Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research

Nature Nanotechnology (2024)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study design, meta-analysis and artificial intelligence modelling of the workflow.
Fig. 2: Feature-based NP design.
Fig. 3: Feature-based data on biological and therapeutic strategies.
Fig. 4: Feature-based preclinical evaluation of inorganic NPs.
Fig. 5: Model selection, adversarial testing and interpretation of supervised ML models.
Fig. 6: Linear discriminant analysis.

Similar content being viewed by others

Data availability

The full database is publicly available via GitHub at https://github.com/RekerLab/NanoAnalysis. Source data are provided with this paper.

Code availability

The code to create our machine learning models and perform the analysis described here is publicly available via GitHub at https://github.com/RekerLab/NanoAnalysis.

References

  1. Mendes, B. B., Sousa, D. P., Conniot, J. & Conde, J. Nanomedicine-based strategies to target and modulate the tumor microenvironment. Trends Cancer 7, 847–862 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Bobo, D., Robinson, K. J., Islam, J., Thurecht, K. J. & Corrie, S. R. Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm. Res. 33, 2373–2387 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Anselmo, A. C. & Mitragotri, S. Nanoparticles in the clinic: an update. Bioeng. Transl. Med. 4, e10143 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Anselmo, A. C. & Mitragotri, S. Nanoparticles in the clinic: an update post COVID-19 vaccines. Bioeng. Transl. Med. 6, e10246 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mendes, B. B. et al. Nanodelivery of nucleic acids. Nat. Rev. Methods Primers 2, 24 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. van der Meel, R. et al. Smart cancer nanomedicine. Nat. Nanotechnol. 14, 1007–1017 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Janjua, T. I., Cao, Y., Yu, C. & Popat, A. Clinical translation of silica nanoparticles. Nat. Rev. Mater. 6, 1072–1074 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Das, C. G. A., Kumar, V. G., Dhas, T. S., Karthick, V. & Kumar, C. M. V. Nanomaterials in anticancer applications and their mechanism of action - a review. Nanomedicine 47, 102613 (2023).

    Article  CAS  PubMed  Google Scholar 

  9. Gavas, S., Quazi, S. & Karpiński, T. M. Nanoparticles for cancer therapy: current progress and challenges. Nanoscale Res. Lett. 16, 173 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Faria, M., Björnmalm, M., Crampin, E. J. & Caruso, F. A few clarifications on MIRIBEL. Nat. Nanotechnol. 15, 2–3 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Faria, M. et al. Minimum information reporting in bio–nano experimental literature. Nat. Nanotechnol. 13, 777–785 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lorenc, A. et al. Machine learning for next-generation nanotechnology in healthcare. Matter 4, 3078–3080 (2021).

    Article  CAS  Google Scholar 

  13. Mitchell, M. J. et al. Engineering precision nanoparticles for drug delivery. Nat. Rev. Drug Discov. 20, 101–124 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Boehnke, N. et al. Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery. Science 377, eabm5551 (2023).

    Article  Google Scholar 

  15. Brockow, K. et al. Experience with polyethylene glycol allergy-guided risk management for COVID-19 vaccine anaphylaxis. Allergy 77, 2200–2210 (2022).

    Article  CAS  PubMed  Google Scholar 

  16. Sellaturay, P., Nasser, S., Islam, S., Gurugama, P. & Ewan, P. W. Polyethylene glycol (PEG) is a cause of anaphylaxis to the Pfizer/BioNTech mRNA COVID-19 vaccine. Clin. Exp. Allergy 51, 861–863 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Stone, C. A. Jr. et al. Immediate hypersensitivity to polyethylene glycols and polysorbates: more common than we have recognized. J. Allergy Clin. Immunol. Pract. 7, 1533–1540.e8 (2019).

    Article  PubMed  Google Scholar 

  18. Chenthamara, D. et al. Therapeutic efficacy of nanoparticles and routes of administration. Biomater. Res. 23, 20 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  PubMed  Google Scholar 

  20. Global Burden of Disease 2019 Cancer Collaboration. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 8, 420–444 (2022).

  21. Alvarez, E. M. et al. The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Oncol. 23, 27–52 (2022).

    Article  Google Scholar 

  22. Chen, Y., Chen, H. & Shi, J. In vivo bio-safety evaluations and diagnostic/therapeutic applications of chemically designed mesoporous silica nanoparticles. Adv. Mater. 25, 3144–3176 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Iscaro, A., Howard, F. N. & Muthana, M. Nanoparticles: properties and applications in cancer immunotherapy. Curr. Pharm. Des. 25, 1962–1979 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Zhou, H. et al. Biodegradable inorganic nanoparticles for cancer theranostics: insights into the degradation behavior. Bioconjug. Chem. 31, 315–331 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Zhang, Y. et al. Prolonged local in vivo delivery of stimuli-responsive nanogels that rapidly release doxorubicin in triple-negative breast cancer cells. Adv. Healthc. Mater. 9, 1901101 (2020).

