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PubMed2026

Zhiling Jiangya decoction treats hypertension in rats: An integrative study of network pharmacology, immune infiltration, molecular simulation, and 16S rDNA sequencing.

OBJECTIVE: This study integrated network pharmacology, immune infiltration analysis, molecular docking, molecular dynamics simulation, ADMET prediction, 16S rDNA sequencing, and rat experiments to elucidate the potential mechanisms underlying the antihypertensive effects of Zhiling Jiangya Decoction (ZLJYD). METHODS: Active compounds and their potential targets were screened from the PubChem, TCMSP, NovoPro, and SwissTargetPrediction databases. Hypertension-related targets were retrieved from the OMIM and GeneCards databases, and overlapping targets were identified. The STRING database and Cytoscape 3.10.1 software were used to construct a protein-protein interaction network and a herb-component-target-disease network. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the key biological processes and signaling pathways involved. Using the CIBERSORT algorithm combined with correlation analysis, we investigated the association between key targets and immune cell infiltration. Molecular docking, molecular dynamics simulations, and ADMET predictions were performed to assess the binding stability and pharmacokinetic properties of the main compounds with their corresponding targets. Finally, the antihypertensive efficacy of ZLJYD was validated using a spontaneously hypertensive rat model, and alterations in gut microbiota were analyzed using 16S rDNA sequencing. RESULTS: A total of 123 active compounds and 267 hypertension-related targets of ZLJYD were identified. Enrichment analysis revealed that these targets were primarily associated with the PI3K-Akt signaling pathway and lipid and atherosclerosis pathways. Immune infiltration analysis suggested that the therapeutic effects of ZLJYD may involve the regulation of follicular helper T cells, naïve B cells, and naïve CD4⁺ T cells. Molecular docking and dynamics simulations supported the stable binding of key compounds to their target proteins, while ADMET predictions indicated favorable pharmacokinetic properties and safety profiles. Rat experiments demonstrated that ZLJYD significantly reduced blood pressure in spontaneously hypertensive rats, partially alleviated gut microbiota dysbiosis, and altered microbial community structure and phylogenetic diversity. CONCLUSION: This study systematically elucidates the potential mechanisms underlying the antihypertensive effects of ZLJYD through multiple components, targets, and pathways, particularly immune regulation and gut microbiota remodeling. These findings provide mechanistic insights into its potential therapeutic application.

16S rDNA sequencing↗

PubMed2026

Structure-based virtual screening, multi-score docking, and molecular dynamics simulation of novel small molecules targeting the epidermal growth factor receptor for potential management of oral squamous cell carcinoma.

UNLABELLED: Oral squamous cell carcinoma (OSCC) is a major global health burden, with epidermal growth factor receptor (EGFR) serving as an important therapeutic target. However, resistance to currently available EGFR inhibitors limits the efficacy of long-term treatment. In this study, a structure-based virtual screening approach was employed using the Mcule database to identify novel small molecules with potential EGFR-inhibitory activity. The top-ranked compounds were subjected to consensus docking using multiple docking platforms and compared with established EGFR inhibitors. The most promising complexes were further evaluated using 500 ns molecular dynamics simulations to investigate their structural stability, conformational flexibility, and binding persistence. ADMET and pharmacokinetic analyses were performed to assess the drug-like and safety profiles. Five lead compounds (C1-C5) demonstrated significant binding affinities toward EGFR, ranging from - 9.9 to - 9.2 kcal/mol, while satisfying the major drug-likeness criteria. Molecular dynamics simulations suggested that C1 and C4 may form relatively stable EGFR-ligand complexes, as supported by stable RMSD convergence and persistent interactions with key active-site residues throughout the simulation period. Trajectory-based interaction analyses further indicated a sustained binding behavior. ADMET profiling predicted favorable oral bioavailability and low predicted toxicity for most compounds, particularly C3 and C5, although a potential risk of cytochrome P450-mediated drug-drug interactions was observed. Overall, the shortlisted compounds exhibited docking and dynamic stability profiles comparable to those of the reference inhibitor Lapatinib. These findings suggest the potential therapeutic relevance of structurally novel EGFR-targeting scaffolds in OSCC and provide a foundation for future experimental validation through in vitro and in vivo studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00722-4.

ADMET↗

PubMed2026

In silico identification of DNMT1 inhibitors from the PlantCyc database through computational approach to assess the anti-cancer potential of nutraceutical compounds in breast cancer.

