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Ferroptosis is a form of iron-mediated regulated cell death driven by lipid peroxidation (LPO). It has not only further improved our understanding of the cell death mechanism but also shown enormous potential in therapeutic applications. While the precise subcellular itinerary of ferroptotic cell death remains a subject of ongoing debate, radical-trapping antioxidants (RTAs) are widely recognized as efficient antiferroptotic agents due to their ability to interrupt LPO chain propagation. Here, we highlight recent pioneering works in the field, showing how probes derived from RTAs serve as powerful chemical tools for resolving the mechanism of ferroptosis across multiple cellular compartments. Show less

Hepatocellular carcinoma (HCC) is a highly refractory malignancy, for which treatment relies on molecule targeted therapy and/or conventional chemotherapy in clinic. However, these approaches generally suffer from limited efficacy or severe toxicity, restricting their applications. Guided by the targeted drug conjugate (TDC) strategy, the pharmacophore of lenvatinib was modified by incorporating DN604 (C₆H₁₀N₂O₅Pt), a carboplatin analogue, to generate a Pt(II) complex Len-604 (C₃₀H₃₃ClN₈O₉Pt). This compound was found to possess the specific capability to bind to fibroblast growth factor receptor 4 (FGFR4) protein both in vitro and in vivo, facilitating targeted delivery of DN604 to tumor sites and consequently triggering serious DNA damage in cancer cells. It exhibited potent cytotoxicity against human hepatocellular carcinoma cell lines HUH-7 and SMMC-7721, with IC₅₀ values of 5.62 and 5.64 μM, respectively. Significantly, in HUH-7 xenograft models, Len-604 exhibited stronger antitumor activity than lenvatinib, while showing lower toxicity than cisplatin and its physical mixture with lenvatinib. Show less

Proteomics has matured into a discipline capable of quantifying nearly every protein encoded by the genome, yet it remains largely blind to the true operational units of physiology: proteoforms. Each proteoform—defined by a specific sequence and post-translationally modified state—represents a unique molecular identity with distinct chemical, functional, and structural properties. This review proposes the proteoform functor: a mathematical map between the abstract proteoform state space and the realised physiological space of biological function—and ultimately complex phenotypes. This mapping is not linear or additive. Rather, it is hierarchical, nonlinear, and context-dependent, reflecting the emergent complexity of life. Without resolving proteoforms, proteomics risks describing shadows of biology rather than its material substance. Deciphering complex phenotypes, demands a shift from bulk protein averages to revealing the precise molecular identities—proteoforms—that give rise to physiology. Show less

We elucidate the mechanism of the manganese-catalyzed N-alkylation of aniline with benzyl alcohol mediated by a bis(1,2,3-triazolylidene) Mn(I) complex through a combination of experimental studies and density functional theory (DFT) calculations. Activation of the precatalyst by a base leads to the formation of an anionic alkoxo complex featuring a deprotonated methylene bridge, which is identified as the catalytically active species. Notably, the methylene linker exhibits previously unrecognized noninnocent behavior, undergoing reversible deprotonation and participating directly in proton-transfer steps of the catalytic cycle. Kinetic isotope effects and deuterium-labeling experiments support the involvement of both hydride transfer and alcohol-assisted proton processes in the rate-determining steps. These findings uncover a new mode of metal-ligand cooperation in triazolylidene-based manganese catalysts and provide mechanistic guidelines for the design of cooperative ligands in base-metal-borrowing hydrogen catalysis. Show less

Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design. Show less

Ferroptosis suppressor protein 1 (FSP1) has emerged as a critical regulator of ferroptosis, an iron-dependent form of programmed cell death with significant therapeutic potential in cancer treatment. Despite rapidly expanding research, current knowledge on FSP1 remains fragmented across various tumor types and experimental contexts. The aim of this review is to systematically integrate the latest evidence regarding the molecular structure, biological functions, and regulatory mechanisms controlling FSP1 expression, emphasizing its involvement in tumor progression and resistance to therapy. Readers can expect comprehensive coverage of FSP1's structural characteristics, enzymatic roles, transcriptional and post-transcriptional regulation, and its pathological significance in hepatocellular carcinoma, colorectal cancer, pancreatic cancer, gastric cancer, breast cancer, lung cancer, and leukemia. We further evaluate emerging therapeutic strategies targeting FSP1 aimed at overcoming resistance and improving clinical outcomes. Relevant studies were systematically identified by searching PubMed, Web of Science, and Embase databases, focusing particularly on the recent and impactful literature to guide future research directions. Show less

