Search Results (1,403)

Background/Objectives: Development of Patient-Reported Outcome Measures (PROMs) in ophthalmology is limited by high patient burden during early validation. We propose an In Silico Pre-validation Framework using Large Language Models (LLMs) to stress-test instruments before clinical deployment. Methods: The LLM generated a PROM questionnaire and a synthetic cohort of 500 distinct patient profiles via a Python-based pipeline. Profiles were instantiated as structured JSON objects with detailed attributes for demographics, lifestyle, and health background, including specific clinical parameters like IOL type (Monofocal, Multifocal, EDOF) and dysphotopsia severity. To eliminate memory bias, a stateless simulation approach was used for test–retest reliability; AI agents were re-instantiated without access to prior conversation history. Psychometric validation included Confirmatory Factor Analysis (CFA) using WLSMV estimation and Differential Item Functioning (DIF). Results: The model demonstrated excellent fit (CFI = 0.962, TLI = 0.951, RMSEA = 0.048, SRMR = 0.063), confirming structural validity. DIF analysis detected no significant bias based on age, sex, or IOL type (0/20 items flagged). Internal consistency was robust (Cronbach’s alpha > 0.80) and stateless test–retest reliability was high (ICC > 0.90), indicating stability independent of algorithmic memory. Convergent validity was established via significant correlations with NEI-VFQ-25 scores (Spearman’s: −0.425 to −0.652,). While responsive to change, known-groups validity reflected realistic clinical overlap. Conclusions: LLM-based pre-validation effectively mirrors complex human response patterns through “algorithmic fidelity.” By identifying structural failure points in silico, this framework ensures PROMs are robust and unbiased before clinical trials, reducing the ethical and logistical burden on real-world populations. Full article

Kratom alkaloids are classified as aromatic pentacyclic indole and substituted carbonyl oxindole alkaloids. This study investigates the metabolism and interactions of indole alkaloids using in silico tools, including ADMET Predictor 13.0™, to assess pharmacokinetic and metabolic profiles. The analysis examined absorption, distribution, metabolism, and excretion (ADME), focusing on cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT) enzyme interactions, drug transporters, and clearance. Most indole alkaloids showed strong substrate interaction and inhibition of CYP3A4 (79–99% confidence) and induction of CYP1A2 (up to 94% confidence). Among UGT enzymes, UGT1A1 demonstrated the highest substrate affinity (97%), while none interacted with UGT2B15. All alkaloids showed strong P-glycoprotein (Pgp) interaction but minimal inhibition of BCRP. Mitralactonine exhibited the highest skin permeability, and Mitralactonal showed maximal jejunal permeability. Most indole alkaloids demonstrated significant blood–brain barrier penetration (up to 99% confidence) and compliance with Lipinski’s rule of five. Predictive modeling indicated notable effects on hepatic microsomal clearance parameters. This investigation offers the first comprehensive in silico ADMET profiling of kratom indole alkaloids, uncovering their CYP3A4 inhibition potential and metabolic liabilities to prioritize candidates for safer therapeutic development, though limited by model biases, applicability domain restrictions, and inability to fully capture biological complexity, stereochemistry, or interindividual variability necessitating experimental in vitro and in vivo validation. Full article

Background/Objectives: The Zika virus (ZIKV) represents an ongoing threat to public health due to its neurological and congenital complications. Even after 10 years since the first major outbreak, correlated with an increase in congenital ZIKV syndrome, there is still no vaccine or treatment for this infection. Among the various existing platforms, DNA vaccines combined with the use of immunoinformatics tools allow for the efficient selection of immunogenic epitopes and immunostimulatory molecules with greater flexibility, in addition to being simple to manufacture and having a higher cost–benefit ratio in production. Methods: In this work, we conducted an integrated approach, combining in silico analyses and in vivo experimental validations, for the development of multi-epitope DNA vaccines against ZIKV. The computational analyses confirmed structural stability, adequate solubility, absence of toxicity, and immune induction potential for constructs based on epitopes from the Envelope (E) and NS1 proteins. Therefore, we evaluated DNA constructs containing the ENV + NS1 epitopes, both with and without fusion to the ssPGIP signal peptide, in BALB/c mice. Results: Both vaccines increased the population of CD4⁺ and CD8⁺ T lymphocytes, in addition to the production of IgG antibodies associated with the Th1 profile. The fusion with ssPGIP broadened the response, stimulating the release of Th1, Th2, and Th17 cytokines, as well as enhancing antibody formation. In contrast, its absence was associated with a slight increase in CD4⁺ and CD8⁺ T cells, accompanied by restricted cytokine production. Conclusions: These results indicate that epitope-targeted techniques offer a viable and safe method for inducing robust immune responses, demonstrating that combining immunoinformatics methods with early preclinical testing is an effective strategy for ZIKV vaccine development. Furthermore, although the present study focused on initial immunogenic characterization, future studies involving viral challenge in a suitable animal model will be essential to conclusively determine the protective efficacy of these vaccine candidates. Full article

