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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In order to improve the dissolution properties of albendazole and solve the problem of dysphagia caused by conventional tablets in patients,the prescription and preparation process of albendazole dispersible tablets were explored based on the solubilization effect.The effects of adding surfactants and preparing inclusion compounds on dissolution were investigated to screen the method of increasing the solubility of albendazole.Adhesives,disintegrants,solubilizers,and lubricants were screened by single factor experiment with compressibility,hardness,disintegrating time,and dissolution as indicators.The formulation and preparation process of albendazole dispersible tablets were optimized by orthogonal test.Three batches of albendazole dispersible tablets were prepared by the optimal prescription and process,the qualities were evaluated in vitro,and the content and dissolution were measured by UV-spectrophotometry.The results show that adding mixed surfactants of sodium dodecyl sulfate(SDS) and Tween 80 can assist dissolution of albendazole in water.The optimal prescription is as follows:3% of SDS,16% of PVPP (with an internal and external ratio of 1∶1),3% of Tween 80,3% of HPMC and 1% of Magnesium Stearate.The albendazole dispersible tablets were prepared with wet granulation.Different batches of albendazole dispersible tablets completely disintegrated within 122 s,the accumulated dissolution of albendazole dispersible tablets within 45 minutes was more than 80%,which met the requirements of Pharmacopoeia of the People's Republic of China(2020).The formulation of albendazole dispersible tablets is reasonable,the preparation process is feasible and the quality is good in vitro,which provides research ideas and a theoretical basis for improving the bioavailability of oral dosage forms of biopharmaceutical classification system (BCS) class Ⅱ compounds.

Climate change associated with global warming is a major warning of the twenty-first century, threatening ecosystems through uncontrolled temperature rises, drought, lack of water with a strong impact on productivity, economy, and worldwide life well-being. In most cases, the poor regions of the planet suffer from a lack of exploitable resources deriving from natural reserves. For this reason, wild vegetables able to grow in deserted areas are attracting increasing attention due to their beneficial properties. Among them, Prosopis cineraria has been recently recognized in the UAE not only as a cultural heritage but also as a potential source of raw materials for agri-food and pharmaceutics still poorly valued. P. cineraria occurs in most of the world's hot arid and semi-arid regions as a native or introduced species and, due to its multiple properties, could be exploited for medical, food, and, more recently, in different growing productivity fields like a luxury, especially in countries like the UAE. The use of actives-rich natural sources offers clear advantages over synthetic compounds in terms of process and product eco-sustainability. In this manuscript, we review the main properties and potential applications of P. cineraria aiming to promote the scientific interest toward the development of innovative approaches in several productive fields, including pharma and cosmetics, exploiting the versatility of materials that can be extracted from the various parts of the plant and discuss commercialization opportunities of the plant to support biodiversity and sustainability. In conclusion, P. cineraria turns out to be a plant able to grow in hostile environments, already providing nutrients for populations of Western Asia and the Indian subcontinent and possibly translatable to poor arid regions.

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.

In the last decades, a variety of nuclear magnetic resonance (NMR) techniques have been applied with success in the field of advanced functional materials, including the important area of drug delivery. In such field, solid-state NMR (SSNMR) is an irreplaceable tool in the arsenal of characterization techniques, offering unique and comprehensive perspectives for the description of chemical structure, spatial connectivity and interfacial phenomena of solid dosage forms. This review focuses on the widespread applications of SSNMR in drug delivery field, an overview of selected case studies is provided, together with possible developments.