Mitochondria are associated with cellular energy metabolism, proliferation, and mode of death. Damage to mitochondrial DNA (mtDNA) greatly affects mitochondrial function by interfering with energy production and the signaling pathway. Monofunctional trinuclear platinum complex MTPC demonstrates different actions on the mtDNA of cancerous and normal cells. It severely impairs the integrity and function of mitochondria in the human lung cancer A549 cells, such as dissipating mitochondrial membrane potential, decreasing the copy number of mtDNA, interfering in nucleoid proteins and polymerase gamma gene, reducing adenosine triphosphate (ATP), and inducing mitophagy, whereas it barely affects the mtDNA of the human kidney 2 (HK-2) cells. Moreover, MTPC promotes the release of mtDNA into the cytosol and stimulates the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway, thus showing the potential to trigger antitumor immunity. MTPC displays significant cytotoxicity against A549 cells, while it exhibits weak toxicity toward HK-2 cells, therefore displaying great advantage to overcome the lingering nephrotoxicity of platinum anticancer drugs. Discrepant effects of a metal complex on mitochondria of different cells mean that targeting mitochondria has special significance in cancer therapy. Show less

Background Psychiatric diseases are often treated with several drugs. In addition, the risk of developing somatic comorbidities which may require drug therapy is higher in patients with than in patients without psychiatric diseases. Further on, the risk of drug-drug interactions (DDI) increases with the number of drugs taken. The aim of this study was to analyze whether already known DDI between psychiatric drugs and somatic medications still occur in everyday clinical practice. Methods We analyzed 9,276 spontaneous adverse drug reaction (ADR) reports from Germany contained in the Show less

Abstract Transcription of ribosomal RNA (rRNA) by RNA Polymerase I (Pol I) is often upregulated in cancer to facilitate rapid cell growth and proliferation, and has emerged as a potential target for chemotherapeutic agents. BMH-21 and Pt(II) chemotherapeutic agent oxaliplatin are well documented as inhibitors of Pol I activity, however the underlying mechanisms for this inhibition are not completely understood. Here, we applied chromatin immunoprecipitation sequencing (ChIP-seq) techniques and immunofluorescence imaging to probe the influence of oxaliplatin and BMH-21 on Pol I machinery. We demonstrate oxaliplatin and BMH-21 induce early nucleolar stress leading to the formation of “nucleolar caps” containing Pol I and upstream binding factor (UBF) which corresponds with broad reductions in ribosomal DNA (rDNA) occupancy of Pol I. Distinct occupancy patterns for the two compounds are revealed in ChIP-seq experiments. Taken together, our findings suggest that in vivo, oxaliplatin does not induce Pol I inhibition via interrupting a specific step in Pol I transcription, while treatment with BMH-21 induced unique polymerase stalling at the promoter and terminator regions of the human ribosomal RNA gene. Show less

In the healthcare industry, the ever-increasing volume of clinical trial data presents challenges for ensuring drug safety and detecting adverse drug reactions (ADRs). This study aims to address the challenge of accurately detecting Serious Adverse Events (SAEs) in pharmacovigilance, a critical component in ensuring drug safety during and after clinical trials. The key problem lies in the underreporting and delayed detection of Adverse Drug Reactions (ADRs) due to the heterogeneous nature of medical data, class imbalance, and the limited scope of traditional monitoring techniques. This study proposes a hybrid AI-driven framework that integrates structured (e.g., patient demographics, lab results) and unstructured data (e.g., clinical notes) to detect ADRs using advanced deep learning and NLP methods. The objective is to outperform traditional signal detection methods and provide interpretable predictions to aid clinicians in real-time. By leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, including Random Forests, Gradient Boosting Machines, and Convolutional Neural Networks (CNNs), our model aims to identify potential ADRs across different patient subgroups. Through meticulous feature engineering and the application of techniques to address data imbalance, our model demonstrates improved accuracy and interpretability in predicting ADRs. The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). These findings suggest that specific demographic and clinical factors significantly influence the likelihood of adverse reactions, offering valuable insights for targeted monitoring and risk mitigation strategies[11]. This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes.•The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data.•Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model's performance. Among the models tested, the CNN model with BERT achieved the highest accuracy, providing valuable insights into the potential of deep learning for enhancing pharmacovigilance practices.•These findings suggest that an inclusion of diverse clinical data when supplied to advanced ML and NLP techniques can significantly improve the detection of ADRs, leading to better alignment with the fundamental principles of Good Clinical Practice (GCP). Show less

Abstract Cells use the covalent attachment of Ubiquitin (Ub) chains to mark proteins for degradation, alter their cellular localization or drive their association. Protein fate is encoded in the distinct poly-Ub linkages, exploiting the vast combinatorial space of linear and branched Ub modifications. AlphaFold has emerged as a powerful tool to predict the structure of protein-protein complexes. However, standard AlphaFold does not consider linkages between individual protein chains, limiting its applicability to Ub chains. The near complete conservation of the ubiquitin sequence and the large number of binding partners suppresses coevolutionary signals, further challenging the prediction of poly-Ub complex structures. We address this challenge, first, by introducing correlated cysteine mutations to induce linkage-specific proximity of Ubs in complex with interacting proteins. Second, we introduce short covalent linker groups in AlphaFold 3 calculations that mimic the isopeptide bonds between linked lysines and Ub C-terminal carboxylates. These two approaches enable the robust structural modeling of complexes involving poly-Ub chains with AlphaFold. The linker approach is general and can be used for other covalent inter-chain connections and to enforce distance restraints for integrative structural modeling. Show less