Background: Three-dimensional printing (3DP) is a promising technology for advancing pharmaceutical research by enabling the production of personalized drug products. However, its progress has been hindered by the conventional trial-and-error approach to excipient selection and optimization. Methods: In this study, the blend module was employed to determine the miscibility parameters—mixing energy (E_mix) and Flory–Huggins interaction parameter (χ) to find the right excipients and drug–excipient ratio and examine the incorporation of plasticizers and lipids to enhance printability. Furthermore, molecular dynamics (MD) simulations were employed to calculate the cohesive energy density (CED) for predicting the dissolution behavior of 3DP formulations. Results: Data from 51 formulations were analyzed, enabling correlation and experimental validation of the in silico predictions. The predicted miscibility values demonstrated a strong correlation with experimental printability results. Furthermore, using a miscibility parameter, it was possible to accurately forecast minor changes in the drug-to-excipient ratio, plasticizer/lipid concentration, and hot-melt extrusion (HME) temperature that affect printability. Hydrophilic carriers exhibited lower CED values corresponding to faster drug release. In contrast, more hydrophobic carriers revealed high CED values, indicating stronger drug entrapment and sustained release. Conclusions: The miscibility parameters and MD-simulated CED values provide a practical framework for early-stage, high-throughput excipient screening. Overall, in silico prediction offers a viable strategy for modeling the entire 3DP workflow, minimizing the need for trial-and-error experimentation, and thereby accelerating the clinical translation of 3DP drug delivery systems. Full article

p53 has long been studied as a major regulator in cellular pathways, resulting in a plethora of information on the structure and function of this protein as a frequently mutated tumor suppressor. Recent studies have demonstrated how the p53 transcription activation domain (TAD) interacts with the BH3-binding pocket of BCL-2 to regulate cell survival/death. While the in vitro studies on p53 and BCL-2 have frequently used truncated and stabilized proteins of p53 to ensure crystallization, these mutated proteins are not naturally observed in cells. Thus, it becomes important to find a way in silico to simulate how a full-length monomer with the unaltered sequence of wild-type (WT) or missense mutant (MT) p53 interacts with BCL-2. Our objective is to provide a predictive insight into how p53 monomers might interact with BCL-2 through the combination of previously published algorithms. Using pre-established computational techniques in silico, the interactions between p53 variants and BCL-2 were compared to existing crystals to ensure the validity of the current method, and the affinities were predicted to explore the strength of these interactions. Here, we found that this protocol was able to replicate some of the amino acid interactions identified in the previous literature, as well as identify affinities between each WT/MT p53 and BCL-2. Most major MT p53 variants are predicted to directly interact with BCL-2, but have a decrease in affinity compared to WT p53, suggesting a potential increase in BCL-2 survival activity. Together, the method described here can potentially be useful as a predictive workflow to inform future studies in vitro and in vivo. Full article

