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Drug Delivery

Poster Rapid Fire 2

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

Friday, October 13, 2023
2:20 PM – 2:25 PM PDT
Location: 348 (Center, Level 3)

    Presenting Author(s)

  • Zilu Zhang (he/him/his)

    PhD Candidate
    Duke University
    Durham, North Carolina, United States

    • Co-Author(s)

  • BM

    Bárbara B. Mendes

    Postdoctoral Researcher
    ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, United States

  • JC

    João Conniot

    Postdoctoral Researcher
    ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, United States

  • DS

    Diana P. Sousa

    PhD Student
    ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, United States

  • JR

    João M. J. M. Ravasco

    Research Scholar
    ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, United States

  • AL

    Andżelika Lorenc

    Research Scholar
    Instituto de Investigação do Medicamento (iMed), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto; Department of Biopharmacy, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, United States

  • TR

    Tiago Rodrigues

    Assistant Professor
    Instituto de Investigação do Medicamento (iMed), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, United States

  • JC

    João Conde

    Assistant Professor
    ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, United States

    • Primary Investigator(s)

  • Daniel Reker, DrSc

    Assistant Professor
    Duke University
    Durham, North Carolina, United States

Inorganic nanoparticles have become an important tool as cancer therapeutics, diagnostics, and theranostics. However, notwithstanding the achievements that have been made, it remains challenging to objectively, comprehensively, and systematically define key design requirements to ensure the success of inorganic nanomedicines in cancer treatment. To discover such design rules, we have curated the world’s largest database of inorganic nanoparticles in preclinical cancer research and use statistics and machine learning to capture trends in nanoparticle design, correlate nanoparticle properties with treatment outcomes, and provide predictive tools that can guide the creation of safe and efficacious nanoparticles for cancer drug delivery.