Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.
Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]
*1Dr. Jegathis Kumar M., 2Karthikeyan S., 3Magesh Kumar· Zenodo (CERN European Organi...· 0 citations
In order to guarantee product safety, efficacy, and regulatory compliance, the pharmaceutical sector needs strong quality control systems. Electronic Quality Management Systems (eQMS) are gradually replacing traditional paper-based Quality Management Systems (QMS) as a result of expanding regulatory requirements and digital technology improvements. An eQMS enhances documentation, traceability, efficiency, and compliance management by integrating quality-related procedures into a single electronic platform. Major advantages of eQMS include enhanced data integrity, improved regulatory compliance, reduction in human error, However, implementation of eQMS is associated with several challenges. Regulations pertaining to electronic systems and data integrity have been established by regulatory bodies such as the USFDA, European Medicines Agency (EMA), ICH, and WHO. Compliance with FDA 21 CFR Part 11 principles is essential for successful implementation of eQMS. The article also highlights future trends such as cloud-based systems, artificial intelligence, predictive analytics, and Industry 4.0 integration that are transforming pharmaceutical quality management. Overall, eQMS represents a significant advancement in pharmaceutical quality assurance and operational excellence.
Atodariya Divyanshi*, Dr. Nidhi Chauhan, Patel Dhruvita, Patel Kiran, Mistry Drashti· Zenodo (CERN European Organi...· 0 citations
Post-surgical bone infection is one of the most challenging complications in orthopaedic surgery due to the hypo vascularization of the affected tissue, bacterial biofilm formation, and inadequate antibiotic penetration to the infection site.[1,2] Currently available antibiotic-loaded systems, including polymethyl methacrylate (PMMA) bone cement, have a number of disadvantages, such as the need for surgical removal and low adaptability to heat-sensitive antibiotics.[3,4] Three-dimensional (3D) printing is a promising technology that allows the fabrication of patient-specific biodegradable scaffolds.[5–7] Such scaffolds can be loaded with antibiotics and possess both osteogenic and antibiotic elution properties.[5–7] The present review highlights recent advances in the development of antibiotic-loaded 3D-printed scaffolds for the treatment of post-surgical infections[8,9], including the description of various scaffolding technologies, materials suitable for 3D printing, methods for antibiotic incorporation, and the mechanism of their antibacterial and osteogenic activity.[5,6] Additionally, the preclinical and clinical evaluation of antibiotic-loaded 3D-printed scaffolds, as well as the regulatory, safety, and translational aspects of their application in orthopaedics and future perspectives, including the use of stimuli-responsive scaffolds and artificial intelligence, are discussed.[10–12] Overall, antibiotic-loaded 3D-printed scaffolds represent a new generation of scaffolds with good prospects for future clinical applications in orthopaedics.[12,13]
Devi Nivedita Sanaboyina*1, A. Lakshmi Naga Venkata Srilikitha2, O. Venu Chandrika3, S. Venkata Ramanjaneyulu4, J. Joy5· Zenodo (CERN European Organi...· 0 citations
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Background: Strengthening health security requires effective strategies for identifying and managing public health risks. As one of the most prevalent oral diseases globally, dental caries represents a significant burden on healthcare systems and serves as a key indicator of community health. The early detection of initial lesions is particularly critical to prevent disease progression and reduce treatment costs. However, conventional diagnostic methods, which rely on visual examination and radiography, are often limited by their subjectivity and time-consuming nature reducing their effectiveness for large-scale screening in public healthcare settings. This challenge highlights the need for intelligent risk detection systems. Artificial intelligence (AI) offers a transformative solution with the potential to shift disease diagnostics from a reactive to a proactive model thereby enhancing population-wide health security. Methods: This review highlights how artificial intelligence is transforming the diagnostic process, using dentistry as a powerful example of its broader potential in public health. However, the study also identifies challenges and limitations, such as restricted sample sizes and variability in AI model architectures. Therefore, the findings suggest that further research is needed to validate and scale the application of AI in clinical settings. By enhancing diagnostic accuracy and efficiency, intelligent systems can serve as a vital component in strengthening health security and improving public health outcomes. Results: All included studies demonstrate that artificial intelligence models, particularly deep neural networks, achieve higher accuracy in diagnosing dental diseases from radiographic images. This finding is especially critical for the identification of proximal carious lesions, which are often missed during routine examinations. The results signify a major advancement in early disease detection capabilities, which is a cornerstone of effective health security systems. Conclusions: This review highlights how artificial intelligence is transforming the diagnostic process in dentistry. However, the study also reveals the problems and pitfalls of AI utilization, such as restricted sample sizes and potential differences in the architecture of AI models. Therefore, the findings suggest that more studies are needed to advance the application of AI in clinical settings, as it appears to enhance diagnostic accuracy and effectiveness.
