Hunter A Miller1, Hermann B Frieboes2,3,4,5. 1. Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA. 2. Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA. hbfrie01@louisville.edu. 3. Department of Bioengineering, Lutz Hall 419, University of Louisville, Louisville, Kentucky, 40292, USA. hbfrie01@louisville.edu. 4. James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA. hbfrie01@louisville.edu. 5. Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA. hbfrie01@louisville.edu.
Abstract
PURPOSE: Nanoparticle-mediated drug delivery and efficacy for cancer applications depends on systemic as well as local microenvironment characteristics. Here, a novel coupling of a nanoparticle (NP) kinetic model with a drug pharmacokinetic/pharmacodynamics model evaluates efficacy of cisplatin-loaded poly lactic-co-glycolic acid (PLGA) NPs in heterogeneously vascularized tumor tissue. METHODS: Tumor lesions are modeled with various levels of vascular heterogeneity, as would be encountered with different types of tumors. The magnitude of the extracellular to cytosolic NP transport is varied to assess tumor-dependent cellular uptake. NP aggregation is simulated to evaluate its effects on drug distribution and tumor response. RESULTS: Cisplatin-loaded PLGA NPs are most effective in decreasing tumor size in the case of high vascular-induced heterogeneity, a high NP cytosolic transfer coefficient, and no NP aggregation. Depending on the level of tissue heterogeneity, NP cytosolic transfer and drug half-life, NP aggregation yielding only extracellular drug release could be more effective than unaggregated NPs uptaken by cells and releasing drug both extra- and intra-cellularly. CONCLUSIONS: Model-based customization of PLGA NP and drug design parameters, including cellular uptake and aggregation, tailored to patient tumor tissue characteristics such as proportion of viable tissue and vascular heterogeneity, could help optimize the NP-mediated tumor drug response.
PURPOSE: Nanoparticle-mediated drug delivery and efficacy for cancer applications depends on systemic as well as local microenvironment characteristics. Here, a novel coupling of a nanoparticle (NP) kinetic model with a drug pharmacokinetic/pharmacodynamics model evaluates efficacy of cisplatin-loaded poly lactic-co-glycolic acid (PLGA) NPs in heterogeneously vascularized tumor tissue. METHODS:Tumor lesions are modeled with various levels of vascular heterogeneity, as would be encountered with different types of tumors. The magnitude of the extracellular to cytosolic NP transport is varied to assess tumor-dependent cellular uptake. NP aggregation is simulated to evaluate its effects on drug distribution and tumor response. RESULTS:Cisplatin-loaded PLGA NPs are most effective in decreasing tumor size in the case of high vascular-induced heterogeneity, a high NP cytosolic transfer coefficient, and no NP aggregation. Depending on the level of tissue heterogeneity, NP cytosolic transfer and drug half-life, NP aggregation yielding only extracellular drug release could be more effective than unaggregated NPs uptaken by cells and releasing drug both extra- and intra-cellularly. CONCLUSIONS: Model-based customization of PLGA NP and drug design parameters, including cellular uptake and aggregation, tailored to patienttumor tissue characteristics such as proportion of viable tissue and vascular heterogeneity, could help optimize the NP-mediated tumor drug response.
Entities:
Keywords:
PLGA nanoparticles; cancer nanotherapy; cancer simulation; mathematical modeling; tumor heterogeneity
Authors: Min Wu; Hermann B Frieboes; Steven R McDougall; Mark A J Chaplain; Vittorio Cristini; John Lowengrub Journal: J Theor Biol Date: 2012-12-07 Impact factor: 2.691
Authors: John P Sinek; Sandeep Sanga; Xiaoming Zheng; Hermann B Frieboes; Mauro Ferrari; Vittorio Cristini Journal: J Math Biol Date: 2008-09-10 Impact factor: 2.259
Authors: Fransisca Leonard; Louis T Curtis; Pooja Yesantharao; Tomonori Tanei; Jenolyn F Alexander; Min Wu; John Lowengrub; Xuewu Liu; Mauro Ferrari; Kenji Yokoi; Hermann B Frieboes; Biana Godin Journal: Nanoscale Date: 2016-01-28 Impact factor: 7.790