Protein Sci. 2025 Apr;34(4):e70083. doi: 10.1002/pro.70083.

ABSTRACT

The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMP discovery using modern deep learning techniques has rarely been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. AMPlify and the companion scripts supporting the AMP mining workflow presented herein are publicly available at https://github.com/bcgsc/AMPlify.

PMID:40100125 | DOI:10.1002/pro.70083

Sarcoidosis Vasc Diffuse Lung Dis. 2025 Mar 18;42(1):15378. doi: 10.36141/svdld.v42i1.15378.

NO ABSTRACT

PMID:40100114 | DOI:10.36141/svdld.v42i1.15378

Biomol Biomed. 2025 Mar 17. doi: 10.17305/bb.2025.12010. Online ahead of print.

ABSTRACT

Differentiating early-stage breast cancer from benign breast masses is crucial for radiologists. Additionally, accurately assessing axillary lymph node metastasis (ALNM) plays a significant role in clinical management and prognosis for breast cancer patients. Chest computed tomography (CT) is a commonly used imaging modality in physical and preoperative evaluations. This study aims to develop a deep learning model based on chest CT imaging to improve the preliminary assessment of breast lesions, potentially reducing the need for costly follow-up procedures such as magnetic resonance imaging (MRI) or positron emission tomography-CT and alleviating the financial and emotional burden on patients. We retrospectively collected chest CT images from 482 patients with breast masses, classifying them as benign (n = 224) or malignant (n = 258) based on pathological findings. The malignant group was further categorized into ALNM-positive (n = 91) and ALNM-negative (n = 167) subgroups. Patients were randomly divided into training, validation, and test sets in an 8:1:1 ratio, with the test set excluded from model development. All patients underwent non-contrast chest CT before surgery. After preprocessing the images through cropping, scaling, and standardization, we applied ResNet-34, ResNet-50, and ResNet-101 architectures to differentiate between benign and malignant masses and to assess ALNM. Model performance was evaluated using sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and the area under the curve (AUC). The ResNet models effectively distinguished benign from malignant masses, with ResNet-101 achieving the highest performance (AUC: 0.964; 95% CI: 0.948-0.981). It also demonstrated excellent predictive capability for ALNM (AUC: 0.951; 95% CI: 0.926-0.975). In conclusion, these deep learning models show strong diagnostic potential for both breast mass classification and ALNM prediction, offering a valuable tool for improving clinical decision-making.

PMID:40100034 | DOI:10.17305/bb.2025.12010

Anal Chem. 2025 Mar 18. doi: 10.1021/acs.analchem.4c07061. Online ahead of print.

ABSTRACT

Tetracyclines are widely used in bacteria infection treatment, while the subtle chemical differences between tetracyclines make it a challenge to accurate discrimination via biosensors. A 3D fluorescence spectrum can provide fingerprint structure information for many analytes, but a single probe-based method is prone to information overlap. Here, aptamers are first reported to obtain abundant information in a ratiometric, 3D fluorescence spectrum for deep learning to accurately discriminate tetracyclines. So, each tetracycline can be related to a distinct, ratiometric, 3D fluorescence spectrum via the strategy of dual biomolecules recognition. One artificial neural network model can efficiently treat this fingerprint information, and the qualitative/quantitative analysis of tetracyclines is successfully realized. The proposed dual biomolecule recognition strategy has been demonstrated to show a higher accuracy than a conventional single probe method. So, the ratiometric 3D fluorescence spectrum can enrich the fingerprint information for deep learning, providing a new strategy for 3D fluorescence-based analytes discrimination.

PMID:40099919 | DOI:10.1021/acs.analchem.4c07061

Mol Inform. 2025 Mar;44(3):e202400159. doi: 10.1002/minf.202400159.

