Search Results (7,352)

Background/Objectives: Recent public and scientific discussions have raised concerns about a possible link between prenatal paracetamol exposure and autism spectrum disorder (ASD). However, pharmacovigilance-based evidence remains scarce, and the role of reporting bias has not been systematically assessed. This study aimed to characterize ASD-related adverse event reports involving paracetamol in the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) and to evaluate potential disproportionality signals, considering demographic, temporal, and geographic patterns. Methods: FAERS data from January 2010 to September 2025 were screened for reports listing paracetamol as the suspect drug and ASD-related Preferred Terms. After excluding duplicates and concomitant drugs, 1776 unique cases were analyzed. Patient demographics, reporter type, and country of origin were summarized descriptively. Disproportionality was calculated using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Information Component (IC), and Empirical Bayes Geometric Mean (EBGM). Results: Among 172,129 paracetamol-associated reports, 1776 (1.03%) included ASD-related terms. All were classified as serious and mostly submitted by consumers (98.6%). Gender was available in 47.7% of cases, showing male predominance (68.8%). Most reports referred to fetal exposure during pregnancy. Nearly all originated from the United States (98.4%). A marked rise was observed after 2022, with 562 reports in 2023 and 1051 in 2025. Disproportionality analyses revealed consistently elevated signals (ROR = 69.8, PRR = 69.2, IC025 = 5.60, EB05 = 48.3). Conclusions: The strong disproportionality signal likely reflects increased public attention and reporting dynamics rather than a causal association. Further integration of pharmacovigilance and epidemiologic data is warranted to clarify the clinical significance of these findings. Full article

Nicotinic acetylcholine receptors (nAChRs) are membrane-bound proteins that mediate fast synaptic transmission throughout the nervous system. A functional nAChR subtype is formed by the combination of multiple subunits arranged as homomeric or heteromeric pentamers, each with a distinct pharmacological profile. Disruption of their neurotransmission contributes to various neuropathologies, emphasizing the need for detailed knowledge of receptor structure, function, subunit composition, dynamics, and potential ligand-binding sites. However, their structural complexity as integral membrane proteins has hindered expression in mammalian cell lines and proven even more challenging to crystallize, limiting insights into ligand interactions. Understanding the molecular determinants governing nAChRs function is essential for the rational design of selective therapeutics targeting neurological disorders. The emergence of a chimeric receptor approach has dramatically improved the ability to study these important proteins and opened new avenues for high-throughput screening in drug discovery efforts. This review explains how the design of chimera constructs using soluble homologs, such as AChBP, provides researchers with an immense opportunity to investigate receptor structure–function relationships and subtype-specific properties, thereby facilitating the development of more effective treatments. Full article

