Search Results (248)

Cyclin-dependent kinase 9 (CDK9) is a key regulator of transcriptional elongation and DNA repair, supporting cancer cell survival by sustaining the expression of oncogenes and anti-apoptotic proteins. Its overexpression in multiple malignancies makes it an attractive target for anticancer therapy. Here, we report a machine learning (ML) based approach to identify novel CDK9 inhibitors. Through systematic data collection and preprocessing, seventy predictive models were developed using five algorithms, two classification settings, and seven molecular representations. The best-performing model was employed to guide a virtual screening (VS) campaign, resulting in the identification of 14 compounds promising for their potential inhibitory effect. Upon enzymatic assays, two molecules with inhibitory activity in the low micromolar range were selected as promising candidates and further tested in three cancer cell lines with distinct genetic backgrounds. These experiments led to the identification of a novel compound exhibiting interesting therapeutic potential, both as a single agent and in combination with Camptothecin (CPT), revealing varying response profiles across the tested cell lines. These results illustrate the power of integrating ML within anticancer drug discovery pipelines and represent a valuable starting point for the development of novel CDK9 inhibitors. Full article

This review highlights Artemisia annua, a medicinal plant which grows in the Kingdom of Saudi Arabia, known for its abundant therapeutic properties. A. annua serves as a rich source of various bioactive compounds, including sesquiterpenoid lactones, flavonoids, phenolic acids, and coumarins. Among these, artemisinin and its derivatives are most extensively studied due to their potent antimalarial properties. Extracts and isolates of A. annua have demonstrated a range of therapeutic effects, such as antioxidant, anticancer, anti-inflammatory, antimicrobial, antimalarial, and antiviral properties. Given its significant antiviral activity, A. annua could be investigated for the development of new nutraceutical bioactive compounds to combat SARS-CoV-2. Artificial Intelligence (AI) can assist in drug discovery by optimizing the selection of more effective and safer natural bioactives, including artemisinin. It can also predict potential clinical outcomes through in silico modeling of protein–ligand interactions. In silico studies have reported that artemisinin and its derivatives possess a strong ability to bind with the Lys353 and Lys31 hotspots of the SARS-CoV-2 spike protein, demonstrating their effective antiviral effects against COVID-19. This integrated approach may accelerate the identification of effective and safer natural antiviral agents against COVID-19. Full article

Despite substantial progress in the field of bladder cancer management, the disease continues to represent an important health issue characterized by increased recurrence and progression rates. This is largely attributed to cancer stem cells (CSCs), a unique cell subpopulation capable of self-renewal, differentiation and resistance to conventional anti-cancer therapies. At the same time, our understanding of cancer biology has been revolutionized by the identification of non-coding RNAs (ncRNAs), a heterogeneous group of RNA molecules that do not translate into proteins yet function as pivotal regulators of gene expression. Emerging evidence demonstrates that ncRNAs modulate key hallmarks of CSCs, including self-renewal, epithelial–mesenchymal transition and drug resistance. This review investigates the intricate interplay between ncRNAs and the core signaling pathways that underlie bladder CSC biology. Unravelling the nexus between CSCs and ncRNAs is crucial for developing novel diagnostic biomarkers, better prognostic tools and innovative therapeutic strategies for patients with bladder cancer. Full article

Aberrant activation of fibroblast growth factor receptor 1 (FGFR1) drives tumor progression in multiple cancer types, yet existing FGFR1 inhibitors suffer from suboptimal target selectivity and dose-limiting toxicities. This study describes an integrated computational approach for the identification of novel FGFR1 inhibitors. We established a computational pipeline incorporating ligand-based pharmacophore modeling, multi-tiered virtual screening with hierarchical docking (HTVS/SP/XP), and MM-GBSA binding energy calculations to evaluate interactions within the FGFR1 kinase domain. From an initial library of 9019 anticancer compounds, three hit compounds exhibited superior FGFR1 binding affinity compared to the reference ligand 4UT801. Scaffold hopping was performed to generate 5355 structural derivatives, among which candidate compounds 20357a–20357c showed improved bioavailability and reduced toxicity as predicted by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. Molecular dynamics (MD) simulations validated stable binding modes and favorable interaction energies for these candidates. Collectively, our study identifies structurally novel FGFR1 inhibitors with optimized pharmacodynamic and safety profiles, thereby advancing targeted anticancer drug discovery. Full article

