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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.

The role and opportunities presented by particulate technologies (due to novel processing methods and advanced materials) have multiplied over the last few decades, leading to promising and ideal properties for drug delivery. For example, the dissolution and bioavailability of poorly soluble drug substances and achieving site- specific drug delivery with a desired release profile are crucial aspects of forming (to some extent) state-of-the-art platforms. Atomisation techniques are intended to achieve efficient control over particle size, improved processing time, improved drug loading efficiency, and the opportunity to encapsulate a broad range of viable yet sensitive therapeutic moieties. Particulate engineering through atomization is accomplished by employing various mechanisms such as air, no air, centrifugal, electrohydrodynamic, acoustic, and supercritical fluid driven processes. These driving forces overcome capillary stresses (e.g., liquid viscosity, surface tension) and transform formulation media (liquid) into fine droplets. More frequently, solvent removal, multiple methods are included to reduce the final size distribution. Nevertheless, a thorough understanding of fluid mechanics, thermodynamics, heat, and mass transfer is imperative to appreciate and predict outputs in real time. More so, in recent years, several advancements have been introduced to improve such processes through complex particle design coupled with quality by-design (QbD) yielding optimal particulate geometry in a predictable manner. Despite these valuable and numerous advancements, atomisation techniques face difficulty scaling up from laboratory scales to manufacturing industry scales. This review details the various atomisation techniques (from design to mechanism) along with examples of drug delivery systems developed. In addition, future perspectives and bottlenecks are provided while highlighting current and selected seminal developments in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: The need for a stability evaluation of [18F]FDG is evident. The main purpose of this study was to make recommendations for determining the shelf life based on the available stability literature and our own two-centre stability studies. Results: We performed a non-systematic literature study to find the most relevant stability data for [18F]FDG. The amount of radioactivity, radio-stabilizers, choice of synthesis, dilution, pH, temperature, storage and the choice of stability tests and acceptance criteria were the most important factors to evaluate for the implementation of good manufacturing practice. Moreover, we discuss some limitations of the study, especially the choice of synthesis, photostability, the environment, temperature and storage. Based on these data, we designed our own two-centre stability studies. All the defined acceptance criteria were met. Conclusions: We have made recommendations for future stability evaluations based on our findings. The most important findings were that the amount of the radio-stabilizer ethanol should be > 0.1 % ethanol for activities up to 4 GBq/mL and > 0.2 % ethanol for activities up to 22.7 GBq/mL to keep [18F]FDG stable. [ABSTRACT FROM AUTHOR]

Keywords: chemical analysis; chromatography; mass spectrometry; spectrometry; sampling; environment; biomedicine; pharmaceutics; food EN chemical analysis chromatography mass spectrometry spectrometry sampling environment biomedicine pharmaceutics food 11767 11767 1 12/28/21 20211215 NES 211215 The 12th International Conference on "Instrumental Methods of Analysis" www.ima2021.gr (accessed on 8 November 2021)), was organized by the Aristotle University of Thessaloniki and National Technical University of Athens, during 20-23 September 2021 as a virtual event, providing the opportunity for high-level analytical scientists from all around the world to promote their relevant research. However, in most of the aforementioned approaches, such optimization is focused on specific compounds, or compound classes, known to be present and/or abundant in the corresponding matrices [3,4]. Detection and chemical identification of organic biomarkers require the use of high-sensitivity techniques to overcome the complexity limitations of the samples in terms of composition and low concentrations of constituent compounds. The analysis of autumn heather honey revealed that phenolic compounds are the most abundant and p-anisaldehyde, 1-methoxy-4-propyl-benzene, and p-anisic acid could serve as potent marker compounds. These compounds were distributed in five major categories, namely, hydroxycinnamic acid derivatives (10 compounds), hydroxybenzoic acid derivatives (6 compounds), flavonoids (25 compounds), phenolic terpenes (carnasol and carnosic acid), and chalcones (arbutin). [Extracted from the article]

[Display omitted] Pharmaceutical nanotechnology research is focused on smart nano-vehicles, which can deliver active pharmaceutical ingredients to enhance their efficacy through any route of administration and in the most varied therapeutical application. The design and development of new nanopharmaceuticals can be very laborious. In recent years, the application of mathematics, statistics and computational tools is emerging as a convenient strategy for this purpose. The application of Quality by Design (QbD) tools has been introduced to guarantee quality for pharmaceutical products and improve translational research from the laboratory bench into applicable therapeutics. In this review, a collection of basic-concept, historical overview and application of QbD in nanomedicine are discussed. A specific focus has been put on Response Surface Methodology and Artificial Neural Network approaches in general terms and their application in the development of nanomedicine to monitor the process parameters obtaining optimized system ensuring its quality profile. [ABSTRACT FROM AUTHOR]

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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.

