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.
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.
AI-Assisted Prompt Engineering Identifies KRT19 as a Genetically Validated Therapeutic Target
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 19 (KRT19) demonstrates strong genetic evidence linking it to disease risk, particularly in fibrotic disorders and cancer. Genome-wide association studies (GWAS) reveal three significant hits at the KRT19 locus, with complete replication across independent cohorts, underscoring the robustness of the association. The lead SNP for liver fibrosis shows a modest but meaningful effect size (OR = 1.28, 95% CI: 1.18–1.39). ClinVar and OMIM report no Mendelian disorders, but common variants highlight KRT19 as a contributor to complex disease. gnomAD analysis shows low loss-of-function constraint (pLI = 0.1), indicating tolerance to inhibition. Importantly, eQTL evidence links increased KRT19 expression to higher risk, supporting KRT19 inhibition as a genetically validated therapeutic strategy with favorable safety potential. This study highlights the effectiveness of AI-driven prompt engineering as a scalable and reproducible framework for precision target prioritization.
Identification and Validation of YWHAZ Using AI Generated Prompts.
Artificial intelligence–assisted prompting offers a transformative strategy for rapidly identifying and contextualizing biological targets. Using Swalife PromptStudio, we present a case study of YWHAZ (14-3-3ζ) which is a multifunctional signaling adaptor that integrates diverse cellular pathways, including PI3K/AKT, MAPK, apoptosis, and cell cycle regulation. Multi-omics profiling reveals consistent overexpression at transcriptomic and proteomic levels, with downstream metabolomic signatures of glycolysis, glutaminolysis, and redox balance. Pathway and network analyses position YWHAZ as a super-hub protein with high degree and betweenness centrality, linking kinase signaling, apoptosis, and metabolic modules. Genetic evidence highlights common regulatory variants associated with cancer and metabolic traits, alongside rare, deleterious LoF mutations linked to neurodevelopmental disorders, confirming essentiality. Collectively, YWHAZ emerges as a high-value but high-risk therapeutic target, best addressed through selective modulation of pathogenic interactions and biomarker-guided strategies in precision medicine. This study highlights the effectiveness of AI-driven prompt engineering as a scalable and reproducible framework for precision target prioritization.
A Multi-Omics and Network Biology Case Study of CLIC1 Using AI Generated Prompts
Artificial intelligence–assisted prompting is accelerating target discovery by integrating multi-omics and network data. Using Swalife PromptStudio, we identify CLIC1, a redox-regulated chloride channel and metamorphic protein linking cytoskeletal, redox, and metabolic modules. Multi-omics analyses show its consistent overexpression in cancers and neuroinflammatory diseases, correlating with poor prognosis. GWAS and ClinVar highlight CLIC1 as a modifier of immune traits and cancer susceptibility, while somatic amplifications confirm its oncogenic role. Network analysis positions it as a high-centrality hub overlapping with cancer hallmarks. Its benign germline pathogenicity profile suggests low toxicity risk, supporting therapeutic targeting. These findings validate prompt-driven approaches for assembling actionable insights and establish CLIC1 as a promising, polypharmacological target across disease contexts.
chloride intracellular channel protein 1 (CLIC1)
Catalase: A Key Regulator of Hydrogen Peroxide Detoxification and Redox Homeostasis in Disease
Scientific prompting is reshaping early-stage drug discovery by transforming large language models (LLMs) into engines for knowledge mining, evidence synthesis, and hypothesis generation. Using the Swalife Prompt Studio platform, we systematically mapped the biology of catalase (CAT), a central regulator of oxidative defense. Analyses confirmed CAT as the non-redundant hub of hydrogen peroxide detoxification, linking peroxisomal metabolism with cellular redox homeostasis. Network biology and multi-omics profiling highlighted CAT as both a structural linchpin and functional bottleneck, consistently dysregulated across cancer, diabetes, neurodegeneration, and aging. Genetic evidence, including rs1001179, reinforced its translational relevance in cancer and vitiligo. Evolutionary conservation of CATs’ interactome underscored its essentiality and therapeutic potential. Beyond this case study, our work establishes autonomous, prompt-driven workflows as scalable, reproducible frameworks for target discovery, accelerating the transition from biological insight to translational opportunity.
