ArticleNature nanotechnology2024
A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.
Article in Nature nanotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Sonogenetic flexible nanogels enabling enhanced calcium overload-induced immunogenic cell death for tumor immunotherapy.Bioactive materials · 2026Article
- The Translational Paradox of Cancer Nanomedicine: Biological, Pharmacokinetic, and Manufacturing Barriers to Clinical Success.Biology · 2026Review
- Review
- Magnetothermally Responsive Mesoporous Silica Nanocarriers: Materials Design, Thermoresponsive Gates and Controlled Drug Release.Nanomaterials (Basel, Switzerland) · 2026Review
- Analysis of Pharmacokinetic-Pharmacodynamic Relationships of Nanoparticles against Tumors.ACS nano · 2026Article
- Article
- Accelerating the clinical translation of bioengineered anticancer therapeutics.Journal of the National Cancer Center · 2026Article
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Nanomaterials for In Vivo Photoacoustic Sensing: Emerging Strategies and Future Outlook.ACS sensors · 2026Review
- Integrative bioinformatics and machine learning reveal an association of LTF and MMP8 with systemic inflammation and lung injury.Respiratory research · 2026Article
- Therapeutic strategies for ischemic heart disease with natural product-based nanomedicines.Journal of nanobiotechnology · 2026Review
- Taming the Immiscibility of Gold, Iron, and Boron to Craft Chemodegradable Nanoparticles for Multimodal Imaging and Radiotherapy.Advanced healthcare materials · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Advancements in Drug Delivery Systems in Glioblastoma Therapy.International journal of molecular sciences · 2026Review
- Designing drug delivery systems: the impact of structural order and disorder for optimized therapeutic outcomes.Nanomedicine (London, England) · 2026Review
- Metalloprotein-Based Nanomedicines: Design Strategies, Functional Mechanisms, and Biomedical Applications.International journal of molecular sciences · 2026Review
- Peptide-Functionalized Iron Oxide Nanoparticles for Cancer Therapy: Targeting Strategies, Mechanisms, and Translational Opportunities.Molecules (Basel, Switzerland) · 2026Review
- Harnessing nanomedicine to target NF-κB signalling in cancer: at the intersection of inflammatory signalling, metabolic reprogramming, and therapeutic innovation.Cell communication and signaling : CCS · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.
Indexed as
Identifiers
38750164What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.