Designing Next-Generation Platforms with Machine Learning to Optimize Immune Cell Engineering for Enhanced Applications

Trends in Immunotherapy

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Designing Next-Generation Platforms with Machine Learning to Optimize Immune Cell Engineering for Enhanced Applications

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https://doi.org/10.54963/ti.v9i4.1402
Jadhav, S., Chakrapani, I., Sivasubramanian, S., RamKrishna, B. V., Mouleswararao, B., & Gangwar, S. (2025). Designing Next-Generation Platforms with Machine Learning to Optimize Immune Cell Engineering for Enhanced Applications. Trends in Immunotherapy, 9(4), 226–244. https://doi.org/10.54963/ti.v9i4.1402
Volume 9 Issue 4 (December 2025): In Progress
Copyright (c) 2025 Sachin Jadhav, I.S. Chakrapani, S Sivasubramanian, Bh. V. RamKrishna, B Mouleswararao, Sanjeev Gangwar
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Authors

  • Sachin Jadhav

    Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra 411048, India
  • I.S. Chakrapani

    Department of Zoology, PRR & VS Govt College, Vidavalur, Andhra Pradesh 52438, India
  • S Sivasubramanian

    Department of Artificial Intelligence and Data Science, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu 600073, India
  • Bh. V. RamKrishna

    Department of Computer Science and Engineering, Vignana Institute of Technology and Science, Deshmukhi, Hyderabad 508284, India
  • B Mouleswararao

    Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India
  • Sanjeev Gangwar

    Department of Computer Applications, VBS Purvanchal University, Jaunpur, Uttar Pradesh 222003, India

Received: 14 July 2025; Revised: 24 July 2025; Accepted: 6 August 2025; Published: 8 December 2025

Immune cell engineering is a cutting-edge strategy in modern immunotherapy, enabling precise, personalized treatments using modified immune cells such as CAR-T and NK cells. This paper introduces an Intelligent Optimized Machine Learning (IoML) framework with Sorting Slap Swarm Optimization (SSSO) to enhance immune cell design. The IoML model employs a ranking-based Deep Neural Network (DNN) to predict therapeutic metrics—cytotoxicity, persistence, safety, and antigen specificity—from high-dimensional biological data. Public datasets including GSE120575, ImmPort, and the Single Cell Portal were used for training and validation. The SSSO algorithm efficiently explores the immune cell design space, selecting optimal configurations based on predicted performance. Simulation results highlight the IoML model as an intelligent, low-overhead solution for personalized immunotherapy. It achieved a classification accuracy of 96.8%, precision of 96.1%, recall of 95.9%, and F1-score of 96.0%, outperforming Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution methods. In ranking analysis, the model attained a score of 0.92 within 30 iterations. Experimental validation confirmed optimized CAR-T cells with cytotoxicity of 96.2%, persistence of 93.8%, and apoptosis resistance of 94.3%, while reducing off-target effects to 3.2%. These findings demonstrate that the IoML–SSSO framework surpasses conventional approaches in accuracy, convergence speed, and biological relevance, effectively identifying top-performing immune cell configurations for next-generation immunotherapy platforms.

Keywords:

Immune Cell Machine Learning Optimization Deep Neural Network (DNN) Immunotherapy

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