Metal-organic frameworks (MOFs), comprised of organic ligands and metal ions/metal clusters via coordinative bonds are highly porous, crystalline materials. Their tunable porosity, chemical composition, size and shape, and easy surface functionalization make this large family more and more popular for drug delivery. There is a growing interest over the last decades in the design of engineered MOFs with controlled sizes for a variety of biomedical applications. This article presents an overall review and perspectives of MOFs-based drug delivery systems (DDSs), starting with the MOFs classification adapted for DDSs based on the types of constituting metals and ligands. Then, the synthesis and characterization of MOFs for DDSs are developed, followed by the drug loading strategies, applications, biopharmaceutics and quality control. Importantly, a variety of representative applications of MOFs are detailed from a point of view of applications in pharmaceutics, diseases therapy and advanced DDSs. In particular, the biopharmaceutics and quality control of MOFs-based DDSs are summarized with critical issues to be addressed. Finally, challenges in MOFs development for DDSs are discussed, such as biostability, biosafety, biopharmaceutics and nomenclature.

In order to enhance the aqueous solubility and stability of etoposide preparation,the central composite design-response surface method was used to optimize the prescription technology of etoposide nano-mixed micelles (ETP mPEG-PLA/P123).Based on the results of single factor test,central composite design-response surface method of three factors and five levels was carried out to optimize the prescription technology with drug dosage,the mass ratio of mPEG-PLA and hydration volume as independent variables,and the entrapment efficiency,drug loading and particle size as dependent variables.The critical micelle concentration (CMC) was determined by fluorescence probe method,and the physicochemical properties and release behavior of the prepared micelle preparations were evaluated.The results showed that the optimal prescription of ETP mPEG-PLA/P123 micelle preparation was that mPEG-PLA∶P123=38∶62,the drug dosage was 5 mg,and the hydration volume was 6 mL.After optimization,the entrapment efficiency and drug loading were 874% and 419%,the particle size was 1156 nm,PDI was 0216,and Zeta potential was -163 mV.The CMC value was 17×10-3 g/L,so mPEG-PLA/P123 micelles had high stability.In vitro release experiments,etoposide could be released from micelles slowly and continuously,up to 80% within 48 h.The optimized ETP mPEG-PLA/P123 nano-mixed micelles were able to enormously improve the aqueous solubility of etoposide,and the micelle preparation had good stability and a certain slow-release effect.The results of this study could provide theoretical guidance for further broadening the range of application of new etoposide preparations.

In a long passage from Letter 95 to Lucilius (§ 15-29), Seneca presents a history of medicine in which he establishes a chronological sequence of the appearance of the different parts of medical science: pharmaceutics, surgery, and dietetics. While the chronological sequence of the birth of the sub-divisions of medicine is quite traditional among medical writers themselves, Seneca’s justification for it is not. According to him, the complexification of primitive medicine is not the consequence of the progress of science and nosological observation, but of a complexification of diseases, itself provoked by a sophistication of lifestyles. This theory stems not from historical, but from moral considerations, which are part of the Platonic tradition, in perfect harmony with Roman Stoic thought.

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.

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.

In order to avoid the irritation of metformin hydrochloride to the stomach and realize its release in the intestine, with metformin hydrochloride as the main drug and chitosan/sodium alginate as the composite support, the optimal formulation and preparation technology of metformin hydrochloride loaded pH-sensitive hydrogel microspheres were selected, and the pH-sensitivity and in vitro drug release characteristics were investigated. The formulation and process were optimized by single factor test and orthogonal test. The morphology was characterized by SEM. The entrapment efficiency and drug-loaded amount were determined by UV. The release degree was studied by rotating basket method. The results show that the optimized prescription is as following: chitosan and sodium alginate with a total concentration of 2%(mass-volume ratio), mass ratio of sodium alginate to chitosan =1∶1, mass ratio of drug to sodium alginate=2∶5, and 3.5% CaCl2(mass-volume ratio). The cumulative release rate is less than 4% in artificial gastric juice within 6 h, and up to 96.4% in artificial intestinal juice within 6 h. The formulation and process are stable and reliable, and the hydrogel microspheres have high mechanical strength, good biodegradability and stability. So it is a new metformin hydrochloride delivery system.

