Journal of Drug Discovery and Health Sciences
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Regulatory Frameworks for Advanced Therapy Medicinal Products (ATMPs): A Comparative Review of FDA, EMA, and CDSCO
Background: Advanced therapy medicinal products (ATMPs) such as gene therapy medicinal products, somatic cell therapy medicinal products, tissue engineered products and combined advanced therapies are one of the fastest growing classes of biological medicines. The biological complexity and individualized manufacturing of these products do not fit well within regulatory systems designed for small-molecule drugs, and the three regulators examined here – the United States Food and Drug Administration (FDA), the European Medicines Agency (EMA), and India’s Central Drugs Standard Control Organization (CDSCO) – have each developed distinct legal and procedural routes for this product class. Aim: To compare the legal basis, institutional architecture, clinical trial oversight, expedited approval pathways, manufacturing and quality expectations, post-approval surveillance needs, and access and reimbursement mechanisms governing advanced therapy medicinal products under the FDA, the EMA, and CDSCO, and examine the extent to which these systems are converging/diverging. Data Sources: Gray literature from well-acknowledged regulatory and health policy organizations, official publications of FDA, EMA and CDSCO, PubMed and Scopus and Web of Science indexed journals focusing on the content published from 2019 to mid-2026. Review Methodology: Our approach was narrative and comparative. Peer-reviewed articles, regulatory guidance documents, and health-policy analyzes on ATMP definition, licensure pathway, clinical trial authorization, expedited designation, manufacturing oversight, and reimbursement were identified. These were thematically synthesized and cross-checked against primary regulatory sources where available. Main Findings: ATMPs are biologics regulated by the FDA under section 351 of the Public Health Service Act by the Center for Biologics Evaluation and Research, with the assistance of the Regenerative Medicine Advanced Therapy designation. The EMA has centrally overseen ATMPs under Regulation (EC) No 1394/2007 via its Committee for Advanced Therapies, a structure currently being reviewed as the European Union completes a broader reform of its pharmaceutical legislation. The Central Drugs Standard Control Organization (CDSCO) oversees cell and gene therapy products along with the Indian Council of Medical Research, the Department of Biotechnology, and the Gene Therapy Advisory and Evaluation Committee under the New Drugs and Clinical Trials Rules, 2019, an arrangement recently strengthened by amendments that put these products under joint central and state oversight. India’s approval last year of NexCAR19, a homegrown and much cheaper CAR-T cell therapy, reflects both the maturing Indian pathway and the ongoing global affordability divide. Conclusion: The FDA and EMA frameworks are demonstrating increasing procedural convergence in quality and comparability expectations, while CDSCO has moved rapidly toward a comprehensive, if still evolving, national framework. Across the three jurisdictions, the principal barriers to equitable patient access to sophisticated therapy medicinal products remain the ability to reimburse and the generation of long-term evidence, rather than scientific review alone before the market.
Biological Products Drug Approval Drug Legislation Cell and Tissuebased Therapy Genetic Therapy Regenerative Medicine
Artificial Intelligence in Regulatory Affairs: Transforming Drug Development, Regulatory Decision-Making, and Global Compliance
Background: Artificial intelligence (AI) and machine learning (ML) are increasingly being used throughout the drug development lifecycle, prompting regulatory agencies to develop new frameworks for their use in evidence generation, clinical trial execution, and post-market surveillance. Objective: To critically appraise current AI/ML applications across drug development and regulatory decision making, compare the regulatory approaches adopted by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and to explore the challenges of global regulatory harmonization and compliance. Data sources: Literature and guidance documents were searched in PubMed/MEDLINE, Scopus- and Web of Science-indexed journals, Nature Portfolio publications and primary regulatory guidance from FDA and EMA, published predominantly between 2021 and 2026. Review Methods: We used a comprehensive narrative review format to accommodate the broad scope of the topic, from applied AI/ML literature to regulatory guidance documents and health-policy commentary. Evidence assessed for source authority, considering agency positions, and highlighting convergence and divergence; not chronological summary. Key Findings: The FDA has transitioned from discussion papers to draft and final guidance to the use of AI in the regulatory decision-making process, device software lifecycle management and predetermined change control, based on its own experience with over 500 submissions of AI components from 2016-2023. The EMA reflection paper sets out a human-centred, risk-based approach covering the whole lifecycle of a medicinal product. AI applications have demonstrated credible retrospective performance improvements in clinical trial recruitment, risk prediction, and pharmacovigilance, but prospective validation and demonstrated impact on trial success or patient safety outcomes remain scarce. The regulators’ own use of generative AI tools has led to concerns over reliability due to hallucinations, continuing to underscore the need for oversight by humans. Conclusion: AI/ML is revolutionizing regulatory affairs in drug development but the current landscape of regulatory structures remains in an early guidance stage of maturity, and global harmonization, algorithmic transparency and prospective outcome validation continue to be the major open challenges to translate the technical promise of AI into reliable regulatory practice.
Artificial Intelligence; Drug Development; Machine Learning; Pharmacovigilance; Clinical Trials as Topic; Government Regulation
Redefining the CADD Paradigm: Harnessing Artificial Intelligence from Target Discovery to Lead Optimization
Artificial intelligence (AI) is reshaping computer-aided drug design (CADD) by enabling data-driven analysis and generation across the early drug-discovery continuum. Conventional CADD approaches—including molecular docking, pharmacophore modelling, quantitative structure-activity relationship analysis and molecular dynamics—remain valuable, but their predictive performance can be constrained by simplified scoring functions, incomplete structural information and the enormous size of chemical space. Machine learning (ML), deep learning, graph neural networks, protein language models, geometric learning and generative models increasingly complement these methods. AI can support disease and target prioritization, protein-structure prediction, binding-site analysis, drug-target interaction prediction, virtual screening, de novo molecular generation, ADMET prediction, retrosynthetic planning and multi-parameter lead optimization. Recent advances such as AlphaFold3, deep-learning docking, ultra-large-library screening and generative molecular design have expanded the scope of structure-based and ligand-based discovery. Importantly, AI-generated candidates must be assessed for potency, selectivity, physicochemical properties, pharmacokinetics, toxicity, synthetic accessibility and uncertainty before experimental testing. Data leakage, bias, activity cliffs, distribution shift, limited prospective validation and interpretability remain significant barriers. This review presents an integrated AI-CADD framework from target identification to lead optimization and emphasizes the design-make-test-analyze cycle as the practical bridge between computational prediction and experimental medicinal chemistry.