*Omar I. Alhawshani· Zenodo (CERN European Organi...· 0 citations
Cannabidiol (CBD) and related phytocannabinoids have attracted sustained pharmaceutical interest for their anti-inflammatory, analgesic and antioxidant properties, yet their clinical utility is persistently constrained by negligible aqueous solubility, extensive hepatic first-pass metabolism, and markedly variable oral bioavailability. Transdermal delivery offers a mechanistic route around these limitations by avoiding the gastrointestinal tract and hepatic first pass altogether. Realising this route in practice, however, requires overcoming the stratum corneum barrier, and a broad family of nanocarriers vesicular lipid systems (liposomes, ethosomes, transfersomes), solid lipid nanoparticles and nanostructured lipid carriers, and mesoporous inorganic carriers such as mesoporous silica nanoparticles (MSN) and magnesium aluminometasilicate (MAS) has been developed to that end. This review synthesises the published literature on nanoparticle-based transdermal delivery of cannabinoids and structurally comparable lipophilic actives, covering carrier chemistry and comparative performance, physical and chemical permeation-enhancement strategies, Quality-by-Design (QbD) approaches to nanoparticle and patch optimisation, general principles of transdermal patch evaluation, in-vitro release and permeation kinetics, stability considerations, preclinical and clinical anti-inflammatory evidence, carrier biosafety, the evolving regulatory landscape for cannabinoid-containing topical products, and the emerging role of artificial intelligence in formulation design. Across this evidence base, mesoporous nanoparticle carriers combined with a Quality-by-Design-optimised transdermal patch represent a mechanistically well-supported and increasingly well-evidenced platform for cannabinoid delivery, though unresolved questions in chronic dermal nanotoxicology and an unsettled regulatory environment remain the principal barriers to clinical translation.
Manish Kumar2 Pare Pankaj Rajendra*1· Zenodo (CERN European Organi...· 0 citations
Current Good Manufacturing Practice (cGMP) represents a fundamental regulatory and quality framework for ensuring that pharmaceutical products are consistently manufactured and controlled according to predefined standards of quality, safety, efficacy, identity, strength, and purity. With increasing complexity in pharmaceutical manufacturing, globalization of supply chains, adoption of advanced technologies, and growing regulatory expectations, cGMP compliance has evolved from a predominantly inspection-oriented approach to a comprehensive, science- and risk-based pharmaceutical quality system. This review provides an overview of the principles, regulatory framework, and essential components of cGMP applicable to pharmaceutical manufacturing. Major areas discussed include the Pharmaceutical Quality System (PQS), personnel and training, premises and equipment, documentation and data integrity, material management, production and in -process controls, quality control, qualification and validation, deviation management, Corrective and Preventive Action (CAPA), change control, Quality Risk Management (QRM), Product Quality Review (PQR), supplier management, and self-inspection. The review also highlights the roles of major regulatory authorities and guidance frameworks, including the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), World Health Organization (WHO), International Council for Harmonisation (ICH), and Central Drugs Standard Control Organization (CDSCO). Particular emphasis is placed on ICH Q9(R1) and ICH Q10 as important frameworks supporting risk-based decision-making, lifecycle management, continual improvement, and effective pharmaceutical quality systems. Emerging trends such as digitalization, continuous manufacturing, Process Analytical Technology (PAT), automation, artificial intelligence, and advanced data analytics are also considered. Effective implementation of cGMP should extend beyond procedural compliance to establish a sustainable quality culture capable of maintaining process control, preventing quality failures, protecting patients, and supporting continuous improvement throughout the pharmaceutical product lifecycle.