Background: Immunomodulatory therapies, including biologic and targeted synthetic disease-modifying antirheumatic drugs (DMARDs) have reshaped the treatment of autoimmune diseases. They alter host defenses, but the current landscape of associated infectious risk is not fully defined. Objective: A scoping review of recent literature was conducted to characterize infectious complications associated with modern immunomodulatory biologic agents, summarize current pathogen patterns, and highlight recommendations for prevention and early recognition in clinical practice. Methods: Following PRISMA-ScR guidelines, a systematic search was performed on Scopus, Science Direct, and PubMed for studies published since 2023. Inclusion criteria focused on adult human subjects, exposure to immunomodulatory therapy, and reported infectious outcomes. Studies focusing exclusively on antineoplastic agents without established use in autoimmune diseases were excluded. After screening 1046 unique records, 16 studies were included in the final review. Findings: High-dose glucocorticoids remain a primary driver of serious infections across autoimmune diseases. Newer agents present mechanism-specific risk profiles. JAK inhibitors are associated with herpes zoster, while TNF-α inhibitors are linked to opportunistic bacterial infections and reactivation of granulomatous infections. B-cell depletion with rituximab correlates with hypogammaglobulinemia and its associated infections, whereas belimumab may offer a lower infection risk in non-renal SLE. Recent post hoc analyses (2023–2025) quantify the elevated risk of herpes zoster with JAK inhibitors compared to TNF inhibitors, particularly in older populations. Conclusions: The infectious risk associated with biologic and targeted DMARDs varies by mechanism. While glucocorticoids remain a primary driver of serious infections, newer data highlights specific vulnerabilities with JAK inhibitors (herpes zoster) and B-cell depletion (hypogammaglobulinemia) that require targeted risk stratification. This review shows the urgent need for individualized risk stratification, targeted prophylaxis (e.g., for Pneumocystis or zoster), and pre-therapy screening to balance therapeutic efficacy with patient safety. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Compared with traditional two-dimensional (2D) models, patient-derived CRC organoids more faithfully preserve the genomic, transcriptomic, and architectural features of primary tumors, making them a powerful intermediate platform bridging basic discovery and clinical translation. Over the past several years, organoid systems have rapidly expanded beyond conventional epithelial-only cultures toward increasingly complex architectures, including immune-organoid co-culture models and mini-colon systems that enable long-term, spatially resolved tracking of tumor evolution. These advanced platforms, combined with high-throughput technologies and clustered regularly interspaced short palindromic repeats (CRISPR)-based functional genomics, have substantially enhanced our ability to dissect CRC mechanisms, identify therapeutic vulnerabilities, and evaluate drug responses in a physiologically relevant context. However, current models still face critical limitations, such as the lack of systemic physiology (e.g., gut–liver or gut–brain axes), limited standardization across platforms, and the need for large-scale, prospective clinical validation. These gaps highlight an urgent need for next-generation platforms and computational frameworks. The development of high-throughput multi-omics, CRISPR-based perturbation, drug screening technologies, and artificial intelligence-driven predictive approaches will offer a promising avenue to address these challenges, accelerating mechanistic studies of CRC, enabling personalized therapy, and facilitating clinical translation. In this perspective, we propose a roadmap for CRC organoid research centered on two major technical pillars: advanced organoid platforms, including immune co-culture and mini-colon systems, and mechanistic investigations leveraging multi-omics and CRISPR-based functional genomics. We then discuss translational applications, such as high-throughput drug screening, and highlight emerging computational and translational strategies that may support future clinical validation and precision medicine. Full article

Objective: The emergence and spread of multi-drug-resistant (MDR) bacteria pose a growing threat in veterinary medicine, particularly in equine hospitals. This study investigated the colonization and infection dynamics of horses undergoing emergency laparotomy with two distinct antibiotic protocols (single-shot versus 5-day protocol) during hospitalization. Methods: Nasal swabs and fecal samples were collected from 67 horses undergoing emergency laparotomy at clinic admission as well as on postoperative days 3 and 10. These were screened for multi-drug-resistant indicator pathogens. As multi-drug-resistant indicator pathogens, methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum β-lactamase (ESBL)-producing Enterobacterales (ESBL-E), and bacteria belonging to the Acinetobacter baumannii complex were defined. Results: Preoperatively, 6.2% of horses tested positive for MRSA and 13% for ESBL-E. An increase in colonization was observed on day 3 postoperatively, with 62.1% of nasal swabs and 86.4% of fecal samples testing positive for MDR organisms. On day 10, 53.4% of nasal swabs and 62.5% of fecal samples tested positive for indicator pathogens. Surgical site infection developed in five horses, two of which tested positive for MRSA in both nasal and wound samples during hospitalization, supporting the potential role of nasal carriage as a source of infection. Furthermore, all horses tested positive for ESBL-E during at least one time-point during hospitalization, and Enterobacterales (MDR in two surgical site infections (SSI)) were involved in all surgical site infections. No significant differences were observed between the two antibiotic treatment groups regarding colonization rates with indicator pathogens during hospitalization. However, the results indicate that hospitalization itself contributes to increased colonization with resistant bacteria. A clear limitation of the study is the restricted number of sampled horses and the lack of environmental contamination data. Non-sampled hospitalized horses with and without antibiotic treatment may have acted as reservoirs for MDR bacteria. Conclusion: The findings emphasize the need for routine environmental monitoring and strict adherence to hygiene protocols in equine clinics to reduce the risk of nosocomial transmission. Ongoing surveillance and infection control strategies are essential to mitigate the spread of MDR pathogens in veterinary settings. Full article

