Search Results (739)

Resistance formation and considerable toxicities limit the application of currently available antiparasitic drugs. Thus, new drug candidates are required. Piperlongumine-based cinnamides are promising antiparasitic compounds. In this study, new synthetic cinnamide derivatives with variable halogen substituents (F, Cl, and Br) were prepared and analyzed. They were tested for activity against Toxoplasma gondii and Leishmania major parasites. Considerable activities against T. gondii parasites were observed for certain chloro- and bromo-substituted cinnamides (IC₅₀ = 1.88–2.72 µM), while activities against L. major were less pronounced. Structure–activity relationships were investigated, which revealed notable relations of anti-toxoplasmal activity with the nature of the applied halogen substituents and a preference for chloro- and bromo-substituents in active compounds. In contrast to piperlongumine, the new active compounds have no methoxy substituents anymore and appear to be suitable for advanced antiparasitic studies. Successful docking of selected derivatives into the colchicine binding site of tubulin provided a strong hint at a possible mode of action for these cinnamides (S-scores of −6.075 and −5.993 kcal/mol). In addition, considerable drug-like properties were determined by ADME-T calculations. Thus, in conclusion, new halo-substituted cinnamides with promising activity against Toxoplasma gondii were identified. The selectivity for Toxoplasma parasites can lead to better drugs for the therapy of toxoplasmosis. Full article

Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures—even at very low levels, termed “subconcussive”—can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain’s largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose–response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management. Full article

Triple-negative breast cancer (TNBC) is a clinically aggressive malignancy with extremely limited effective targeted therapies. Natural products are promising alternatives for anticancer drug discovery, whereas integrated computational approaches serve as efficient tools for novel lead identification. Herein, four novel spiro-polycyclic sesquiterpene [4+2] trimers (Inubritantrimers A–D) and eight synthetic derivatives from Inula britannica L. were investigated via DFT calculations at the ωB97xD/6-311++G(2d,p) level (for geometric, electronic, spectral, and reactivity parameters), network pharmacology, molecular docking against seven core breast cancer-related targets, 500 ns all-atom molecular dynamics (MD) simulation, and MM/PBSA analysis. The results showed that the endo-type cycloaddition products had superior structural stability, with all reactions thermodynamically spontaneous (ΔG < 0). Compound 11 exhibited the most potent and balanced binding activity, with a docking free energy of −13.45 kcal/mol to MTOR; MD and MM/PBSA confirmed stable complex formation (total binding free energy −21.13 kcal/mol), driven predominantly by hydrophobic interactions. This study first established a comprehensive stereochemistry–electronic structure–property–activity relationship for this rare sesquiterpene trimer class and identified compound 11 as a promising MTOR-targeted TNBC lead. It provided a theoretical basis for developing high-efficiency, low-toxicity natural anticancer agents. Full article

Conventional tumor therapies and traditional nanocarrier delivery systems both suffer from multiple limitations. Traditional Chinese medicine polysaccharides (TCMPs), which possess excellent biocompatibility, low toxicity, structurally modifiable characteristics and inherent pharmacological activities, have emerged as promising functional materials for constructing antitumor drug delivery systems and are being gradually applied in tumor combination therapy. This review systematically summarizes the common structural characteristics, biological functions, and structure–activity relationships of representative TCMPs. It further discusses the principal modification strategies used to construct TCMPs-based drug delivery systems and highlights recent progress in their applications in immunotherapy-centered combination therapy. In addition, it outlines their mechanistic basis and therapeutic potential for improving drug delivery efficiency and antitumor efficacy. This review provides theoretical references for the optimal design of such novel systems and guidance for their applications in precision treatment of tumors, while also pointing out some challenges in the translational research of such systems. Full article

