  Objective:We propose a novel iterative-optimizationinspired direct targeting
strategy (DTS) for smart nanosystems, which harness swarms of externally
manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for
knowledgeaided tumor sensitization and targeting. We aim to demonstrate through
computational experiments that the proposed DTS can significantly enhance the
accumulation of MNPs in the tumor site, which serve as a contrast agent in
various medical imaging modalities, by using the shortest possible
physiological routes and with minimal systemic exposure. Methods: The epicenter
of a tumor corresponds to the global maximum of an externally measurable
objective function associated with an in vivo tumor-triggered biophysical
gradient; the domain of the objective function is the tissue region at a high
risk of malignancy; swarms of externally controllable magnetic nanoswimmers for
tumor sensitization are modeled as the guess inputs. The objective function may
be resulted from a passive phenomenon such as reduced blood flow or increased
kurtosis of microvasculature due to tumor angiogenesis; otherwise, the
objective function may involve an active phenomenon such as the fibrin formed
during the coagulation cascade activated by tumortargeted "activator"
nanoparticles. Subsequently, the DTS can be interpreted from the iterative
optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are
continuously updated according to the gradient of the objective function in
order to find the optimum (i.e., tumor) by moving through the domain (i.e.,
tissue under screening). Along this line of thought, we propose the
computational model based on the gradient descent (GD) iterative method to
describe the GD-inspired DTS, which takes into account the realistic in vivo
propagation scenario of nanoswimmers.