AI-Driven Validation of PKP3 as a Multifaceted Oncogenic Hub
Artificial intelligence–assisted prompting offers a transformative strategy for rapidly identifying and contextualizing biological targets. Using Swalife PromptStudio, we present a case study of Plakophilin 3 (PKP3)which is a desmosomal protein that functions as both a structural anchor and a signaling regulator, with emerging significance in human disease. Genetic evidence reveals a dual role: rare homozygous loss-of-function variants cause ectodermal dysplasia/skin fragility syndrome, confirming its essential role in epithelial integrity, while common regulatory variants identified by genome-wide association studies link PKP3 overexpression to psoriasis risk. Additionally, somatic copy-number amplifications in carcinomas drive PKP3 upregulation, correlating with tumor progression and poor prognosis. Despite modest effect sizes for common variants, the combination of replicated GWAS signals, pathogenic rare mutations, and cancer amplifications provides strong validation. These findings establish PKP3 as a genetically anchored, translationally relevant therapeutic target in inflammatory and oncologic indications.
Target Identification Plakophilin-3 (PKP3)
Unveiling ESR1's Network Centrality through Multi-Omics Fusion and AI: Implications for Next-Generation Endocrine Therapies
Estrogen Receptor 1 (ESR1) remains a cornerstone therapeutic target in estrogen receptor-positive (ER+) breast cancer, driving ~70% of cases through dysregulated endocrine signalling. This study leverages the Swalife PromptStudio platform, an AI-native framework integrating large language models for structured target deconvolution, to generate a comprehensive multi-dimensional profile of ESR1. Through literature mining, multi-omics integration (transcriptomics, proteomics, metabolomics), gene ontology/pathway mapping, protein interaction networks, and genetic evidence analysis, we affirm ESR1's exceptional biomarker stability, network centrality, and translational relevance. Key findings include near-perfect fold-change consistency across omics layers (r ≈ 0.78 mRNA-protein correlation), dominant enrichment in estrogen signalling with crosstalk to PI3K-Akt/MAPK/mTOR pathways, and high-frequency somatic mutations (e.g., Y537S, D538G) in ~35% of AI-resistant tumours. These insights highlight ESR1's role in resistance mechanisms, metabolic reprogramming, and post-translational modifications, underscoring opportunities for next-generation therapies like oral SERDs (e.g., Elacestrant), PROTACs, and combination strategies with CDK4/6 inhibitors. This AI-accelerated approach exemplifies rapid, reproducible target validation, paving the way for precision oncology advancements
The Role of EGFR in Triple-Negative and Basal-Like Breast Cancer: Signalling Mechanisms and Drug Resistance
Breast cancer remains the most prevalent cancer among women globally, with aggressive subtypes such as triple-negative breast cancer (TNBC) posing significant therapeutic challenges. The Epidermal Growth Factor Receptor (EGFR), a receptor tyrosine kinase, is a pivotal driver in these cancers through its roles in cell proliferation, survival, and metastasis. EGFR’s structure includes extracellular ligand-binding and intracellular kinase domains that activate multiple oncogenic pathways, chiefly MAPK and PI3K/AKT. Dysregulation in breast cancer occurs via overexpression, activating mutations, and autocrine loops, predominantly in TNBC and basal-like subtypes, correlating with poor prognosis. Therapeutic strategies using monoclonal antibodies and tyrosine kinase inhibitors have met limited success, often hindered by tumour heterogeneity and resistance mechanisms. Multi-omics and pathway analyses affirm EGFR’s role as a central signalling hub intertwined with critical pathways such as MAPK/ERK and PI3K/AKT/mTOR, and reveal metabolic and phosphorylation signatures highlighting its functional activity. Genetic evidence reflects modest yet significant breast cancer risk associations with predominantly missense variants. Challenges remain in biomarker refinement and overcoming resistance, emphasising combination therapies and novel inhibitors as future directions. This integrated analysis underscores EGFR’s continued promise as a therapeutic target in breast cancer, especially for aggressive subtypes lacking effective treatment options.
Swalife AI-Powered Network Analysis Tool: An Intelligent Platform for Herb-Disease-Protein Interaction Mining
The integration of artificial intelligence (AI) in biomedical research has revolutionized data analysis, yet researchers continue to face challenges in efficiently identifying and interpreting complex relationships among herbs, diseases, and protein targets. Traditional literature review and database exploration methods are time-intensive, fragmented, and often fail to capture the multi-dimensional nature of biological interactions involved in herbal therapeutics. To address this gap, the Swalife AI-Powered Network Analysis Tool has been developed as an intelligent platform that automates literature mining, data integration, and network visualization. By combining data from PubMed and UniProt, the tool constructs herb-disease-protein interaction networks, enabling users to uncover potential molecular targets and biological pathways relevant to drug discovery, systems pharmacology, and personalized medicine. The tool allows users to input disease and herb parameters to retrieve related scientific articles, identify target proteins, and visualize interactive biological networks. Its features such as downloadable data outputs and graphical mapping facilitate in-depth exploration of molecular mechanisms underlying herbal actions. The platform ultimately accelerates hypothesis generation, enhances research efficiency, and bridges the gap between traditional medicine and modern molecular biology.
