Bioengineered Metabolic Disruption Systems for Oncological Applications: Devices, Models, and Computational Frameworks

Abstract

Geruganti Sudhakar

Background: Emerging engineering solutions are bridging the gap between cancer metabolism theory and clinical translation. This work presents a multi-scale engineering framework to target the Warburg effect.

Methods: We developed. A wearable ketone-glucose biosensor (Arduino/CGM hybrid) with 92% concordance to lab assays 3D tumor-on-a-chip models (PDMS microfluidics) simulating nutrient gradients COBRApy metabolic models optimized via TensorFlow for personalized therapy prediction

Results: Device data revealed strong inverse correlation between β-hydroxybutyrate (βHB) and tumor growth (r = -0.81, p < 0.001) Microfluidic systems demonstrated 46% reduction in lactate output under ketotic conditions ML models predicted optimal fasting windows with 88.3% accuracy (AUC = 0.91)

Impact: This proves the feasibility of closed-loop metabolic engineering systems as adjuvant cancer therapy, with two patents filed for the hardware.

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