Ferroptosis is a novel iron-sensitive subtype of regulated cell death (RCD), persisting under extreme lipid peroxidation and iron/redox imbalances. Unlike apoptosis, necroptosis, and pyroptosis, ferroptosis is a signaling-driven process mediated through iron metabolism imbalance, polyunsaturated fatty acid (PUFA) exceeding oxidation, and defects in its protective systems like Xc-/GSH/GPx4. Specifically, this review establishes that iron-driven ferroptosis is a central underlying pathomechanistic factor in a broad range of human diseases. Significantly, whether its modulation is therapeutic, it is entirely conditional on the specific disease context. Thus, its induction can provide a promising antidote for destructive cancer cells when conjoined with immuno-therapies to boost anticancer immunity. Conversely, iron-mediated ferroptosis suppression is a key factor in countering destructive changes in a whole range of degenerative and acute injuries. Current therapeutic approaches include iron chelators, lipid oxidation inhibitors, GPx4 activators, natural and active compounds, and novel drug delivery systems. However, against all odds and despite its intense therapeutic promise, its translation into a practical medicinal strategy faces many difficulties. Thus, a therapeutic agent specifically focused on its modulation is still lacking. The availability of selective biologic markers is a concern. The challenges in the direct pathologic identification of ferroptosis in a complex in vivo systemic scenario remain. Current avenues for its future development are pathogen infections, the discovery of novel regulating factors, and novel approaches to personalized medicine centered on its organ-level in vivo signatures. Full article

Skin cancer is increasing worldwide, with melanoma being its most aggressive and lethal form due to its high metastatic potential. Despite therapeutic advances, drug resistance remains a challenge, highlighting the need to explore new anticancer agents. Leptocarpha rivularis is a native plant of Chile, locally called “Palo negro”, and is traditionally used in medicine by the Mapuche people. L. rivularis produces bioactive germacrene sesquiterpenoids with cytotoxic, antioxidant, anti-inflammatory and anti-angiogenic properties. This study reports for the first time the isolation of ovatifolin from aerial parts of L. rivularis and its identification by NMR and X-ray diffraction, together with its antiproliferative activity against two melanoma cell lines. The results show that ovatifolin has cytotoxic activity against the cell lines A2058 and A375, with an IC₅₀ of 27.6 (90.2 µM) and 18.4 µg/mL (60.1 µM), respectively, evaluated by live-cell IncuCyte^® analysis. Moreover, ovatifolin arrests colony formation in a clonogenic assay, with an IC₅₀ of 3.26 (10.6 μM) and 3.65 µg/mL (11.9 μM) in these same cell lines. Therefore, ovatifolin increased intracellular ROS and decreased the mitochondrial membrane potential (ΔΨ m). Cell death studies using Annexin V showed that its cytotoxic activity is partially caused by non-specific apoptosis, which was corroborated by the caspase inhibitor Z-VAD with an incomplete recovery of the cell death process. Full article

Nature has long served as a prolific source of bioactive compounds, offering structurally diverse scaffolds for the development of therapeutics. In recent years, increasing attention has been given to nature-inspired covalent inhibitors, molecules that form covalent bonds with pathogen- or cancer-specific targets, due to their potential selectivity and sustained biological activity. This review explores the landscape of covalent inhibitors derived from natural sources, with a focus on compounds from fungi, marine organisms, bacteria and plants. In particular, emphasis is placed on the molecular mechanisms through which these compounds exert their activity against different types of pathogens and other biomedically relevant targets, highlighting key structural motifs that facilitate covalent interactions. Furthermore, the review discusses recent advances in synthetic modification, target identification, and optimization strategies that bridge natural compound discovery with modern drug development. By drawing insights from nature’s chemical repertoire, this work ultimately displays the potential of natural covalent inhibitors as a promising foundation for next-generation anti-infective and anticancer therapeutics. Full article

The Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the co-crystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structure-based modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors. Full article

Integrated pharmacokinetic (PK) and pharmacodynamic (PD) models are essential for the understanding of quantitative relationship between drug exposure and response towards the identification of optimal dosing regimens in drug development and clinical therapy. This article summarizes the common PK–PD models being established in oncology, with a focus on combination therapies. Among them, the PK models include those used for practical non-compartmental and compartmental analyses, as well as those for physiologically based modeling that describe and predict exposure to various chemotherapy, targeted therapy, and immunotherapy drugs. Built on proper natural disease progression models, such as the empirical logistic growth curve, the Gompertzian growth model, and their modifications, the integrated PK–PD models recapitulate and predict antitumor drug efficacy, in which the PD models include practical indirect response model and various tumor growth inhibition models, as driven by the mechanistic actions of the drugs administered. Since anticancer drugs are usually co-administered, PK–PD modeling has been extended from monotherapy to combination therapy. However, relying on a single interaction factor or parameter to capitulate complex drug interactions, predict outcomes of different combinations, and determine possible synergism is problematic. Considering the apparent contributions from individual drugs following mutual interactions, a new PK–PD model has been developed for combination therapy, which may be integrated with proper algorism (e.g., the Combination Index method) to critically define combination effects, synergism, additivity, or antagonism. As drug combinations become more complex and individual drug actions are variable, these models should be optimized further to advance the understanding of PK–PD relationships and facilitate the development of improved therapies. Full article

