Search Results (26)

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

Background: Ovarian cancer is a major challenge in oncology due to high mortality rates, especially in advanced stages, despite current therapeutic approaches relying on chemotherapy and surgery. The search for novel therapeutic strategies is driven by the need for more effective treatments. This study focuses on novel xanthone derivatives modified with a morpholine ring, aiming to improve anticancer efficacy. Methods: In silico studies were conducted using ProTox III and SwissADME databases to assess the toxicity and ADME properties of the synthesized compounds. Molecular changes in cellular stress-related genes were investigated through qPCR in two ovarian cancer cell lines (TOV-21G and SKOV-3) following treatment with the compounds. Results: In silico analyses predicted high gastrointestinal absorption and blood–brain barrier permeability for the derivatives. Compounds exhibited varying toxicity and metabolic profiles. qPCR revealed significant alterations in genes related to antioxidant enzymes, molecular chaperones, and xenobiotic metabolism, indicating potential mechanisms of action and cellular responses to the compounds. Conclusions: The study demonstrates the potential of novel xanthone derivatives as promising candidates for ovarian cancer therapy, with implications for enhancing therapeutic efficacy and addressing drug resistance. Further research is warranted to elucidate the precise mechanisms underlying the observed effects and to develop tailored treatment strategies leveraging these agents. Full article

Antibody–drug conjugates (ADCs) are promising cancer therapeutics, but optimizing their cytotoxic payloads remains challenging. We present DumplingGNN, a novel hybrid Graph Neural Network architecture for predicting ADC payload activity and toxicity. Integrating MPNN, GAT, and GraphSAGE layers, DumplingGNN captures multi-scale molecular features using both 2D and 3D structural information. Evaluated on a comprehensive ADC payload dataset and MoleculeNet benchmarks, DumplingGNN achieves state-of-the-art performance, including BBBP (96.4% ROC-AUC), ToxCast (78.2% ROC-AUC), and PCBA (88.87% ROC-AUC). On our specialized ADC payload dataset, it demonstrates 91.48% accuracy, 95.08% sensitivity, and 97.54% specificity. Ablation studies confirm the hybrid architecture’s synergy and the importance of 3D information. The model’s interpretability provides insights into structure–activity relationships. DumplingGNN’s robust toxicity prediction capabilities make it valuable for early safety evaluation and biomedical regulation. As a research prototype, DumplingGNN is being considered for integration into Omni Medical, an AI-driven drug discovery platform currently under development, demonstrating its potential for future practical applications. This advancement promises to accelerate ADC payload design, particularly for Topoisomerase I inhibitor-based payloads, and improve early-stage drug safety assessment in targeted cancer therapy development. Full article

Predicting the toxicity of drug molecules using in silico quantitative structure–activity relationship (QSAR) approaches is very helpful for guiding safe drug development and accelerating the drug development procedure. The ongoing development of machine learning techniques has made this task easier and more accurate, but it still suffers negative effects from both the severely skewed distribution of active/inactive chemicals and relatively high-dimensional feature distribution. To simultaneously address both of these issues, a binary ant colony optimization feature selection algorithm, called BACO, is proposed in this study. Specifically, it divides the labeled drug molecules into a training set and a validation set multiple times; with each division, the ant colony seeks an optimal feature group that aims to maximize the weighted combination of three specific class imbalance performance metrics (F-measure, G-mean, and MCC) on the validation set. Then, after running all divisions, the frequency of each feature (descriptor) that emerges in the optimal feature groups is calculated and ranked in descending order. Only those high-frequency features are used to train a support vector machine (SVM) and construct the structure–activity relationship (SAR) prediction model. The experimental results for the 12 datasets in the Tox21 challenge, represented by the Modred descriptor calculator, show that the proposed BACO method significantly outperforms several traditional feature selection approaches that have been widely used in QSAR analysis. It only requires a few to a few dozen descriptors for most datasets to exhibit its best performance, which shows its effectiveness and potential application value in cheminformatics. Full article

