TY - JOUR T1 - Target Site Delivery and Residence of Nanomedicines: Application of Quantitative Systems Pharmacology JF - Pharmacological Reviews JO - Pharmacol Rev SP - 157 LP - 169 DO - 10.1124/pr.118.016816 VL - 71 IS - 2 AU - Jessie L.-S. Au AU - Roberto A. Abbiati AU - M. Guillaume Wientjes AU - Ze Lu A2 - Ishikawa, Yoshihiro Y1 - 2019/04/01 UR - http://pharmrev.aspetjournals.org/content/71/2/157.abstract N2 - Quantitative systems pharmacology (QSP), an emerging field that entails using modeling and computation to interpret, interrogate, and integrate drug effects spanning from the molecule to the whole organism to forecast treatment outcomes, is expected to enhance the efficiency of drug development. Since late 2017, the U.S. Food and Drug Administration has advocated the use of an analogous approach of model-informed drug development. This review focuses on issues pertaining to nanosized medicines (NP) and the potential utility of QSP to determine NP delivery and residence at extracellular or intracellular targets in vivo. The kinetic processes governing NP disposition and transport, interactions with biologic matrix components, binding and internalization in cells, and intracellular trafficking are determined, sometimes jointly, by NP properties (e.g., dimension, materials, surface charge and modifications, shape, and geometry) and target tissue properties (e.g., perfusion status, vessel pore size and wall thickness, vessel and cell density, composition of extracellular matrix, and void volume fraction). These various determinants, together with the heterogeneous tissue structures and microenvironment factors in solid tumors, lead to environment-, spatial-, and time-dependent changes in NP concentrations that are difficult to predict. Adding to the complexity is the recent discovery that NP surface-coating protein corona, whose composition depends on NP properties and which undergoes continuous evolution with time and local protein environments, is yet another unpredictable variable. Examples are provided to demonstrate the potential utility of QSP-based multiscale modeling to capture the physicochemical and biologic processes in equations to enable computational studies of the key kinetic processes in cancer treatments. ER -