Purpose: Medicinal plants are the richest source of structurally diverse biologically active compounds. Herbal leads discovery can also be accelerated using artificial intelligence and multi omics technologies. This review evaluates and summarizes the applications of artificial intelligence (AI), multi-omics, synthetic biology, and biomanufacturing in advancing research for herbal therapeutics. Virtual computational screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Method: We comprehensively examine literature on virtual computational screening, deep learning, molecular docking, multi-omics platforms, genome mining, CRISPR-based synthetic biology, and emerging tools like organ-on-a-chip systems and nanobiotechnology using PubMed, ScienceDirect and Google Scholar. Result: Virtual screening, deep learning, and molecular docking extremely accelerate lead identifications, structure-activity predictions, and target identification. Simultaneously, multi-omics platforms evaluate system-level metabolic networks, regulatory pathways, and host–microbiome interactions. Genome mining and CRISPR-based synthetic biology unlock cryptic metabolic pathways and enable scalable, sustainable production of high-value natural products. Furthermore, tools like organ-on-a-chip systems and nanobiotechnology bridge the gap toward precision herbal medicine and standardization. Conclusion: By integrating these multidisciplinary strategies, herbal drug discovery is evolving from empirical observation toward predictive, sustainable, and mechanism-driven pharmaceutical science. Finally, key challenges like data standardization, explainable AI, experimental validation, and regulatory approval must be addressed for clinical translation.
Ansh Mishra1, Aarti Yadav2, Govind Gupta2, Priya3, Km. Deeksha4*· Zenodo (CERN European Organi...· 0 citations
Background: Sepsis is a life-threatening clinical syndrome caused by a dysregulated host response to infection resulting in organ dysfunction. Bloodstream infections are important contributors to sepsis and may rapidly progress to severe systemic inflammation, organ dysfunction, and septic shock. Despite advances in antimicrobial therapy and critical care, delayed diagnosis, antimicrobial resistance, altered pharmacokinetics, and biological heterogeneity continue to complicate the management of sepsis. Objective: This review summarizes current evidence on the epidemiology, etiology, pathophysiology, clinical manifestations, diagnostic approaches, pharmacotherapeutic management, antimicrobial resistance, antimicrobial stewardship, and emerging therapeutic strategies in sepsis and bloodstream infections. Methods: A narrative literature review was conducted using PubMed/MEDLINE, Google Scholar, Scopus, ScienceDirect, and the Cochrane Library. Literature published primarily between 2015 and 2025 was considered, with selected landmark studies and guidelines published before 2015 included where relevant. Peer-reviewed studies, clinical guidelines, randomized controlled trials, cohort studies, systematic reviews, meta-analyses, and relevant narrative reviews addressing sepsis and its pharmacotherapeutic management were considered. Results: Contemporary management of sepsis involves early recognition, appropriate empirical antimicrobial therapy, microbiological investigation, source control, fluid resuscitation, vasopressor support when required, and individualized antimicrobial dosing. Antimicrobial resistance has increasingly complicated empirical treatment and highlights the importance of antimicrobial stewardship, pharmacokinetic/pharmacodynamic optimization, therapeutic drug monitoring, and timely de-escalation. Emerging approaches include biomarker-guided management, immune phenotyping, endothelial and glycocalyx protection, microbiome-directed strategies, and artificial intelligence-assisted prediction. Conclusion: Effective management of sepsis requires an integrated and individualized approach combining rapid diagnosis, appropriate antimicrobial therapy, hemodynamic stabilization, source control, antimicrobial stewardship, and multidisciplinary critical care. Future research should focus on precision medicine approaches that account for the biological heterogeneity of sepsis and enable individualized therapeutic decision-making.