Detection of botulinum neurotoxins (BoNTs) involves a combination of technical challenges that call for the execution of inter-laboratory proficiency tests (PTs) to define the performance and ease of implementation of existing diagnostic methods regarding representative BoNT toxin-types spiked in clinical, food, or environmental matrices. In the framework of the EU project EuroBioTox, we organized an international proficiency test for the detection and quantification of the clinically relevant BoNT/A, B, E, and F sero- and subtypes including concentrations as low as 0.5 ng/mL. BoNTs were spiked in serum, milk, and soil matrices. Here, we evaluate the results of 18 laboratories participating in this PT. Participants have implemented a wide array of detection methods based on functional, immunological, and mass spectrometric principles. Methods implemented in this proficiency test notably included endopeptidase assays either coupled to mass spectrometry (Endopep-MS) or enzyme-linked immunosorbent assays (Endopep-ELISA). This interlaboratory exercise pinpoints the most effective and complementary methods shared by the greatest number of participants, also highlighting the importance of combining the training of selected methods and of distributing toxin reference material to reduce the variability of quantitative data. Full article

Background: Pseudomonas aeruginosa is a pathogen that causes infections in animals and humans, with veterinary implications including ear infections in dogs, respiratory diseases in cats, and mastitis in ruminants. In humans, it causes severe hospital-acquired infections, particularly in immunosuppressed patients. This study aimed to identify and assess the prevalence of specific virulence factors in Pseudomonas aeruginosa isolates. Methods: We analyzed 98 Pseudomonas aeruginosa isolates from various animal samples (dogs, cats, ruminants, fowl) from northeastern Poland in 2019–2022 for virulence-related genes (toxA, exoU, exoT, exoS, lasB, plcN, plcH, pldA, aprA, gacA, algD, pelA, endA, and oprF) by PCR and assessed biofilm formation at 48 and 72 h. Genomic diversity was assessed by ERIC-PCR. Results: The obtained results showed that all strains harbored the pelA gene (100%), while the lowest prevalence was found for pldA (24%) and exoU (36%). Regardless of the animal species, strong biofilm forming ability was prevalent among the strains after both 48 h (75%) and 72 h (74%). We obtained as many as 87 different genotyping profiles, where the dominant one was profile ERIC-48, observed in four strains. Conclusions: No correlation was found between presence or absence of determined genes and the nature of infection. Similarly, no correlation was found between biofilm-forming genes and biofilm strength. The high genetic diversity indicates challenges for effective prevention, emphasizing the need for ongoing monitoring and research. Full article

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body’s response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR’s architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety. Full article

Pyrogens are fever-inducing substances routinely investigated in health products through tests such as the Rabbit Pyrogen Test (RPT), the Limulus Amebocyte Lysate (LAL), and the Monocyte Activation Test (MAT). However, the applications of the MAT for medical devices and biomaterials remain limited. This work aimed to overview the studies evaluating the pyrogenicity of medical devices and biomaterials using the MAT, highlighting its successes and potential challenges. An electronic search was performed by December 2023 in PubMed, Scopus, and Web of Science, identifying 321 records which resulted in ten selected studies. Data were extracted detailing the tested materials, MAT variants, interferences, and comparisons between methods. Methodological quality was assessed using the ToxRTool, and the results were synthesized descriptively. The selected studies investigated various materials, including polymers, metals, and natural compounds, employing the different biological matrices of the MAT. Results showed the MAT’s versatility, with successful detection of pyrogens in most materials tested, though variability in sensitivity was noted based on the material and testing conditions. Challenges remain in optimizing protocols for different material properties, such as determining the best methods for direct contact versus eluate testing and addressing the incubation conditions. In conclusion, the MAT demonstrates significant potential as a pyrogen detection method for medical devices and biomaterials. However, continued research is essential to address existing gaps, optimize protocols, and validate the test across a broader range of materials. Full article