Artificial intelligence; computer-aided drug design; virtual screening; generative chemistry; lead optimization
Artificial Intelligence-enhanced Computer-aided Drug Design: Recent Advances, Applications and Challenges
CADD has, for four decades, provided the computational scaffolding on which rational drug discovery is built, encompassing structure-based and ligand-based approaches such as molecular docking, molecular dynamics, pharmacophore modelling, and QSAR analysis. Over the last decade this discipline has been transformed by the infusion of AI, giving rise to what is increasingly termed AI-driven drug design (AIDD). Deep neural networks, generative models, graph neural networks, and transformer-based language models now support nearly every stage of the discovery pipeline, from target identification and highly accurate protein structure prediction, through de novo molecular generation and virtual screening of ultra-large chemical libraries, to the prediction of ADMET properties and the optimisation of clinical trial design. This review synthesises recent advances in AI-enhanced CADD, covering key methodological families (generative adversarial networks, variational autoencoders, diffusion models, graph neural networks, and transformers), landmark applications including AlphaFold and its successors, deep-learning-guided antibiotic and antifibrotic drug discovery, and AI-designed molecules that have advanced into clinical trials. It further examines the persistent challenges that temper enthusiasm for the field — data scarcity and bias, limited model interpretability, poor generalisation beyond training distributions, weak experimental validation, and unresolved regulatory and ethical questions — and outlines emerging directions, including multimodal foundation models, physics-informed learning, and closed-loop autonomous laboratories, that are likely to shape the next generation of AI-enhanced drug discovery.
Artificial intelligence; computer-aided drug design; deep learning; generative models; drug discovery
Stem Cell-Based Therapies for Chronic Wound Healing: Current Evidence and Future Perspectives
Background: Chronic wounds, including diabetic foot ulcers, venous leg ulcers, and ischemic wounds, fail to progress through the normal phases of healing and impose a substantial clinical and economic burden. Stem cell-based therapies, principally mesenchymal stem cells (MSCs) and their derivatives, have been investigated as regenerative alternatives to conventional wound management. Objective: To critically appraise current clinical and mechanistic evidence for stem cell-based therapies in chronic wound healing, compare outcomes across cell sources and wound aetiologies, and examine the regulatory and safety considerations governing their translation into practice. Data Sources: PubMed/MEDLINE, Scopus- and Web of Science-indexed journals, and publications from Frontiers, MDPI, Springer Nature, Wiley, PLOS, and SAGE were searched for literature published predominantly between 2019 and 2026, supplemented by landmark earlier studies and current US Food and Drug Administration (FDA) regulatory guidance. Review Methods: A comprehensive narrative review format was adopted to accommodate the methodological breadth of the field, spanning mechanistic, preclinical, clinical trial, and regulatory literature. Evidence was appraised for study design, cell source, and outcome measures, with emphasis on comparing findings across wound aetiologies rather than sequential summarisation. Key Findings: Meta-analyses of randomised controlled trials indicate that MSC-based therapy improves ulcer healing rate and reduces amputation risk in diabetic foot ulcers relative to conventional care, with evidence certainty rated low to moderate by GRADE criteria. Evidence in venous leg ulcers and critical limb ischemia remains limited to small, mostly phase I/II trials. Cell-free MSC-derived extracellular vesicles and induced pluripotent stem cell (iPSC)-derived lineages show mechanistic promise but remain confined chiefly to preclinical and early translational studies. Regulatory classification under the US FDA’s 351/361 framework materially determines the evidentiary and manufacturing burden applicable to a given product, and tumorigenicity and manufacturing variability remain unresolved safety considerations for pluripotent-derived cells. Conclusion: Stem cell-based therapies, particularly MSC-based interventions, show reproducible benefit for diabetic foot ulcer healing in randomised trials, while evidence for other chronic wound types and for cell-free and pluripotent-derived approaches remains preliminary. Continued translation should be paced to standardised manufacturing, long-term safety follow-up, and harmonised regulatory classification.
Wound Healing; Mesenchymal Stem Cells; Diabetic Foot; Regenerative Medicine; Induced Pluripotent Stem Cells; Extracellular Vesicles
Engineered Exosomes versus Synthetic Nanoparticles for Drug Delivery
Two developing, but promising, platforms to optimize therapeutic targeting, bioavailability and safety in drug delivery research are engineered exosomes and synthetic nanoparticles. The exosomes that are engineered out of naturally occurring extracellular vesicles have become further of interest because of their intrinsic biocompatibility, lack of immunogenicity and capacity to traverse biological barriers, such as the blood-brain barrier. Innovations in genetic and chemical engineering have also added to their targeting capabilities and cargo-carrying capacity, which makes them potentially successful vectors in the use of precision medicine. Synthetic nanoparticles, in contrast, have better structural tunability, scaling fabrication, and high drug encapsulation efficiency, but are related to increased immunogenicity and toxicity potentials. New trends show a major advance in optimizing both systems in treatment of cancers/neurological as well as systemic disorders, and new hybrid strategies have tried to combine the advantages of both systems. Nonetheless, there are still issues with big-scale production, standardization, controlling biodistribution, and long-term safety. There is a piece of comparative evidence indicating that, although synthetic nanoparticles are currently leading in clinical translation because of manufacturing maturity, engineered exosomes perform better biologically in complex biological conditions. In general, the two delivery systems are set to complete each other in the field of next generation nanomedicine especially with bioengineering, synthetic biology and nanofabrication finding new leaps in technological advancements.