An AI Framework for Target-Based Lead Optimization: The SwALife Approach
The increasing demand for rapid and cost-effective drug discovery necessitates the integration of artificial intelligence (AI) into traditional computational chemistry workflows. The SwALife Target & Lead Optimizer represents an advanced AI-assisted platform that facilitates protein -ligand interaction analysis, lead molecule optimization, and pharmacokinetic evaluation. By combining protein structure data (PDB format) and molecular descriptors (SMILES/InChIKey), the tool enables iterative optimization of small molecules to enhance their binding affinity, drug-likeness, and bioavailability. This paper presents the architecture, methodology, and case study outcomes demonstrating the efficiency of SwALife in optimizing drug-like compounds against target proteins.
Innovative Formulations in Neurodegenerative Pharmacology: From Nano emulsions to Controlled Release Technologies
Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) present formidable challenges for pharmacotherapy, largely due to the restrictive blood-brain barrier (BBB) [1]. Traditional small molecule drugs often fail to reach the therapeutic concentrations in the brain, necessitating novel delivery approaches. Cutting edge nanoscale formulations-including nanoemulsions, dendrimers, and controlled-release implants are being engineered to enhance CNS targeting and drug bioavailability. These platforms can bypass or exploit BBB transport pathways (e.g. receptor- mediated or intranasal routes) and enable sustained release, higher payloads, and targeting ligands for disease specific tissues [2]. This review provides a comprehensive overview of BBB transport mechanisms, formulation technologies, and their applications in ND therapy. We highlight specific examples (e.g. dopamine-loaded albumin/PLGA nanoparticles in PD or NO-releasing liposomal systems in AD) and discuss challenges in translation (toxicity, immunogenicity, scale-up) and future directions for personalized neurotherapeutics.
Integrative Network Pharmacology: Multilayer Omics, Bioinformatics, and Polypharmacological Drug Design
Network pharmacology represents a transformative shift from "one drug–one target" paradigm to a systems-level approach that integrates biological networks, omics data, and computational intelligence. This comprehensive review articulates the conceptual foundations, architecture, and practical applications of network pharmacology in drug discovery and translational medicine. By incorporating multilayer omics—genomics, transcriptomics, proteomics, metabolomics, and single-cell data—with bioinformatics tools and graph theory, researchers can delineate disease modules, predict polypharmacological interactions, and repurpose drugs with increased precision. The work discusses cutting-edge computational strategies including graph neural networks, similarity network fusion, and explainable AI for robust modeling and interpretation. Case studies in oncology, neurodegeneration, infectious diseases, autoimmunity, and ethnopharmacology underscore the method’s potential in revealing novel mechanisms and multi-target therapeutics. Challenges such as incomplete interactomes, data heterogeneity, and model opacity are acknowledged, with solutions proposed through dynamic modeling, FAIR data practices, and ethical AI frameworks. Ultimately, network pharmacology emerges as a pivotal discipline enabling precision medicine through integrative, predictive, and mechanistically informed drug design.
Integrative Pharmacovigilance: Monitoring Adverse Effects in Herbal, Nutraceutical, and Conventional Therapies
Herbal teas, plant-based powders, and nutraceutical supplements are now common parts of modern treatment plans. You can find these products in most pharmacies, and they are often advertised on wellness websites, which is why so many people use them with regular medications. Even though these products are becoming more popular, many of them are not covered by strict pharmaceutical rules. Because of this, there are big holes in the records and knowledge of their possible negative effects. Patients may be at risk for unexpected health problems because there is not enough government oversight. This is especially true when these supplements are taken without a doctor's advice. To deal with this problem, it is very important to improve post-market surveillance by using integrated pharmacovigilance systems. It is also important to encourage patients and healthcare providers to talk openly about using these kinds of products. These steps can work together to make safety monitoring stronger, lower the number of avoidable bad outcomes, and raise public awareness of the risks that come with using complementary therapies that aren't regulated or used correctly.