Leveraging in silico models for the rational design of graphene quantum DOT-based drug delivery systems for breast cancer: an applied pharmaceutics perspective
Abstract
Delivering chemotherapy to aggressive forms of breast cancers including triple-negative breast cancer (TNBC) is hindered by poor drug bioavailability, limited tumor penetration, and off-target toxicity. While conventional carriers often struggle with biological barriers, Graphene Quantum Dots (GQDs) offer a versatile solution. This review examines how computational design—specifically density functional theory (DFT) and molecular dynamics (MD) simulations—is accelerating the development of GQDs as precision nanocarriers. Due to their ultrasmall size, GQDs penetrate deep into the dense tumor microenvironment (TME), while surface functionalization with ligands like folic acid ensures selective targeting. These "smart" carriers can be engineered for pH-responsive drug release, triggered by the acidic conditions of the tumor to minimize side effects. Beyond delivery, the intrinsic fluorescence and high drug-loading capacity of GQDs enable a "theranostic" approach, combining real-time imaging with therapy. By integrating in silico modeling with nanotechnology, these advancements point toward more adaptive, personalized treatments for TNBC.
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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