Breast cancer accounts for a disproportionate share of global cancer-related deaths, with 670,000 fatalities and 2.3 million new diagnoses recorded in women during 2022 alone. Existing treatment modalities carry considerable toxicity burdens, and resistance to available agents remains an unresolved clinical problem. DNA methyltransferase 1 (DNMT1), the enzyme chiefly responsible for maintaining genome-wide methylation patterns during DNA replication, has been mapped out as a high-value target in breast cancer because its dysregulation silences tumour suppressor genes through promoter hypermethylation. The present work involves hierarchical in silico workflow to screen 4549 plant-derived compounds from the PlantCyc database (v16.0.3) against the human DNMT1 catalytic domain (PDB ID: 4WXX). Ten top-scoring compounds were taken forward for molecular docking via AutoDock Vina; Quercetin and Kaempferol both recorded the highest binding affinities at -9.5 kcal/mol, Wogonin (-9.3 kcal/mol) and Xanthohumol (-8.1 kcal/mol) also emerged as strong binders. Pharmacokinetic evaluation using ADMET-AI confirmed that all 10 compounds met Lipinski's rule of five, with human intestinal absorption values at or above 0.98. Wogonin and Xanthohumol were selected for a 100 ns all-atom molecular dynamics (MD) simulation in GROMACS due to their well-rounded ADMET profiles and limited existing data on their specific interactions with DNMT1 in breast cancer. Across all measured trajectory metrics, backbone RMSD, residue fluctuation, radius of gyration, solvent-accessible surface area, and intermolecular hydrogen bond count, Wogonin formed a more stable, compact complex. These findings suggest that Wogonin and Xanthohumol are non-toxic nutraceutical candidates suitable for DNMT1 targeted epigenetic therapy, with computational foundation strong enough to facilitate future in vitro and in vivo validation work.

Humans↗

PubMed2026

Computational discovery of emodin-based anthraquinones as PARP-1 inhibitors with relevance to ovarian and prostate cancer.

Cancer is a disease characterized by genomic instability and aberrant DNA repair. Poly (ADP-ribose) polymerase-1 (PARP-1) represents a well-established therapeutic target, particularly in ovarian and prostate cancer. However, the currently approved PARP inhibitors face challenges such as resistance, toxicity, and reduced efficacy. The search for alternative scaffolds has therefore become increasingly urgent. In this study, we used an integrated approach combining computer-aided methods to search for potential lead compounds among emodin-based anthraquinone derivatives as PARP-1 inhibitors. Using a PASS-based QSAR approach, drug-likeness prediction, and in silico ADMET assessment, we pre-screened a large set of anthraquinones and identified several potential hits for interaction with PARP-1. These hits were studied using molecular docking with the PARP-1 catalytic domain (PDB ID: 7KK4). The most stable and compact complexes were further explored by 500 ns molecular dynamics (MD) simulations and various dynamic properties (RMSD, RMSF, Rg, SASA, MolSA, hydrogen bonds, PCA, DCCM). The key finding of this study is that several emodin-derived anthraquinones exhibited binding behavior and ADMET profiles comparable to, or better than, the reference PARP-1 inhibitor. Among them, CID-10425624 emerged as the most promising candidate, exhibiting stable binding, reduced conformational fluctuation, compact complex formation, persistent hydrogen-bond interactions, and enhanced dynamic residue correlations within the PARP-1 catalytic domain. These findings suggest that the anthraquinone scaffold can provide a valuable starting point for developing structurally distinct PARP-1 inhibitors. In summary, this study identified several emodin-derived anthraquinones, particularly CID-10425624, as computationally prioritized lead candidates for PARP-1 inhibition, providing a novel anthraquinone-based scaffold for further experimental validation and optimization.

Anthraquinones↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Polyphenol-Rich Opuntia ficus-indica Cladodes: An Integrated Metabolomic, In Vivo and In Silico Study Supporting Their Hypolipidemic and Hepatoprotective Effects.

Background: Hyperlipidemia is a major risk factor for cardiometabolic disorders, including non-alcoholic fatty liver disease (NAFLD), and is closely associated with oxidative stress. Opuntia ficus-indica (OFI) cladodes are recognized as a rich source of bioactive phytochemicals; however, the molecular mechanisms underlying their metabolic benefits remain incompletely understood. Objectives: This study aimed to comprehensively evaluate the hypolipidemic and hepatoprotective potential of a polyphenol-rich O. ficus-indica cladode extract (OCE) using an integrated approach combining in vivo evaluation, untargeted metabolomics (UHPLC-Orbitrap-MS/MS), molecular docking, and ADMET prediction. Methods: Hyperlipidemic mice fed a high-fat diet (HFD) were treated with OCE, while molecular docking was performed on ten major annotated phytochemicals against twelve key proteins involved in lipid metabolism and cholesterol homeostasis, including HMGCR, FAS, PPARα, PCSK9, and NPC1L1, using simvastatin as the reference compound. Results: OCE treatment significantly improved plasma and hepatic lipid profiles, improved glucose homeostasis, and markedly reduced hepatic malondialdehyde (MDA) levels, indicating attenuation of oxidative stress. Histopathological analysis further supported a pronounced hepatoprotective effect, with a substantial reduction in hepatic steatosis. Untargeted metabolomics enabled the annotation of 102 metabolites, putatively identifying piscidic acid as the predominant phenolic constituent together with a diverse profile of flavonoids and phenolic acids. Molecular docking supported the potential contribution of these phytochemicals to the regulation of lipid metabolism through favorable interactions with multiple therapeutic targets, while ADMET prediction suggested an overall favorable pharmacokinetic and toxicity profile despite the lower intestinal permeability predicted for glycosylated derivatives. Conclusions: Overall, these findings support O. ficus-indica cladodes as a promising source of dietary bioactive compounds with potential applications in the nutritional management and prevention of hyperlipidemia and related cardiometabolic disorders.

