Biopress Journal of Advanced Pharmacology (BJAP)
Biopress Journal of Advanced Pharmacology (BJAP) is a leading peer-reviewed, open-access journal published under the prestigious Biopress Journals series, dedicated to advancing cutting-edge research across all domains of pharmacological sciences. As an international platform, we bring together researchers, clinical pharmacologists, pharmaceutical scientists, and healthcare professionals to share transformative discoveries that bridge fundamental research with therapeutic applications.
Role of Vetiveria Zizanioides Linn. In The Treatment of Hepatotoxicity: A Review
Hepatotoxicity, a prevalent form of liver injury caused by xenobiotics, pharmaceuticals, and environmental pollutants, necessitates effective therapeutic interventions. Vetiveria zizanioides Linn., an aromatic grass widely used in traditional medicine systems like Ayurveda, has garnered attention for its potential hepatoprotective properties. This review consolidates and analyzes scientific evidence from peer-reviewed studies to elucidate the therapeutic role of Vetiveria zizanioides in managing hepatotoxicity. A systematic literature search across databases including PubMed, Scopus, and Web of Science, covering publications from 2000 to 2023, identified in vitro, in vivo, and limited clinical studies. The analysis highlights that bioactive constituents such as vetiverol, khusimol, and essential oils from vetiver roots exert hepatoprotective effects by mitigating oxidative stress, reducing lipid peroxidation, and elevating antioxidant enzyme activities like superoxide dismutase and catalase. Preclinical models exposed to hepatotoxins such as carbon tetrachloride, paracetamol, and heavy metals show significant reductions in elevated liver enzymes (e.g., alanine aminotransferase, aspartate aminotransferase) and improved histopathological profiles. Mechanistic insights point to the modulation of inflammatory pathways, inhibition of cytochrome P450-mediated toxin activation, and enhancement of cellular detoxification processes. Despite these promising outcomes, gaps persist in standardized dosing, long-term safety, and human trials. This review advocates for Vetiveria zizanioides as a viable natural adjunct in hepatotoxicity treatment, emphasizing the need for further clinical validation and phytochemical standardization to harness its full therapeutic potential.
Green Remedies for the Mind: Translation Potential of Medicinal Plants in Future Mental Healthcare
Stress and anxiety are becoming more common mental health issues in modern culture, owing primarily to lifestyle changes, work pressure, and social barriers. Conventional pharmaceutical therapies are effective, but they are frequently linked with side effects, dependence, and expensive costs. In this context, medicinal plants present a potential, safer, and more cost-effective option for stress and anxiety relief. Various ancient medical systems, such as Ayurveda, Unani, and ancient Chinese Medicine, have long used plant-based therapies to promote mental health. Several medicinal plants, including Withania somnifera (Ashwagandha), Bacopa monnieri (Brahmi), Ocimum sanctum (Tulsi), Valeriana officinalis, and Passiflora incarnata, have been shown to have anxiolytic and stress-relieving properties through mechanisms involving neurotransmitter modulation, antioxidant activity, and hypothalamic-pituitary-adrenal (HPA) axis regulation. This article presents an overview of medicinal plants used to treat stress and anxiety, including their active elements, pharmacological activities, and scientific data supporting their efficacy. The increased interest in green therapies demonstrates their potential to improve mental health with few side effects.
AI-Driven Reverse Pharmacology & Accelerated Drug Repurposing Using Ancient Indian Manuscripts – A Comprehensive Review
This paper presents a comprehensive literature review on AI-driven drug repurposing, contrasting traditional pharmacological methods with advanced AI-based approaches to elucidate their respective strengths and limitations. It systematically explores key computational techniques—such as network pharmacology, molecular docking, and deep learning—and their role in identifying drug-target interactions and predicting novel therapeutic indications. Special emphasis is placed on the digitalization and AI validation of traditional medical knowledge, particularly reverse pharmacology’s “bedside-to-bench” paradigm, which transforms historical clinical narratives into testable hypotheses. Through analysis of prominent case studies, the paper highlights both the transformative potential of AI integration in accelerating drug repurposing and the persistent challenges related to data heterogeneity and regulatory complexities. The vision of this review is to establish a holistic framework that synergizes computational innovations with ancestral wisdom, positioning AI-driven reverse pharmacology as a paradigm shift with the capacity to revolutionize drug discovery and therapeutic innovation. The mission is to provide critical insights and guide future research toward overcoming existing barriers, thereby enhancing the effectiveness and scope of medication repurposing strategies.