ABSTRACT

The challenges of drug discovery from hit identification to clinical development sometimes involves addressing scaffold hopping issues, in order to optimise molecular biological activity or ADME properties, or mitigate toxicology concerns of a drug candidate. Docking is usually viewed as the method of choice for identification of isofunctional molecules, i. e. highly dissimilar molecules that share common binding modes with a protein target. However, the structure of the protein may not be suitable for docking because of a low resolution, or may even be unknown. This problem is frequently encountered in the case of membrane proteins, although they constitute an important category of the druggable proteome. In such cases, ligand-based approaches offer promise but are often inadequate to handle large-step scaffold hopping, because they usually rely on molecular structure. Therefore, we propose the Interaction Fingerprints Profile (IFPP), a molecular representation that captures molecules binding modes based on docking experiments against a panel of diverse high-quality proteins structures. Evaluation on the LH benchmark demonstrates the interest of IFPP for identification of isofunctional molecules. Nevertheless, computation of IFPPs is expensive, which limits its scalability for screening very large molecular libraries. We propose to overcome this limitation by leveraging Metric Learning approaches, allowing fast estimation of molecules IFPP similarities, thus providing an efficient pre-screening strategy that in applicable to very large molecular libraries. Overall, our results suggest that IFPP provides an interesting and complementary tool alongside existing methods, in order to address challenging scaffold hopping problems effectively in drug discovery.

PMID:40099892 | DOI:10.1002/minf.202400159

J Comput Chem. 2025 Mar 30;46(8):e70081. doi: 10.1002/jcc.70081.

ABSTRACT

With the development of artificial neural networks (ANNs), its applications in chemistry have become increasingly widespread, especially in the prediction of various molecular properties. This work introduces the X2-PEC method, that is, the second generalization of the X1 series of ANN methods developed in our group, utilizing pair energy correction (PEC). The essence of the X2 model lies in its feature vector construction, using overlap integrals and core Hamiltonian integrals to incorporate physical and chemical information into the feature vectors to describe atomic interactions. It aims to enhance the accuracy of low-rung density functional theory (DFT) calculations, such as those from the widely used BLYP/6-31G(d) or B3LYP/6-31G(2df,p) methods, to the level of top-rung DFT calculations, such as those from the highly accurate doubly hybrid XYGJ-OS/GTLarge method. Trained on the QM9 dataset, X2-PEC excels in predicting the atomization energies of isomers such as C6H8 and C4H4N2O with varying bonding structures. The performance of the X2-PEC model on standard enthalpies of formation for datasets such as G2-HCNOF, PSH36, ALKANE28, BIGMOL20, and HEDM45, as well as a HCNOF subset of BH9 for reaction barriers, is equally commendable, demonstrating its good generalization ability and predictive accuracy, as well as its potential for further development to achieve greater accuracy. These outcomes highlight the practical significance of the X2-PEC model in elevating the results from lower-rung DFT calculations to the level of higher-rung DFT calculations through deep learning.

PMID:40099806 | DOI:10.1002/jcc.70081

Sarcoidosis Vasc Diffuse Lung Dis. 2025 Mar 18;42(1):15604. doi: 10.36141/svdld.v42i1.15604.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease. Lung cancer (LC) is among the most crucial comorbidity factors in patients with IPF. IPF patients that are diagnosed with LC have a reduced mean survival time. Therapeutic strategies for LC in patients with IPF need to be adapted according to the individual treatment risk. Life-threatening acute exacerbation (AE) of IPF may occur in association with cancer treatment, thereby severely restricting the therapeutic options for IPF-associated LC. Because LC and anticancer treatments can worsen the prognosis of IPF, the prevention of LC is as critical as managing patients with IPF.

PMID:40100103 | DOI:10.36141/svdld.v42i1.15604

Elife. 2025 Mar 18;13:RP96892. doi: 10.7554/eLife.96892.

ABSTRACT

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

PMID:40100251 | DOI:10.7554/eLife.96892

Protein Sci. 2025 Apr;34(4):e70091. doi: 10.1002/pro.70091.