Animals↗

PubMed2026

Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses.

Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.

Humans↗

PubMed2026

Discovery of NAT-6-321056 as a novel modulator of VEGFR2 signaling to suppress tumor angiogenesis.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a master regulator of angiogenesis and cancer progression. However, current VEGFR2 modulators face significant challenges, including off-target toxicity and acquired resistance, underscoring the urgent need for novel therapeutic agents with improved efficacy and safety profiles. Here, we reported that virtual screening of 39,442 natural products from the ZINC natural products-derived library, coupled with molecular docking and molecular dynamics (MD) simulations to evaluate the binding stability of candidate compounds, identified NAT-6-321056 as a highly promising modulator of VEGFR2 signaling. Biological evaluations demonstrated that NAT-6-321056 exerted potent inhibition on the growth of a broad spectrum of cancer cells, including both solid tumors and hematological malignancies. In EA.hy 926 endothelial cells and SK-N-DZ neuroblast cells, the compound significantly suppressed proliferation, migration, and invasion. Microscale thermophoresis (MST) confirmed direct binding of NAT-6-321056 to VEGFR2 with favorable affinity. Kinase profiling against a panel of 33 kinases indicated that NAT-6-321056 exhibited a multi-kinase modulation profile. Mechanistic studies revealed that NAT-6-321056 suppressed the expression of hypoxia-inducible factor 1-alpha (HIF-1α) and was associated with reduced VEGFR2 phosphorylation and attenuation of the downstream ERK/JNK/AKT signaling pathways. Moreover, NAT-6-321056 exhibited robust in vivo anti-angiogenic effects in both the chick chorioallantoic membrane (CAM) assay and transgenic zebrafish vascular fluorescence imaging models. Computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction suggested acceptable drug-like properties. Collectively, these findings demonstrated that NAT-6-321056 is a promising modulator of VEGFR2 signaling with potent anti-angiogenic activity and represents a viable candidate for cancer therapy.

Vascular Endothelial Growth Factor Receptor-2↗

PubMed2026

Design, synthesis and biological evaluation of hydroxybenzothiazole-linked benzothiazole/benzoxazole conjugates as potent dual α-amylase and α-glucosidase inhibitors.

The current study focuses on the synthesis and evaluation of novel Hydroxybenzothiazole-Linked Benzothiazole/Benzoxazole Conjugates to target Diabetes Mellitus (DM) by inhibiting α-amylase and α-glucosidase. Spectroscopic methods, including 1H and 13C NMR spectroscopy, were employed to confirm the structures of newly synthesized conjugates. The findings of in-vitro analysis displayed that the synthesized derivatives inhibited α-amylase and α-glucosidase enzymes with IC50 values ranging from 3.65 ± 0.20 μM to 32.15 ± 3.20 μM on α-amylase and 5.92 ± 0.80 μM to 35.60 ± 3.40 μM on α-glucosidase, in contrast to the reference drug Acarbose (α-amylase IC50 = 8.25 ± 0.80 μM; α-glucosidase IC50 = 10.75 ± 1.10 μM). Among the series 9a-9f and 10a-10f, analogs 10 f, 10b, 9b, and 9e displayed superior anti-diabetic activity compared to the reference drug Acarbose. The inhibitory activity of these conjugates can be attributed to their favorable and stable interactions with critical amino acid residues of targeted enzymes, as revealed through molecular docking analysis. ADMET predictions and drug-likeness evaluations showed favorable pharmacokinetic features, while DFT investigations revealed electronic insights related to bioactivity. Experimental outcomes and in silico support display that these potent Hydroxybenzothiazole-Linked Benzothiazole/Benzoxazole Conjugates were comparable to an existing diabetic mellitus inhibitor while conserving an acceptable safety profile, specifying potential for further therapeutic development and optimization against diabetic Mellitus.