As the important active ingredients of Astragali Radix (AR), Astragalus polysaccharides (APs) have therapeutic potential for multiple diseases including nervous system diseases, cardiovascular diseases, diabetes mellitus, and cancer. A large number of cell experiments combined with animal experiments have shed light on the therapeutic mechanisms and therapeutic effects of APs on a variety of diseases. However, the clinical application of APs is not widespread, except for the use of injected APs in the clinical adjuvant therapy of cancer. Due to the excessive molecular weight, bulky, low solubility and negatively charged characteristics, APs have low bioavailability which limits their clinical application. With the deepening of researches on the pharmaceutics of APs, the nanocrystals and moderate structural modification enormously boost the bioavailability, which may expand the application of APs. This review summarizes the studies in pharmacodynamic properties and pharmaceutics of APs, with the purpose of providing experimental researches and clinical application data for expanding the clinical development through expounding the therapeutic mechanisms and pharmaceutical researches of APs.

In the past few decades, additive manufacturing (AM) has been developed and applied as a cost-effective and versatile technique for the fabrication of geometrically complex objects in the medical industry. In this review, we discuss current advances of AM in medical applications for the generation of pharmaceuticals, medical implants, and medical devices. Oral and transdermal drugs can be fabricated by a variety of AM technologies. Different types of hard and soft clinical implants have also been realized by AM, with the goal of producing tissue-engineered constructs. In addition, medical devices used for diagnostics and treatment of various pathological conditions have been developed. The growing body of research on AM reveals its great potential in medical applications. The goal of this review is to highlight the usefulness and elucidate the current limitations of AM applications in the medical field.

An artificial intelligence-based predictive model was developed using a support vector machine to investigate the solubility data of the drug Busulfan drug in supercritical carbon dioxide. The data for simulations were collected from literature. The model was trained and implemented in order to determine the correlation between the solubility values and the input parameters, namely, temperature and pressure. These parameters were used as the inputs as they are known to have a significant effect on the solubility of Busulfan in supercritical carbon dioxide. In the artificial intelligence model, a polynomial model with kernel function was applied to the data, and the model’s findings were compared with measured data for fitting. Good agreement was observed between the model’s outputs and the measured data with coefficient of determination greater than 0.99.

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.

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.

Several attempts have been made to encapsulate indomethacin (IND), to control its sustained release and reduce its side effects. To develop a successful formulation, drug release from a polymeric matrix and subsequent biodegradation need to be achieved. In this study, we focus on combining microfluidic and artificial intelligence (AI) technologies, alongside using biomaterials, to generate drug-loaded polymeric microparticles (MPs). Our strategy is based on using Poly (D,L-lactide-co-glycolide) (PLGA) as a biodegradable polymer for the generation of a controlled drug delivery vehicle, with IND as an example of a poorly soluble drug, a 3D flow focusing microfluidic chip as a simple device synthesis particle, and machine learning using artificial neural networks (ANNs) as an in silico tool to generate and predict size-tunable PLGA MPs. The influence of different polymer concentrations and the flow rates of dispersed and continuous phases on PLGA droplet size prediction in a microfluidic platform were assessed. Subsequently, the developed ANN model was utilized as a quick guide to generate PLGA MPs at a desired size. After conditions optimization, IND-loaded PLGA MPs were produced, and showed larger droplet sizes than blank MPs. Further, the proposed microfluidic system is capable of producing monodisperse particles with a well-controllable shape and size. IND-loaded-PLGA MPs exhibited acceptable drug loading and encapsulation efficiency (7.79 and 62.35%, respectively) and showed sustained release, reaching approximately 80% within 9 days. Hence, combining modern technologies of machine learning and microfluidics with biomaterials can be applied to many pharmaceutical applications, as a quick, low cost, and reproducible strategy.

When carrying out policy of import substitution in pharmaceutical industry first of all it is necessary to pay attention to the problems existing in this industry today. On the basis of the analysis of industry of pharmaceutics in Russia, the authors have revealed the main problems of industry, and also the possible directions of carrying out policy of import substitution are created.