Evaluating the Impact of AI on Drug Discovery and Clinical Transition
Artificial intelligence (AI) is reshaping the medicament production by transfigure drug realization, optimizing evolution pipelines, and amplify clinical transitions. Traditional drug discovery is hindered by lofty attrition rates, long schedule, and escalating costs, and ameliorate clinical success rates, whereas AI-driven approaches leverage machine learning, deep neural matrix, and generative models to analyse vast abstracts, envision atomic properties, and design novel admixtures with unprecedented efficiency. These capabilities compress discovery cycles from years to months and intensify patient stratification and reconciling trial designs, contributing to up to 85% advancement rates in Phase I–III trials. AI’s integration into translational medicine bridges the gap between laboratory fact-finding and clinical application, facilitating biomarker discovery and personalized treatment strategies. Despite its life-changing potential, provocation remain in data quality, model interpretability, and regulatory validation, necessitating hybrid human-AI approaches. Breakthroughs such as AlphaFold’s protein structure prediction and generative adversarial webbing for de novo drug design have demonstrated corporeal acceleration, reducing discovery cycles from years to months. AI further strengthens translational panacea by integrating real-world evidence, biomarker discovery, and patient stratification, thereby improving clinical trial triumph rates and advancing individualize therapeutics. Applications span target identification, virtual screening, ADMET prediction, drug repurposing, and trial optimization, with ventures like Insilico Medicine, Benevolent AI, and Exscientia successfully advancing AI-designed molecules into clinical juncture. Despite these advances, challenges remain in data quality, model interpretability, and regulatory substantiate, underscoring the need for hybrid human-AI approaches. This review explores AI’s multifaceted role across drug discovery, repurposing, optimization, and clinical trials, highlighting actuality applications and future directions in simply drug innovation. Overall, AI represents a transformative force in pharmaceuticals, bridging the gap between laboratory research and clinical practice, while promising faster, safer, and more cost-effective therapeutic innovations.
ER-Mitochondria Crosstalk in Mitophagy Failure: Implication for Neurodegenerative Disorders
The critical association between mitochondria-associated membranes (MAMs), which are endoplasmic reticulum (ER)-mitochondria contact sites, and mitophagy failure in neurodegenerative diseases is the subject of this review. Because of the high energy demands and restricted regeneration of neurons, they rely on specific mechanisms of mitochondrial quality control (MQC) including biogenesis, dynamics, and mitophagy to counteract ROS damage, bioenergetic deficits or collapse of proteostasis. By activating PINK1-Parkin-dependent ubiquitination of outer mitochondrial membrane proteins or receptor-mediated pathways, mitochondria degrades dysfunctional mitochondria through mitophagy, while its impairment damages toxic organelles, exacerbating diseases like PD, AD and amyotrophic lateral sclerosis (ALS). Additionally, MAMs function as active centers for Ca2+ mechanistic interactions. How does this explain the development of Parkinson's disease? Despite the maintenance of optimal 10-65 nm spacing by tethering proteins like MFN2, VAPB-PTPIP51, and PDZD8, disruptions such as MNF2 mutations in Charcot-MarieTooth disease or TDP-43 aggregates on ALS cause wide gaps, blunt signaling, (via PINK1/Parkin recruitment) or stall binding to LC3 adaptors. In this state, disease-specific insults converge as: - synuclein overloads MAM in PD, A/tau disrupts Ca2+ flux in AD, mutant SOD1/VAPB impairs contacts in the ALS, and mHTT blocks HD mitophagy blockade, ROS surges, inflammation (NLRP3 at MAFs), and neuronal death. By discussing mitophagy pathways, MAM architecture and crosstalk functions as well as failure mechanisms and disorder links, this synthesis concludes: "MAM stabilizers (e.g, the anti-tumor P-like pridopidine), with or without food coke, inducer[M] and ER stress modulators (4-PBA] are therapeutically effective in returning homeostasis to normal cells.". In the future, super-resolution imaging, CRISPR screens and biomarkers will be used for precision interventions balancing clearance with biogenesis.
Fisetin as a Neuroprotective Flavonoid in Alzheimer's Disease; Targeting Bdnf and TAU Pathways
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that causes cognitive decline, synaptic dysfunction, neuronal loss, and characteristic pathologies such as Tau hyperphosphorylation and neurofibrillary tangle formation. In addition to amyloid and Tau pathology, impaired brain-derived neurotrophic factor (BDNF) signaling, oxidative stress, and chronic neuroinflammation all play important roles in disease development. Current therapeutic approaches only provide symptomatic alleviation, emphasizing the critical need for diseasemodifying medicines capable of addressing numerous pathogenic pathways. Fisetin, a naturally occurring dietary flavonoid, has received increased interest due to its extensive neuroprotective potential in AD. Preclinical investigations show that fisetin has antioxidant and anti-inflammatory properties while also improving neuronal survival and synaptic plasticity. Fisetin regulates BDNF signaling by increasing BDNF expression and activating downstream pathways such as Trk-B, CREB, PI3K/Akt, and MAPK, so promoting learning and memory processes. Fisetin reduces Tau pathology by suppressing hyperphosphorylation and aggregation. It does this by regulating important kinases such glycogen synthase kinase-3β (GSK-3β), which helps stabilize microtubules and maintain neuronal integrity. Despite convincing preclinical evidence, the clinical translation of fisetin is hampered by issues such as inadequate bioavailability, insufficient human research, and a lack of knowledge of its pharmacokinetics. Future research should focus on enhancing fisetin delivery using innovative formulations, verifying its efficacy in clinical trials, and investigating synergistic effects with existing AD medications. Fisetin stands out as a promising multitarget option for Alzheimer's disease prevention and treatment since it targets both BDNF-mediated neurotrophic support and Tau-associated neurodegenerative pathways.