D. Rasagna*· World Journal of Pharmacy an...· 0 citations
Background: Non-muscle invasive bladder cancer (NMIBC) constitutes the majority of bladder cancer cases and is primarily treated with intravesical Bacillus Calmette-Guérin (BCG) immunotherapy. However, up to 40% of patients do not respond to BCG, highlighting the need for reliable predictive tools to guide personalized treatment strategies. Artificial intelligence (AI) has emerged as a promising approach to address this challenge by integrating complex clinical and biological data. Methods: A comprehensive literature search was conducted across multiple databases up to November 2025. Studies evaluating AI techniques—including deep learning, machine learning, radiomics, and multimodal models—for predicting BCG response were included. Data on model performance, input modalities, and clinical applicability were analyzed using narrative synthesis. Results: AI-based models demonstrated superior predictive performance compared to traditional clinical risk stratification tools. Deep learning models showed high accuracy, particularly with histopathological and genomic data, though interpretability remains limited. Classical machine learning approaches offered improved transparency with comparable performance. Radiomics enabled non-invasive prediction of tumor characteristics and immune microenvironment features. Multimodal models integrating imaging, pathology, molecular, and clinical data consistently achieved the highest predictive accuracy. Certain AI models also demonstrated potential in guiding treatment selection and identifying patients unlikely to benefit from BCG therapy. Conclusion: AI-based predictive models hold significant potential for improving treatment decision-making and enabling personalized therapy in NMIBC. However, challenges such as limited prospective validation, data heterogeneity, and lack of standardization must be addressed before routine clinical implementation.
1Evangelin Gabriel Rajesh Kumar, 2Monisha Krishnakumar, 3*Lashika L. K.· Zenodo (CERN European Organi...· 0 citations
In the face of the growth and planned growth of industrial developments in artificial intelligence, embodied AI, immersive media/virtual reality, and the fintech industry and cryptocurrencies – and the energy requirements for each of these and all of them together – this paper examines the question of whether the plans for growth and future development in these areas is probable, possible, problematic, or impossible. Additionally, the examination is situated atop both state-based and criminal threats to use the Internet and mobile Internet to wreak havoc and instability.
Thom Gencarelli· Roger Williams University -...· 0 citations
By-products generated throughout the entire pear (Pyrus spp.) industrial chain, including peels, branches, leaves, and unripe fruits, are abundant sources of high-value bioactive components. Current research indicates that bioactive compounds such as phenolics, triterpenoids, and arbutin exhibit significant "spatiotemporal heterogeneity", characterized by considerably higher concentrations in peels, branches, leaves, and unripe fruits compared to mature pulp. This distribution pattern underscores the superiority of these by-products as raw materials for extraction. Regarding extraction techniques, while traditional organic solvent extraction is well-established, its application is constrained by issues of solvent residues and environmental toxicity. A comparative analysis reveals that green, efficient extraction technologies offer a promising solution to overcome the limitations of traditional methods regarding mass transfer efficiency and solvent residues. These technologies represent a critical pathway for the valorization of pear processing by-products, provided that extraction strategies are flexibly selected based on the physical characteristics of the raw materials and the stability of the target components. Bioactive components from pears possess immense development potential in the fields of skin-whitening cosmetics and gut health-promoting foods. However, future research should prioritize multi-scale industrial validation, artificial intelligence-driven multi-objective process optimization, and the in-depth elucidation of synergistic interactions among components, aiming to advance the comprehensive, high-value utilization of pear resources.
Yue ZHAO, Wei ZHOU, Shaohua ZHU et al.· DOAJ (DOAJ: Directory of Ope...· 0 citations
