Search Results (6,556)

Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

Extracellular vesicles (EVs) are mediators of intercellular communication and serve as promising tools for drug discovery and targeted therapies. These lipid bilayer-bound nanovesicles facilitate the transfer of functional proteins, RNAs, lipids, and other biomolecules between cells, thereby influencing various physiological and pathological processes. This review outlines the molecular mechanisms governing EV biogenesis and cargo sorting, emphasizing the role of key regulatory proteins in modulating selective protein packaging. We explore the critical involvement of EVs in various disease microenvironments, including cancer progression, neurodegeneration, and immunological modulation. Their ability to cross biological barriers and deliver bioactive cargo makes them desirable candidates for precise drug delivery systems, especially in neurological and oncological disorders. Moreover, this review highlights advances in engineering EVs for the delivery of RNA therapeutics, CRISPR-Cas systems, and targeted small molecules. The utility of EVs as diagnostic tools in liquid biopsies and their integration into personalized medicine and companion diagnostics are also discussed. Patient-derived EVs offer dynamic insights into disease states and enable real-time treatment stratification. Despite their potential, challenges such as scalable isolation, cargo heterogeneity, and regulatory ambiguity remain significant hurdles. Recent studies have reported novel pharmacological approaches targeting EV biogenesis, secretion, and uptake pathways, with emerging regulators showing promise as drug targets for modulating EV cargo. Future directions include the standardization of EV analytics, scalable biomanufacturing, and the classification of EV-based therapeutics under evolving regulatory frameworks. This review emphasizes the multifaceted roles of EVs and their transformative potential as therapeutic platforms and biomarker reservoirs in next-generation precision medicine. Full article

Background/Objectives: The rise in antibiotic resistance presents a serious and urgent global health challenge, emphasizing the need to develop new therapeutic compounds. This study focuses on the design and evaluation of a novel series of hybrid molecules that combine the 2-quinolone and 1,2,3-triazole pharmacophores, both recognized for their broad-spectrum antimicrobial properties. Methods: A library of 29 candidate molecules was first designed using in silico techniques, including QSAR modeling, ADMET prediction, molecular docking, and molecular dynamics simulations, to optimize antibacterial activity and drug-like properties. The most promising compounds were then synthesized and characterized by ¹H and ¹³C NMR APT, mass spectrometry (MS), Fourier-transform infrared (FT-IR) spectroscopy, and UV-Vis spectroscopy. Results: Antibacterial evaluation revealed potent activity against both Gram-positive and Gram-negative bacterial strains, with minimum inhibitory concentration (MIC) values ranging from 0.019 to 1.25 mg/mL. Conclusions: These findings demonstrate the strong potential of 2-quinolone–triazole hybrids as effective antibacterial agents and provide a solid foundation for the development of next-generation antibiotics to combat the growing threat of bacterial resistance. Full article

Background/Objectives: Drug discovery is a lengthy and expensive process, taking an average of 10 years and more than USD 2 billion from target discovery to drug approval. It is even more challenging in complex diseases due to disease heterogeneity and limited knowledge about the underlying mechanisms. We present a novel computational framework that integrates network analysis, statistical mediation, and deep learning to identify causal target genes and repurposable small-molecule candidates. Methods: We applied weighted gene co-expression network analysis (WGCNA) and bidirectional mediation analysis (causal WGCNA) to transcriptomic data from idiopathic pulmonary fibrosis (IPF) patients to identify genes causally linked to the disease phenotype. These genes were used as a phenotypic signature for deep learning-based compound screening using the DeepCE model. Results: Using RNA-seq data from 103 IPF patients and 103 controls, we identified seven significantly correlated modules and 145 causal genes. Five of these genes (ITM2C, PRTFDC1, CRABP2, CPNE7, and NMNAT2) were predictive of disease severity in IPF. Our compound screening identified several promising candidates, such as Telaglenastat (GLS1 inhibitor), Merestinib (MET kinase inhibitor), and Cilostazol (PDE3 inhibitor), with significant inverse correlation with the IPF-specific gene signature. Conclusions: This study demonstrates the utility of combining causal inference and deep learning for drug discovery. Our framework identified novel gene targets and therapeutic candidates for IPF, offering a scalable strategy for phenotype-driven drug discovery and repurposing. Full article

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

Since its discovery, the BTK inhibitor ibrutinib has redefined the standard treatments for hematological cancers, such as chronic lymphocytic leukemia (CLL). However, concerns exist regarding its secondary effects in humans and its occasional lack of efficacy in certain malignancies. Therefore, combined therapies with ibrutinib have emerged as promising new approaches. In this study, we aimed to explore its therapeutic potential through different approaches. For this purpose, we combined this drug with the BH3 mimetics ABT-199 and ABT-737, which inhibit anti-apoptotic members of the Bcl-2 family, and with the PDK1 inhibitor dichloroacetate (DCA), respectively. As cell models, we used ex vivo samples from patients and also selected the in vitro CLL cell line Mec-1, generating two sub-lines overexpressing Bcl-XL and Mcl-1, a common feature in this cancer. Results demonstrated a synergistic effect for both approaches, in all tumor cells tested, for both cytostatic and cytotoxic effects. Mechanistically, the expression of Bcl-2-family proteins was explored, exhibiting increases in pro-apoptotic, but also in anti-apoptotic, proteins upon ibrutinib treatment and a relative increase in the amount of the pro-apoptotic protein PUMA after treatment with DCA. Our data provides new insights into combined therapies with ibrutinib for CLL, which further expands our knowledge and the potential of this drug for cancer treatment. Full article

