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BACKGROUND: Adrenal insufficiency is usually diagnosed in children who will need lifelong hydrocortisone therapy. However, medicines for pediatrics, in terms of dosage and acceptability, are currently unavailable. RESEARCH DESIGN AND METHODS: Semi-solid extrusion (SSE) 3D printing (3DP) was utilized for manufacturing of personalized and chewable hydrocortisone formulations (printlets) for an upcoming clinical study in children at Vall d'Hebron University Hospital in Barcelona, Spain. The 3DP process was validated using a specific software for dynamic dose modulation. RESULTS: The printlets contained doses ranging from 1 to 6 mg hydrocortisone in three different flavor and color combinations to aid adherence among the pediatric patients. The pharma-ink (mixture of drugs and excipients) was assessed for its rheological behavior to ensure reproducibility of printlets through repeated printing cycles. The printlets showed immediate hydrocortisone release and were stable for 1 month of storage, adequate for prescribing instructions during the clinical trial. CONCLUSIONS: The results confirm the suitability and safety of the developed printlets for use in the clinical trial. The required technical information from The Spanish Medicines Agency for this clinical trial application was compiled to serve as guidelines for healthcare professionals seeking to apply for and conduct clinical trials on 3DP oral dosage forms.

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Rising research and development costs, currently exceeding $3.5 billion per novel drug, reflect a five-decade decline in pharmaceutical R&D efficiency. While recent reports suggest a potential turnaround, this review offers a systems-level analysis to explore whether this marks a structural shift or transient reversal. We analyzed financial data from the 200 largest pharmaceutical firms, novel drug approvals, and more than 80 000 clinical trials between 2012 and 2023. Our analysis revealed that despite recent stabilization, the pharmaceutical industry continues to face challenges, particularly due to elevated late-stage clinical attrition, suggesting that a sustained turnaround in R&D efficiency remains elusive.

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The primary aim of this study is to assess the significance of top management commitment in the context of Lean 4.0 implementation within the pharmaceutical manufacturing industry in Ghana. The study seeks to understand and evaluate the overall effectiveness and achievements associated with adopting Lean 4.0. Employing a positivist mindset, the research utilizes an explanatory quantitative research design and a survey technique. Data collected from 181 employees of pharmaceutical companies in Ghana undergo analysis using SmartPLS (version 4) and IBM SPSS version 26. The study employs a combination of descriptive statistics to summarise data characteristics and inferential statistics to test various hypotheses related to Lean 4.0 adoption. The analysis reveals that the successful integration of lean methods and Industry 4.0 technologies requires meticulous management. Simultaneously, individual implementations of lean principles and Industry 4.0 technologies positively impact business performance. Surprisingly, the study does not observe a substantial positive influence of Lean 4.0 on corporate performance, suggesting that immediate improvements in efficiency or profitability may not result from the adoption of this framework. This research contributes to the field by highlighting the need for careful management in integrating lean methods and Industry 4.0 technologies. It also emphasizes the positive impacts of lean principles and Industry 4.0 technology on business performance. The unexpected finding regarding the lack of immediate improvements in corporate efficiency or profitability with Lean 4.0 adoption prompts considerations of initial implementation challenges or the organization's need for time to adapt to this integrated approach.

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The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

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Introduction: Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future. Methods: The current literature about the latest business and scientific evolutions and trends was reviewed. Results: In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient. Conclusion: Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

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This article addresses the research and development (R&D) productivity challenge of the pharmaceutical industry, focusing on United States Food and Drug Administration (FDA)-related new drug approvals of the top 20 pharmaceutical companies (2014-2023). We evaluated the degree of innovation in new drugs to determine the innovativeness of these leading companies. A key finding of our analysis is the decline in the number of new drugs approved by the FDA for these leading companies over the investigated time period. This trend suggests that some of the leading companies are losing ground in R&D innovation, raising concerns about their ability to sustain competitive advantage, ensure long-term market success, and maintain viable business models.

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Three-dimensional printing is one of the emerging technologies that is gaining interest from the pharmaceutical industry as it provides an opportunity to customize drugs according to each patient's needs. Combining different active pharmaceutical ingredients, using different geometries, and providing sustained release enhances the effectiveness of medicine. One of the most innovative uses of 3D printing is producing fabrics, medical devices, medical implants, orthoses, and prostheses. This review summarizes the various 3D printing techniques such as stereolithography, inkjet printing, thermal inkjet printing, fused deposition modelling, extrusion printing, semi-solid extrusion printing, selective laser sintering, and hot-melt extrusion. Also, discusses the drug relies profile and its mechanisms, characteristics, and applications of the most common types of 3D printed API formulations and its recent development. Here, Authors also, summarizes the central flow of 3D food printing process and knowledge extension toward personalized nutrition.

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Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.