Engineered exosomes; synthetic nanoparticles; drug delivery systems; nanomedicine; targeted therapy; extracellular vesicles; cancer therapy; blood–brain barrier.
The Influence of Transformational Leadership on Burnout and Turnover Intention Among Psychiatric Nurses in Correctional Facilities
Psychiatric nurses in correctional settings are faced with highly complex environments with high patient acuity, security issues, emotional needs, workload stressors, and organizational resource constraints. These circumstances may make individuals more susceptible to workplace burnout and may enhance their resolve to quit their job, posing difficulties for job security and continuity of mental health care. The purpose of this study is to investigate how transformational leadership affects psychiatric nurses in correctional facilities in terms of burnout and intention to leave. It aims to examine whether the leadership behaviors of inspirational motivation, individualized consideration, intellectual stimulation and supportive communication can decrease occupational stress and increase the nurses’ intention to stay. The study suggests a quantitative correlational research method involving the use of standardized instruments for assessing psychiatric nursing staffs’ transformational leadership, burnout, and intention to leave. Relationships between the study variables can be assessed using descriptive statistics, correlation analysis, regression analysis and mediation testing. The proposed framework assumes that higher transformational leadership will be linked to lower burnout and to lower intention to leave, and higher levels of burnout will be linked to higher intention to leave. Results could help correctional healthcare administrators to design leadership behaviors and staff interventions that foster staff wellbeing, staff commitment, and staff retention and support sustainable psychiatric nursing services.
Transformational leadership; psychiatric nurses; correctional facilities; burnout; turnover intention; nurse retention; workplace well-being; correctional healthcare.
Salivary metabolomics as an early predictor of childhood ECC before lesion formation
Early childhood caries (ECC) remains one of the most prevalent chronic oral diseases affecting children worldwide, with conventional diagnostic approaches often detecting disease only after irreversible enamel damage has occurred. Salivary metabolomics has emerged as a promising non-invasive strategy for identifying biochemical alterations associated with ECC susceptibility before visible lesion formation. By analyzing complex metabolic profiles present in saliva, this approach can detect early changes related to microbial activity, carbohydrate metabolism, host responses, inflammation, and oxidative stress. Advanced metabolomic platforms, including mass spectrometry and nuclear magnetic resonance-based techniques, enable the identification of potential biomarker patterns that may predict caries risk and progression. Integration of salivary metabolomics with microbiome analysis, proteomics, and artificial intelligence-driven predictive models may improve early risk assessment and support personalized preventive dental care. This molecular-based approach has the potential to transform ECC management from reactive treatment toward proactive prevention by enabling timely interventions, targeted behavioral modifications, and individualized therapeutic strategies. However, clinical implementation requires standardized protocols, validation of reliable biomarkers, and development of practical chairside diagnostic technologies. Salivary metabolomics represents a promising frontier in precision pediatric dentistry for early ECC prediction and prevention.
Salivary metabolomics; Early childhood caries; ECC prediction; Biomarkers; Pediatric dentistry; Oral microbiome; Precision dentistry; Preventive dentistry; Artificial intelligence; Noninvasive diagnostics.
Digital twin simulation to predict orthodontic relapse before treatment completion
Orthodontic relapse remains one of the greatest challenges in achieving long-term treatment stability, often resulting from complex interactions among biological remodeling, biomechanical forces, patient compliance, and retention strategies. Recent advances in digital dentistry have introduced digital twin technology as a transformative approach for creating dynamic, patient-specific virtual models that continuously integrate clinical, imaging, and treatment data throughout orthodontic care. By combining three-dimensional imaging, intraoral scanning, artificial intelligence, biomechanical simulation, and predictive analytics, digital twins enable clinicians to monitor treatment progression in real time and forecast the likelihood of relapse before active treatment is completed. This proactive approach supports individualized treatment planning, optimization of force application, timely modification of therapeutic strategies, and personalized retention protocols to improve long-term outcomes. Digital twin simulation also enhances clinical decision-making by providing visual representations of anticipated tooth movement and treatment responses, thereby facilitating better communication between clinicians and patients. Although challenges related to data integration, computational demands, model validation, interoperability, and ethical management of patient information remain, ongoing developments in artificial intelligence and digital technologies continue to strengthen the feasibility of clinical implementation. Digital twin simulation has the potential to redefine predictive orthodontics by enabling precision-based, adaptive, and preventive treatment strategies that minimize relapse risk while improving treatment efficiency, stability, and patient-centered care.
Digital twin, orthodontic relapse, artificial intelligence, predictive simulation, personalized orthodontics, biomechanical modeling, three-dimensional imaging, digital dentistry, treatment planning, precision orthodontics.
AI optimization of complete denture design using facial muscle dynamics.
Complete denture therapy remains a fundamental treatment for edentulous patients, yet achieving optimal retention, stability, comfort, and facial esthetics continues to present clinical challenges due to individual variations in oral anatomy and neuromuscular function. Recent advances in artificial intelligence (AI) and digital dentistry have created new opportunities to improve denture design through the analysis of facial muscle dynamics. By integrating three-dimensional facial scanning, digital impression techniques, jaw motion tracking, and machine learning algorithms, AI can evaluate dynamic facial expressions and masticatory movements to generate personalized denture designs that better adapt to each patient’s functional characteristics. AI-driven predictive models facilitate optimization of tooth arrangement, occlusal relationships, flange contours, and border extensions, thereby enhancing prosthesis stability and minimizing the need for repeated clinical adjustments. The incorporation of computer-aided design and computer-aided manufacturing technologies further supports accurate fabrication and efficient clinical workflows. Although challenges such as data standardization, algorithm validation, equipment costs, and clinical integration remain, AI-assisted denture design demonstrates considerable potential to improve treatment outcomes and patient satisfaction. Continued research and interdisciplinary collaboration are expected to refine predictive modeling techniques and establish intelligent, patient-centered prosthodontic solutions that combine functional efficiency, esthetic excellence, and long-term clinical success in complete denture rehabilitation.