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., (© 2024. The Author(s).)

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., Competing Interests: No potential conflict of interest was declared by authors., (© 2024 The Authors.)

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., (© 2024. The Author(s).)

The prevalence of metabolic dysfunction associated-steatotic liver disease (MASLD) (formerly known as nonalcoholic fatty liver disease; NAFLD) is estimated at around 32% of the world's population, resulting in a major healthcare concern in recent times. Current pharmaceutical methods lack efficacy for the treatment of the disease because of suboptimal pharmacokinetic parameters including poor bioavailability, short half-life, and premature clearance. Designing an efficient drug delivery system that provides a protective environment is critical for addressing these challenges. Such a system should aim to enhance the cellular uptake of drugs, improve their bioavailability, and reduce the chances of rapid clearance. Here, we developed nanoengineered natural cell membrane-derived nanoparticles (CMNs) incorporated with a model drug, rosuvastatin, in the bilayer assembly of CMNs to reduce the accumulation of lipids in hepatocytes, a hallmark of MASLD. We used a cell extrusion technique to develop self-assembled CMNs with precise size control compared to the cell shearing method. Interestingly, the prepared CMNs were found to be nonphagocytic, representing around 1.13% of phosphatidylserine receptors on healthy cells, which allows the possibility of their use as stealth nanoparticles for drug delivery. Furthermore, CMNs exhibit higher drug-loading efficiency, excellent cytocompatibility, and enhanced cellular internalization capabilities. Moreover, we show that the delivery of rosuvastatin-loaded CMNs in the in vitro MASLD model efficiently reduced hepatocyte lipid accumulation, including total cholesterol (26.8 ± 3.1%) and triglycerides (11.8 ± 0.8%), compared to the negative control. Taken together, the nanoengineered biomimetic CMNs enhance the drug's bioactivity in hepatic cells, establishing a foundation for further investigation of this drug delivery system in treating MASLD.

Background: Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue., Objective: The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs)., Methods: A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach., Results: A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention., Conclusions: The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed., Trial Registration: INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/., (©Yiqun Miao, Yuan Luo, Yuhan Zhao, Mingxuan Liu, Huiying Wang, Ying Wu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.07.2024.)

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare., Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN., Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA., Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase., Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs., (© Suncheol Heo, Eun-Ae Kang, Jae Yong Yu, Hae Reong Kim, Suehyun Lee, Kwangsoo Kim, Yul Hwangbo, Rae Woong Park, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Hyojung Jung, Yebin Chegal, Jae-Hyun Lee, Yu Rang Park. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Distributed and point-of-care (POC) manufacturing facilities enable an agile pharmaceutical production paradigm that can respond to localized needs, providing personalized and precision medicine. These capabilities are critical for narrow therapeutic index drugs and pediatric or geriatric dosing, among other specialized needs. Advanced additive manufacturing, three-dimensional (3D) printing, and drop-on-demand (DoD) dispensing technologies have begun to expand into pharmaceutical production. We employed a quality by design (QbD) approach to identify critical quality attributes (CQAs), critical material attributes (CMAs), and critical process parameters (CPPs) of a POC pharmaceutical manufacturing paradigm. This theoretical framework encompasses the production of active pharmaceutical ingredient (API) "inks" at a centralized facility, which are distributed to POC sites for DoD dispensing into/onto delivery vehicles ( e.g. , orodispersible films, capsules, single liquid dose vials). Focusing on the POC dispensing/dosing processes, QbD considerations and cause-and-effect analyses identified the dispensed API quantity and solid-state form (CQAs), as well as the nozzle diameter, system pressure channel, and number of drops dispensed (CPPs) for detailed investigation. Final assay quantification and content uniformity CQAs were measured from demonstrative levothyroxine sodium single-dose liquid vials of glycerin/water, meeting the standard acceptance values. Each POC facility is unlikely to maintain full quality control laboratory capabilities, requiring the development of appropriate atline or inline methods to ensure quality control. We developed control strategies, including atline ultraviolet-visible (UV-vis) verification of the API ink prior to dispensing, inline drop counting during dispensing, intermediate atline-dispensed volume checks, and offline batch confirmation by liquid chromatography-tandem mass spectrometry (LC-MS/MS) following production.

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.

