ArticleInternational journal of nanomedicine2022
Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.
Article in International journal of nanomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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
74 citing papers in PubMed, 1 synthesis or guideline pooled it, 150 citations in OpenAlex.
- Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.ACS nano · 2023Pooled it
- Polymeric Materials in Cancer Immunotherapy: Advances, Challenges, and Future Directions.Polymer science & technology (Washington, D.C.) · 2026Review
- Review
- Analysis of Pharmacokinetic-Pharmacodynamic Relationships of Nanoparticles against Tumors.ACS nano · 2026Article
- Are similar nanoforms comparable in biodistribution? Data-driven approaches for optimizing nanomedicine design.Materials today. Nano · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Biomimetic Targeted Drug Delivery for Liver Failure in Abdominal Sepsis: Focus on Autologous Erythrocyte Ghosts.International journal of molecular sciences · 2026Review
- Iron-Based Nanoparticles as Delivery Tools.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Light-Activated Iron Oxide Nanoparticles in Cancer Treatment: Synergistic Roles in Photothermal and Photodynamic Therapy.Cancers · 2026Review
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Polysaccharide-functionalized gold, silver, and iron oxide nanoparticles for siRNA delivery: The role of artificial intelligence in design and optimization.Materials today. Bio · 2026Review
- Bioengineering of extracellular vesicles with scaffold proteins for drug delivery.Journal of nanobiotechnology · 2026Review
- Advances in green-synthesized magnetic nanoparticles for targeted cancer therapy: mechanisms, applications, and future perspectives.European journal of medical research · 2026Review
- The landscape of nanomedical clinical trials.Nano today · 2026Article
- Toward oral nanomaterial-based drug delivery systems for hepatocellular carcinoma therapy: evidence mapping, route-specific validation, and translational challenges.Frontiers in pharmacology · 2026Review
- Nanomaterial-based strategies to overcome sorafenib resistance in hepatocellular carcinoma: from mechanistic insights to translational applications.Frontiers in oncology · 2026Article
- Deep Tumor Penetration Using Nanoparticle Delivery Systems: Programmed Design Strategies and Emerging Evaluation Platforms.International journal of nanomedicine · 2026Review
- Radiolabeled Iron-Based Nanomaterials for Cancer Diagnosis and Therapy.International journal of nanomedicine · 2026Review
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R Conclusion: This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.
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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.