Quinazolinone derivatives are well-known anticancer agents; anticancer properties are also part of the broad spectrum of biological activity of coumarins. Conjugates containing quinazolin-4(3H)-one and coumarin fragments linked by polymethylene bridges of varying lengths were designed to improve properties of both parental compounds and create new anticancer or antibacterial agents. 3-{3-[(4-Methyl-2-oxo-2H-chromen-7-yl)oxy]propyl}quinazolin-4(3H)-one was synthesized as the base compound. It demonstrated moderate cytotoxicity against leukemia (K562 and HL60) and neuroblastoma (SH-SY5Y) cells in vitro, combined with relatively low acute, subacute, and chronic toxicity in vivo. Conjugates with various substituents and linkers were then synthesized to evaluate the structure–activity relationship. A study of the synthesized compounds on cell cultures showed that the introduction of a methyl substituent into the benzene ring of the coumarin fragment led to both an increase in cytotoxicity and expansion of its spectrum of action. Testing of the hybrids against Gram-positive and Gram-negative bacteria revealed that the introduction of halogens into the quinazoline fragment in the compounds or the elongation of the linker led to the emergence of pronounced antibacterial properties, which were most clearly manifested against Acinetobacter baumanii. The possibility of directing activity of quinazoline-4(3H)-one–coumarin hybrids by varying the substituents and the length of the linker was shown. Full article

Antiretroviral drugs (ARVDs) remain the cornerstone of HIV/AIDS management, but their therapeutic efficacy and safety are highly influenced by bioconversion processes such as hepatic metabolism and enzymatic transformation. Variability in metabolic pathways, mediated by cytochrome P450 enzymes and other liver-based systems, contributes to interindividual differences in drug response, toxicity, and resistance. Recent advances in artificial intelligence, particularly artificial neural networks (ANNs), offer promising tools for modeling and optimizing these complex bioconversion processes. ANNs are capable of learning nonlinear relationships from high-dimensional datasets, making them ideal for predicting the pharmacokinetic parameters, enzyme–substrate interactions, and metabolic stability of ARVDs. This review explores the emerging role of ANNs in understanding and optimizing the metabolic transformation of antiretroviral agents. Key applications are discussed, including prediction of drug–enzyme interactions, in silico modeling of hepatic clearance, and simulation of enzyme kinetics. The integration of molecular descriptors, omics data, and clinical parameters into ANN models allows for improved prediction accuracy and personalized therapy. Furthermore, ANN-based tools can aid in early-stage drug development by identifying metabolic liabilities and guiding structural modifications to enhance metabolic stability. Despite their potential, challenges such as data scarcity, model interpretability, and standardization remain. Future research should focus on hybrid models combining ANN with mechanistic pharmacokinetics, the incorporation of real-world patient data, and validation against experimental outcomes. Overall, ANNs represent a powerful approach to optimizing ARVDs bioconversion, with the potential to improve efficacy, reduce toxicity, and support the development of next-generation antiretroviral therapies Full article

Traditional pharmacotherapy is often constrained by suboptimal bioavailability and systemic toxicity. Biomolecularly inspired nano-drug delivery systems (nano-DDS) have emerged as precise platforms to overcome these barriers by orchestrating molecular interactions at the bio-nano interface. This review systematically evaluates the molecular recognition mechanisms and biochemical principles governing nano-DDS performance. We systematically evaluate how passive targeting relies on the EPR effect—dictated by the nanocarrier’s physicochemical properties—and how active targeting exploits ligand-receptor affinity to enhance cellular uptake. Special emphasis is placed on bioresponsive strategies that utilize pathological cues—such as pH gradients, redox potential, and enzymatic activity—for intelligent, on-demand drug release. Furthermore, we discuss structure-function relationships in lipid, polymeric, and biologically derived systems, highlighting their roles in modulating therapeutic signaling in oncology and inflammatory diseases. Finally, translational hurdles and emerging AI-driven molecular design strategies are critically examined. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusions: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads. Full article