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Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

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Pharmacy compounding, the art and science of preparing customized medications to meet individual patient needs, is on the verge of transformation. Traditional methods of compounding often involve manual and time-consuming processes, presenting challenges in terms of consistency, dosage accuracy, quality control, contamination, and scalability. However, the emergence of cutting-edge technologies has paved a way for a new era for pharmacy compounding, promising to redefine the way medications are prepared and delivered as pharmacy-tailored personalized medicines. In this multi-site study, more than 30 hospitals and community pharmacies from eight countries in Europe utilized a novel automated dosing approach inspired by 3D printing for the compounding of non-sterile propranolol hydrochloride tablets. CuraBlend® excipient base, a GMP-manufactured excipient base (pharma-ink) intended for automated compounding applications, was used. A standardized study protocol to test the automated dosing of tablets with variable weights was performed in all participating pharmacies in four different iterative phases. Integrated quality control was performed with an in-process scale and NIR spectroscopy supported by HPLC content uniformity measurements. In total, 6088 propranolol tablets were produced at different locations during this study. It was shown that the dosing accuracy of the process increased from about 90% to 100% from Phase 1 to Phase 4 by making improvements to the formulation and the hardware solutions. The results indicate that through this automated and quality controlled compounding approach, extemporaneous pharmacy manufacturing can take a giant leap forward towards automation and digital manufacture of dosage forms in hospital pharmacies and compounding pharmacies.

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Current trends in drug design notably consider so-called privileged scaffolds as the core structural fragments with decisive impact on affinity to properly chosen biological targets, potency, selectivity and toxicological characteristics of drugs and prospective drug candidates. Fruquintinib (1) is a novel synthetic selective inhibitor of vascular endothelial growth factor receptor (VEGFR) isoforms, i.e., VEGFR-1, VEGFR-2 and VEGFR-3. The therapeutic agent (1) consists of a flat bicyclic heteroaromatic ring, in which two nitrogens are suitablyincorporated, a core bicyclic heteroaromatic ring - privileged (substituted) benzofuran scaffold, and a pair of hydrogen bond (H-bond) donor and acceptor group, i.e., amide functional moiety. Fruquintinib (1) was first approved in China for the treatment of metastatic colorectal cancer, a severe malignant disease with a high mortality rate. The review article offered a brief insight into the topic of privileged structures, their drug- -like ranges of several parameters, pharmacodynamic characteristics of fruquintinib (1) and various in silico descriptors characterizing drug's structural and physicochemical properties (molecular weight, number of heavy atoms, number of aromatic heavy atoms, fraction of sp3 C-atoms, number of H-bond acceptors, number of H-bond donors, total polar surface area, molar refractivity, molecular volume as well as parameters of lipophilicity and solubility). Some of these descriptors were related to pharmacokinetics and distribution of fruquintinib (1), and, in addition, might help predict its ability to cross passively the blood-brain barrier (BBB). Moreover, a possible connection between the induction potential on cytochrome P450 isoenzymes (CYP1A2 and CYP3A4) and passive transport of a given drug into the central nervous system via BBB was investigated. Current clinical experience and future directions regarding of fruquintinib (1) were also briefly outlined.

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Background: Recent acute anaphylaxis guideline updates have identified remaining unmet needs based on currently available therapeutic options as a critical focus. Objective: We compared the pharmacokinetic, pharmacodynamic, safety, and tolerability profiles of intranasal epinephrine with intramuscular epinephrine administered by autoinjector and manual syringe. Methods: An open-label, 3-period crossover study was conducted in 116 healthy adult volunteers to assess the bioavailability of a single 13.2 mg intranasal dose of epinephrine compared to a 0.3 mg intramuscular autoinjector and a 0.5 mg manual syringe. Patients with epinephrine concentrations of 50, 100, and 200 pg/mL at 10, 20, 30, and 60 minutes after dosing were also evaluated. Results: Pharmacokinetic parameters for the 13.2 mg intranasal dose exceeded those of the 0.3 mg autoinjector with a rapid and higher maximum observed concentration (intranasal, 429.4 pg/mL; autoinjector, 328.6 pg/mL) and greater systemic exposure (AUC0-360; intranasal, 39,060 pg∙min/mL; autoinjector, 17,440 pg∙min/mL). Similar results were observed compared to the 0.5 mg manual syringe. Pharmacokinetic parameters for opposite-nostril and same-nostril dosing were higher than both intramuscular doses, except time to reach maximum observed concentration, which was bracketed between the 2 intramuscular doses (intranasal opposite and same nostril, 20 minutes; autoinjector, 14.9 minutes; manual syringe, 45 minutes). Similar effects on blood pressure and heart rate were observed for intranasal and autoinjector administration. Intranasal epinephrine was safe and well tolerated. No serious or unexpected adverse events were reported, confirming results from earlier clinical studies. Conclusions: Bidose epinephrine spray addresses the unmet medical and patient needs for a needle-free, convenient, and effective dose-delivery system for self-administration of epinephrine that is as good as or better than the 0.3 mg autoinjector.