Benzothiazoles↗

PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗

PubMed2026

Harnessing the Power of Large Language Models for Drug Discovery: A Systematic Review of Current Applications and Future Directions.

INTRODUCTION: The demand for inventive approaches to drug discovery has increased due to the rising costs, time, and failure rates in pharmaceutical research. Large Language Models (LLMs), with their sophisticated natural language processing and generative capabilities, have become potent instruments that have the potential to revolutionize biomedical research. The function of LLMs in different phases of drug development is methodically examined in this article. METHODS: The PRISMA 2020 principles were adhered to in this systematic study. A thorough search for research published between 2018 and 2025 was done using PubMed, Scopus, Web of Science, and Google Scholar. The search terms "large language model," "transformer," "drug discovery," and important sub-domains (such as "de-novo design" and "ADMET") were merged, and two reviewers independently screened the results. Predetermined inclusion and exclusion criteria were used to filter studies for relevance. 98 studies out of the 1,285 records that were initially retrieved met the requirements for the final qualitative synthesis. RESULTS: 98 studies that demonstrated the use of LLMs in various drug discovery domains were found during the review. These covered molecular generation, genomics, protein-ligand modeling, ADME/T and toxicity profiling, drug-target interaction and DTI prediction, and biomedical text mining. 42 different LLM-based tools were mapped, including BioBERT, SciSpacy, Drug- LLM, DNA-BERT, GPT-4, and ChatGPT. Predictive accuracy, hypothesis creation, target prioritization, and multi-modal data integration all showed notable gains with these techniques. DISCUSSION: By providing scalable, precise, and effective solutions for data-driven drug discovery, LLMs are revolutionizing the pharmaceutical industry. They allow for the creation of hypotheses and individualized insights across multi-modal biological data, and they perform better than conventional approaches in a number of subdomains. Improvements in performance were task-dependent; the most consistent gains occurred for biomedical text mining, disease-genedrug relationship mapping and drug-target interaction prediction tasks. Yet most evidence for clinical applications is still derived from retrospective studies and benchmark datasets, suggesting a higher need for prospective validation. CONCLUSION: There is revolutionary potential in incorporating LLMs into drug discovery processes. Clinical translation and regulatory uptake will depend heavily on collaborative validation, ethical deployment, and standardization as models become more multimodal and interpretable. Before normal use, extensive prospective benchmarking and head-to-head comparisons with established chemoinformatics pipelines are necessary.

De novo design↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2026

Development of metal-free one-pot sequential synthesis of carbazolyl-thiazolidinones as anti-leukemic agents with potential β-catenin/c-MYC pathway modulation: from synthesis to in vitro and in silico profiling.

Cancer remains a leading cause of mortality worldwide, necessitating the development of new, selective, and potent therapeutic agents. In this study, a novel, metal-free, one-pot sequential synthetic approach was developed for the synthesis of carbazolyl-thiazolidinone (CTZD) derivatives via the reaction of N-octylcarbazole-3-carbaldehyde with variety of aromatic and aliphatic primary and secondary amines and thioglycolic acid. This strategy efficiently yielded a diverse range of CTZD derivatives (4a-p) in moderate to high yields (20-95%). The synthesized compounds were characterized by FTIR, NMR (1H, 13C, DEPT, APT), and HRMS. Their in vitro cytotoxicity was tested on human leukemia cell lines NB4, K562 and U937 using MTT assays, where four derivatives (4e, 4i, 4j, and 4o) exhibited potent, concentration-dependent antiproliferative activity over the tested concentration range (1.25-10 μM). As c-MYC is a key regulator of cell proliferation, qRT-PCR analysis demonstrated that these four derivatives significantly downregulated c-MYC mRNA expression, with compound 4j producing the greatest reduction, suggesting a potential association with modulation of the Wnt/β-catenin pathway. DNA fragmentation analysis showed no detectable late-stage apoptosis, indicating that the observed c-MYC downregulation and antiproliferative effects were not associated with late-stage apoptotic cell death. The ADME/T analysis of all compounds showed favorable pharmacokinetic profiles with prediction of good oral absorption (HIA >92%) and no hERG I liability. Molecular docking studies demonstrated strong binding affinities of these compounds to β-catenin protein (PDB ID: 7ZRB) with compound 4i showing strongest affinity with ΔG = -8.10 kcal/mol via H-bonds with Ser473, Asn430, Arg469 and His470 amino acid residues. The developed metal-free synthesis provided a sustainable route to bioactive carbazolyl-thiazolidinones, and derivatives 4e, 4i, 4j, 4o could be promising leads for targeting Wnt/β-catenin/c-MYC signaling in leukemia.

Humans↗