Artificial intelligence, complete dentures, facial muscle dynamics, digital dentistry, machine learning, facial scanning, prosthodontics, CAD/CAM, occlusal optimization, personalized denture design.
Digital twin technology for personalized endodontic treatment planning using CBCT, intraoral scans, and patient-specific biomechanical simulations
Digital twin technology is emerging as a transformative approach in personalized healthcare by creating a dynamic virtual representation of an individual patient that continuously integrates clinical, anatomical, and biological data. In endodontics, the combination of cone-beam computed tomography (CBCT), intraoral scanning, and patient-specific biomechanical simulations enables clinicians to construct highly accurate digital replicas of teeth and surrounding structures for treatment planning. These virtual models facilitate detailed visualization of root canal anatomy, simulation of instrumentation and obturation procedures, assessment of stress distribution, and prediction of potential treatment outcomes before clinical intervention. Artificial intelligence further enhances the digital twin by automating image segmentation, integrating electronic dental records, and continuously updating patient-specific models throughout treatment. This technology has the potential to improve diagnostic precision, optimize treatment strategies, minimize procedural complications, and support personalized clinical decision-making. Additionally, digital twins may facilitate long-term monitoring of healing and restoration performance through continuous data integration. Despite promising developments, widespread implementation remains limited by challenges related to data standardization, computational complexity, interoperability, regulatory considerations, and validation in large-scale clinical studies. Continued advances in artificial intelligence, imaging technologies, and computational modeling are expected to accelerate the clinical adoption of digital twin systems, ultimately supporting predictive, precision, and patient-centered endodontic care.
Intraoral scanning; Artificial intelligence; Biomechanical simulation; Personalized treatment planning; Precision dentistry.
Artificial intelligence-assisted prediction of instrument separation during root canal preparation based on canal anatomy and instrument stress
Instrument separation remains one of the most challenging procedural complications during root canal preparation, often compromising treatment efficiency, prognosis, and long-term clinical outcomes. Recent advances in artificial intelligence (AI) have created new opportunities to predict the likelihood of instrument fracture by integrating anatomical, mechanical, and procedural data before and during endodontic treatment. AI models can analyze cone-beam computed tomography images to identify complex canal morphology, including severe curvature, narrow canals, bifurcations, and calcifications, while simultaneously incorporating information related to instrument design, alloy properties, kinematics, and estimated mechanical stress. Machine learning and deep learning algorithms enable the identification of high-risk clinical scenarios, allowing clinicians to select appropriate instruments, modify preparation strategies, and minimize excessive torsional and cyclic fatigue. AI-assisted predictive systems also support personalized treatment planning, improve procedural consistency, and reduce operator-dependent variability through evidence-based decision support. Despite these advantages, widespread clinical implementation requires robust multicenter datasets, standardized validation protocols, seamless integration with digital endodontic workflows, and transparent model interpretation to ensure clinician confidence and patient safety. As digital dentistry continues to evolve, AI-assisted prediction of instrument separation has the potential to transform preventive risk assessment, optimize root canal preparation, and enhance the precision, safety, and overall success of modern endodontic therapy.
instrument separation, root canal preparation,
canal anatomy, machine learning, deep learning, cyclic fatigue, torsional stress, cone-beam computed tomography, predictive endodontics.
Copyright (c) 2026 Journal of Drug Discovery and Health Sciences
AI-assisted real-time detection of canal morphology during endodontic treatment using an operating microscope
The integration of artificial intelligence (AI) with operating microscopes represents a significant advancement in modern endodontics by enhancing the real-time detection of complex root canal morphology during treatment. Accurate identification of root canal anatomy is essential for successful endodontic therapy, as undetected canals and anatomical variations remain major causes of treatment failure. Conventional diagnostic approaches, including periapical radiography, cone-beam computed tomography, and magnification through operating microscopes, have substantially improved clinical outcomes but continue to rely heavily on the clinician’s expertise and interpretation. AI-assisted image analysis introduces automated recognition of canal structures through advanced machine learning and deep learning algorithms capable of processing microscopic images in real time. These technologies can improve the detection of accessory canals, calcified canals, canal bifurcations, and other anatomical complexities while providing immediate clinical guidance during instrumentation. The integration of AI with digital imaging workflows has the potential to increase diagnostic accuracy, reduce procedural errors, improve treatment efficiency, and support minimally invasive endodontic procedures. Despite challenges related to algorithm validation, data standardization, equipment integration, and regulatory considerations, ongoing technological advancements continue to expand the clinical applicability of AI-assisted systems. The incorporation of intelligent image analysis into operating microscopy is expected to transform endodontic practice by improving precision, consistency, and long-term treatment outcomes.
Artificial intelligence, E n d o d o n t i c s , O p e r a t i n g microscope, Root canal morphology, Deep learning, Machine learning, Computer vision, Real-time detection, Digital dentistry, Image analysis.
Salivary microRNA profile as a predictor of irreversible pulpitis and postoperative pain
Irreversible pulpitis is a common inflammatory condition of the dental pulp that presents significant diagnostic and therapeutic challenges in endodontic practice. Conventional diagnostic approaches, including clinical examination, pulp sensibility tests, and radiographic assessment, often provide indirect information regarding pulpal status and may not accurately reflect the underlying biological processes. Consequently, there is growing interest in identifying objective molecular biomarkers capable of improving diagnostic precision and predicting treatment outcomes. Salivary microRNAs have emerged as promising candidates due to their stability, non-invasive accessibility, and regulatory roles in inflammation, immune responses, and tissue repair. Alterations in salivary microRNA expression have been associated with inflammatory changes within the dental pulp and may provide valuable information for distinguishing irreversible pulpitis from healthy or reversibly inflamed pulp tissues. Furthermore, specific microRNA expression patterns have shown potential for predicting postoperative pain intensity, enabling clinicians to identify high-risk patients and implement personalized pain management strategies. Advances in molecular diagnostics, biosensor technology, and artificial intelligence have further enhanced the feasibility of integrating salivary microRNA profiling into routine clinical practice. Although additional large-scale clinical validation and standardized analytical protocols are required, current evidence suggests that salivary microRNA profiling represents a promising approach for improving diagnostic accuracy, prognostic assessment, and personalized endodontic care while supporting the broader transition toward precision dentistry.
