Predictive Control of Nanoparticle Protein Corona: A Computational Study using the AMDCF Memory-Driven Framework

When nanoparticles are injected into the bloodstream, they are rapidly coated by a layer of proteins, forming the "Protein Corona". The composition of this corona dictates the biological fate of the nanoparticle, including immune clearance and cellular targeting. Controlling this dynamic and noisy biological interface is a major challenge in nanomedicine. In this paper, we introduce a computational approach using the AMDCF (Adaptive Memory-Driven Coupling Framework) to actively steer the corona composition. By modeling the competitive binding kinetics of Albumin (target), IgG (immune threat), and Fibrinogen, we demonstrate that the AMDCF kernel utilizes a non-Markovian memory feedback to suppress IgG adsorption. The dynamic safety valve acts as an immune-response regulator, preventing the numerical and biological instability of uncontrolled protein competition. This work establishes a novel theoretical foundation for the algorithmic design of stealth nanoparticles.