Nexus between in silico and in vivo models to enhance clinical translation of nanomedicine

Abstract In cancer, one of the main barriers to effective chemotherapy is inefficient drug delivery. The delivery of drugs to solid tumors involves various biochemical, biophysical, and mechanical processes, occurring over a wide range of length and time scales. Nanotechnology-based research in targeted drug delivery to solid tumors has led to a breakthrough in cancer treatment. However, many challenges remain related to inadequate tissue penetration, ineffective tumoral distribution, insufficient accumulation of drugs, loss of targeting ability, and various safety concerns. Mathematical and computational modeling allows for controlled study of these processes which is often not possible, or not economical, through empirical methods. Different computational models have been used to simulate nano-sized-drug delivery to solid tumors in order to investigate efficacy, understand biological phenomena, and select optimal anticancer treatment strategies. These models are classified as: discrete (quantum mechanics, molecular dynamics, Monte Carlo, and coarse-grained), continuous (pharmacokinetic/pharmacodynamics, finite element, and finite volume), or hybrid models. Using in vivo and in silico models, this paper reviews several key issues related to the use of nanoparticles as anticancer drug delivery vehicles: specifically, injection into the circulatory system, transvascular extravasation, distribution in the interstitium, cellular uptake, and drug release from nanocarriers. Adjustable nanocarrier design parameters, static targeting strategies (active/passive), and dynamic targeting strategies (internal/external stimuli-responsive) for nano-sized-drug delivery systems are discussed. Further, endogenous- and exogenous-based stimuli-responsive nano-engineered drug delivery systems are introduced for timed, destination-specific drug release. Clinical translation of nanomedicine can be accelerated through the integration of mathematical modeling techniques with modern imaging techniques and in vitro technologies.