Salivary microRNA, irreversible pulpitis, postoperative pain, endodontics, salivary biomarkers, molecular diagnostics, precision dentistry, artificial intelligence, inflammation, predictive biomarkers.
Biopesticides Production, Formulation and Applications: A Short Review
Rising public concern over the environmental and health risks of chemical pesticides has accelerated the shift toward safer, more sustainable pest control methods. Biopesticides—sourced from microorganisms, plant extracts, and natural biochemical compounds—offer an eco-friendly alternative to synthetic chemicals. Their development involves selecting, cultivating, and processing biological agents to ensure both effectiveness and stability. Advanced formulation techniques improve performance, extend shelf life, and adapt application methods to various agricultural contexts, such as liquid suspensions, wettable powders, and granules. Effective use of biopesticides depends on factors like target pests, environmental conditions, and alignment with integrated pest management (IPM) strategies. Despite hurdles such as short shelf life, regulatory challenges, and production costs, ongoing advancements in biotechnology and formulation are enhancing their viability. With continued investment in research, education, and supportive policies, biopesticides hold strong promise for reducing reliance on synthetic pesticides and promoting sustainable agriculture. This review examines the classification, production, and current status of biopesticides, with a focus on their role in Integrated Pest Management (IPM) and their adoption in India. The review highlights recent advancements, market trends, and regulatory frameworks shaping global and national biopesticide usage.
Biopesticides, Integrated Pest Management, Microbial pesticides, Trichoderma
Advanced Chromatographic and Spectroscopic Method Development for Biomarker Identification and Validation in Clinical Biochemistry
The application of advanced chromatographic and spectroscopic methods has revolutionized the process of biomarker identification and validation in clinical biochemistry, thus enhancing analytical sensitivity, specificity and reproducibility. Several analytical techniques like liquid chromatography–mass spectrometry (LC-MS/MS), gas chromatography–mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR), offer strong bases for the identification of complex biological molecules involved in various diseases. The present study discusses recent advances in method development, optimization and validation in the field of biomarker discovery in clinical diagnostics and personalized medicine. The combination of multi-omics, automation and advanced mass spectrometry technologies has improved clinical biomarker reliability and applicability for translation. In addition, analytical validation parameters such as sensitivity, specificity, precision and accuracy are still key for clinical acceptance and regulatory compliance. The study highlights the growing importance of advanced analytical technologies in improving early disease detection, therapeutic monitoring, and precision healthcare.
Navigating Cultural Divergence and Fostering Inclusive Well-Being: A Mental Health-Centered Approach to Global Pharmaceutical Team Dynamics
Globalization has greatly changed the operational system of the pharmaceutical industry and this has created culturally diverse teams that work across the geographical as well as institutional boundaries. Although this diversity increases the innovation, sharing of knowledge as well as the capacity to solve global problems, it also presents a problem concerning communication barriers, cultural misunderstandings and psychological strains between the team members. These issues raise the question of the significance of introducing mentally oriented approaches to management behaviors in organizations that will support inclusive health and well-being and resilient collaboration. This paper discusses the cultural divergence dynamics of the international pharmaceutical work teams and ways that the mental health-oriented organizational structure can promote the inclusive working environment. The qualitative approach to the analysis based on the literature on cross-cultural psychology, organizational well-being, and global workforce management allows the researcher to focus on several important variables that determine the team cohesion and employee psychological well-being in multinational drug-making companies. The discussion highlights how culturally responsive leadership, inclusive communication patterns, cross-cultural competency training, and integrated mental health support systems can be used to enhance employee resilience and productivity. The evidence indicates that the organizations with high emphasis on the concept of psychological safety and cultural inclusiveness have a stronger stance to utilize the innovative potential of diverse teams and reduce the adverse impact of cultural fragmentation. The paper also reveals the strategic significance of mental health initiatives being aligned with diversity and inclusion policies to reinforce the team dynamics and organizational performance. By integrating a mental health-based perspective on workforce management, the pharmaceutical companies in the world can reposition cultural diversity as the source of conflict to creativity, teamwork and long term institutional sustainability.
Global pharmaceutical teams; Cultural diversity; Inclusive well-being; Workplace mental health; Cross-cultural collaboration; Organizational resilience; Team dynamics.
Assessment of Acute Toxicity and Behavioral Disruptions in Indian Major Carp (Labeo Rohita) Under Pyrthrum Stress
The extensive use of pesticides in agriculture has raised serious concerns regarding their inadvertent entry into aquatic ecosystems and subsequent toxicity to non-target organisms. Pyrethrum, a botanical insecticide derived from Tanacetum cinerariifolium, is often regarded as an eco-friendly alternative to synthetic pesticides; however, its toxic effects on aquatic fauna remain insufficiently explored. The present study evaluates the acute toxicity and associated behavioral alterations induced by pyrethrum in the Indian major carp Labeo rohita. Acute toxicity bioassays were conducted under laboratory conditions following APHA guidelines, and median lethal concentrations (LC₅₀) were determined for 24, 48, 72, and 96 hours of exposure. The LC₅₀ values exhibited a time-dependent decline, indicating increased toxicity with prolonged exposure, with the lowest LC₅₀ recorded at 96 hours (21.40 µg/L). Mortality increased with both concentration and exposure duration, reaching 100% at higher concentrations. In addition to lethality, pronounced behavioral and physiological disturbances were observed, including erratic swimming, surfacing and gulping, avoidance responses, excessive mucus secretion, reduced opercular movement, fin damage, and body discoloration. Dissolved oxygen levels declined significantly in exposure tanks, suggesting elevated metabolic stress and impaired respiration. These findings demonstrate that pyrethrum is highly toxic to Labeo rohita even at low concentrations and induces severe behavioral disruptions. The study underscores the ecological risks associated with botanical insecticides and highlights the need for cautious application and stringent regulation to protect aquatic ecosystems.