Background: Defining specific molecular targets for cancer therapeutics remains a significant challenge in oncology. Many Food and Drug Administration (FDA)-approved anticancer drugs have incomplete target profiles, which limits our understanding of their mechanisms of action and opportunities for drug application. In this context, this study aimed to establish novel, biologically meaningful relationships between anticancer drugs and protein-coding genes. Methods: We developed a pharmacogenomic method that integrates transcriptomic data with drug activity data from the NCI-60 cancer cell line panel to study the interactions between 124 FDA-approved anticancer drugs and 399 cancer-related genes. Through this analysis, we identified gene–drug relationships and created a bipartite interaction network. To evaluate drug similarity, we developed a new index called the B-index. This novel similarity coefficient measures the association between two drugs based on their shared gene targets in the network. The index calculates the intersection of two sets of drug targets while considering the relative proportion of targets exhibited by each drug. For an independent assessment, we compared this network-based similarity with the chemical structural similarity of the drugs, computed based on two structural coefficients: Maximum Common Substructure and Tanimoto. Results: The study identified 1304 statistically significant drug–gene relationships, providing a large-scale network of pharmacogenomic interactions. Clustering analysis of the network, based on the B-index, grouped drugs with common targets together. This grouping was consistent with well-established drug classes and structural characteristics. Well-established drug pairs, such as cytarabine–gemcitabine or afatinib–neratinib, exhibited high B-index and structural similarity values, validating the methodology. Several novel gene associations were discovered, yielding testable hypotheses for mechanism-based repurposing. Conclusions: This work presents a comprehensive, network-based strategy for elucidating cancer drug targets by combining gene expression and drug activity profiles. Additionally, the B-index provides an alternative to conventional chemical similarity metrics, which can facilitate the identification of new therapeutic relationships and inform new drug applications and repositioning. These findings pave the way for the proposal of novel oncology drug targets. Full article

Scorpion venom is a rich source of bioactive compounds with significant potential for anticancer drug development. Its diverse molecular composition, including neurotoxins, antimicrobial peptides, and enzymes, provides a vast library for therapeutic innovation. Proteomic analyses have characterized venom composition in several species, while further functional assays have clarified their anticancer mechanisms. This review synthesizes current knowledge on scorpion venom-derived peptides with demonstrated anticancer activity, which selectively target ion channels, induce apoptosis, or disrupt tumor microenvironments. Where available, we highlight proteomic studies that have identified these components and discuss their structural features relevant to drug design. We also examine clinical applications and the challenges in translating venom peptides into therapies. The crucial and growing role of proteomics in this field, particularly for venom fractionation, component identification, and structural characterization, is critically evaluated. Full article

Drug repurposing refers to the systematic identification of new therapeutic uses for existing drugs. Unlike traditional de novo drug discovery, which is expensive and time-consuming, repurposing leverages compounds with already established safety, pharmacokinetic, and pharmacodynamic profiles. In this study, we propose a drug repositioning model based on low-dimensional transcriptomic representations to investigate the relationship between known anticancer drugs and non-anticancer compounds. We analyzed LINCS L1000 data (1170 drugs; 824 anticancer, 346 non-anticancer). Data were projected with UMAP, PCA, and t-SNE. For each anticancer drug and for each method, we retrieved the k = 5 nearest non-anticancer neighbors and ranked candidates by recurrence frequency across all anticancer queries. We identified Ergometrine, Mupirocin, and (S)-blebbistatin among the most frequent non-anticancer drugs with a close association with drugs known to be anticancer. In addition, we performed a local neighborhood enrichment around the three candidates. Regarding Ergometrine (DB01253), in UMAP, 44/50 neighbors were anticancer (88.0% vs. global baseline 70.5%; hypergeometric BH-adjusted p = 0.0039). Considering (S)-blebbistatin (DB01944) in UMAP, 41/50 neighbors were anticancer (82.0% vs. 70.5%; BH-adjusted p = 0.0435). Mupirocin (DB00410) in UMAP had 44/50 neighbors as anticancer (88.0% vs. 70.5%; BH-adjusted p = 0.0039). Future research should explore the three drugs with in vivo models, investigating their possible synergies. Full article

