug nanoparticles can be formed and controlled for better solubility and consequently bioavailability. We modeled the solubility of the drug namely desoxycorticosterone acetate (DA). The temperature and pressure are two inputs for the developed models, whereas the target output is the solubility of DA in the solvent. Indeed, models with two inputs and one output are developed in this study. This dataset contains 45 rows of data collected at five distinct temperatures and nine pressure levels. Three novel models are employed here including: HHO-MLP, HHO-RR, and HHO–KNN for theoretical determination of the drug solubility in the solvent. They are in fact MLP, Ridge Regression, and KNN models coupled with Harris Hawks optimizer (HHO) for hyper-parameter optimization. The HHO-MLP model has the best performance among models with R2 of 0.995, RMSE error rate of 0.194 and maximum error equal to 0.327. The model's findings can be used for estimation of the solubility data in wide range of T and P.

Solid can be divided into three main categories such as bulk, particles and molecules. However, the particle productions, as well as distribution of size, are very crucial to achieve particular properties for any specific industrial uses. This research aim is to develop empirical relations using an artificial neural network (ANN) for the sonofragmentation experimental results in terms of number fractions for different lengths and widths of particles corresponding to ultrasonic amplitude equal to 10%. For the ANN model, three and four hidden layers are chosen for lengths and widths models, respectively. Then it empirically calculated the number of fractions for different lengths and widths of a particle was compared with the population balance equation (PBE). To further strengthen the empirical expression, the ANN result was also compared with the population balance model and found to be agreed well with the PBE simulations. The model was trained and validated using the measured (experimental) data of the number fraction. The model was found to be valid and reliable, with R2 equal to or greater than 0.95 in both training and validation.

Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.

The aims of this systematic review are to explore the possibilities of using the positron annihilation lifetime spectroscopy (PALS) method in the pharmaceutical industry and to examine the application of PALS as a supportive, predictive method during the research process. In addition, the review aims to provide a comprehensive picture of additional medical and pharmaceutical uses, as the application of the PALS test method is limited and not widely known in this sector. We collected the scientific literature of the last 20 years (2002–2022) from several databases (PubMed, Embase, SciFinder-n, and Google Scholar) and evaluated the data gathered in relation to the combination of three directives, namely, the utilization of the PALS method, the testing of solid systems, and their application in the medical and pharmaceutical fields. The application of the PALS method is discussed based on three large groups: substances, drug delivery systems, and medical devices, starting with simpler systems and moving to more complex ones. The results are discussed based on the functionality of the PALS method, via microstructural analysis, the tracking of ageing and microstructural changes during stability testing, the examination of the effects of excipients and external factors, and defect characterization, with a strong emphasis on the benefits of this technique. The review highlights the wide range of possible applications of the PALS method as a non-invasive analytical tool for examining microstructures and monitoring changes; it can be effectively applied in many fields, alone or with complementary testing methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Patients, hospitals, sensors, researchers, providers, phones, and healthcare organisations are producing enormous amounts of data in both the healthcare and drug detection sectors. The real challenge in these sectors is to find, investigate, manage, and collect information from patients in order to make their lives easier and healthier, not only in terms of formulating new therapies and understanding diseases, but also to predict the results at earlier stages and make effective decisions. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. These increases are driven by advances in technology, which allow for the analysis of ever-larger data sets. Big Data (BD) has the potential to transform drug development and safety testing by providing new insights into the effects of drugs on human health. However, harnessing this potential involves several challenges, including the need for specialised skills and infrastructure. In this survey, we explore how BD approaches are currently being used in the pharmacology, toxicology, and pharmaceutics fields; in particular, we highlight how researchers have applied BD in pharmacology, toxicology, and pharmaceutics to address various challenges and establish solutions. A comparative analysis helps to trace the implementation of big data in the fields of pharmacology, toxicology, and pharmaceutics. Certain relevant limitations and directions for future research are emphasised. The pharmacology, toxicology, and pharmaceutics fields are still at an early stage of BD adoption, and there are many research challenges to be overcome, in order to effectively employ BD to address specific issues. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to develop a lamellar liquid crystal system consisting of temperature-insensitive and mild non-ionic surfactants, we construct the phase diagram of sucrose laurate (L1695)/sorbitan monolaurate (Span 20)/water pseudo-ternary systems at 25 ℃ based on the direct visual inspection and polarized microscopy. In contrast to the phase diagrams of polyoxyethylene alkyl ethers and co-surfactant mixtures reported in relevant studies, the single-phase region of the liquid crystalline phase in sucrose laurate (L1695)/sorbitan monolaurate (Span 20)/water pseudo-ternary systems is shown to be narrower. Moreover, the liquid crystal phase behaviors of the sucrose laurate/sorbitan mono-laurate (Span 20)/water pseudo-ternary systems are examined in depth applying cryo-SEM, X-ray scattering measurements, rheological test and low field NMR experiments. In the abovementioned aqueous systems with a L1695 to Span 20 mass ratio (between 53.9: 46.1 and 43.3:56.7), sucrose mono-laurate combines with a certain number of sorbitan monolaurate molecules to form a single lamellar liquid crystalline phase. Furthermore, the present experimental results as well as their analysis could give the quantitative playground for the interaction between sugar-based surfactants in aqueous system, which has rarely been investigated compared with the phase behavior of polyoxyethylene alkyl ethers in aqueous solution. [Display omitted] • Phase behavior of sucrose laurate/sorbitan mono-laurate/water pseudo-ternary systems has been examined in-depth. • Sucrose mono-laurate combines with Span 20 molecules in a certain ratio to form the lamellar liquid crystals. • Rheology of the lamellar liquid crystals formed in sugar-based surfactant mixtures has been investigated thoroughly. [ABSTRACT FROM AUTHOR]