ABSTRACT

Increasing insights into how sequence motifs in intrinsically disordered regions (IDRs) provide functions underscore the need for systematic motif detection. Contrary to structured regions where motifs can be readily identified from sequence alignments, the rapid evolution of IDRs limits the usage of alignment-based tools in reliably detecting motifs within. Here, we developed SHARK-capture, an alignment-free motif detection tool designed for difficult-to-align regions. SHARK-capture innovates on word-based methods by flexibly incorporating amino acid physicochemistry to assess motif similarity without requiring rigid definitions of equivalency groups. SHARK-capture offers consistently strong performance in a systematic benchmark, with superior residue-level performance. SHARK-capture identified known functional motifs across orthologs of the microtubule-associated zinc finger protein BuGZ. We also identified a short motif in the IDR of S. cerevisiae RNA helicase Ded1p, which we experimentally verified to be capable of promoting ATPase activity. Our improved performance allows us to systematically calculate 10,889 motifs for 2695 yeast IDRs and provide it as a resource. SHARK-capture offers the most precise tool yet for the systematic identification of conserved regions in IDRs and is freely available as a Python package (https://pypi.org/project/bio-shark/) and on https://git.mpi-cbg.de/tothpetroczylab/shark.

PMID:40100159 | DOI:10.1002/pro.70091

Protein Sci. 2025 Apr;34(4):e70084. doi: 10.1002/pro.70084.

ABSTRACT

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small-molecule data in the PDB necessitates specialized tools for effective analysis and visualization. PDBe has developed a number of tools, including PDBe CCDUtils (https://github.com/PDBeurope/ccdutils) for accessing and enriching ligand data, PDBe Arpeggio (https://github.com/PDBeurope/arpeggio) for analyzing interactions between ligands and macromolecules, and PDBe RelLig (https://github.com/PDBeurope/rellig) for identifying the functional roles of ligands (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. The enhanced ligand annotations and data generated by these tools are presented on the novel PDBe-KB ligand pages, offering a comprehensive overview of small molecules and providing valuable insights into their biological contexts (example page for Imatinib: https://pdbe.org/chem/sti). By improving the standardization of ligand identification, adding various annotations, and offering advanced visualization capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

PMID:40100137 | DOI:10.1002/pro.70084

J Exp Bot. 2025 Mar 18:eraf122. doi: 10.1093/jxb/eraf122. Online ahead of print.

ABSTRACT

Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As a central phenotypic trait, fruit development and its relative growth rate (RGR) result from interactions between gene regulation, metabolism and environment. In the present study, we carried out a multispecies transcriptomic analysis of nine different fruits. To illustrate fruit transcriptomes, transcripts were first compared using multivariate methods, revealing main similar profiles. They were then used as variables to predict four growth traits, i.e. RGR, developmental progress, fruit weight and protein content, using generalised linear models (GLMs) to decipher the mechanisms involving gene expression in development. The predictions were very satisfactory despite disparities when the model did not include the entire panel of fruit species. Based on orthogroups derived from BLAST and annotated consensus sequences from gene ontology (GO) terminology, variables annotated for metabolic processes, especially those involving cell wall carbohydrates and proteins, were found to be the most effective in predicting growth. In addition, predictions were improved for RGR when introducing a seven-day lag between transcript contents and growth traits, suggesting the necessity of considering the proteins produced to enhance phenotypic trait predictions. These original results showed that growth traits can be predicted very well with GLMs based on orthogroups from multi-species transcriptomes.

PMID:40099514 | DOI:10.1093/jxb/eraf122

Expert Opin Drug Discov. 2025 Mar 18. doi: 10.1080/17460441.2025.2481262. Online ahead of print.