Dam constructions alter the river flow, leading to a cascade of physical, chemical, and biological changes in the ecosystem’s structure and function. This study presents a systematic framework for assessing the impact of these built structures on adjacent surface water bodies. The approach integrates mandatory long-term monitoring data with a multivariate statistical approach (Partial Least Squares Discriminant Analysis, PLS-DA) to provide a robust assessment of fourteen of Bulgaria’s major and significant reservoirs’ influence on nearby rivers and streams. Datasets for studied reservoirs include basic physicochemical parameters, and for 8 out of 14 dams—potentially toxic elements (PTEs). To assess the influence of each reservoir on the river, two sampling locations were selected per dam: upstream (U) and downstream (D). Results for the water quality parameters, identified as significant discriminators in each PLS-DA model, are presented. A clear upstream dominance was observed for Pchelina, Saedinenie, and Ticha, a strong downstream pattern was observed for Dospat and Yovkovtsi, and a mixed spatial pattern for the remaining dams. The hierarchical clustering revealed three groups of parameters studied. The first cluster (EC, NO₂⁻, NO₃⁻, TN) likely reflects diffuse inputs. The second cluster (TP, PO₄³⁻) describes the relationship between total and dissolved phosphorus fractions. The third cluster (pH, NH₄⁺, DO, BOD) highlights organic matter decomposition and oxygen dynamics. The results highlight that reservoir impacts are governed by the interplay of hydrological conditions, catchment characteristics, and in-reservoir biogeochemical processes, leading to distinct functional behaviours such as retention, transformation, or release of substances. Full article

The increasing prevalence of fungal infections caused by Candida species, together with rising antifungal resistance, highlights the urgent need for novel therapeutic agents with improved efficacy and safety. In this study, a series of 3-substituted rhodanine derivatives (3–6) were synthesized and evaluated as potential multifunctional compounds combining antifungal activity and ferric reducing capacity in the FRAP assay. The compounds were characterized using FT-IR and NMR spectroscopy and assessed for their physicochemical and pharmacokinetic profiles through in silico ADME analysis. Biological evaluation revealed that compounds 3 and 5 exhibited the most promising antifungal activity against a panel of clinically relevant Candida strains, with compound 5 demonstrating broad-spectrum, predominantly fungicidal effects. In contrast, compounds bearing a bulky 4-chlorobenzoyl substituent (4 and 6) showed reduced activity, indicating the importance of structural features for antifungal efficacy. Ferric reducing capacity assessment using the FRAP assay confirmed that all compounds possess reducing activity, with compounds 3 and 6 showing the highest potential. Safety evaluation using zebrafish (Danio rerio) embryos and larvae revealed concentration-dependent toxicity for all compounds. Notably, compounds 5 and 6 exhibited significant embryotoxicity and neurobehavioral effects at low micromolar concentrations, whereas compound 3 demonstrated a more favorable safety profile, with minimal impact on development and locomotor activity. Taken together, these results indicate that compound 3 provides a balanced combination of antifungal activity and reduced toxicity, while compound 5 represents a highly active but more toxic derivative. The observed structure–activity relationships emphasize the importance of carefully tuning substituent-dependent properties to optimize both biological activity and safety, supporting the continued investigation of rhodanine-based multifunctional antifungal agents targeting fungal proliferation and ferric reducing properties. Full article

Quantifying the mixture effects on humans exposed remains challenging because mixture components are correlated and may act bidirectionally by exhibiting nonlinear dose-response relationships, which may contribute to subclinical organ dysfunction. The liver is a vital organ in the body with broad functions, making it vulnerable to injury as it is the first organ exposed to circulating toxicants, which can precipitate hepatic damage. Our study’s objective was to evaluate the combined and component-specific associations of a multi-chemical exposure mixture of heavy metals, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (dioxins), and polychlorinated dibenzofurans (furans), with liver biomarkers, and to compare concentration-based results with the toxic equivalent (TEQ) potency of the weighted results for dioxin-like compounds. In an unweighted analytic sample of U.S. adults from NHANES 2003–2004 with 947 complete cases, we examined heavy metals (cadmium, lead, and mercury), PCBs (12 congeners), dioxins (7 congeners), and furans (10 congeners) in relation to eight liver biomarkers (albumin, ALP, ALT, AST, GGT, LDH, total bilirubin, and total protein). We applied multi-exposure linear regression, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR), with parallel TEQ-based models using WHO 2005 TEFs for dioxin-like PCBs, dioxins, and furans. Across mixture methods, the mixture structure was chemically sparse, with a limited set of recurring contributors. Total bilirubin showed the most consistent positive mixture association across qgcomp and BKMR and persisted under TEQ weighting, with prominent PCB- and dioxin-like contributions (notably PCB81/PCB TEQs and dioxin-related components). Albumin demonstrated inverse mixture patterns in BKMR and TEQ-BKMR, with dioxin-like components (notably Dioxin3 and Dioxin3_TEQ) repeatedly emerging as key drivers. For ALT, ALP, AST, GGT, LDH, and total protein, overall mixture effects were frequently attenuated or null in qgcomp despite structured component weights, indicating bidirectional sub-mixtures and internal counterbalancing. BKMR PIPs similarly concentrated on a small number of dominant predictors (e.g., lead for ALP, mercury for ALT, PCB28 for AST, and cadmium and PCB189 for LDH), while interaction summaries provided limited evidence of stable non-additivity. Using multiple complementary mixture methods, we identified outcome-specific mixture patterns suggesting hepatobiliary vulnerability. TEQ concordance supports toxicological relevance of the dioxin-like axis, while metals and non–dioxin-like mechanisms likely contribute additional pathways. Full article