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Biotechnology and small pharma companies must recognize the opportunity presented by FDA Breakthrough Therapy Designation (BTD) to expedite drug development for serious conditions. This paper evaluates BTD economic and developmental impacts on such companies. Analysis of 29 BTD events from 2017 to 2022 revealed immediate share price boosts and improved drug approval rates, alongside faster development times. Yet, long-term share prices underperformed relative to the broader market, underscoring the persistent risks in pharma investments. Although offering new insights, the limited sample size calls for further investigation.

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Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

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Inkjet printing (IJP) is an additive manufacturing process that selectively deposits ink materials, layer-by-layer, to create 3D objects or 2D patterns with precise control over their structure and composition. This technology has emerged as an attractive and versatile approach to address the ever-evolving demands of personalized medicine in the healthcare industry. Although originally developed for nonhealthcare applications, IJP harnesses the potential of pharma-inks, which are meticulously formulated inks containing drugs and pharmaceutical excipients. Delving into the formulation and components of pharma-inks, the key to precise and adaptable material deposition enabled by IJP is unraveled. The review extends its focus to substrate materials, including paper, films, foams, lenses, and 3D-printed materials, showcasing their diverse advantages, while exploring a wide spectrum of therapeutic applications. Additionally, the potential benefits of hardware and software improvements, along with artificial intelligence integration, are discussed to enhance IJP's precision and efficiency. Embracing these advancements, IJP holds immense potential to reshape traditional medicine manufacturing processes, ushering in an era of medical precision. However, further exploration and optimization are needed to fully utilize IJP's healthcare capabilities. As researchers push the boundaries of IJP, the vision of patient-specific treatment is on the horizon of becoming a tangible reality.

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OBJECTIVE To study the improvement effects of arbutin on myocardial fibrosis （MF） model rats and its mechanism. METHODS The network pharmacology was used to predict the potential target of arbutin in improving MF and molecular docking was used to validated. Totally 50 SD rats were given isoprenaline subcutaneously （5 mg/kg， once a day， for 14 consecutive days） to induce the MF model. Modeled rats were randomly divided into model group， captopril group （9 mg/kg）， arbutin low-dose， medium-dose and high-dose groups （50， 100， 200 mg/kg）， with 10 rats in each group. Another 10 healthy rats were included as normal group. Each group was given the corresponding drugs， once a day， for 28 consecutive days. Twenty-four hours after the final administration， electrocardiograms and heart-related indexes [heart weight index （HWI）， left ventricular weight index （LVWI）] of rats were detected； the levels of creatine kinase （CK）， lactate dehydrogenase （LDH）， N-terminal pro-brain natriuretic peptide （NT-proBNP） and type Ⅰ collagen （Col Ⅰ） and Col Ⅲ were detected in myocardial tissue of rats； the pathological changes of myocardial tissue were observed， and protein and mRNA expressions of adenosine deaminase （ADA） and adenosine kinase （ADK） were detected in the myocardial tissue of rats. RESULTS The results of network pharmacology showed that the main targets of arbutin improving MF were ADA and ADK. The results of molecular docking showed that arbutin bind stably with ADA and ADK. The results of experimental verification showed that compared with model group， the amplitude of ST and T waves in electrocardiogram were improved in administration groups， and the symptoms of atrial flutter were alleviated； HWI （except for arbutin medium-dose group）， LVWI， the levels of CK， LDH， NT-proBNP， Col Ⅰ and Col Ⅲ in the myocardial tissue of rats were decreased significantly （P＜0.05）； the degree of myocardial fibrosis in rats decreased； protein and mRNA expressions of ADA and ADK in the myocardial tissue were significantly increased （P＜0.05）. CONCLUSIONS Arbutin can improve cardiac fibrosis and cardiac function of MF model rats， the mechanism of which may be associated with up-regulating protein and mRNA expressions of ADA and ADK，influencing the nucleotide metabolism and collagen generation. zhangminghao@hactcm.edu.cn

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This commentary explores issues of professional identity, fairness and discovery in the history of psychiatry in the light of Walter Benjamin's (1892-1940) philosophy of history, especially his concept of Jetztzeit (now-time) and the profession's relationship with the founder and owners of Purdue Pharma LP.

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A quarter of a century ago, designer peptide drugs finally broke through the glass ceiling. Despite the resistance by big pharma, biotechnology companies managed to develop injectable peptide-based drugs, first against orphan or other small volume diseases, and later for conditions affecting large patient populations such as type 2 diabetes. Even their lack of gastrointestinal absorption could be utilized to enable successful oral dosing against chronic constipation. The preference of peptide therapeutics over small molecule competitors against identical medical conditions can be achieved by careful target selection, intrachain and terminal amino acid modifications, appropriate conjugation to stability enhancers and chemical space expansion, innovative delivery and administration techniques and patient-focused marketing strategies. Unfortunately, however, pharmacoeconomical considerations, including the strength of big pharma to develop competing small molecule drugs, have somewhat limited the success of otherwise smart peptide-based therapeutics. Yet, with increasing improvement in peptide drug modification and formulation, these are continuing to gain significant, and growing, acceptance as desirable alternatives to small molecule compounds.