Background/Objectives: The study of biological activity of plants and their metabolites is an important approach for the discovery of new active material. However, little is known of the properties of the Microlicia genus. In addition to natural products, nanotechnology demonstrates considerable potential in pharmacotherapy. The utilization of nanoemulsions holds considerable promise in enhancing the efficacy of drugs, reducing dose, and therefore, lowering of toxic effects. Methods: In this context, antimicrobial and trypanocidal activities were evaluated to the free and encapsulated essential oil from M. graveolens in oil-in-water (o/w) nanoemulsion. Results: This oil is composed mainly of cis-pinocarvyl acetate (~80.0%). The nanoemulsions were prepared by phase inversion method and showed mean particle size of 58 nm, polydispercity index of 0.09, pH 7.8, zeta potential of −21.9 mV, electrical conductivity of 0.38 mS/cm, and good stability. The essential oil was active against all five Gram-positive bacteria tested, and the formulation enhanced this ability. The cytotoxicity effect on L929 cells was also reduced after encapsulation of this oil in o/w nanoemulsion. In addition, the oil and the nanoemulsion were able to inhibit the growth of Trypanosoma cruzi. Conclusions: Thus, the development of a nanoemulsion loaded with M. graveolens essential oil is an easy and low-cost way to obtain and deliver the cis-pinocarvyl acetate compound as well as allow its use in the treatment of diseases caused mainly by the genus Listeria and Staphylococcus. Full article

Rapidly proliferating tumor cells exhibit elevated demands for nutrients and energy to support their uncontrolled growth, with glucose serving as a key metabolic substrate. Glucose is transported into cells via facilitated diffusion mediated by glucose transporters (GLUTs), after which it undergoes a series of enzymatic reactions to generate energy. To accommodate their heightened metabolic needs, cancer cells frequently overexpress GLUTs, thereby enhancing glucose uptake. Notably, aerobic glycolysis—commonly referred to as the “Warburg effect”—has been identified as the predominant pathway of glucose metabolism within tumor tissues, even in the presence of adequate oxygen levels. Consequently, the conjugation of chemotherapeutic agents, including metallodrugs, to glucose-mimicking substrates holds significant potential for achieving tumor-specific intracellular drug delivery by exploiting the elevated glucose uptake characteristic of cancer cells. Moreover, in recent years, glycosylation of metal scaffolds has been extended to the development of bioactive metallodrugs for applications other than cancer treatment, such as potential tumor imaging, antiviral, antimicrobial, antiparasitic and anti-neurodegenerative agents. Accordingly, major advancements in the design of metal-based glycoconjugates for medicinal applications are here summarized and critically discussed, focusing on related results and discoveries published subsequently to our previous (2015) review article on the topic. Full article

Sulfur Fluoride Exchange (SuFEx) chemistry represents an emerging class of click reactions that has found broad applications in drug discovery and materials science. Traditionally, SuFEx reactivity has been regarded as the exclusive privilege of sulfur and fluorine. Accordingly, the scaffolds exhibiting SuFEx-like reactivity without sulfur or fluorine have remained underdeveloped. Indeed, SuFEx reactions may represent a more generalizable mode of chemical reactivity. By enhancing the electrophilicity of the carbonyl group and increasing the steric hindrance around the carbon center, we identified disubstituted Meldrum’s acid as a novel carbon-based scaffold with SuFEx-like reactivity. Various O-, S-, and N-nucleophiles are viable exchange partners in the presence of Barton’s base or DBU. In addition to the original method, a catalytic protocol was developed and successfully applied to drug derivatization, including the gram-scale modification of acetaminophen. Full article

Enteroviruses, a diverse genus within the Picornaviridae family, are responsible for a wide range of human infections, including hand, foot, and mouth disease, respiratory disease, aseptic meningitis, encephalitis, myocarditis, and acute flaccid paralysis. Despite their substantial global health burden and the frequent emergence of outbreaks, no specific antiviral therapies are currently approved for clinical use against non-polio enteroviruses. This review provides a comprehensive overview of the current landscape of antiviral strategies targeting enteroviruses, including direct-acting antivirals such as capsid binders, protease inhibitors, and viral RNA polymerase inhibitors. We also examine the potential of host-targeting agents that interfere with virus–host interactions essential for replication. Emerging strategies such as immunotherapeutic approaches, RNA interference, CRISPR-based antivirals, and peptide-based antivirals are also explored. Furthermore, we address key challenges, including viral diversity, drug resistance, and limitations in preclinical models. By highlighting recent advances and ongoing efforts in antiviral development, this review aims to guide future research and accelerate the discovery of effective therapies against enterovirus infections. Full article