Predictive Modeling Analysis to Predict Complications after Laparotomy: A PRISMA-Based Systematic Review
Background Postoperative complications following laparotomy remain a major contributor to surgical morbidity, mortality, prolonged hospitalization, and increased healthcare burden. Traditional perioperative risk assessment tools demonstrate limited predictive accuracy at the individual patient level, prompting increasing interest in predictive modeling approaches, including machine learning–based techniques, to enhance risk stratification and clinical decision-making. Objective To systematically evaluate predictive modeling approaches used to forecast postoperative complications after laparotomy and to analyze their methodological characteristics, predictive performance, and clinical applicability. Methods A PRISMA-based systematic review was conducted to identify studies evaluating predictive models developed to estimate postoperative complications following laparotomy or major abdominal surgery. Electronic databases were systematically searched for relevant studies published within the defined time frame. Eligible studies included those employing traditional statistical models or machine learning–based algorithms for postoperative risk prediction. Data extraction focused on study design, patient population, predictors used, modeling techniques, performance metrics, validation methods, and reported clinical outcomes. Risk of bias and methodological quality were evaluated in accordance with established systematic review and prediction modeling reporting standards. Results The included studies demonstrated increasing utilization of machine learning–based approaches alongside traditional regression models for predicting postoperative complications. Predictive performance across studies showed moderate to good discrimination, commonly reported using AUROC values ranging from approximately 0.70 to 0.85. Models incorporating dynamic perioperative variables including intraoperative parameters and early postoperative physiological data demonstrated improved predictive performance compared with models relying solely on static preoperative predictors. However, external validation remained limited, and calibration reporting was inconsistent. Conclusions Predictive modeling represents a promising strategy for improving early identification of high-risk patients undergoing laparotomy. Machine learning approaches offer potential advantages in handling complex perioperative data; however, their superiority over traditional statistical models appears context-dependent. Greater emphasis on external validation, methodological transparency, and integration into clinical decision-support systems is essential for translating predictive models into routine surgical practice.
Artificial Intelligence–Driven Molecular Docking for Selection of Antibacterial Agents in Periodontitis Therapy: From Pathophysiology to Target Validation
Periodontitis is a chronic inflammatory disease that leads to destruction of the supporting structures of teeth and eventually tooth loss. The disease is initiated by pathogenic bacteria and modified by host immune responses. Although mechanical cleaning remains the foundation of therapy, antibiotics are often required in moderate to severe cases. However, empirical drug selection, increasing resistance, and variable patient response limit treatment success. Artificial intelligence (AI) and molecular docking have recently emerged as powerful tools for improving precision in antimicrobial selection. These technologies allow prediction of drug–target interactions, screening of potential molecules, and optimization of therapy before clinical use. This review discusses the pathophysiology of periodontitis, the role of antibiotics such as minocycline and clindamycin, and the growing importance of AI-assisted molecular docking in periodontal drug discovery and validation.
Green Nanomedicine & Drug Delivery: Emerging Role of Nanoparticles in Targeted Therapy
In contemporary drug delivery methods, green nanomedicine has become a viable and biocompatible strategy. By integrating concepts of green chemistry with nanotechnology, ecofriendly nanoparticles are manufactured using biological sources such as plants, microbes, and biopolymers. These green-synthesized nanoparticles exhibit higher therapeutic efficacy, lower toxicity, and improved targeting ability compared to traditional nanocarriers. This study covers the concept of green nanomedicine, types of nanoparticles employed in drug delivery, their involvement in targeted therapy, analytical characterisation techniques, therapeutic uses, associated obstacles, and future possibilities in clinical translation.
A Comprehensive Review on Berberis Aristata DC: Phytochemistry, Pharmacology, and Therapeutic Potential
Berberis aristata DC, commonly known as Indian barberry or Daruharidra, is a medicinal plant extensively used in traditional systems of medicine such as Ayurveda, Siddha, and Unani. The plant is particularly valued for its rich content of isoquinoline alkaloids, of which berberine is the most prominent and pharmacologically active constituent. In recent years, scientific research has increasingly focused on berberine and Berberis aristata due to their wide spectrum of pharmacological activities, including antidiabetic, hypolipidemic, antimicrobial, antiinflammatory, hepatoprotective, neuroprotective, and anticancer effects. This literature review aims to provide a comprehensive and systematic evaluation of the botanical characteristics, phytochemical composition, pharmacological properties, mechanisms of action, clinical evidence, safety profile, and future prospects of Berberis aristata, with particular emphasis on berberine. Although berberine demonstrates significant therapeutic potential across multiple disease conditions, its clinical application is limited by poor oral bioavailability and pharmacokinetic challenges. Recent advancements in formulation strategies and drug delivery systems aimed at enhancing berberine absorption are also discussed. Overall, Berberis aristata represents a promising medicinal plant with potential applications in modern pharmacotherapy; however, further well-designed clinical studies and standardization efforts are required to establish its efficacy and safety conclusively.