ABSTRACT

INTRODUCTION: Drug discovery is a complex and multifaceted process driven by scientific innovation and advanced technologies. Next-Generation Sequencing (NGS) platforms, encompassing both short-read and long-read technologies, have revolutionized the field by enabling the high-throughput and cost-effective analysis of DNA and RNA molecules. Continuous advancements in NGS-based technologies have enabled their seamless integration across preclinical and clinical workflows in drug discovery, encompassing early-stage drug target identification, candidate selection, genetically stratified clinical trials, and pharmacogenetic studies.

AREA COVERED: This review provides an overview of the current and potential applications of NGS-based technologies in drug discovery and development process, including their roles in novel drug target identification, high-throughput screening, clinical trials, and clinical medication studies. The review is based on literature retrieval from the PubMed and Web of Science databases between 2018 and 2024.

EXPERT OPINION: As technologies advance rapidly, NGS enhances accuracy and generates vast datasets. These datasets are extensively integrated with other heterogeneous data in systems biology and are mined using machine learning to extract significant insights, thereby driving progress in drug discovery.

PMID:40099494 | DOI:10.1080/17460441.2025.2481262

Rev Salud Publica (Bogota). 2023 Mar 1;25(2):103211. doi: 10.15446/rsap.V25n2.103211. eCollection 2023 Apr.

ABSTRACT

The case of a 46-year-old male patient is presented. He was admitted to the emergency department with a clinical picture of oppressive chest pain of 10/10 intensity on the pain analog scale, which had been evolving for one hour. After evaluation and based on electrocardiographic and laboratory findings, the patient was diagnosed with acute myocardial infarction with st elevation. Additionally, aortic dissection and hypertensive emergency with end-organ damage to the heart were suspected due to intense precordial pain and blood pressure readings of 230/120 mmHg. As part of the therapeutic approach, 3 mg of intravenous morphine diluted in 10 ml of 0.9 % saline solution were administered. Following administration, the patient exhibited suspected hypersensitivity. Therefore, a suspected adverse event assessment was performed using the Naranjo algorithm, and it was established that the effects of morphine were plausible (category probable).

PMID:40099127 | PMC:PMC11254130 | DOI:10.15446/rsap.V25n2.103211

Hum Vaccin Immunother. 2025 Dec;21(1):2475616. doi: 10.1080/21645515.2025.2475616. Epub 2025 Mar 18.

ABSTRACT

Enhanced Passive Safety Surveillance was used to detect safety signals before the peak period of immunization with quadrivalent inactivated influenza vaccines (IIV4) in Finland (standard dose [SD]) and Germany (high dose [HD]) in the 2023-24 season. The primary objective was to evaluate adverse drug reactions (ADRs) occurring ≤7 days following IIV4 vaccination. Enrolled participants were vaccinated in routine clinical care settings and encouraged to report ADRs. Exposure data and ADR reports were collected in a near real-time manner using an electronic system. Vaccinee reporting rate (RR) with 95% confidence interval (CI) was calculated as the number of vaccinees reporting ≥ 1 ADR divided by total number of vaccinees. In Finland for SD-IIV4, among 1,003 vaccinees aged ≥ 6 months, 81 reported a total of 192 suspected ADRs occurring ≤ 7 days following vaccination (vaccinee RR 8.08%; 95% CI 6.46, 9.94). In Germany for HD-IIV4, among 1,075 vaccinees aged ≥ 60 years, 15 reported 46 ADRs that occurred in ≤ 7 days of vaccination (vaccinee RR 1.40%; 95% CI 0.78, 2.29). No safety signal was detected during this surveillance. The 2023-24 season surveillance did not suggest any clinically significant changes in safety profile compared with previously reported safety data for SD-IIV4 and HD-IIV4.

PMID:40098448 | DOI:10.1080/21645515.2025.2475616

Rev Salud Publica (Bogota). 2023 Jul 1;25(4):107594. doi: 10.15446/rsap.V25n4.107594. eCollection 2023 Aug.