A series of cyclometalated platinum-(II) complexes bearing neutral isocyanide or acyclic diaminocarbene ancillary ligands were designed and developed. Their photophysical properties were systematically studied in different polymer systems: poly-(methyl methacrylate), polystyrene, poly-(isobornyl acrylate), and copolymers based on them. The dependence of luminescent characteristics on the concentration of the doped complex (0.5-10 wt %), composition, and properties of the polymer material was investigated as key factors for the measurement of quantum yields, excited-state lifetimes, and spectral profiles in routine studies. Show less

In nature, life is inherently dissipative. Cells continuously consume energy (such as ATP) to sustain homeostasis, drive metabolism, and respond dynamically to environmental cues. Inspired by this principle, we develop a synthetic protocell system that exhibits dissipative behavior and initiates metabolic-like processes. Our design features synthetic vesicles formed from a cationic surfactant, which undergo a fuel-driven transformation into coacervate protocells via liquid-liquid phase separation. Dissipation is achieved through alkaline phosphatase (ALP)-catalyzed ATP hydrolysis, driving the reverse transition from coacervates back to vesicles. The distinct physicochemical properties and internal organization of vesicle and coacervate protocells enable us to design functional regulators capable of producing secondary signals, such as fluorescence and enzymatic products. This work offers a strategy for engineering enzymatic reaction-regulated dissipative behaviors of protocell systems that emulate key aspects of cellular metabolism, representing a step toward synthetic life-like systems with dynamic behavior and functional complexity. Show less

AbstractPhotoactivatable metal complexes offer the prospect of novel drugs with low side effects and new mechanisms of action to combat resistance to current therapy. We highlight recent progress in the design of platinum, ruthenium, iridium, gold and other transition metal complexes, especially for applications as anticancer and anti‐infective agents. In particular, understanding excited state chemistry related to identification of the bioactive species (excited state metallomics/pharmacophores) is important. Photoactivatable metallodrugs are classified here as photocatalysts, photorelease agents and ligand‐activated agents. Their activation wavelengths, cellular mechanisms of action, experimental and theoretical metallomics of excited states and photoproducts are discussed to explore new strategies for the design and investigation of photoactivatable metallodrugs. These photoactivatable metallodrugs have potential in clinical applications of Photodynamic Therapy (PDT), Photoactivated Chemotherapy (PACT) and Photothermal Therapy (PTT). Show less

LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 38 million PubMed abstracts and 6.6 Show less

High-Grade Serous Ovarian Cancer (HGSOC) is the most common and lethal subtype of ovarian cancer, known for its high aggressiveness and extensive genomic alterations. Typically diagnosed at an advanced stage, HGSOC presents formidable challenges in drug therapy. The limited efficacy of standard treatments, development of chemoresistance, scarcity of targeted therapies, and significant tumor heterogeneity render this disease incurable with current treatment options, highlighting the urgent need for novel therapeutic approaches to improve patient outcomes. In this study we report a straightforward and stereoselective synthetic route to novel Pd(II)-vinyl and -butadienyl complexes bearing a wide range of monodentate and bidentate ligands. Most of the synthesized complexes exhibited good to excellent in vitro anticancer activity against ovarian cancer cells. Particularly promising is the water-soluble complex bearing two PTA (1,3,5-triaza-7-phosphaadamantane) ligands and the Pd(II)-butadienyl fragment. This compound combines excellent cytotoxicity towards cancer cells with substantial inactivity towards non-cancerous ones. This derivative was selected for further studies on ex vivo tumor organoids and in vivo mouse models, which demonstrate its remarkable efficacy with surprisingly low collateral toxicity even at high dosages. Moreover, this class of compounds appears to operate through a ferroptotic mechanism, thus representing the first such example for an organopalladium compound. Show less

Drug discovery has seen dramatic change over the last 25 years. The vertically integrated large company model prevalent for more than 50 years has at least partly been replaced with a more distributed drug discovery enterprise that includes large numbers of small research organizations. Show less

Academic Editor: Sabrina Venditti Received: 22 May 2025 Revised: 5 July 2025 N6-methyladenosine (m6A) represents the most common and thoroughly investigated RNA modification and exerts essential functions in regulating gene expression through influencing the RNA stability, the translation efficiency, alternative splicing, and nuclear export processes. The rapid development of high-throughput sequencing approaches, including miCLIP and MeRIP-seq, has profoundly transformed epitranscriptomics research. Show less