Background/Objectives: Aromatase inhibitors (AIs)—specifically, letrozole, anastrozole and exemestane—represent the current gold standard for patients with estrogen-receptor-positive breast cancer (ER + BC). This narrative review highlights potential interactions between nutrients and AIs, elucidating their molecular mechanisms involved. Methods: A comprehensive search was conducted across the PubMed, ScienceDirect, Google Scholar, and Scopus databases to identify scientific publications and elucidate recommended dietary regimes for ER + BC patients treated with AIs. Results: Certain bioactive substances found in licorice, rosemary, juniper, cannabis, and citrus fruits exhibit intrinsic aromatase-inhibiting effects. Additionally, other nutrients and compounds—including honey, ginger, turmeric, sweet potatoes, pomegranates, bitter melon, dark sweet cherries, resveratrol, and vitamins D and C—contribute to treatment outcomes through their demonstrated antiproliferative properties. Certain natural compounds, such as soy, cow’s milk, sesame seeds, and sesame oil, require caution due to their potential estrogen-like effects which could diminish the anti-estrogenic efficacy of AIs. Conclusions: These considerations hold significant weight in this context, as the management of oncological patients—particularly women with ER + BC—requires an integrated perspective. Antineoplastic treatment must be supported by appropriate nutrition to enhance antitumor efficacy and improve the patient’s quality of life. The data presented herein are derived from in vitro, in silico, and animal model studies and await validation in large patient cohorts. Nevertheless, these findings pave the way for future research to elucidate these molecular phenomena in humans and to establish clinically significant conclusions for ER + BC patients. Full article

Affordable, flexible surveillance tools are needed to detect SARS-CoV-2 and other pathogens in wildlife. Standard nucleic acid amplification tests (NAATs) are reliable but restricted to predefined targets, limiting their ability to detect co-infections or emerging pathogens. To address this, we adapted a targeted next-generation sequencing (tNGS) panel for mesocarnivores to the Oxford Nanopore Technologies (ONT) MinION platform and combined it with a SARS-CoV-2 whole-genome sequencing assay. Merging both assays before library preparation enables simultaneous SARS-CoV-2 detection, variant identification, and broader pathogen screening. The MinION platform also improves turnaround time because sequencing can begin immediately on small numbers of samples, reducing costs in low-volume workflows. We converted our validated carnivore tNGS panel from the Ion Torrent system to MinION, optimizing amplification conditions, primer pools, and barcoding for multiplexing. Analytical sensitivity was measured using contrived wildlife samples spiked with serial dilutions of SARS-CoV-2 and tested in parallel with a commercial NAAT. Diagnostic sensitivity was assessed using contrived positives, and specificity was evaluated using NAAT-negative wildlife samples and in silico analyses. All 161 wildlife samples were NAAT-negative. MinION tNGS detected SARS-CoV-2 down to Ct 34 and produced ≥ 99% genome coverage for Ct ≤ 24 while simultaneously identifying additional pathogens. Diagnostic sensitivity and specificity were 96.7% and 100%. This workflow offers a low-cost, scalable approach for comprehensive wildlife pathogen surveillance. Full article

Background/Objectives: The escalating antimicrobial resistance crisis underscores the urgent need to explore underexplored ecological niches as reservoirs of novel bioactive compounds. The Brazilian Caatinga, a unique semi-arid biome, represents a promising reservoir for microbial discovery. Methods: In this study, we report the polyphasic characterization of Streptomyces galbus I339, a strain isolated from Caatinga soil. Whole-genome sequencing and phylogenomic analysis confirmed its taxonomic identity. In silico mining of the genome was conducted to assess biosynthetic potential. This genetic promise was experimentally validated through an integrated metabolomic approach, including liquid chromatography-tandem mass spectrometry (LC-MS/MS), nuclear magnetic resonance (NMR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS) profiling. The anti-mycobacterial activity of the crude extract was evaluated against Mycobacterium tuberculosis. Results: The strain S. galbus I339 possesses a 7.55 Mbp genome with a high GC content (73.17%). Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Metabolomic analyses confirmed the production of actinomycins and identified abundant diketopiperazines. Furthermore, the crude extract exhibited antimycobacterial activity, with a potent MIC of 0.625 µg/mL. Conclusions: The convergence of genomic and metabolomic data not only validates the expression of a fraction of this strain’s biosynthetic arsenal but also highlights a significant untapped potential, with the majority of BGCs remaining silent under the tested conditions. Our work establishes S. galbus I339 as a compelling candidate for biodiscovery and underscores the value of integrating genomics and metabolomics to unlock the chemical diversity of microbes from extreme environments. Full article