ABSTRACT

OBJECTIVE: To analyze the reports of orphan diseases in Bogotá, in order to describe the epidemiological profile, based on the cases reported to the Public Health System (Sivigila), from January 2019 to March 2022.

METHODS: A descriptive and cross-sectional study was carried out in which the cases reported to Sivigila in Bogotá were analyzed in the period between January 2019 and March 2022. Absolute and relative frequencies, frequency distribution and prevalences and averages of different variables were calculated. notified in the notification sheets.

RESULTS: From January 2019 to March 2022, 10,399 patients with orphan diseases have been notified to Sivigila in Bogotá, of which 56.25% (5,849) are female and 43.75% (4,550) are female. male sex. 87.10% (9,060) of the cases belong to the contributory regime. The town with the highest number of reports was Suba with 15.85% (1,294). The most reported orphan diseases were: multiple sclerosis with 13.1% (1,363), amyotrophic lateral sclerosis with 4.04% (421) and Guillain-Barre syndrome with 3.6% (374). A patient with an orphan disease in Bogotá takes 61.3 months on average from the beginning of their symptoms to obtaining a diagnosis (SD 101.9).

CONCLUSIONS: From the notification to Sivigila in Bogotá, compared to the global prevalence, there is an under-registration of patients with orphan diseases and the delay in the diagnosis of these diseases is evident.

PMID:40098659 | PMC:PMC11648384 | DOI:10.15446/rsap.V25n4.107594

J Bone Metab. 2025 Feb;32(1):21-30. doi: 10.11005/jbm.24.787. Epub 2025 Feb 28.

ABSTRACT

BACKGROUND: This study aims to investigate the effect of genetic polymorphisms of vitamin D receptor (VDR), estrogen receptor 1 (ER1), and Col1a1 on the response to bisphosphonate (BP) therapy in women with postmenopausal osteoporosis (OP).

METHODS: Twenty-one women with postmenopausal OP who received alendronate, ibandronate, or zoledronic acid for one year were enrolled in this study. Bone mineral density (BMD) at the lumbar spine and femoral neck were assessed by dual energy X-ray absorptiometry at baseline and after 12 months. Serum osteocalcin levels were measured at baseline and after 12 months. Polymorphic sites of the genes encoding ER1, VDR and Col1a1 proteins were amplified by polymerase chain reaction and examined using restriction fragment length polymorphism. Response to BP treatment and change in osteocalcin levels were compared among women with different gene polymorphisms.

RESULTS: Ratio of responders to treatment regarding improvements in the BMD of lumbar spine and femoral neck was adequate in 76% and 62%, respectively. There was no significant difference in treatment response regarding BMD in either region or change in serum osteocalcin levels among different gene polymorphisms.

CONCLUSIONS: These findings did not support the potential role of VDR BsmI, Col1a1 Sp1, ER1 PvuII, or XbaI polymorphisms in predicting the response to BP therapy in women with postmenopausal OP. Further investigation with larger prospective studies is required.

PMID:40098426 | DOI:10.11005/jbm.24.787

Clin Transl Sci. 2025 Mar;18(3):e70200. doi: 10.1111/cts.70200.