Ginkgo Biloba’s Neuroprotective Potential in Alzheimer’sDementia
Alzheimer's disease (AD), which is characterized by increasing cognitive impairment and neuroinflammation, is a growing global health concern. Ginkgo biloba extract (standardized as EGb 761) has attracted a lot of interest among botanical therapies because to its many neuroprotective qualities. The main reason for its effectiveness is the combination of terpene lactones and flavonoid glycosides, which work in concert to tackle the underlying pathophysiology of dementia via multiple different pathways. Ginkgo biloba's strong antioxidant capability is the primary basis of its neuroprotective potential. The extract reduces the oxidative stress that usually precedes neuronal death in AD patients by scavenging reactive oxygen species (ROS) and increasing the activity of superoxide dismutase. Additionally, Ginkgo biloba exhibits a special capacity to maintain mitochondrial integrity, which stabilizes cellular energy production and prevents pro-apoptotic signals from being activated. In addition to protecting cells, the extract modifies the amyloid cascade by preventing peptides from aggregating into harmful oligomers, hence lowering the "plaque load" that interferes with synaptic transmission. According to clinical studies, ginkgo biloba is especially useful for stabilizing or delaying the deterioration of cognitive abilities and enhancing the ability of people with mild-to-moderate dementia to perform activities of daily living (ADL). The clinical data is still a little inconsistent because of variations in extract concentrations and patient demographics, even though it also improves cerebral microcirculation and lowers blood viscosity, offering a secondary vascular advantage. Ginkgo biloba ultimately functions as a clinically useful, well-tolerated adjunct therapy that tackles the complexity of neurodegeneration through a multi-target pharmacological approach, even though it is not a disease-modifying solution.
Neurobehavioral Consequences of Circadian Rhythm Disturbances: Insights into Mood Disorders and Cognitive Decline
Circadian rhythms are endogenous biological oscillations with an approximately 24-hour periodicity that synchronize behavioural and physiological processes with the environmental light–dark cycle. In humans, the circadian timing system regulates sleep-wake behaviour, hormonal secretion, neurotransmission, and higher cognitive functions. This system is hierarchically organised, with the suprachiasmatic nucleus (SCN) acting as the central pacemaker and coordinating peripheral clocks through transcription-translation feedback loops involving core clock genes such as CLOCK, BMAL1, PER, and CRY. Disruption of circadian rhythms, driven by light exposure, shift work, irregular sleep schedules, aging, and chronic stress, has emerged as a major contributor to neurobehavioral dysfunction. Growing evidence links circadian misalignment to mood disorders, including depression, bipolar disorder, and anxiety, as well as to cognitive decline. Mechanistically, circadian disruption impairs sleep-dependent memory consolidation, alters synaptic plasticity, dysregulates neuroendocrine pathways such as the hypothalamic-pituitary–adrenal axis, and disturbs the rhythmic release of neurotransmitters including serotonin, dopamine, glutamate, and GABA. At the molecular level, clock gene dysregulation, neuroinflammation, mitochondrial dysfunction, oxidative stress, and reduced neurogenesis collectively compromise emotional regulation and cognitive resilience. Age-related weakening of circadian rhythmicity further increases vulnerability to mood instability and neurodegenerative processes. This review integrates molecular, neurochemical, behavioural, and clinical evidence linking circadian rhythm disturbances to mood disorders and cognitive decline, and highlights emerging chronotherapeutic, light-based, pharmacological, and lifestyle interventions aimed at restoring circadian alignment and promoting brain health.
Whole-Exome Sequencing in Cancer Chemoprevention: Tracking Mutational Trajectories in High-Risk Populations
Whole-exome sequencing (WES) has revolutionized understanding of cancer chemoprevention by enabling comprehensive characterization of mutational landscapes in high-risk populations and monitoring of clonal evolution during prevention interventions. This review synthesizes current knowledge on WES applications in cancer prevention, emphasizing tracking of mutational trajectories from normal epithelium through dysplasia to invasive carcinoma. We examine discovery of driver mutations and mutational burden as chemoprevention biomarkers, computational approaches for inferring genetic progression (PhylogicNDT), and longitudinal monitoring of clonal evolution using circulating cell-free DNA in prevention trials. Particular emphasis is placed on oral squamous cell carcinoma (OSCC) and oral epithelial dysplasia (OED) as model systems where WES has identified key early driver mutations (TP53, NOTCH1, PIK3CA, CDKN2A) enabling molecular stratification of malignant transformation risk and personalization of prevention strategies. The review discusses universal cancer screening with WES identifying occult cancer predisposition syndromes, prospectively validated genetic progression models (gMART) predicting dysplasia-to-carcinoma risk, and emerging single-cell sequencing approaches revealing subclonal heterogeneity driving prevention failures. Integration of WES with spatial transcriptomics and single-cell RNA-seq provides unprecedented mechanistic insight into how dysplastic lesions acquire transformative mutations, enabling identification of optimal intervention windows during clonal evolution. Clinical applications including window-of-opportunity trials, real-time ctDNA monitoring of chemopreventive efficacy, and evolution-informed adaptive treatment strategies are discussed. We address critical challenges including tumor heterogeneity interpretation, distinguishing driver from passenger mutations in premalignant lesions, clinical translation of discovery findings, and cost-effectiveness of universal WES screening in prevention cohorts.