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma - DIPG), is the primary cause of brain tumor-related death in pediatric patients. DIPG is characterized by a median survival of <12 months from diagnosis, harboring the worst 5-year survival rate of any cancer. Corticosteroids and radiation are the mainstay of therapy; however, they only provide transient relief from the devastating neurological symptoms. Numerous therapies have been investigated for DIPG, but the majority have been unsuccessful in demonstrating a survival benefit beyond radiation alone. Although many barriers hinder brain drug delivery in DIPG, one of the most significant challenges is the blood-brain barrier (BBB). Therapeutic compounds must possess specific properties to enable efficient passage across the BBB. In brain cancer, the BBB is referred to as the blood-brain tumor barrier (BBTB), where tumors disrupt the structure and function of the BBB, which may provide opportunities for drug delivery. However, the biological characteristics of the brainstem's BBB/BBTB, both under normal physiological conditions and in response to DIPG, are poorly understood, which further complicates treatment. Better characterization of the changes that occur in the BBB/BBTB of DIPG patients is essential, as this informs future treatment strategies. Many novel drug delivery technologies have been investigated to bypass or disrupt the BBB/BBTB, including convection enhanced delivery, focused ultrasound, nanoparticle-mediated delivery, and intranasal delivery, all of which are yet to be clinically established for the treatment of DIPG. Herein, we review what is known about the BBB/BBTB and discuss the current status, limitations, and advances of conventional and novel treatments to improving brain drug delivery in DIPG., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Withania somnifera (L.) Dunal (W. somnifera) is a herb commonly known by its English name as Winter Cherry. Africa is indigenous to many medicinal plants and natural products. However, there is inadequate documentation of medicinal plants, including W. somnifera, in Africa. There is, therefore, a need for a comprehensive compilation of research outcomes of this reviewed plant as used in traditional medicine in different regions of Africa. Methodology: Scientific articles and publications were scooped and sourced from high-impact factor journals and filtered with relevant keywords on W. somnifera. Scientific databases, including GBIF, PubMed, NCBI, Google Scholar, Research Gate, Science Direct, SciFinder, and Web of Science, were accessed to identify the most influential articles and recent breakthroughs published on the contexts of ethnography, ethnomedicinal uses, phytochemistry, pharmacology, and commercialization of W. somnifera. Results: This critical review covers the W. somnifera ethnography, phytochemistry, and ethnomedicinal usage to demonstrate the use of the plant in Africa and elsewhere to prevent or alleviate several pathophysiological conditions, including cardiovascular, neurodegenerative, reproductive impotence, as well as other chronic diseases. Conclusion: W. somnifera is reportedly safe for administration in ethnomedicine as several research outcomes confirmed its safety status. The significance of commercializing this plant in Africa for drug development is herein thoroughly covered to provide the much-needed highlights towards its cultivations economic benefit to Africa. [ABSTRACT FROM AUTHOR]

Milling by mechanical means is vital unit operation in pharmaceutical processing which can be used for controlling particle size reduction. This approach can be also used for mechanochemical synthesis of pharmaceutical cocrystals. However, controlling the particle size of cocrystal during the milling process is challenging due to complexity of the process and different mechanisms involved. In this study, artificial neural network (ANN) approach was performed to predict the size of particles during mechanochemical synthesis of pharmaceutical cocrystals in ball milling operation. Theophylline was used as active pharmaceutical ingredient (API) and 4-aminobenzoic acid as co-former in the experiments. Different types of excipients including hydroxypropylmethylcellulose (HPMC), lactose, microcrystalline cellulose (MCC) polyvinylpyrrolidone (PVP) and polyethylene glycol (PEG) were used to see the effect of excipients on the cocrystals particle size. ANN was developed considering excipient type, excipient percentage, jar size, milling time as inputs, while median particle size (d50) was considered as the response, and representative of particle size. Two hidden layers were considered in developing ANN, and a combination of linear and nonlinear functions was used as transfer function. ANN was trained and validated with measured data, and R2 greater than 0.99 was obtained for the training and validation, with RMSE values of 1.06 and 3.89 for training and validation, respectively. The results were used to provide a design space for understanding the cocrystal particle size variation during ball milling process. It was indicated that the largest particles were formed using PEG as excipient, and increase in particle size over the milling time. The latter was attributed to the electrostatic attraction forces between the particles, thus aggregation and agglomeration of particles as the milling time exceeds a threshold. Furthermore, larger particles were obtained with the larger jar size. It turned out that faster cocrystallization rate occurred after only 5 min of milling in 25 mL jar compared to 25 min in 10 mL milling jar. The developed methodology has been shown to be robust and can be used as predictive tool for designing pharmaceutical cocrystallization using ball milling, and controlling the particle size during size reduction process. [ABSTRACT FROM AUTHOR]

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.