Ovarian cancer (OC) remains one of the most lethal gynecologic malignancies due to late diagnosis, rapid progression, and frequent chemoresistance. Despite advances in targeted therapy, durable responses are uncommon, underscoring the need for novel multitarget agents capable of modulating key oncogenic networks. Medicarpin, a natural pterocarpan phytoalexin, exhibits diverse pharmacological activities; however, its molecular mechanisms in OC are poorly defined. This study employed an integrative in silico framework combining network pharmacology, pathway enrichment, molecular docking, and survival analysis to elucidate medicarpin’s therapeutic landscape in OC. A total of 107 overlapping targets were identified, resulting in a dense protein–protein interaction network enriched in kinase-mediated and apoptotic signaling pathways. Ten hub genes were emphasized: CASP3, ESR1, mTOR, PIK3CA, CCND1, GSK3B, CDK4, PARP1, CHEK1, and ABL1. Gene Ontology and KEGG analyses demonstrated substantial enrichment in the PI3K–Akt/mTOR and prolactin signaling pathways. Docking revealed the stable binding of medicarpin to CASP3 (−6.13 kcal/mol) and ESR1 (−7.68 kcal/mol), supporting its dual regulation of hormonal and apoptotic processes. Although CASP3 and ESR1 expression alone lacked prognostic significance, their network interplay suggests synergistic relevance. Medicarpin exhibits multitarget anticancer potential in OC by modulating kinase-driven and hormone-dependent pathways, warranting further experimental validation. Full article

Aspergillus oryzae and Aspergillus flavus are closely related species within the Aspergillus section Flavi, sharing approximately 99.5% genomic similarity. Despite this similarity, they differ markedly in their ability to produce aflatoxin, a carcinogenic mycotoxin synthesized by the aflatoxin biosynthesis gene cluster (ABGC). Species and strains included within section Flavi display diverse deletion patterns in the ABGC at the sequence level. In this study, we performed an in-depth comparative analysis of the ABGC of 30 strains belonging to section Flavi, including isolates obtained from nuruk. The analysis revealed that A. oryzae exhibits distinct large-scale or locus-specific deletions in the ABGC compared to other related species. Based on these unique deletion patterns, we designed four primer sets to distinguish A. oryzae from A. flavus by comparing the sizes of PCR amplicons. Application of these primer sets to nuruk-derived isolates enabled successful species differentiation with 92% accuracy. To further validate this method, in silico PCR analysis was conducted using publicly available genomes of A. oryzae (116) and A. flavus (482), confirming that the developed biomarkers could consistently distinguish between the two close species. The primer sets are expected to serve as a rapid, accurate, and practical method for distinguishing A. oryzae from A. flavus. Full article

Background: Chromobacterium violaceum is an emerging multidrug-resistant (MDR) Gram-negative bacterium associated with severe septicemia, abscess formation, and high mortality, particularly in immunocompromised individuals. Increasing antimicrobial resistance and the absence of approved vaccines underscore the urgent need for alternative preventive strategies. Traditional vaccine approaches are often inadequate against genetically diverse MDR pathogens, prompting the use of computational immunology and reverse vaccinology for vaccine design. Objectives: This study aimed to design and characterize a novel multi-epitope subunit vaccine (MEV) candidate against C. violaceum using a comprehensive pangenome-guided subtractive proteomics and immunoinformatics pipeline to identify conserved antigenic targets capable of eliciting strong immune responses. Methods: Comparative genomic analysis across eight C. violaceum strains identified 3144 core genes. Subtractive proteomics filtering yielded two essential, non-homologous, surface-accessible, and antigenic proteins—penicillin-binding protein 1A (Pbp1A) and organic solvent tolerance protein (LptD)—as vaccine targets. Cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes were predicted and integrated into a 272-amino-acid MEV construct adjuvanted with human β-defensin-4A using optimal linkers. The construct was evaluated through structural modeling, molecular docking with TLR4, molecular dynamics simulation, immune simulation, and in silico cloning into the pET-28a(+) vector. Results: The MEV construct exhibited strong antigenicity, non-allergenicity, and non-toxicity, with stable tertiary structure and favorable physicochemical properties. Docking and dynamics simulations demonstrated high binding affinity and stability with TLR4 (ΔG = −16.2 kcal/mol), while immune simulations predicted durable humoral and cellular immune responses with broad population coverage (≈89%). Codon optimization confirmed high expression potential in E. coli K12. Conclusions: The pangenome-guided immunoinformatics approach enabled the identification of conserved antigenic proteins and rational design of a promising multi-epitope vaccine candidate against MDR C. violaceum. The construct exhibits favorable immunogenic and structural features, supporting its potential for experimental validation and future development as a preventive immunotherapeutic against emerging MDR pathogens. Full article