Clonal Evolution and Genomic Stability as Endpoints of Chemoprevention: Evidence from WES Studies
Cancer chemoprevention has historically relied on clinical endpoints (tumor incidence, survival) as measures of efficacy, requiring decades of follow-up and large patient cohorts. Whole-exome sequencing-enabled molecular monitoring of clonal evolution and genomic stability now offers mechanistic alternatives enabling real-time assessment of whether chemopreventive agents successfully suppress high-risk clones or inadvertently select for resistant populations. This comprehensive review synthesizes evidence that clonal evolution dynamics measured through circulating cell-free DNA (cfDNA) monitoring, subclonal architecture reconstruction, and variant allele frequency changes represent sensitive, biologically-meaningful endpoints of chemoprevention efficacy. We examine how WES reveals whether prevention agents achieve true elimination of transformative clones versus mere delay of inevitable progression, a critical distinction with different clinical implications. Suppression of high-risk clones carrying driver mutations (TP53, NOTCH1, PIK3CA) is discussed alongside mechanisms including genomic instability enhancement, immune checkpoint activation, and senescence induction. Integration of clonal evolution endpoints with genomic stability assessment quantified through measures including mutational burden dynamics, copy number variation stability, and DNA repair gene expression provides comprehensive evaluation of chemoprevention-induced evolutionary constraints. Case studies demonstrate successful clonal suppression in chronic lymphocytic leukemia under targeted therapy, osimertinib-resistant lung cancer clones under combination approaches, and emerging oral dysplasia suppression through immune checkpoint blockade. Critical challenges including distinguishing adaptive clonal responses from true suppression, heterogeneity in clonal fitness landscapes, and translating molecular suppression to clinical benefit are addressed. The review discusses adaptive trial designs leveraging clonal evolution endpoints, mathematical modeling of clonal evolutionary trajectories, and future integration with spatial transcriptomics and single-cell technologies enabling unprecedented resolution of prevention mechanisms.
Mutation Signatures and Genome Protection: Applications of WES in Chemoprevention Trials
Whole-exome sequencing (WES) has emerged as a transformative technology for understanding cancer initiation and progression through the lens of mutational signatures characteristic patterns of somatic mutations imprinted on cancer genomes by underlying mutagenic processes. This review synthesizes current knowledge on the application of WES-derived mutation signatures as biomarkers and endpoints in cancer chemoprevention trials, with emphasis on genomic approaches to preventing malignant transformation. We examine mechanistic underpinnings of major carcinogen-specific signatures (tobacco, ultraviolet, alcohol, aging), discuss technical considerations for implementing WES as a clinical platform, and outline strategies for integrating mutation signature analysis into adaptive trial designs. Particular focus is given to oral squamous cell carcinoma (OSCC) prevention, where loss of heterozygosity (LOH) profiling and emerging somatic mutation burden metrics offer unprecedented opportunities for patient enrichment and molecular monitoring. We review the paradigm shift from histologic endpoints (intraepithelial neoplasia) to genomic endpoints (tumor mutational burden, signature composition, clonal evolution), and discuss how mutation signatures can guide personalized chemoprevention through synthetic lethal approaches targeting DNA damage response deficiencies. The integration of multi-omic biomarkers, liquid biopsy, and adaptive trial designs promises to accelerate the clinical translation of genome protection strategies, ultimately improving cancer prevention outcomes in high-risk populations.
Integrating Whole-Exome Sequencing with Clinical and Histopathological Endpoints in Chemoprevention Studies: A Framework for Mechanistically Informed Prevention Trials
The integration of whole-exome sequencing (WES) with classical clinical and histopathological endpoints represents a paradigm shift in cancer chemoprevention trial design, enabling mechanistically informed patient stratification and molecular monitoring of intervention efficacy. This review examines the state-of-the-art in multi-endpoint chemoprevention studies, with emphasis on (1) correlating somatic mutations detected via WES with histopathological dysplasia grade and regression, (2) designing chemoprevention trials with composite endpoints combining molecular, histological, and clinical measures, and (3) strengthening causal inference regarding biomarker-guided interventions through mediation analysis, reverse validation, and mechanistic studies. We synthesize evidence from landmark studies including the Oral Cancer Prediction Longitudinal Study (LOH validation), Erlotinib Prevention of Oral Cancer (EPOC) trial, Mallery freeze-dried black raspberry trial, and emerging protocols such as the SAVER (Sodium Valproate for Epithelial Dysplasia) trial. Key challenges addressed include the dysplasia paradox (wherein histology is a poor independent predictor of progression), the surrogate endpoint paradox (wherein biomarker improvement does not guarantee clinical benefit), intratumor heterogeneity, field cancerization, and technical standardization of WES for clinical-grade implementation. We provide a practical framework for integrating WES data with endpoints, discuss FDA biomarker qualification pathways, and outline future directions including multi-omic integration, liquid biopsy-based monitoring, and artificial intelligence-driven discovery of progression biomarkers.