    Article  CAS  Google Scholar 

  26. Conde, J., Oliva, N., Zhang, Y. & Artzi, N. Local triple-combination therapy results in tumour regression and prevents recurrence in a colon cancer model. Nat. Mater. 15, 1128–1138 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kwong, B., Gai, S. A., Elkhader, J., Wittrup, K. D. & Irvine, D. J. Localized immunotherapy via liposome-anchored anti-CD137 + IL-2 prevents lethal toxicity and elicits local and systemic antitumor immunity. Cancer Res. 73, 1547–1558 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li, W. et al. Hyaluronic acid ion-pairing nanoparticles for targeted tumor therapy. J. Control. Release 225, 170–182 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Lei, C. et al. Local release of highly loaded antibodies from functionalized nanoporous support for cancer immunotherapy. J. Am. Chem. Soc. 132, 6906–6907 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fransen, M. F., van der Sluis, T. C., Ossendorp, F., Arens, R. & Melief, C. J. M. Controlled local delivery of CTLA-4 blocking antibody induces CD8+ T-cell-dependent tumor eradication and decreases risk of toxic side effects. Clin. Cancer Res. 19, 5381–5389 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Ishihara, J. et al. Matrix-binding checkpoint immunotherapies enhance antitumor efficacy and reduce adverse events. Sci. Transl. Med. 9, eaan0401 (2017).

    Article  PubMed  Google Scholar 

  32. Errington, T. M., Denis, A., Perfito, N., Iorns, E. & Nosek, B. A. Challenges for assessing replicability in preclinical cancer biology. eLife 10, e67995 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    Article  CAS  Google Scholar 

  34. Cheng, Y.-H., He, C., Riviere, J. E., Monteiro-Riviere, N. A. & Lin, Z. Meta-analysis of nanoparticle delivery to tumors using a physiologically based pharmacokinetic modeling and simulation approach. ACS Nano 14, 3075–3095 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhong, R. et al. Hydrogels for RNA delivery. Nat. Mater. https://doi.org/10.1038/s41563-023-01472-w (2023).

  36. Lasagna-Reeves, C. et al. Bioaccumulation and toxicity of gold nanoparticles after repeated administration in mice. Biochem. Biophys. Res. Commun. 393, 649–655 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Hatakeyama, H., Akita, H. & Harashima, H. A multifunctional envelope type nano device (MEND) for gene delivery to tumours based on the EPR effect: a strategy for overcoming the PEG dilemma. Adv. Drug Deliv. Rev. 63, 152–160 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. Harris, J. M., Martin, N. E. & Modi, M. Pegylation. Clin. Pharmacokinet. 40, 539–551 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. Suk, J. S., Xu, Q., Kim, N., Hanes, J. & Ensign, L. M. PEGylation as a strategy for improving nanoparticle-based drug and gene delivery. Adv. Drug Deliv. Rev. 99, 28–51 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Zhang, M. et al. Influencing factors and strategies of enhancing nanoparticles into tumors in vivo. Acta Pharm. Sin. B 11, 2265–2285 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nguyen, L. N. M. et al. The exit of nanoparticles from solid tumours. Nat. Mater. 22, 1261–1272 (2023).

    Article  CAS  PubMed  Google Scholar 

  42. Setyawati, M. I. et al. Titanium dioxide nanomaterials cause endothelial cell leakiness by disrupting the homophilic interaction of VE–cadherin. Nat. Commun. 4, 1673 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Shamay, Y. et al. Quantitative self-assembly prediction yields targeted nanomedicines. Nat. Mater. 17, 361–368 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Reker, D. et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nat. Nanotechnol. 16, 725–733 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bannigan, P. et al. Machine learning models to accelerate the design of polymeric long-acting injectables. Nat. Commun. 14, 35 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Caballero, D. et al. Precision biomaterials in cancer theranostics and modelling. Biomaterials 280, 121299 (2022).

