Trends in Immunotherapy

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Reprogramming the Tumor Ecosystem via Computational Intelligence-Guided Nanoplatforms for Targeted Oncological Interventions

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https://doi.org/10.54963/ti.v9i3.1286
Jadhav, S., Aruna, C., Choudhary, V., Gamini, S., Kapila, D., & Reddy, C. S. P. (2025). Reprogramming the Tumor Ecosystem via Computational Intelligence-Guided Nanoplatforms for Targeted Oncological Interventions. Trends in Immunotherapy, 9(3), 210–226. https://doi.org/10.54963/ti.v9i3.1286
Volume 9 Issue 3 (2025)
Copyright (c) 2025 Sachin Jadhav, Chittineni Aruna, Vijay Choudhary, Sridevi Gamini, Dhiraj Kapila, C. S. Preetham Reddy
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Authors

  • Sachin Jadhav

    School of Engineering and Technology, Pimpri Chinchwad University Pune, Maharashtra 412106, India
  • Chittineni Aruna

    Department of Computer Science and Engineering, KKR & KSR Institute of Technology and Sciences, Vinjanam‑ padu, Guntur, Andhra Pradesh 522107, India
  • Vijay Choudhary

    IPS Academy Institute of Engineering and Science, Indore, Madhya Pradesh 452012, India
  • Sridevi Gamini

    Department of Electronics and Communication Engineering, Aditya University, Surampalem, Andhra Pradesh 533437, India
  • Dhiraj Kapila

    Department of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
  • C. S. Preetham Reddy

    Department of Electronics and Communication Engineering, K L Deemed to be University, Guntur, Andhra Pradesh 522302, India

Received: 31 May 2025; Revised: 16 June 2025; Accepted: 09 July 2025; Published: 5 September 2025

The Tumor Microenvironment (TME) resists conventional treatments by sending out signals that weaken the immune system. Tumor cells vary significantly, and treatments can lead to resistance. This paper focuses on constructing an effective module for reprogramming the tumor ecosystem using Computational Intelligence-Guided Nanoplatforms. It hypothesizes that computationally optimized fuzzy deep learning, integrated with nanoplatform-mediated drug delivery, can dynamically reprogram tumor-infiltrating T-cells to overcome immunosuppression in the TME, thereby enhancing cytotoxicity and therapeutic response. A novel nanotechnology-integrated deep fuzzy learning framework—Seagull Optimized Sugeno Fuzzy Deep Learning (SgOSF-DL)—is proposed to reprogram T-cell behavior in real-time. Multi-omic data from tumor-infiltrating T-cells are encoded and analyzed using fuzzy logic to determine their immune state (suppressed, exhausted, or active), guided by key biomarkers such as Granzyme B and PD-1. The optimized model governs the release of IL-21 and checkpoint inhibitors via nanoplatforms composed of PLGA, gold nanoshells, and iron oxide particles. Fuzzy rules are formulated using optimized parameters to evaluate the TME. Simulation results confirm that the proposed SgOSF-DL model accurately distinguishes between cancer and healthy cells. It alters cancer behavior by reducing tumor burden, lowering PD-1, and boosting Granzyme B expression. The model achieves 96.5% accuracy in classifying T-cell states, reduces tumor count by 69.2%, and decreases PD-1 expression by 61% for active immune function. It also offers faster therapeutic classification (0.017 seconds) with an activation consistency of 92.8%. Fuzzy logic enables transparent decision-making, aiding clinicians in understanding the treatment rationale.

Keywords:

Tumor Microenvironment (TEM) Seagull Deep Learning Oncology T‑Cells Reprogramming

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