Background and Objectives: Clinically meaningful drug–drug interactions may be overlooked in oncology. Proton pump inhibitors (PPIs) may modulate outcomes with immune checkpoint inhibitors (ICIs) by altering the gut microbiome, altering the immune milieu, and affecting transporter interactions. We evaluated whether concomitant PPI use affects survival among patients with metastatic non-small cell lung cancer (NSCLC) treated with nivolumab. Materials and Methods: We retrospectively included patients with metastatic NSCLC who received second-line nivolumab across five oncology centers (January 2020–June 2023). Patients were grouped as concomitant PPI users vs. non-users. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan–Meier method and compared with the log-rank test; multivariable Cox models assessed independent associations. Results: A total of 194 patients were screened, of whom 30 were excluded according to predefined criteria. The final analysis included 164 patients—85 PPI users and 79 non-users. Median OS was 26.1 months (95% CI 15.5–36.7) in PPI users and 29.3 months (22.2–36.4) in non-users; this difference was not statistically significant (p = 0.54). Median PFS was 6.2 months (3.7–8.6) in PPI users vs. 10.2 months (7.1–13.2) in non-users (p = 0.04). In multivariable analysis, absence of concomitant PPI use (No vs. Yes) was independently associated with longer PFS (HR = 0.52, 95% CI 0.24–0.89, p = 0.03), whereas PPI use was not associated with OS (HR = 0.96, 95% CI 0.67–1.61, p = 0.83). Conclusions: Concomitant PPI use during nivolumab therapy was associated with significantly shorter PFS and a numerical reduction in OS in real-world metastatic NSCLC. Where clinically feasible, the need for PPIs should be re-evaluated before and during ICI therapy. Full article

Organoid culture represents a sophisticated biological model that surpasses traditional two-dimensional (2D) methods and animal models in physiological relevance and cost-effectiveness. Current organoid systems derive from adult, fetal, and induced pluripotent stem cells, providing innovative platforms for studying organ development, disease pathogenesis, and drug discovery. Recent technological advances now enable respiratory organoids to significantly contribute to respiratory disease research. This review comprehensively synthesizes the development of respiratory organoid models and their applications in studying major respiratory diseases, including pulmonary fibrosis, chronic obstructive pulmonary disease (COPD), and lung cancer. It further evaluates the transformative potential of these models in advancing respiratory disease research. Respiratory organoids uniquely model disease mechanisms and drug responses in human-specific microenvironments, enabling pathogenesis studies of respiratory diseases. They serve as functional platforms for drug screening and personalized therapy development. Future integration of multi-organoid systems with precision medicine promises to redefine respiratory disease research paradigms. Full article

Background: Cardiovascular polypharmacy inherently amplifies the risk of drug–drug interactions (DDIs), yet most studies remain limited to isolated drug pairs or predefined high-risk classes, without mapping the systemic architecture through which interactions accumulate. Objectives: To characterize the burden, severity, and network structure of potential DDIs in a real-world cohort of hospitalized cardiovascular patients using interaction profiling combined with graph-theoretic network analysis. Methods: This retrospective observational study included 250 hospitalized cardiovascular patients. All home medications at admission were analyzed using the Drugs.com interaction database, and a drug interaction network was constructed to compute topological metrics (i.e., degree, betweenness, and eigenvector centrality). Results: Polypharmacy was highly prevalent, with a mean of 7.7 drugs per patient, and 98.4% of patients exhibited at least one potential DDI. A total of 4353 interactions were identified, of which 12.1% were classified as major, and 35.2% of patients presented high-risk profiles with ≥3 major interactions. Interaction burden showed a strong correlation with medication count (r = 0.929). Network analysis revealed a limited cluster of hub medications, particularly pantoprazole, furosemide, spironolactone, amiodarone, and perindopril, that disproportionately governed both interaction density and high-severity risk. Conclusions: These findings move beyond conventional pairwise screening by demonstrating how interaction risk propagates through interconnected therapeutic networks. The study supports the integration of hub-focused deprescribing, targeted monitoring strategies, and network-informed clinical decision support to mitigate DDI risk in cardiovascular polypharmacy. Future studies should link potential DDIs to clinical outcomes and validate network-based prediction models in prospective settings. Full article