    Article  CAS  PubMed  Google Scholar 

  48. Zhao, Y. et al. A comparison between sphere and rod nanoparticles regarding their in vivo biological behavior and pharmacokinetics. Sci. Rep. 7, 4131 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kolhar, P. et al. Using shape effects to target antibody-coated nanoparticles to lung and brain endothelium. Proc. Natl Acad. Sci. USA 110, 10753–10758 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhang, M., Kim, H. S., Jin, T. & Moon, W. K. Near-infrared photothermal therapy using EGFR-targeted gold nanoparticles increases autophagic cell death in breast cancer. J. Photochem. Photobiol. B 170, 58–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Jo, Y. et al. Chemoresistance of cancer cells: requirements of tumor microenvironment-mimicking in vitro models in anti-cancer drug development. Theranostics 8, 5259–5275 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Guo, B. et al. Molecular engineering of conjugated polymers for biocompatible organic nanoparticles with highly efficient photoacoustic and photothermal performance in cancer theranostics. ACS Nano 11, 10124–10134 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Li, Z. et al. Small gold nanorods laden macrophages for enhanced tumor coverage in photothermal therapy. Biomaterials 74, 144–154 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. Das, P., Delost, M. D., Qureshi, M. H., Smith, D. T. & Njardarson, J. T. A survey of the structures of US FDA approved combination drugs. J. Med. Chem. 62, 4265–4311 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Fernandes Neto, J. M. et al. Multiple low dose therapy as an effective strategy to treat EGFR inhibitor-resistant NSCLC tumours. Nat. Commun. 11, 3157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kim, M. H. et al. The effect of VEGF on the myogenic differentiation of adipose tissue derived stem cells within thermosensitive hydrogel matrices. Biomaterials 31, 1213–1218 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  58. Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. in Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).

  59. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. in Advances in Neural Information Processing Systems (eds Guyon, I. et al.) Vol. 30 (Curran Associates, Inc., 2017).

Download references

Acknowledgements

This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-StG-2019-848325), the Duke Science and Technology Initiative and the National Institutes of Health NIGMS grant R35GM151255. We acknowledge Fundação para a Ciência e a Tecnologia (FCT) for financial support in the framework of the PhD grant 2020.06638.BD (D.P.S.), the Duke Department of Biomedical Engineering for support through a BME Fellowship (Z.Z.), the National Science Foundation (NSF) for support through the Graduate Research Fellowship DGE2129754 (L.A.O.) and the ERASMUS+ programme (A.L.).

Author information

Author notes

  1. These authors contributed equally: Bárbara B. Mendes, Zilu Zhang.

Authors and Affiliations

  1. ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas (NMS|FCM), Universidade NOVA de Lisboa, Lisbon, Portugal

    Bárbara B. Mendes, João Conniot, Diana P. Sousa, João M. J. M. Ravasco & João Conde

  2. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Zilu Zhang, Lauren A. Onweller & Daniel Reker

  3. Instituto de Investigação do Medicamento (iMed), Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal

    Andżelika Lorenc & Tiago Rodrigues

  4. Department of Biopharmacy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland

    Andżelika Lorenc

  5. Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA

    Daniel Reker

Authors
  1. Bárbara B. Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zilu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Conniot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diana P. Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. João M. J. M. Ravasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lauren A. Onweller
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andżelika Lorenc
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tiago Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Reker
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. João Conde
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J. Conde conceived the idea and concept of the study. D.R. conceived the ML platform. T.R. conceived the data curation. B.B.M., J. Conniot, D.P.S., J.M.J.M.R. and J. Conde collected all of the data from the published manuscripts, organized the dataset and calculated the correlations. Z.Z., L.A.O. and A.L. conducted the data analysis, text mining and designed, implemented and evaluated the ML models. J. Conde, D.R. and T.R provided guidance and supervised the work. All authors contributed to the writing and editing of the paper, and all authors approved the final version of the paper.

Corresponding authors

Correspondence to Tiago Rodrigues, Daniel Reker or João Conde.

Ethics declarations

Competing interests

J. Conde and T.R. are co-founders and shareholders of TargTex SA Targeted Therapeutics for Glioblastoma Multiforme. J. Conde is a member of the Global Burden of Disease (GBD) consortium from the Institute for Health Metrics and Evaluation (IHME), University of Washington, USA, and member of the Scientific Advisory Board of Vector Bioscience, Cambridge. T.R. acts as a consultant to the pharmaceutical, biotechnology and technology industry and is a full member of the Acceleration Consortium, University of Toronto. D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a scientific mentor for Start2 and serves on the scientific advisory board of Areteia Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Nanotechnology thanks Natalie Boehnke, Karolina Jagiełło and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Results and Discussion, Figs. 1– 9, Tables 1–8 and Refs. 1–11.

Source data

Source Data Fig. 2

Alluvial plot source data for Fig. 2c.

Source Data Fig. 4

Raw data for Fig. 4d, alluvial plot source data for Fig. 4e and statistical source data for Fig. 4f.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mendes, B.B., Zhang, Z., Conniot, J. et al. A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01673-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41565-024-01673-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing