Retinoblastoma is a malignant growth affecting retina. An original combination of modified Non-Markov and Gompertzian computational approaches is proven of being a reliable tool for prediction of tumor selective accumulation of the bivalent metal isotopes (25Mg, 43Ca, 60Co, 67Zn, …) — releasing nanoparticles in human retinoblastoma cells. This mathematical model operates with a starting point of the discriminative drug uptake caused by a gap-like distinction between the neighboring malignant and normal cell proliferation rates. This takes into account both pharmacokinetic and pharmacodynamic peculiarities of PMC16, fullerene-C60 based nanoparticles, known for their unique capabilities for a cancer-targeted delivery of paramagnetic metal isotopes followed by an essential chemotherapeutic effect. Being dependent on a tumor growth rate but not on the neoplasm steady state mass, a randomized level of drug accumulation in retinoblastoma cells has been formalized as a predictive paradigm suitable to optimize an ongoing PMC16 preclinical research.
A Robust Approach to Enhance Tumor-selective Accumulation of Nanoparticles