Individuals across their lifespan may experience hearing loss from medications or chemicals, prompting concern about ototoxic environmental exposures. This study applies computational modeling as a screening-level hazard identification and chemical prioritization approach and is not intended to constitute a human health risk assessment or to estimate exposure- or dose-dependent ototoxic risk. We evaluated in silico drug-induced ototoxicity models on 80 environmental chemicals, excluding 4 with known ototoxicity, and analyzed 76 chemicals using fingerprinting, similarity assessment, and machine learning classification. We compared predicted environmental ototoxicants with ototoxic drugs, paired select polychlorinated biphenyls with the antineoplastic drug mitotane, and used PCB 177 as a case study to construct an ototoxicity pathway. A systems biology framework predicted and compared molecular targets of mitotane and PCB 177 to generate a network-level mechanism. The consensus model (accuracy 0.95 test; 0.90 validation) identified 18 of 76 chemicals as potential ototoxicants within acceptable confidence ranges. Mitotane and PCB 177 were both predicted to disrupt thyroid-stimulating hormone receptor signaling, suggesting thyroid-mediated pathways may contribute to auditory harm; additional targets included AhR, transthyretin, and PXR. Findings indicate overlapping mechanisms involving metabolic, cellular, and inflammatory processes. This work shows that integrated computational modeling can support virtual screening and prioritization for chemical and drug ototoxicity risk assessment. Full article

Background/Objectives: Capric acid (CA)–therapeutic deep eutectic systems (THEDES) are emerging as a distinct class of biofunctional matrices capable of reshaping drug solubilization, permeability, and bioactivity. Methods: Relevant studies on CA–THEDES were identified through targeted database searches and screened for evidence on their design, mechanisms, and pharmaceutical performance. Results: This review synthesizes current evidence on their structural design, mechanistic behavior, and pharmaceutical performance, revealing several unifying principles. Across multiple drug classes, CA consistently drives strong, directional hydrogen bonding and drug amorphization, resulting in marked solubility enhancement and stabilization of non-crystalline or supersaturated states relative to crystalline drugs or conventional solvent systems. Its amphiphilic C10 chain further contributes to membrane fluidization, which explains the improved transdermal and transmucosal permeation repeatedly observed in CA-THEDES. Additionally, synergistic antimicrobial and anticancer effects reported in several systems confirm that CA acts not only as a solvent component but as a bioactive co-therapeutic. Collectively, the reviewed data show that CA serves as a structurally determinant element whose dual hydrogen-bonding and membrane-interacting roles underpin the high pharmaceutical performance of these systems. However, gaps remain in long-term stability, toxicological profiling, and regulatory classification. Emerging Artificial Intelligence (AI) and Machine Learning (ML)-guided predictive approaches offer promising solutions by enabling rational selection of eutectic partners, optimal ratios, and property optimization through computational screening. Conclusions: Overall, CA-THEDES represent a rationally designable platform for next-generation drug delivery, where solvent functionality and therapeutic activity converge within a single, green formulation system. Full article

This paper examines the contrasting yet complementary approaches of the Constrained Disorder Principle (CDP) and Stefan Hell’s deterministic optical nanoscopy for managing noise in complex systems. The CDP suggests that controlled disorder within dynamic boundaries is crucial for optimal system function, particularly in biological contexts, where variability acts as an adaptive mechanism rather than being merely a measurement error. In contrast, Hell’s recent breakthrough in nanoscopy demonstrates that engineered diffraction minima can achieve sub-nanometer resolution without relying on stochastic (random) molecular switching, thereby replacing randomness with deterministic measurement precision. Philosophically, these two approaches are distinct: the CDP views noise as functionally necessary, while Hell’s method seeks to overcome noise limitations. However, both frameworks address complementary aspects of information extraction. The primary goal of microscopy is to provide information about structures, thereby facilitating a better understanding of their functionality. Noise is inherent to biological structures and functions and is part of the information in complex systems. This manuscript achieves integration through three specific contributions: (1) a mathematical framework combining CDP variability bounds with Hell’s precision measurements, validated through Monte Carlo simulations showing 15–30% precision improvements; (2) computational demonstrations with N = 10,000 trials quantifying performance under varying biological noise regimes; and (3) practical protocols for experimental implementation, including calibration procedures and real-time parameter optimization. The CDP provides a theoretical understanding of variability patterns at the system level, while Hell’s technique offers precision tools at the molecular level for validation. Integrating these approaches enables multi-scale analysis, allowing for deterministic measurements to accurately quantify the functional variability that the CDP theory predicts is vital for system health. This synthesis opens up new possibilities for adaptive imaging systems that maintain biologically meaningful noise while achieving unprecedented measurement precision. Specific applications include cancer diagnostics through chromosomal organization variability, neurodegenerative disease monitoring via protein aggregation disorder patterns, and drug screening by assessing cellular response heterogeneity. The framework comprises machine learning integration pathways for automated recognition of variability patterns and adaptive acquisition strategies. Full article