Challenges, Ethics, and Cost-Effectiveness of Using Whole Exome Sequencing for Chemoprevention Assessment
Whole-exome sequencing (WES) has emerged as a powerful tool for enhancing cancer chemoprevention by enabling genomic risk stratification, pharmacogenomic tailoring, and mechanistic insight beyond traditional clinical risk models. This comprehensive review evaluates the feasibility, ethical considerations, regulatory landscape, and cost-effectiveness of implementing WES in cancer prevention settings. We synthesize evidence demonstrating that WES can identify high- and moderate-penetrance germline variants, inform polygenic risk assessment, and guide personalized chemoprevention and surveillance strategies, particularly in enriched high-risk populations. However, widespread adoption is constrained by substantial infrastructure requirements, challenges in defining evidence-based surveillance intervals, and unresolved ethical issues surrounding incidental findings, informed consent, genetic discrimination, and equity across diverse ancestries. Regulatory frameworks for WES-guided prevention remain heterogeneous and continue to evolve, while economic evaluations suggest context-dependent cost-effectiveness influenced by population risk, healthcare system structure, and downstream intervention costs. We propose an integrated, equity-centered implementation framework emphasizing standardized risk protocols, genetic counseling integration, transparent consent processes, and prospective evidence generation to responsibly advance WES-guided precision chemoprevention.
Omics-Based Biomarkers for Predicting Chemopreventive Response to Herbal Formulations
Cancer chemoprevention using herbal formulations represents a potentially valuable yet underutilized strategy for reducing cancer incidence and mortality. Despite substantial preclinical and epidemiological evidence supporting the anticancer potential of plant-derived compounds, clinical translation has been limited by pronounced inter-individual variability and the absence of validated biomarkers capable of predicting chemopreventive response. Recent advances in omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics, enable systems-level characterization of the complex, multi-target mechanisms that typify herbal agents and provide new opportunities for biomarker discovery. This review critically evaluates current evidence on omics-based biomarkers relevant to herbal cancer chemoprevention, emphasizing mechanistic insights, analytical approaches, and translational limitations. Molecular signatures associated with representative polyphenolic compounds, including curcumin, epigallocatechin gallate, resveratrol, quercetin, and emodin, are discussed across multiple omics layers. Emerging strategies for multi-omics integration, including network-based and machine-learning approaches, are examined alongside challenges related to reproducibility, standardization, and clinical generalizability. Key barriers to implementation, including formulation heterogeneity, limited prospective human studies, and gaps in regulatory qualification, are highlighted. Integration of omics technologies with evidence-based herbal medicine, often termed herbogenomics, may inform precision prevention strategies, contingent upon rigorous validation and harmonized regulatory frameworks.
Pharmacological Modulation of DNA Damage Response Pathway in Breast Cancer Therapy: A Comprehensive Review
Breast cancer remains a leading cause of cancer-related morbidity and mortality among women worldwide. A hallmark of cancer cells, genomic instability, often arises from defects in the DNA damage response (DDR) pathways—complex cellular networks that detect, signal, and repair DNA lesions. This comprehensive review explores the pharmacological modulation of key DDR pathways, including base excision repair (BER), nucleotide excision repair (NER), mismatch repair (MMR), homologous recombination (HR), and non-homologous end joining (NHEJ), in the context of breast cancer therapy. Central to DDR activation are damage sensors such as the MRN complex and PARP1, which recognize DNA lesions and recruit transducer kinases—ATM, ATR, to propagate damage signals and coordinate repair. The review also discusses mechanisms of therapeutic resistance, tumor heterogeneity, and the role of combination therapies to overcome these challenges. Furthermore, the importance of predictive biomarkers and personalized DDR profiling for optimizing treatment outcomes is highlighted. We discuss clinical study of DDR-targeting agents such as PARP inhibitors (e.g., Olaparib), mechanisms of resistance, and future directions in harnessing DDR for improved therapeutic outcomes in breast cancer patients.
The Role of Natural Products In Modern Pharmacology
The article focuses on the current approach to understanding pharmaceutical products effect.it describes a paradigm shift in views on pharmaceutical action in correlation to the processes occurring under pathology .The paper discusses the role of regulatory processes in the development of a disease and a recovery .it points out the necessity of disease registration in order to early start a therapy .the author has also analyzed problems of modern pharmacology paying attention at the inability of monitoring the drug product effect during the treatment process and limited awareness of primary pharmaceutical interrelations with biological substrate .it covers the issues of Therapeutical efficiency depending on body oxygen saturation .special attention is paid to the water medium of a body directly related to the development of pharmaceutical effects .the article emphasizes the challenges of pharmaceutical effect depending on a dosage ,structural –functional heterogeneity of tissues , consecution of pathological development ,choice of its diagnostics etc.it outlines the most important ways of increasing the pharmaceutical efficiency based on the primary pharmacological effect.
Myostatin Gene Editing Across Vertebrates, Comparative Insights from CRISPR, TALENs, and AAV-Mediated Strategies
Myostatin (MSTN/GDF-8) is a conserved negative regulator of skeletal muscle growth in vertebrates. Recent advances in genome-editing technologies, including CRISPR/Cas9, TALENs, and AAV-mediated approaches, have enabled precise MSTN knockout across mammals, fish, and avian species. MSTN inhibition consistently enhances muscle mass through hyperplasia and hypertrophy, elevates myogenic transcription factors such as MyoD and Myf5, and modulates metabolic pathways, including glucose homeostasis and adipose deposition. MSTN knockout also affects systemic physiology, including the gut–muscle axis, bone density, and neuromuscular function, revealing pleiotropic effects beyond muscle growth. Comparative studies in rabbits, pigs, channel catfish, zebrafish, chickens, and mice demonstrate species-specific outcomes, highlighting the importance of tailored strategies for therapeutic or agricultural applications. While MSTN-targeted interventions show promise for muscle-wasting disorders, sarcopenia, Duchenne muscular dystrophy, and cultured-meat production, challenges remain, including off-target effects, reproductive and skeletal trade-offs, and limited clinical translation. This review synthesizes recent research on MSTN knockout, emphasizing conserved mechanisms, phenotypic consequences, and translational potential, providing a framework for future studies aimed at safely exploiting MSTN inhibition to enhance muscle mass and metabolic health.