- Pyrethrum, Lethal Concentrations, Labeo rohita, Acute Toxicity, Mortality, LC₅₀
Effect of Azadirachta indica, Pongamia glabra, and Adhatoda vasica on Termites, Arthropods, Shoot Borers and Coleopteran Pests
The excessive use of synthetic chemical pesticides for the management of insect pests in agriculture has led to serious environmental, ecological, and health concerns, necessitating the exploration of eco-friendly alternatives. The present investigation evaluated the bioefficacy of three botanical pesticides—Azadirachta indica (neem), Pongamia glabra (karanja), and Adhatoda vasica (adhatoda)—against major insect pests of sugarcane, including termites (Odontotermes obesus), shoot borers (Chilo infuscatellus), orthopterans, and coleopteran pests. Field and laboratory experiments were conducted in Sultanpur district, Uttar Pradesh, using a randomized block design with botanical treatments applied as foliar sprays and soil drenches at different concentrations, along with a synthetic chemical pesticide and an untreated control for comparison. Observations were recorded on pest infestation, mortality, repellency, growth, and reproductive parameters. The results showed that all botanical treatments significantly reduced pest infestation compared to the control. Azadirachta indica extract and the combined botanical formulation exhibited the highest efficacy among all, achieving 70–72% reduction in termite infestation and 69–70% reduction in shoot borer incidence, values comparable to the synthetic pesticide. Mortality and repellency assays further confirmed the strong insecticidal and deterrent effects of neem and combined botanical treatments, with mortality exceeding 75% after 72 hours of exposure. The study demonstrates that botanical pesticides, particularly neem-based and combined formulations, possess strong potential as sustainable, biodegradable, and environmentally safe alternatives to chemical pesticides and can be effectively integrated into integrated pest management (IPM) strategies for sugarcane cultivation.
Pesticides, Azadirachta indica, Pongamia glabra, Adhatoda vasica, Termite, Biodegradable.
Copyright (c) 2025 Journal of Drug Discovery and Health Sciences
Evaluation of Antidiabetic Efficacy Herbal Ethanolic Extracts inAlloxan-Induced Diabetes Rats
Diabetes is traditionally treated with the huge climber plant Bauhinia vahlii Wight & Arn. The evaluated of antidiabetic effect of Bauhinia vahlii ethanolic extracts leaves and stem at doses of 200 mg/kg and 400 mg/kg using an experimental model by alloxan-induced diabetes in albino rats. The acute toxicity tests revealed that the extracts were not harmful, as there were no deaths at the dosages 2000 mg/kg. The groups were divided into herbal extracts doses, standard drug Glibenclamide at 5 mg/kg, diabetic control group, and normal control group. Blood sugar concentrations were regular monitor and measured for 21st days, and the levels of glucose in the blood after fasting were measured on days 0, 7, 14, and 21st in each of the animal groups. The remarkable antidiabetic effects significant (????<0.001) at dose of 400 mg/kg of Bauhinia vahlii leaves extract comparable to glibenclamide diabetes control. This beneficial effect is probably due to the existence of flavonoids and phenolic substances, which might enhance the release of insulin.
Bauhinia vahlii, Antidiabetic activity and Alloxan induced diabetes.
To study the phytochemical constituent of Murraya koenigii leaves
Introduction: Murraya koenigii (curry leaves) is a widely used medicinal plant known for its traditional applications in treating various ailments due to its rich phytochemical composition. Understanding the qualitative and quantitative presence of its bioactive compounds can provide scientific validation for its ethnomedicinal use. Aim: The present study aimed to perform a comprehensive phytochemical investigation of Murraya koenigii leaves to identify and quantify the major secondary metabolites responsible for its pharmacological activities. Materials and Methods: Qualitative phytochemical screening was conducted using standard chemical tests to detect the presence of alkaloids, flavonoids, tannins, saponins, glycosides, phenolics, terpenoids, and steroids. Quantitative analysis involved spectrophotometric and gravimetric methods to estimate the Total Phenolic Content (TPC), Total Flavonoid Content (TFC), and Total Alkaloid Content. Results: Qualitative analysis confirmed the presence of all tested phytoconstituents, with distinctive color changes and precipitate formation supporting their identification. Quantitatively, TPC was found to be as high as 133.59 mg GAE/g dry weight (DW), indicating potent antioxidant potential. TFC was reported at 1.24% (w/w), reflecting significant flavonoid concentration, and Total Alkaloid Content was measured at 23.73%, suggesting strong pharmacological activity. Conclusion: The study confirms that Murraya koenigii leaves are a rich source of diverse bioactive secondary metabolites. These findings scientifically validate the plant’s traditional therapeutic use and highlight its potential for future development of herbal formulations or pharmaceutical applications. Further studies focusing on the isolation and characterization of individual compounds are warranted to explore their specific mechanisms of action and therapeutic efficacy.
Analytical methods, Antioxidants, Anti-inflammatory, Extraction method, Flavonoids, Antidiabetic, etc.