Background/Objectives: The high volatility of volatile drugs significantly restricts their clinical applicability. Although excipients capable of strong interactions can reduce volatilization, conventional screening methods rely on empirical trial-and-error, resulting in low efficiency and high resource consumption. To address this limitation, this study introduces an artificial intelligence (AI)-driven strategy for screening drug–excipient interactions. Using d-borneol as a model drug, this approach aims to efficiently identify strongly interacting excipients and develop stable nano-formulations. Methods: High-throughput simulations were performed using the Protenix structure prediction model to evaluate interactions between d-borneol and 472 FDA-approved excipients. The top 50 candidate excipients were selected based on these simu-lations. Molecular docking and stability experiments were conducted to validate the predictions. Results: Molecular docking and stability experiments confirmed the consistency between predicted and experimental results, validating the model’s reliability. Among the candidates, soybean phospholipid (PC) was identified as the optimal excipient. A lyophilized liposomal formulation prepared with PC significantly suppressed the volatilization of d-borneol and improved both thermal and storage stability. Mechanistic investigations indicated that d-borneol stably incorporates into the hydro-phobic region of phospholipids, enhancing membrane ordering via hydrophobic interactions without disturbing the polar headgroups. Conclusions: This study represents the first application of a structure prediction model to excipient screening for volatile drugs. It successfully addresses the stability challenges associated with d-borneol and offers a new paradigm for developing nano-formulations for volatile pharmaceuticals. Full article

Over the past two decades, pharmaceutical peptides have emerged as a powerful alternative to traditional small molecules, offering high potency, specificity, and low toxicity. However, most computational drug discovery tools remain optimized for small molecules and need to be entirely adapted to peptide-based compounds. Molecular docking algorithms, commonly employed to rank drug candidates in early-stage drug discovery, often fail to accurately predict peptide binding poses due to their high conformational flexibility and scoring functions not being tailored to peptides. To address these limitations, we present PepScorer::RMSD, a novel machine learning-based scoring function specifically designed for pose selection and enhancement of docking power (DP) in virtual screening campaigns targeting peptide libraries. The model predicts the root-mean-squared deviation (RMSD) of a peptide pose relative to its native conformation using a curated dataset of protein–peptide complexes (3–10 amino acids). PepScorer::RMSD outperformed conventional, ML-based, and peptide-specific scoring functions, achieving a Pearson correlation of 0.70, a mean absolute error of 1.77 Å, and top-1 DP values of 92% on the evaluation set and 81% on an external test set. Our PLANTS-based workflow was benchmarked against AlphaFold-Multimer predictions, confirming its robustness for virtual screening. PepScorer::RMSD and the curated dataset are freely available in Zenodo Full article

Cervical cancer remains a significant global health burden, disproportionately affecting women in low- and middle-income countries despite being preventable. Since 2018, rapid advances in molecular profiling, immunotherapy, refinement of minimally invasive surgery, and targeted therapeutics have transformed diagnostic and therapeutic paradigms. This narrative review synthesizes clinical and translational progress across the continuum of care from 2018 to 2025. We summarize the evolving landscape of precision screening—including HPV genotyping, DNA methylation assays, liquid biopsy, and AI-assisted cytology—and discuss their implications for global elimination goals. Surgical management has shifted toward evidence-based de-escalation with data from SHAPE, ConCerv, and ongoing RACC informing fertility preservation and minimally invasive approaches. For locally advanced disease, KEYNOTE-A18 establishes pembrolizumab plus chemoradiation as a new curative standard, while INTERLACE underscores the benefit of induction chemotherapy. In the metastatic setting, survival outcomes have improved with the integration of checkpoint inhibitors (KEYNOTE-826, BEATcc, EMPOWER-Cervical 1), vascular-targeted therapies, and antibody–drug conjugates, including tisotumab vedotin and emerging HER2 and TROP-2–directed agents. We further highlight emerging biomarkers—PD-L1, TMB, MSI status, HPV integration patterns, APOBEC signatures, methylation classifiers, ctHPV-DNA—and their evolving role in treatment selection and surveillance. Future directions include neoadjuvant checkpoint inhibition, PARP-IO combinations, HER3-directed ADCs, DDR-targeted radiosensitizers, HPV-specific cellular therapies, and AI-integrated precision medicine. Collectively, these advances are reshaping cervical cancer care toward biologically individualized, globally implementable strategies capable of accelerating WHO elimination targets. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article