Colorectal cancer (CRC) remains a significant global health burden. While early-stage CRC has a high survival rate, most patients are diagnosed with advanced disease, necessitating more effective and less toxic therapeutic targets. This review examines recent advancements, challenges, and future directions in targeted therapies for CRC, focusing on HER inhibitors. We assess the efficacy of monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs) and explore strategies to overcome resistance mechanisms. Targeted therapies like cetuximab and panitumumab have improved outcomes for CRC patients with wild-type KRAS. However, resistance mechanisms and intra- and inter-tumour heterogeneity limit their effectiveness. Recent advancements include the development of dual TKIs, antibody/drug conjugates (ADCs), bispecific antibodies, and CAR-T cells against HER family members and other targets that are showing promise in preclinical and clinical trials. Targeted therapies have transformed CRC treatment, but more research is needed to overcome some of the current challenges, such as late diagnosis and the heterogenous nature of CRC, as well as the discovery of more reliable biomarkers for response to the therapy and patient selection. Future research should focus on identifying novel biomarkers of diagnostic, prognostic, and predictive value, developing next-generation inhibitors, drug repurposing, and combining small-molecule targeted therapies with immunotherapy. Such advances could ultimately help increase both the treatment options and outcomes for patients with CRC. Full article

Biological-sequence representation methods are pivotal for advancing machine learning in computational biology, transforming nucleotide and protein sequences into formats that enhance predictive modeling and downstream task performance. This review categorizes these methods into three developmental stages: computational-based, word embedding-based, and large language model (LLM)-based, detailing their principles, applications, and limitations. Computational-based methods, such as k-mer counting and position-specific scoring matrices (PSSM), extract statistical and evolutionary patterns to support tasks like motif discovery and protein–protein interaction prediction. Word embedding-based approaches, including Word2Vec and GloVe, capture contextual relationships, enabling robust sequence classification and regulatory element identification. Advanced LLM-based methods, leveraging Transformer architectures like ESM3 and RNAErnie, model long-range dependencies for RNA structure prediction and cross-modal analysis, achieving superior accuracy. However, challenges persist, including computational complexity, sensitivity to data quality, and limited interpretability of high-dimensional embeddings. Future directions prioritize integrating multimodal data (e.g., sequences, structures, and functional annotations), employing sparse attention mechanisms to enhance efficiency, and leveraging explainable AI to bridge embeddings with biological insights. These advancements promise transformative applications in drug discovery, disease prediction, and genomics, empowering computational biology with robust, interpretable tools. Full article

The convergence of artificial intelligence (AI) and drug discovery is accelerating the pace of therapeutic target identification, refining of drug candidates, and streamlining processes from laboratory research to clinical applications. Despite these promising advances, the inherent opacity of AI-driven models, especially deep-learning (DL) models, poses a significant “black-box" problem, limiting interpretability and acceptance within the pharmaceutical researchers. Explainable artificial intelligence (XAI) has emerged as a crucial solution for enhancing transparency, trust, and reliability by clarifying the decision-making mechanisms that underpin AI predictions. This review systematically investigates the principles and methodologies underpinning XAI, highlighting various XAI tools, models, and frameworks explicitly designed for drug-discovery tasks. XAI applications in healthcare are explored with an in-depth discussion on the potential role in accelerating the drug-discovery processes, such as molecular modeling, therapeutic target identification, Absorption, Distribution, Metabolism, and Excretion (ADME) prediction, clinical trial design, personalized medicine, and molecular property prediction. Furthermore, this article critically examines how XAI approaches effectively address the black-box nature of AI models, bridging the gap between computational predictions and practical pharmaceutical applications. Finally, we discuss the challenges in deploying XAI methodologies, focusing on critical research directions to improve transparency and interpretability in AI-driven drug discovery. This review emphasizes the importance of researchers staying current on evolving XAI technologies to realize their transformative potential in fully improving the efficiency, reliability, and clinical impact of drug-discovery pipelines. Full article

This review covers article and patent data obtained mostly within the period 2013–2024 on the synthesis and biological activity of quinazolines [a]-annelated by five-membered heterocycles. Pyrrolo-, (iso)indolo-, pyrazolo-, indazolo-, (benz)imidazo-, (benz)thiazolo-, and triazolo- [a]quinazoline systems have shown multiple potential activities against numerous targets. We highlight that most research efforts are directed to design of anticancer, antibacterial, anti-inflammatory, and other agents of azolo[a]quinazoline nature. This review emphases both the medicinal chemistry aspects of pyrrolo[a]-, (iso)indolo[a]-, and azolo[a]quinazolines and the comprehensive synthetic strategies of quinazolines annelated at the N(1)–C(2) bond from the perspective of drug development and discovery. Full article