Predicting drug delivery efficiency into tumor tissues through molecular simulation of transport in complex vascular networks

Evan P. Troendle, Ayesha Khan, Peter C. Searson, Martin B. Ulmschneider

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15 Citations (Scopus)
273 Downloads (Pure)


Efficient delivery of anticancer drugs into tumor tissues at maximally effective and minimally toxic concentrations is vital for therapeutic success. At present, no method exists that can predict the spatial and temporal distribution of drugs into a target tissue after administration of a specific dose. This prevents accurate estimation of optimal dosage regimens for cancer therapy. Here we present a new method that predicts quantitatively the time-dependent spatial distribution of drugs in tumor tissues at sub-micrometer resolution. This is achieved by modeling the diffusive flow of individual drug molecules through the three-dimensional network of blood-vessels that vascularize the tumor, and into surrounding tissues, using molecular mechanics techniques. By evaluating delivery into tumors supplied by a series of blood-vessel networks with varying degrees of complexity, we show that the optimal dose depends critically on the precise vascular structure. Finally, we apply our method to calculate the optimal dosage of the cancer drug doxil into a section of a mouse ovarian tumor, and demonstrate the enhanced delivery of liposomally administered doxorubicin when compared to free doxorubicin. Comparison with experimental data and a multiple-compartment model show that the model accurately recapitulates known pharmacokinetics and drug-load predictions. In addition, it provides, for the first time, a detailed picture of the spatial dependence of drug uptake into tissues surrounding tumor vasculatures. This approach is fundamentally different to current continuum models, and reveals that the target tumor vascular topology is as important for therapeutic success as the transport properties of the drug delivery platform itself. This sets the stage for revisiting drug dosage calculations.
Original languageEnglish
Pages (from-to)221-234
Early online date8 Nov 2018
Publication statusE-pub ahead of print - 8 Nov 2018


  • Tumor model
  • Molecular mechanics
  • Personalized medicine
  • Vasculature
  • Drug delivery
  • Doxil


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