MET exon 14 (METex14) skipping mutations differ from other non-small cell lung cancer (NSCLC) genomic biomarkers as they result in aberrantly spliced MET transcripts and increased MET-signaling. However, the most accurate method for their detection remains debated. We conducted a retrospective study of previously identified METex14 skipping NSCLC samples by using different, commercially available, diagnostic targeted DNA- /RNA-Next-Generation Sequencing (NGS) panels. We primarily used small DNA-NGS panels covering the 5′ splice site of METex14 and supplemented by targeted RNA sequencing for selected cases. Using this approach, we identified <0.2% patients with METex14 mutations. Due to this low frequency, we validated and introduced complementary NGS testing using combined DNA/RNA-panels. This resulted in an increased number of METex14-positive patients (3.5%) and allowed us to identify METex14 skipping transcripts. Collectively, data from our cohort (n = 34) demonstrated that optimal diagnostics of METex14 variants require a complementary DNA-NGS performed with targeted panels covering both METex14 splice sites, and RNA-NGS. Consequently, we propose a new workflow for interpretation of concordant and discordant findings in METex14 detection. Finally, the potential of DNA-identified METex14 variants to cause aberrant splicing was in silico assessed by the MaxEntScan tool, providing a quantitative approach to splicing disruption. Interestingly, we also identified two novel variants located inside METex14, which also produced the METex14 skipping transcript despite being located outside the canonical splice sites. The altered binding site resulting from these exonic mutations was in silico determined by SpliceTransformer. Full article

Background/Objectives: Mecasin (KCHO-1) is a standardized multi-herb formulation containing diverse bioactive compounds predicted to engage multiple molecular targets. This study applied an integrative network pharmacology approach to explore how Mecasin may interact with Alzheimer’s disease (AD)-related molecular networks. Methods: Bioactive constituents from 9 herbs were screened through OASIS and PubChem, and their predicted targets were cross-referenced with 8886 AD-associated genes from GeneCards. Overlapping genes were analyzed using protein–protein interaction mapping, Gene Ontology, and KEGG to identify potential Mecasin–AD core nodes and pathways. Co-expression, co-regulation, and molecular docking analyses were performed to further characterize mechanistic relevance. Results: Network integration identified 6 core genes—AKT1, STAT3, IL6, TNF, EGFR, and IL1B—positioned within signaling pathways related to neuronal survival, inflammatory regulation, and cellular stress responses, including FoxO, JAK–STAT, MAPK, and TNF pathways. Molecular docking suggested that several Mecasin compounds may interact with targets such as AKT1 and TNF. Conclusions: These in silico findings indicate that Mecasin, a multi-component formulation containing numerous phytochemicals that generate broad compound–target associations, may interface with interconnected neuroimmune pathways relevant to AD. While exploratory, the results highlight potential multi-target mechanisms that merit further investigation and provide a systems-level framework to inform future experimental validation. Full article