Human Respiratory Viruses: Mechanisms, Epidemiology, Morbidity, and Contemporary Challenges to Global Health
Among the most prevalent infectious pathogens in the globe, respiratory viruses generate an enormous number of chronic as well as acute illnesses. Multiple viruses may spread through the airways and cause illnesses that include deadly pneumonia and nasal collapse, to a small infection of the upper respiratory tract. These viruses include coronaviruses, rhinoviruses and adenoviruses, influenza-like viruses, and the syncytial virus of the respiratory tract (RSV). These viruses utilise strategies for entering host cells, spreading, and resisting the immune system, causing an assortment of clinical manifestations and results. The pathogenesis and infection processes of important human respiratory viruses are summarised in this article, together with their epidemiological traits and range of related diseases. The causes of illness and transmission mechanisms underlying important human viral respiratory infections have been outlined in the current piece, in conjunction with the epidemiological patterns and many associated ailments. It emphasises issues such as shifts in the environment and climate, difficulties with vaccinations, the misuse of medicines and antivirals, viral mutation, outbreaks and repetition of illnesses, and healthcare disparities. The study ends by highlighting the relevance of better monitoring, inoculation efforts, infection prevention approaches, and research into emerging treatments in order to decrease the influence of viruses that cause breathing problems on global health.
The Impact of Artificial Intelligence on the Process of Drug Discovery and Development
Artificial Intelligence (AI) is revolutionizing the field of drug discovery and development by offering innovative solutions to longstanding challenges such as high costs, lengthy timelines, and high rates of failure. This review comprehensively explores how AI is reshaping drug discovery through advanced data analysis, predictive modelling, drug repurposing, biomarker discovery, clinical trial optimization, and drug design. We delve into recent advancements, ongoing challenges, and future directions, supported by recent literature and case studies. AI's transformative impact promises to enhance efficiency, precision, and cost-effectiveness in the pharmaceutical industry, ultimately benefiting patients and the healthcare ecosystem.
Atropine and Myopia Management: Current Evidence, Mechanisms, and Clinical Implications
Myopia, especially in children and young adults, has become a serious global public health issue. The increasing prevalence of high myopia and its associated ocular complications has prompted interest in pharmacological interventions aimed at slowing axial elongation. Of these, the non-selective muscarinic antagonist atropine has shown notable effectiveness in slowing the progression of myopia.&nbsp; This review summarizes current evidence on the use of low-dose atropine in myopia management, elucidates its proposed mechanisms of action, evaluates dosage-dependent outcomes, and discusses clinical implications for optometric practice. Low-dose atropine (0.01–0.05%) has been proven to be a safe and effective treatment option with few adverse effects by recent large-scale studies like the ATOM and LAMP trials. Despite ongoing debates regarding the optimal concentration, rebound effects, and long-term safety, atropine remains the most widely studied pharmacologic agent for myopia control. Understanding its clinical application, pharmacodynamics, and integration with optical interventions offers valuable insight for eye care practitioners striving to combat the global myopia epidemic.
From Virtual Model to Clinical Reality: Review on AI Enabled In Silico Twins in Organ Transplantation
The major difficulties seen in organ transplantation are organ rejection and infection during the surgery, shortage of organs and mainly the donor-recipient matching. By finding solutions to these problems the AI enabled In Silico twins plays a key role in development seen in organ transplantation.[3] The combination of AI In Silico twins, a virtual replica of individual patients created by using physiological, biological and clinical data. These virtual models can predict the organ rejection by [6] immunosuppressive therapy, organ function and disease progression and also help in medical treatment based on the type of organ. The AI enabled In Silico twins provide a real time data and gives the information about the organ function and donor selection. By combining the AI in organ transplantation medical procedure, the positive outcomes will be enhanced. The In-Silico twins technology will reduce ethical risks and minimize the clinical trials and clinical errors in patients. This technology gives a safer procedure by creating the virtual replica by using the physiology of organs and predict better donor-recipient matching and modify the clinical practice and improve the transplant success rapidly developing through AI and it is continuously evolving in all sectors; the virtual twins could soon become an essential tool making the transplant safer and more successful for patients throughout the world.