Ethical and Regulatory Consideration of Dynamic Receptor inPharmacology
The foundation of contemporary pharmacotherapeutics is the study of the relationship between medications and cellular receptors, or receptor pharmacology. In order to guide responsible research and application, ethical and regulatory considerations have grown in importance as scientific discoveries reveal complex receptor processes and new therapeutic targets. A significant addition to the recent studies explores the fundamental frameworks governing translational pharmacology and receptor-based drug development. The ethical paradigms pertaining to receptor research are thoroughly focussed on data integrity, animal welfare, informed consent, and clinical trial transparency. The ethical quandaries that frequently emerge when novel receptor targets include central nervous system receptors, genetic alterations, or customized medical techniques are brought to light. In accordance with international ethical standards, preclinical receptor studies employing animal models concentrate particular emphasis on the 3Rs (Replacement, Reduction, and Refinement). The chapter examines regional and global regulatory frameworks that govern the development of receptor-targeted drugs, such as those established by the FDA, EMA, ICH, and CDSCO. Good Laboratory Practices (GLP), Good Clinical Practices (GCP), and the changing regulatory environment surrounding biosimilars, monoclonal antibodies, and receptor-based gene treatments are all highlighted. This chapter provides young researchers and students with the necessary information to successfully negotiate the moral and legal terrain of receptor pharmacology, bridging the gap between theoretical pharmacology and regulatory compliance. This work encourages a culture of responsible innovation and generates safe, efficient, and socially responsible medicinal discoveries by fusing ethical reasoning with regulatory requirements.
Pharmacotherapeutics, Receptor based delivery, Reverse Pharmacology, Biosimilars, Monoclonal antibodies, etc.
Bioprinting and Organ-on-a-Chip Models: A Revolution in Preclinical Drug Testing
Preclinical drug development currently relies heavily on animal models and simplistic two-dimensional (2D) cell cultures, which often fail to predict human physiological responses, leading to high attrition rates in clinical trials. This review explores the transformative potential of advanced in vitro models, specifically 3D bioprinting and organ-on-a-chip (OoC) technologies, to bridge the gap between bench research and clinical application. Bioprinting enables the fabrication of complex, multi-cellular tissue constructs with precise architectural control, mimicking native tissue organization. Organ-on-a-chip platforms utilize microfluidics to recapitulate the dynamic mechanical and biochemical microenvironments essential for organ function. Literature analysis reveals that these technologies significantly enhance the physiological relevance of preclinical screening for drug efficacy and toxicity. Bioprinted tumor models offer superior platforms for anticancer drug testing, while OoC systems excel in modeling pharmacokinetic profiles (ADME) and complex organ-level pathologies. The integration of patient-derived cells with these platforms further enables personalized medicine approaches. While challenges such as vascularization, scalability, and regulatory standardization persist, the convergence of bioprinting and OoC technology promises to drastically reduce reliance on animal testing, lower drug development costs, and improve clinical trial success rates.
Engineering the Islet: A Renaissance in 3D Bioprinting for Beta Cell Regeneration
Diabetes is driven by the impairment or destruction of the pancreas' insulin-producing beta cells a significant global health burden. That burden is primarily due to the limitations of our current treatments: mainly insulin therapy and transplanting pancreatic islets. Those options are restricted by factors like donor shortages, the risk of the immune system rejecting the transplant and the fact they don't fully restore the body's natural glucose-regulating mechanisms. As a result, researchers are turning to three-dimensional bioprinting technology to generate those beta cells from scratch. This review looks at the building blocks of 3D bioprinting, the materials and techniques being used to create pancreatic beta cells—and where we stand on that research. We also examine the potential for those engineered beta cells to be used in future treatments for diabetes. We discuss the ongoing challenges and the research needed to overcome them. The key to that will be collaboration across disciplines. By combining advanced bioprinting methods with regenerative medicine, we may be able to fundamentally change how we treat diabetes mellitus.
3-D bioprinting, bioink, islet of pancreas, Type-1 Diabetes (T1D), Type-2 Diabetes (T2D), tissue regeneration, engineered beta cell
Formulation, Optimization and Evaluation of Indigoferaaspalathoides extract-loaded silver nanogel for wound healingapplications: A QBD-based approach
This study presents the formulation and optimization of a silver nanogel incorporating Indigofera aspalathoides extract (IAE) for potential wound healing applications. The nanogel was synthesized using the precipitation method, maintaining constant amounts of IAE and aqueous solution while varying the carbopol concentration (1–2% w/v) as the gelling agent. A Central Composite Rotatable Design was employed to optimize the formulation, followed by comprehensive physicochemical characterization. The nanogel's in vitro drug release profile, zeta potential, polydispersity index (PDI), particle size, viscosity, pH, homogeneity, and stability studies were conducted. The optimized formulation exhibited a yellowish-brown appearance, with a pH of 5.8 ± 0.2, viscosity of 9412 cP, and a particle size of 98 nm with a PDI of 0.204, indicating uniform dispersion. The zeta potential was measured at –22 mV, suggesting moderate stability. In vitro release studies showed a cumulative drug release of 86.28% over 8 hours and 98.06 % over 24 hrs. Scanning Electron Microscopy confirmed the spherical morphology of the nanoparticles. These results indicate that the IAE-loaded silver nanogel demonstrates favorable physicochemical properties and a controlled drug release profile, suggesting its potential as an effective topical delivery system for wound healing. Future studies should focus on in vivo evaluations, long-term stability, and clinical trials to further validate its therapeutic efficacy and safety.
Impact of Telemedicine on Patient Care in India
Telemedicine has transformed healthcare delivery in India, addressing challenges of accessibility, cost, and quality in la diverse and densely populated nation. This review examines telemedicine’s impact on patient care, focusing on its role in enhancing access, improving health outcomes, reducing costs, and overcoming barriers in India. Drawing from peer-reviewed studies and practitioner insights, the article highlights applications in chronic disease management, mental health, maternal care, and infectious diseases, with examples like eSanjeevani and SCARF telepsychiatry programs. Benefits include reduced travel costs, improved care continuity, and increased access for rural populations. However, challenges such as digital literacy, internet connectivity, and regulatory gaps persist. Practitioner opinions underscore telemedicine’s potential to reduce COVID-19 risks and streamline workflows, though concerns about legal liabilities and diagnostic accuracy remain. Two figures illustrate telemedicine workflows and adoption trends, and a table summarizes key Indian applications. The review advocates for policy reforms, infrastructure improvements, and culturally tailored solutions to ensure equitable telemedicine access. Future research should focus on cost-effectiveness and long-term outcomes to strengthen telemedicine’s role in India’s healthcare system.
