Currently submitted to: JMIR Bioinformatics and Biotechnology
Date Submitted: Mar 31, 2026
Open Peer Review Period: Apr 23, 2026 - Jun 18, 2026
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Selective Cancer Cell Elimination via a 2-DG-Conjugated Nanobot Framework: PhysiCell Agent-Based Simulation Study
ABSTRACT
Background:
Conventional oncology faces a significant challenge in achieving high selectivity, with standard chemotherapy selectivity indices (SI) often limited to 2–5×, resulting in severe off-target toxicity. There is an urgent need for "smart" therapeutic platforms that can verify cellular identity before activating a cytotoxic payload.
Objective:
The objective of this study was to verify the mechanistic coherence and selectivity of a proposed 2-Deoxy-D-Glucose (2-DG)-conjugated nanobot framework. Specifically, the research aimed to demonstrate how a six-stage "AND-gate" logic—exploiting GLUT1/3 overexpression, intracellular lactate (Warburg effect), and catalase depletion—could control cancer growth while ensuring 100% healthy cell survival.
Methods:
An agent-based simulation was implemented using the PhysiCell 1.10.4 framework. The model simulated a 2 mm × 2 mm tumor microenvironment containing a heterogeneous population of 456 agents (253 cancer cells and 203 healthy cells). The simulation modeled the biochemical kinetics of the 2-DG-boronate-VitC conjugate, including GLUT-mediated absorption, hexokinase-triggered bond cleavage, and lactate-dependent identity verification. A 20% drug-resistant subpopulation with elevated catalase levels was included to reflect clinical reality.
Results:
In silico validation demonstrated a 73.5% reduction in the cancer cell population within 24 simulation hours. Critically, the model showed a 0% mortality rate for healthy cells across all timepoints, resulting in a theoretical selectivity index that is effectively infinite. The simulation confirmed that the "AND-gate" logic successfully prevented Vitamin C release in environments with low lactate (1–2 mM), while the intracellular catalase in healthy cells provided a secondary biological safety barrier against peroxide generation.
Conclusions:
This computational framework provides a validated roadmap for a novel class of programmable therapeutics. By applying rigorous Quality Assurance (QA) principles to biological modeling, the study demonstrates that selective cancer elimination is achievable by combining metabolic sensing with autonomous agent logic. The results justify moving to Phase 2: molecular synthesis and in vitro kinetic testing.
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