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Updated: Jan 26, 2026

A Comprehensive Procedure to Evaluate the In Vivo Performance of Cancer Nanomedicines
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Mathematical modeling in cancer nanomedicine: a review

Prashant Dogra1, Joseph D Butner1, Yao-Li Chuang2

  • 1Mathematics in Medicine Program, The Houston Methodist Research Institute, HMRI R8-122, 6670 Bertner Ave, Houston, TX, 77030, USA.

Biomedical Microdevices
|April 6, 2019
PubMed

Insights

Mathematical modeling enhances understanding of nano-bio interactions for improved cancer nanomedicine delivery. This review explores how these models aid in overcoming barriers for effective targeted cancer treatment.

Area of Science:

  • Oncology
  • Nanotechnology
  • Biomedical Engineering

Background:

  • Cancer remains a leading global healthcare challenge, necessitating advancements in drug delivery.
  • Nanotechnology offers potential solutions through nanocarriers for targeted delivery of cytotoxic agents.
  • Current cancer nanomedicine faces challenges in clinical translation due to biophysical barriers.

Purpose of the Study:

  • To review literature on mathematical modeling of nano-bio interactions.
  • To understand how these models improve nanocarrier transport to tumors.
  • To facilitate better tumor delivery efficacy in cancer nanomedicine.

Main Methods:

  • Comprehensive literature review of mathematical modeling studies.
  • Analysis of models focusing on nano-bio interactions.
  • Examination of models addressing tumor delivery barriers.

Main Results:

  • Mathematical modeling is crucial for understanding nanomaterial behavior in biological systems.
  • Models provide insights into physicochemical and physiological factors affecting nanocarrier transport.
  • Understanding nano-bio interactions is key to overcoming delivery challenges.

Conclusions:

  • Mathematical modeling is instrumental in advancing cancer nanomedicine.
  • Further development and application of these models can improve targeted drug delivery efficacy.
  • Bridging the gap between research and clinical translation requires deeper insights from modeling.
Keywords:
Agent-based modelingCancer treatmentDrug transportMechanistic modelingMultiscalePharmacokinetics and pharmacodynamics

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