Background: Radiation-induced pulmonary fibrosis (RPF) remains a major burden of successful lung cancer radiotherapy. Clinically validated drugs targeting RPF remains scarce. Methods: We employed a transcriptome-based drug repurposing approach using REMEDY, a computational platform built on the Library of Integrated Network-Based Cellular Signatures (LINCS). Differentially expressed genes (DEGs) derived from radiation-induced lung injury (RILI) models were used as a query to identify compounds capable of reversing pro-fibrotic expression profile. Among top-ranked candidates, homoharringtonine (HHT), an FDA-approved protein synthesis inhibitor, was selected for experimental validation. Anti-fibrotic effects of HHT were assessed using an optimized in vitro fibrotic model based on activation of MRC-5 human lung fibroblasts. Complementary in silico molecular docking analyses were also conducted to explore the mechanistic basis of HHT’s actions. This represents the first transcriptome-guided, LINCS-based drug repurposing study applied specifically to radiation-induced pulmonary fibrosis, utilizing RPF-derived molecular signatures rather than general fibrosis-related datasets. Results: HHT significantly attenuated key fibrotic phenotypes, including fibroblast proliferation, myofibroblast differentiation, and extracellular matrix (ECM) production. Notably, HHT suppressed expression of cyclin D1 and α-smooth muscle actin (α-SMA), and reduced collagen deposition. Mechanistic investigations revealed that HHT modulates two pro-fibrotic pathways: RhoA/ROCK and Wnt/β-catenin signaling. Molecular docking further suggested that HHT may directly interact with fibrosis-related receptors such as integrins and Frizzled, providing structural insight into its anti-fibrotic potential. These findings underscore the novelty of reassigning HHT to a radiation-specific fibrotic context using a signature-reversal strategy uniquely tailored to RPF biology. Conclusions: Our findings identify HHT as a promising treatment of RPF, offering a dual mechanism of action—interruption of protein synthesis and targeted inhibition of fibrotic signaling pathways. This study highlights the value of computational drug repurposing platforms for accelerating therapeutic discovery. Further preclinical investigations are warranted to evaluate HHT’s in vivo efficacy and clinical applicability in RPF. Full article

Background/Objectives: Colombia harbors exceptional plant diversity, comprising over 31,000 formally identified species, of which approximately 6000 are classified as useful plants. Among these, 2567 species possess documented food and medicinal applications, with several traditionally utilized for managing febrile illnesses. Despite the global burden of dengue virus infection affecting millions annually, no specific antiviral therapy has been established. This study aimed to identify potential anti-dengue compounds from Colombian medicinal flora through machine learning-based quantitative structure–activity relationship (QSAR) modeling. Methods: An optimized XGBoost algorithm was developed through Bayesian hyperparameter optimization (Optuna, 50 trials) and trained on 2034 ChEMBL-derived activity records with experimentally validated anti-dengue activity (IC₅₀/EC₅₀). The model incorporated 887 molecular features comprising 43 physicochemical descriptors and 844 ECFP4 fingerprint bits selected via variance-based filtering. IC₅₀ and EC₅₀ endpoints were modeled independently based on their pharmacological distinction and negligible correlation (r = −0.04, p = 0.77). Through a systematic literature review, 2567 Colombian plant species from the Humboldt Institute’s official checklist were evaluated (2501 after removing duplicates and infraspecific taxa), identifying 358 with documented antiviral properties. Phytochemical analysis of 184 characterized species yielded 3267 unique compounds for virtual screening. A dual-endpoint classification strategy categorized compounds into nine activity classes based on combined potency thresholds (Low: pActivity ≤ 5.0, Medium: 5.0 < pActivity ≤ 6.0, High: pActivity > 6.0). Results: The optimized model achieved robust performance (Matthews correlation coefficient: 0.583; ROC-AUC: 0.896), validated through hold-out testing (MCC: 0.576) and Y-randomization (p < 0.01). Virtual screening identified 276 compounds (8.4%) with high predicted potency for both endpoints (“High-High”). Structural novelty analysis revealed that all 276 compounds exhibited Tanimoto similarity < 0.5 to the training set (median: 0.214), representing 145 unique Murcko scaffolds of which 144 (99.3%) were absent from the training data. Application of drug-likeness filtering (QED ≥ 0.5) and applicability domain assessment identified 15 priority candidates. In silico ADMET profiling revealed favorable pharmaceutical properties, with Incartine (pIC₅₀: 6.84, pEC₅₀: 6.13, QED: 0.83), Bilobalide (pIC₅₀: 6.78, pEC₅₀: 6.07, QED: 0.56), and Indican (pIC₅₀: 6.73, pEC₅₀: 6.11, QED: 0.51) exhibiting the highest predicted potencies. Conclusions: This systematic computational screening of Colombian medicinal flora demonstrates the untapped potential of regional biodiversity for anti-dengue drug discovery. The identified candidates, representing structurally novel chemotypes, are prioritized for experimental validation. Full article