A Comprehensive Review of Pharmacognostic Techniques for theIdentification and Standardization of Herbal Drugs
The global resurgence in the use of herbal medicines underscores the need for rigorous scientific validation and standardization of plant-based products. Pharmacognosy, a critical discipline within pharmaceutical sciences, offers a systematic framework for the identification, authentication, and quality assessment of medicinal plants through morphological, anatomical, physicochemical, and phytochemical analyses. This review provides a comprehensive overview of classical and modern pharmacognostic techniques used in the standardization of herbal drugs. Initial macroscopic and organoleptic evaluations, followed by detailed microscopic analysis including powder and tissue microscopy, are foundational in determining authenticity. Physicochemical parameters such as ash values, extractive values, and moisture content play a vital role in detecting adulteration and ensuring quality. Advanced techniques such as chromatography (TLC, HPTLC, HPLC), spectroscopy (UV-Vis, FTIR, NMR), and DNA barcoding have significantly enhanced the precision of plant identification and metabolite profiling. The integration of molecular tools with traditional pharmacognostic practices further strengthens the quality control process, facilitating the development of standardized herbal formulations. Challenges such as regional variability, environmental influences, and lack of uniform global standards persist, yet emerging technologies like artificial intelligence, metabolomics, and portable diagnostic tools offer promising solutions. This review emphasizes the importance of combining multidisciplinary approaches to ensure the safety, efficacy, and reproducibility of herbal medicines, promoting their acceptance in mainstream healthcare systems worldwide.
Determination of Lethal Concentrations of Profenofos and Azadirachtin on Labeo Rohita and Analysis of Electrolyte Imbalance Following Sub-Lethal Exposure
This study explores the acute and sub-lethal toxic effects of two commonly used pesticides—Profenofos (an organophosphate) and Azadirachtin (a biopesticide)—on the freshwater fish Labeo rohita. The 96-hour LC₅₀ values were determined through controlled laboratory exposures, revealing Profenofos to be suggestively more toxic (0.08 mg/L) than Azadirachtin (50.0 mg/L). Sub-lethal exposures at 50% of LC₅₀ concentrations over 96 hours and 28 days were shown to assess their effect on fish electrolyte balance. Measurements of sodium, potassium, and calcium levels indicated a progressive decline, with Profenofos causing more severe disturbances compared to Azadirachtin. These findings suggest that even low concentrations of these pesticides can harm osmoregulation and physiological stability in aquatic organisms. The study highlights the ecological risks associated with pesticide contamination in aquatic ecosystems and the need for regulatory analysis on pesticide usage near freshwater bodies.
Profenofos, Azadirachtin, sub-lethal toxicity, electrolyte imbalance, pesticide pollution, aquatic toxicology.
Therapeutic Implication of the Transdermal Patches in Treatment of the Chronic Diseases
The evolving landscape of drug delivery systems has ushered in innovative approaches that transcend the limitations of conventional routes. Among these, transdermal drug delivery systems (TDDS) have emerged as a promising platform, offering a non-invasive and patient-compliant alternative for the management of chronic diseases. This review aims to explore the therapeutic implications of transdermal patches, particularly focusing on their role in ensuring sustained drug release, improving pharmacokinetics, and enhancing therapeutic efficacy. The primary objective of this review is to critically evaluate the design, types, mechanisms, and clinical applications of transdermal patches in the treatment of long-term conditions such as hypertension, diabetes, cardiovascular disorders, hormonal imbalances, and chronic pain. The purpose is to provide a comprehensive understanding of how these systems minimize first-pass metabolism, reduce dosing frequency, and improve patient adherence—factors crucial in managing chronic illnesses that require consistent therapeutic levels over prolonged durations. Recent advancements such as microneedle-enhanced delivery, iontophoresis, and nanoparticle-incorporated patches have significantly broadened the scope of TDDS, enabling the delivery of large molecules, peptides, and biologics. This review also discusses the selection criteria for polymers and permeation enhancers, challenges in skin permeability, and regulatory considerations involved in the development of transdermal systems. TDDS represent a transformative strategy in chronic disease management by offering controlled, targeted, and safe drug delivery. Their growing relevance in personalized medicine and potential integration with smart wearable technologies make them a focal point in future pharmaceutical innovation and clinical practice.
Transdermal patches, Therapeutic efficacy, Pharmacokinetics, Pharmacodynamics, Personalized medicines, Chronic disease etc.
Smart Polymers in Controlled Drug Release: Mechanisms and Clinical Applications
Smart polymers, also referred to as stimuli-responsive polymers, represent a transformative class of materials in the field of drug delivery. These polymers have the unique ability to undergo reversible or irreversible physical and chemical changes in response to external or internal stimuli such as pH, temperature, light, enzymes, and redox conditions. Their adaptability enables precise temporal and spatial control over drug release, which is especially advantageous in targeting diseased tissues while minimizing systemic side effects. Controlled drug delivery systems based on smart polymers offer significant improvements over conventional approaches by enhancing bioavailability, prolonging circulation time, and providing site-specific action. Recent advances in polymer synthesis, nanotechnology, and biomedical engineering have led to the development of sophisticated delivery platforms such as hydrogels, micelles, dendrimers, and nanogels that respond to biological uses. This review discusses the classification, mechanisms of action, and architectural diversity of smart polymers, as well as their clinical applications in oncology, diabetes, infection control, and neurological diseases. Current challenges and future perspectives in clinical translation and regulatory approval are also addressed, highlighting the potential of smart polymers to revolutionize personalized drug delivery strategies.