ABSTRACT

Data on the effects of Roux-en-Y gastric bypass (RYGB) surgery on lansoprazole pharmacokinetics in morbidly obese patients are limited. This study aimed to evaluate the impact of RYGB surgery on the pharmacokinetic profile of lansoprazole in Thai morbidly obese patients. Participants received 30 mg of lansoprazole twice daily for 7 days before surgery and continued the regimen for 6 weeks post-surgery. Plasma lansoprazole concentrations were measured at predose (0), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, and 8 h after dosing, both pre- and post-surgery, using a validated high-performance liquid chromatography technique. CYP2C19 genotyping classified participants as normal metabolizers (*1/*1) or intermediate metabolizers (*1/*2 and *1/*3). Pharmacokinetic parameters, including the area under the plasma concentration-time curve from 0 to 8 h (AUC0-8 h), maximum plasma concentration (Cmax), and time to maximum concentration (Tmax), were compared before and after surgery. A total of 13 patients (mean age 37.0 ± 3.9 years; body mass index 54.0 ± 4.8 kg/m2) were enrolled. Post-surgery, AUC0-8 h and Cmax decreased by 16% (p = 0.009) and 31% (p = 0.003), respectively, while Tmax remained unchanged. A 30% reduction in Cmax (p = 0.007) was observed in CYP2C19 normal metabolizers, whereas no significant changes were noted in intermediate metabolizers. In conclusion, RYGB surgery significantly reduced lansoprazole systemic exposure, particularly in CYP2C19 normal metabolizers. Further studies are needed to explore the clinical implications of these pharmacokinetic changes and develop optimized treatment strategies for post-RYGB patients. Trial Registration: ClinicalTrials.gov identifier: TCTR20220118001.

PMID:40098302 | DOI:10.1111/cts.70200

Pharmacogenomics. 2025 Mar 17:1-14. doi: 10.1080/14622416.2025.2479414. Online ahead of print.

ABSTRACT

Receptor tyrosine kinase pathways are frequently deregulated in cancer. Inhibiting these pathways with small molecule inhibitors or monoclonal antibodies has become a crucial addition to the therapeutic armamentarium in oncology. Since the introduction of drugs that target receptor tyrosine kinase pathways, it has become evident that not all patients respond to treatment. Therefore, biomarkers to predict response and benefit of drugs targeting tyrosine kinases have been sought. Monoclonal antibodies targeting the Epidermal Growth Factor Receptor (EGFR), one of the four receptors of the EGFR family were among the first targeted therapies used in solid tumors. Two drugs of this class, cetuximab and panitumumab, have been used in patients with metastatic colorectal cancer initially without any biomarker requirement. Soon, it became clear that responses were mostly observed in patients without mutations in KRAS oncogene. Currently, additional mutations of the pathway, including non-exon 2 mutations in KRAS, mutations in the homologous GTPase NRAS, in kinase BRAF and PIK3CA and other pathway proteins, have been added in the evaluation for responsiveness prediction to cetuximab and panitumumab. In this review, the predictive biomarker landscape for anti-EGFR monoclonal antibody inhibitors in metastatic colorectal cancers with no extended RAS and BRAF mutations will be examined.

PMID:40097366 | DOI:10.1080/14622416.2025.2479414

Nat Biotechnol. 2025 Mar 17. doi: 10.1038/s41587-025-02616-w. Online ahead of print.

NO ABSTRACT

PMID:40097678 | DOI:10.1038/s41587-025-02616-w

Nat Nanotechnol. 2025 Mar 17. doi: 10.1038/s41565-025-01877-5. Online ahead of print.

ABSTRACT

The active transport and retention principle is an alternative mechanism to the enhanced permeability and retention effect for explaining nanoparticle tumour delivery. It postulates that nanoparticles actively transport across tumour endothelial cells instead of passively moving through gaps between these cells. How nanoparticles transport across tumour endothelial cells remains unknown. Here we show that nanoparticles cross tumour endothelial cells predominantly using the non-receptor-based macropinocytosis pathway. We discovered that tumour endothelial cell membrane ruffles capture circulating nanoparticles, internalize them in intracellular vesicles and release them into the tumour interstitium. Tumour endothelial cells have a higher membrane ruffle density than healthy endothelium, which may partially explain the elevated nanoparticle tumour accumulation. Receptor-based endocytosis pathways such as clathrin-mediated endocytosis contribute to nanoparticle transport to a lesser extent. Nanoparticle size determines the degree of contribution for each pathway. Elucidating the nanoparticle transport mechanism is crucial for developing strategies to control nanoparticle tumour delivery.

PMID:40097646 | DOI:10.1038/s41565-025-01877-5