Diphtheria toxin (DT) is the main virulence factor of Corynebacterium diphtheriae, C. ulcerans and C. pseudotuberculosis. Moreover, new Corynebacterium species with the potential to produce diphtheria toxin have also been described. Therefore, the detection of the toxin is the most important test in the microbiological diagnosis of diphtheria and other corynebacteria infections. Since the first demonstration in 1888 that DT is a major virulence factor of C. diphtheriae, responsible for the systemic manifestation of the disease, various methods for DT detection have been developed, but the diagnostic usefulness of most of them has not been confirmed on a sufficiently large group of samples. Despite substantial progress in the science and diagnostics of infectious diseases, the Elek test is still the basic recommended diagnostic test for DT detection. The challenge here is the poor availability of an antitoxin and declining experience even in reference laboratories due to the low prevalence of diphtheria in developed countries. However, recent and very promising assays have been developed with the potential for use as rapid point-of-care testing (POCT), such as ICS and LFIA for toxin detection, LAMP for tox gene detection, and biosensors for both. Full article

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts—with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks—significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks. Full article

Drug discovery aims to keep fueling new medicines to cure and palliate many ailments and some untreatable diseases that still afflict humanity. The ADME/Tox (absorption, distribution, metabolism, excretion/toxicity) properties of candidate drug molecules are key factors that determine the safety, uptake, elimination, metabolic behavior and effectiveness of drug research and development. The predictive technique of ADME/Tox drastically reduces the fraction of pharmaceutics-related failure in the early stages of drug development. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, artificial intelligence techniques such as Graphormer are showing increasing promise and usefulness to perform custom models for molecule modeling tasks. However, Graphormer and other transformer-based models do not consider the molecular fingerprint, as well as the physicochemicals that have been proved effective in traditional computational drug research. Here, we propose an enhanced model based on Graphormer which uses a tree model that fully integrates some known information and achieves better prediction and interpretability. More importantly, the model achieves new state-of-the-art results on ADME/Tox properties prediction benchmarks, surpassing several challenging models. Experimental results demonstrate an average SMAPE (Symmetric Mean Absolute Percentage Error) of 18.9 and a PCC (Pearson Correlation Coefficient) of 0.86 on ADME/Tox prediction test sets. These findings highlight the efficacy of our approach and its potential to enhance drug discovery processes. By leveraging the strengths of Graphormer and incorporating additional molecular descriptors, our model offers improved predictive capabilities, thus contributing to the advancement of ADME/Tox prediction in drug development. The integration of various information sources further enables better interpretability, aiding researchers in understanding the underlying factors influencing the predictions. Overall, our work demonstrates the potential of our enhanced model to expedite drug discovery, reduce costs, and enhance the success rate of our pharmaceutical development efforts. Full article

The Pseudomonas putida strain was primarily identified and tested in vitro against antibiotic sensitivity for several antibiotics using the disc diffusion method. This isolate was also tested against sensitivity to carvacrol oil (c) and formic acid (f). The genotyping of Pseudomonas spp. and virulotyping for P. putida isolate was carried out and verified by 16S rDNA-PCR amplification. Furthermore, we assessed the efficacy of carvacrol oil and formic acid in vivo for treatment of P. Putida infection. For the in vivo challenge, 180 fish (Nile tilapia, Oreochromis niloticus) were divided into six groups: (G1: control (unchallenged), G2: carvacrol prophylaxis (3 g/kg), G3: formic acid prophylaxis (5 mL/kg), G4: control positive (challenged), G5: carvacrol treatment (3 g/kg), and G6: formic acid treatment (5 mL/kg); 30 fish per group) with three replicates. Following the challenge, nitric oxide and lysozyme activity were measured as essential indicators for fish immunity. The antioxidant parameters including SOD and catalase were computed to reflect the antioxidant status. Furthermore, relative percent survival (RPS) and mortality percent were evaluated to indicate functional immunity. The findings of the antibiotic sensitivity test showed that ciprofloxacin exhibited the largest inhibition zone. Additionally, formic acid (f) displayed the greatest inhibition zone compared to carvacrol oil (c) and was more effective in stimulating the immune-antioxidant response compared to carvacrol oil. The tested exotoxin A (tox A), exoenzyme S (exo S), and the nan1 associated-virulence genes were identified in the P. putida isolate. Overall, the current study verified the virulence of P. putida and highlighted the promising role of dietary addition of formic acid for enhancing the immune-antioxidant indicators and for mitigating P. putida infection. Future studies could be devoted to this field. Full article

Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical toxicity that is trained based on thousands of chemicals with known toxicity. The performance of the HNN-Tox was compared with other machine-learning methods, including Random Forest (RF), Bootstrap Aggregation (Bagging), and Adaptive Boosting (AdaBoost). We also analyzed the model performance dependency on varying features, descriptors, dataset size, route of exposure, and toxic dose. The HNN-Tox model, trained on 59,373 chemicals annotated with known LD50 and routes of exposure, maintained its predictive ability with an accuracy of 84.9% and 84.1%, even after reducing the descriptor size from 318 to 51, and the area under the ROC curve (AUC) was 0.89 and 0.88, respectively. Further, we validated the HNN-Tox with several external toxic chemical datasets on a large scale. The HNN-Tox performed optimally or better than the other machine-learning methods for diverse chemicals. This study is the first to report a large-scale prediction of dose-range chemical toxicity with varying features. The HNN-Tox has broad applicability in predicting toxicity for diverse chemicals and could serve as an alternative methodology approach to animal-based toxicity assessment. Full article

The detection of early coronary atherosclerosis (ECA) is still a challenge and the mechanism of endothelial dysfunction remains unclear. In the present study, we aimed to identify differentially expressed genes (DEGs) and the regulatory network of miRNAs as well as TFs in dysfunctional endothelium to elucidate the possible pathogenesis of ECA and find new potential markers. The GSE132651 data set of the GEO database was used for the bioinformatic analysis. Principal component analysis (PCA), the identification of DEGs, correlation analysis between significant DEGs, the prediction of regulatory networks of miRNA and transcription factors (TFs), the validation of the selected significant DEGs, and the receiver operating characteristic (ROC) curve analysis as well as area under the curve (AUC) values were performed. We identified ten genes with significantly upregulated signatures and thirteen genes with significantly downregulated signals. Following this, we found twenty-two miRNAs regulating two or more DEGs based on the miRNA–target gene regulatory network. TFs with targets ≥ 10 were E2F1, RBPJ, SSX3, MMS19, POU3F3, HOXB5, and KLF4. Finally, three significant DEGs (TOX, RasGRP3, TSPAN13) were selected to perform validation experiments. Our study identified TOX, RasGRP3, and TSPAN13 in dysfunctional endothelium and provided potential biomarkers as well as new insights into the possible molecular mechanisms of ECA. Full article

The impact of neurodegenerative diseases (ND) is becoming unbearable for humankind due to their vast prevalence and the lack of efficacious treatments. In this scenario, we focused on imidazoline I₂ receptors (I₂-IR) that are widely distributed in the brain and are altered in patients with brain disorders. We took the challenge of modulating I₂-IR by developing structurally new molecules, in particular, a family of bicyclic α-iminophosphonates, endowed with high affinity and selectivity to these receptors. Treatment of two murine models, one for age-related cognitive decline and the other for Alzheimer’s disease (AD), with representative compound B06 ameliorated their cognitive impairment and improved their behavioural condition. Furthermore, B06 revealed beneficial in vitro ADME-Tox properties. The pharmacokinetics (PK) and metabolic profile are reported to de-risk B06 for progressing in the preclinical development. To further characterize the pharmacological properties of B06, we assessed its neuroprotective properties and beneficial effect in an in vitro model of Parkinson’s disease (PD). B06 rescued the human dopaminergic cell line SH-SY5Y from death after treatment with 6-hydroxydopamine (6-OHDA) and showed a crucial anti-inflammatory effect in a cellular model of neuroinflammation. This research reveals B06 as a putative candidate for advancing in the difficult path of drug discovery and supports the modulation of I₂-IR as a fresh approach for the therapy of ND. Full article