Airway mucus is a complex viscoelastic gel composed mainly of water, glycoproteins, lipids, enzymes, minerals, etc. Among them, glycoproteins are the main factors determining mucus’s gel-like rheology. Airway mucus forms a protective barrier by secreting mucin, which represents a barrier for absorption, especially for more lipophilic drugs. It rapidly removes drugs from the airway through the physiological mucus clearance mechanism so drugs cannot remain in the lungs or reach the airway epithelial tissue for a long time. Significant progress has been made in enhancing drug lung deposition recently, but strategies are still needed to help drugs break through the lung mucosal barrier. Based on the physiopathological mechanisms of airway mucus, this paper reviews and summarizes strategies to enhance drug penetration and retention in the airway mucosa mediated by nano-delivery systems, including mucosal permeation systems, mucosal adhesion systems, and enzyme-modified delivery systems. On this basis, the potential and challenges of nano-delivery systems for improving airway mucus clearance are revealed. New ideas and approaches are provided for designing novel nano-delivery systems that effectively improve drug retention and penetration in the airway mucus layer.

Chlorothiazide, with the brand name Diuril, is used as a diuretic as well as an antihypertensive drug. This medicine with very slight solubility in water and low permeability is categorized in the class IV of Biopharmaceutical Classification System (BCS) and possess low bioavailability. Therefore, enhancement of the drug solubility would be of great importance to reduce its dosage and consequently side effects. Decrement of Chlorothiazide particles size to micro/nano scale using a supercritical carbon dioxide (scCO2)-based method can be an efficient approach to enhance its bioavailability and therapeutic efficiency. To select and design a proper supercritical micronization/nanonization method, solubility of Chlorothiazide in scCO2 should be determined which is conducted in this work by gravimetric method. In this study, solubility of Chlorothiazide in scCO2 was obtained at various operating conditions (308–338K and 130–290 bar). It was found between 0.417 × 10−5 to 1.012 × 10−5 mol mol−1 (mole fraction) for Chlorothiazide. Moreover, the obtained values were correlated through five empirical models (Chrastil, Mendez-Santiago and Teja (MST), Kumar- Johnston (K-J), Bartle, and Garlapati-Madras), as well as SRK and PR equations of state. All of the mentioned models have shown satisfactory correlation accuracy for the drug solubility. Meanwhile, the K-J model with the minimum AARD% value of 3.15 and the PR-EoS with the mean AARD% value of 6.51 have the highest precision to fit the experimental data. Also, the extrapolative ability of the mentioned empirical models to predict the Chlorothiazide solubility outside the considered range of operating conditions was investigated.