Effect of Natrum Muriaticum in Various Plant Under Salt Stress Condition - Evidence Based Study
The present study investigates the effects of Natrum muriaticum on tomato, cow pea, and basil leaves under salt stress conditions. Salt stress is a significant abiotic stress that can negatively impact plant growth and productivity. Natrum muriaticum, a homeopathic remedy, has been reported to have potential benefits in mitigating salt stress in plants. The study aimed to evaluate the effects of Natrum muriaticum on the growth, biochemical, and physiological parameters of tomato, cow pea, and basil leaves under salt stress conditions. The results showed that Natrum muriaticum treatment significantly improved the growth parameters, such as shoot length, root length, and leaf area, in all three plant species under salt stress conditions. The biochemical analysis revealed that Natrum muriaticum treatment increased the content of proline, a compatible solute that helps in osmoregulation, and antioxidant enzymes, such as superoxide dismutase and catalase, which scenge reactive oxygen species and protect plants from oxidative damage. Results showed that Natrum Mur treatment improved growth, physiological, and biochemical parameters, as well as enhanced yield and fruit quality. The 12X and 30X potencies showed more pronounced effects, suggesting a potential dose-response relationship.
mitigation of salt stress(abiotic stress)
Spatiotemporal Pharmacology and Drug Action in Dynamic Microenvironments
Spatiotemporal pharmacology is a young field representing a combination of spatial and temporal aspects of pharmacologic action improve the understanding of drug behaviours in dynamic micro environment of cells. Advancement of this method can clarify tissue-specific responses, mismatched drug distribution and off-target effects with localization of drugs in the sub-tissue environment and temporal dynamics of effect. Advanced imaging technologies, spatial omics and microphysiological systems increases the understanding of cellular heterogeneity, drug‒target interactions and pharmacokinetic‒pharmacodynamic relationships in biological models. Spatial and temporal pharmacology plays an important role in complex biological environments such as the tumour and immunologic microenvironment, in which protein renaturation and resulting changes in structure and function directly affect the therapeutic efficacy and development of resistance. Due to the various limitations like complexity of data and large financial requirements, new efforts should be made to overcome these challenges by developing AI-enhanced analytics and future generations of spatial technologies that can be used to speed up the process of drug discovery, improves the knowledge and support designing of effective therapeutic plans.
Validation of KRT17 as a Multi- Dimensional Oncogenic Driver for Cancer Using AI
Artificial intelligence–assisted prompting offers a transformative strategy for rapidly identifying and contextualizing biological targets. Using Swalife PromptStudio, we present a case study of (Keratin 17) KRT17 that stands out as a compelling therapeutic target in oral cancer, where its genetic, molecular, and systems-level evidence converge on a pathogenic role. In tumors, KRT17 is consistently overexpressed, acting not only as a structural keratin but also as a signaling regulator. Multi-omics profiling demonstrates strong transcript–protein concordance, high biomarker potential (AUC ≈ 0.89), and novel links to cancer metabolism, suggesting it supports glycolysis while suppressing TCA flux. Pathway mapping places KRT17 centrally within the MAPK cascade, regulating apoptosis resistance and epithelial–mesenchymal transition, processes critical for invasion and metastasis in oral squamous cell carcinoma. Protein interaction analysis identifies it as a conserved hub-bottleneck alongside druggable partners like EGFR. Collectively, these data highlight KRT17 inhibition as a genetically validated and mechanistically grounded strategy for oral cancer therapy.
Role of ACTB in Breast Cancer EMT and Metastatic Progression: An Integrated Multi-Omics and Network-Based Study
Beta-actin (ACTB), a major cytoskeletal protein, plays a crucial role in the epithelial-to-mesenchymal transition (EMT) that facilitates breast cancer metastasis. By driving actin cytoskeletal remodelling, ACTB enables cancer cells to acquire migratory and invasive phenotypes. This study integrates multi-omics data, pathway and protein interaction analyses, and genetic evidence to characterise ACTB's role in breast cancer progression. The findings highlight ACTB’s pivotal function as a network hub orchestrating cell motility and invasion via regulation of cytoskeletal dynamics and signalling pathways such as TGF-β, Wnt/β-catenin, and Rho GTPases. While direct therapeutic targeting of ACTB is limited by its ubiquitous essentiality, associated actin-binding proteins, post-translational modifications, and metabolic dependencies emerge as promising intervention points. ACTB and its modifications also show potential as prognostic biomarkers for aggressive breast cancer subtypes. This comprehensive profiling advances understanding of ACTB’s pathogenic role and guides future anti-metastatic therapy development.
Harnessing AI to Reveal CTNNB1’s Central Role in Breast Cancer Progression and Drug Discovery
CTNNB1 encodes β-catenin, a multifunctional protein critically involved in cell adhesion and canonical Wnt signaling, fundamental for cellular homeostasis, gene regulation, and proliferation. In breast cancer, aberrant activation of the Wnt/β-catenin pathway is commonly observed, particularly in aggressive subtypes such as triple-negative breast cancer (TNBC), where elevated β-catenin correlates with high tumor grade and poor prognosis. Unlike colorectal cancer, CTNNB1 mutations are rare in breast cancer; pathway dysregulation is driven primarily by alterations in Wnt ligands, receptors, and loss of E-cadherin-mediated β-catenin sequestration. CTNNB1 signaling also supports breast cancer stem cell maintenance, metastasis, and therapy resistance, underscoring its therapeutic relevance. Current clinical strategies targeting this pathway include PORCN inhibitors, direct β-catenin interaction inhibitors, and natural compounds, although challenges remain in patient selection and toxicity management. Emerging immunotherapies and combination regimens offer promising avenues. Multi-omics and network analyses reaffirm CTNNB1 as a master regulator with robust biomarker strength and central network involvement, particularly in oncogenic processes. This evolving landscape highlights CTNNB1 as a high-priority target for precision oncology in breast cancer.