In recent years, Marine Natural Products (MNPs) have emerged as a significant source for anticancer drug discovery, as many natural products can offer structural diversity, unique mechanisms of action, and relatively low toxicity. This article provides a systematic review of MNPs with reported anticancer activities from 2020 to 2024. These compounds are classified into seven major categories: terpenoids, alkaloids, sterols, polyketides, peptides and proteins, polysaccharides, and macrolides. For each category, we elaborate on the marine sources, structural identification, in vitro anticancer activity, and preliminary structure–activity relationships. We found that sponges and marine-derived fungi are the most abundant sources of highly active compounds. Furthermore, knowledge graph-based analysis reveals that oxygen- and nitrogen-containing heterocycles constitute the core pharmacophores, and target prediction further indicates that MNPs exert anticancer effects through coordinated modulation of a multi-target network involving kinases, proteasomes, and nuclear receptors. This review contributes significantly to a deeper understanding of recent advances (2020–2024) in MNPs and provides critical guidance for promoting the development of innovative anticancer drugs derived from marine resources. Full article

Cancers are intricate and multifactorial diseases. Despite progress in medicine, there are still some obstacles in their treatment due to drug resistance, the toxicity of combination therapy and lack of drug selectivity toward cancer cells. The solution to this may be multi-target directed ligands (MTDLs), which have gained more and more popularity over the years. This review presents a comprehensive overview of novel potential multi-targeted derivatives of nitrogen-containing heterocycles, as imidazole, pyrazole, 1,2,3-triazole and 1,2,4-triazole. The review gathers the selected literature from 2006 to 2026. The analysis focuses on the potency of the inhibitory activity of selected molecules against a variety of molecular targets, as well as on their interactions with protein binding sites. Additionally, the structure-activity relationship (SAR) studies within the collected series are included. The discussion may contribute to the development of new multi-target anticancer agents. Full article

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure–performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations. Full article

Assessing genotoxicity, specifically gene mutations and chromosomal aberrations, is fundamental to chemical risk assessment. Notably, the early identification of an aneugenic mechanism is of crucial importance, allowing, in principle, for a threshold-based risk assessment approach. To investigate this issue while pushing towards innovation in risk assessment by leveraging New Approach Methodologies, in silico approaches stand out as a particularly promising avenue. Building on these premises and given the lack of QSAR models for aneuploidy in the public domain, the present study exploited the genotoxicity-relevant alert lists implemented in the OECD QSAR Toolbox to base the investigation of structure-activity relationships for aneuploidy. To address the lack of relevant structured data resources, a dataset of 65 confirmed aneugenic substances was specifically curated and designed for the study. The results highlighted widely differing performances among the various profilers, confirming a general limited discriminatory power for aneuploidy. On the other hand, a granular analysis of the results from individual structural alerts enabled the successful isolation of some features associated with the aneugenic mode of action. Moreover, a subset of tubulin-binding chemicals was investigated to determine whether targeting a specific protein improves the characterization of toxicological alerts. The findings provide a refined definition of specific toxicity determinants for tubulin binders and serve as a promising tool for early hazard assessment, potentially informing relevant AOPs. While the computational approach appears promising, the overarching challenge that emerges is the limited availability of well-curated experimental data. In fact, reliable data on aneuploidy are scarce and fragmented across the literature. Furthermore, existing compilations of micronucleus study results are often complicated by conflicting interpretations. Full article