Transport and Reaction Modeling of Nanocarriers for Cancer Therapeutics: Experimental and in silico approaches. Issue 23 (2022)

• Record Type: Journal Article
• Authors: Kavousanakis, Mihalis; Bhatavdekar, Omkar; Sofou, Stavroula; Kevrekidis, Ioannis
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 23(2022)
• Page Start: 104
• Page End: 108
• Publication Date: 2022
• Subjects: modeling of complex biological systems -- solid tumors -- drug delivery -- reaction-diffusion -- population modeling; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.023
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Reaction-diffusion mathematical modeling plays a fundamental role in understanding and optimizing the performance of heterogeneous catalysts. The successful exploitation of all catalyst sites in these porous heterogeneous materials bears a striking analogy -conceptually and mathematically- with successfully delivering drugs throughout avascular regions of solid tumors: the drugs need to ``react with" and kill (almost) all of the constituent cancer cells. We use reaction-diffusion modeling to describe non-intuitive cocktails of engineered biomaterials –with diverse drug delivery properties– with the goal of identifying 'geographically complementary' behaviors of transport, reaction, and cell kill. The aim is to "leave no cancer cell behind". We have experimentally demonstrated in mouse models that effectively killing solid tumors does not require cocktails of different drugs. It is the spatiotemporal uniformity of the delivered drug that is important, rather than a variety of drugs, or the total drug delivered per mass of the tumor. No single delivery carrier possesses the plethora of diverse properties needed to enable uniform and prolonged delivery of therapeutics within the entire volume of the avascular regions of solid tumors. We, therefore, use combinations of different delivery carriers of the same drug – carriers with complementary properties, enabled by the mathematical modeling predictions.