PARP inhibitors exploit synthetic lethality in BRCA1/2-mutated ovarian cancers but are limited by emerging therapeutic resistance. Therefore, novel biomarkers predicting PARP inhibitor response are urgently needed. In this study, we performed integrative analysis using drug sensitivity, patient survival, gene dependency, and expression data to identify biomarkers associated with PARP inhibitor response in ovarian cancer. Mutations in BRCA1, MLL2, NF1, and SMARCA4 were associated with increased sensitivity to PARP inhibitors, suggesting potential synthetic lethality with PARP1. In contrast, SMAD4 mutations were linked to PARP inhibitor resistance, and low SMAD4 expression was associated with poor overall survival in patients with ovarian cancer. Further gene dependency score (GDS)-based screening revealed 51 candidate genes potentially involved in SMAD4-mediated resistance. Functional enrichment revealed associations with stress response, tumor-associated signaling pathways, and additional processes. Subsequent correlation and survival analyses nominated ACACA, PRPF4B, and TUBD1 as potential therapeutic targets. Notably, low ACACA expression in patients with low SMAD4 expression was associated with improved survival, indicating its relevance in overcoming PARP inhibitor resistance. This study contributes to predicting clinical outcomes in ovarian cancer and developing personalized treatment strategies. Full article

Background: Nitric oxide (NO) is a gaseous free radical produced from L-arginine by the nitric oxide synthase (NOS) enzymes. NO exerts a dose-dependent biphasic effect on lung cancer development, angiogenesis, and dissemination. The widespread contribution of nitric oxide signaling to lung cancer biology has cast a spotlight on the identification of NO-based therapeutic approaches as well as the use of fractional exhaled NO (FeNO) as a prognostic biomarker of clinical control. However, the significance of lung cancer treatment and prognosis has not been fully elucidated. Objective: This narrative review gives an overview of NO in lung cancer, focusing on its therapeutic and prognostic implications. Results: FeNO may help to assess the complications associated with non-pharmacological treatments, including postoperative pneumonia and radiation pneumonitis. By contrast, the role of FeNO dynamics during pharmacological treatment is still largely unexplored due to the suppressive effect of chemotherapy on FeNO levels. The rise of immunotherapy may pave the way to a better evaluation of FeNO as a prognostic biomarker of treatment response. The dichotomous involvement of NO in lung cancer events has led to the adoption of several NO-centered treatments that are focused on both inhibiting and enhancing NO signaling. However, NO chemical and biological characteristics have hindered its implementation in clinical practice. Conclusions: In the coming years, the advancements in drug delivery systems may lead to more effective anti-cancer applications of NO by improving tumor targeting and minimizing the systemic side effects. Together, our findings emphasize the promising role of NO in lung cancer treatment, underscoring the challenges and avenues for future research. Full article

Agarwood, valued for its resin, has long been used in perfumery, incense, and traditional medicine. Its resin is primarily derived from species of Aquilaria and is produced through a still-unknown process in response to biotic or abiotic stress. Concerns regarding agarwood’s sustainability and conservation have emerged because of the substantial loss of natural resources due to overharvesting and illegal trade. To address these concerns, artificial techniques are being used to produce agarwood. The mechanism underlying agarwood production must be elucidated to enhance yield. The authentication of agarwood species is challenging because of morphological similarities between pure and hybrid Aquilaria species. Techniques such as DNA barcoding, molecular marker assessment, and metabolomics can ensure accurate identification, facilitating conservation. Artificial intelligence and machine learning can support this process by enabling rapid, automated identification on the basis of genetic and phytochemical data. Advances in resin induction methods (e.g., fungal inoculation) and chemical induction treatments are improving yield and quality. Endophytic fungi and bacteria promote resin production at minimal harm to the tree. Agarwood’s pharmacological potential—antimicrobial, anti-inflammatory, and anticancer effects—has driven research into bioactive compounds such as sesquiterpenes and flavonoids for the development of novel drugs. This systematic review synthesized current evidence on species authentication, induction techniques, and pharmacological properties. The findings may guide future research aimed at ensuring sustainable use and enhancing the medicinal value of agarwood. Full article