Optimizing Targeted Drug Delivery Using LSTM and TLR-Enhanced Molecular Communication in Cancer Therapy

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : Ashwini Katkar, Vinitkumar Dongre

10.14445/23488379/IJEEE-V11I6P125

How to Cite?

Ashwini Katkar, Vinitkumar Dongre, "Optimizing Targeted Drug Delivery Using LSTM and TLR-Enhanced Molecular Communication in Cancer Therapy," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 6, pp. 234-241, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P125

Abstract:

In cancer treatment, the continuous aim is to find innovative solutions that effectively target malignant cells while limiting harm to healthy tissues. Molecular Communication (MC) has developed as a promising approach for targeted drug delivery, but congestion issues often hamper it. This study introduces a novel framework that combines Long Short-Term Memory (LSTM) congestion control with Toll-Like Receptor (TLR) feedback mechanisms to enhance drug delivery efficiency in cancer therapy. Using COMSOL simulations to determine congestion angles, LSTM models are trained, giving an accuracy rate of 97.75%. Through extensive computational modeling, the proposed approach significantly reduces congestion and improves the targeting and elimination of cancer cells. This research represents a key advancement in cancer treatment, enabling the precise, safe, and efficient delivery of drugs directly to cancer cells.

Keywords:

Cancer cell, Drug delivery, LSTM, Molecular Communication, Targeted drug.

References:

[1] Neda Sharifi et al., “Overcoming Channel Uncertainties in Touchable Molecular Communication for Direct-Drug-Targeting-Assisted Immuno-Chemotherapy,” IEEE Transactions on NanoBioscience, vol. 19, no. 2, pp. 249-258, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Dipak D. Gadade et al., “Strategies for Cancer Targeting: Novel Drug Delivery Systems Opportunities and Future Challenges,” Targeted Cancer Therapy in Biomedical Engineering, pp. 1-42, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Tadashi Nakano, Yutaka Okaie, and Athanasios V. Vasilakos, “Throughput and Efficiency of Molecular Communication between Nanomachines,” 2012 IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, pp. 704-708, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Luca Felicetti et al., “TCP-Like Molecular Communications,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 12, pp. 2354-2367, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Luca Felicetti, Mauro Femminella, and Gianluca Reali, “Congestion Control in Molecular Cyber-Physical Systems,” IEEE Access, vol. 5, pp. 10000-10 011, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mauro Femminella, Gianluca Reali, and Athanasios V. Vasilakos, “A Molecular Communications Model for Drug Delivery,” IEEE Transactions on NanoBioscience, vol. 14, no. 8, pp. 935-945, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mayisha Alam et al., “Conjugate Congestion Control Based Transport Layer Protocol for Molecular Communication in Body Area Nanonetworks (BANs),” 2018 5th International Conference on Networking, Systems and Security (NSysS), Dhaka, Bangladesh, pp. 1-6, 2018.  
[CrossRef] [Google Scholar] [Publisher Link]
[8] Shirin Salehi, Naghmeh S. Moayedian, and Eduard Alarcón, “Diffusion-Based Molecular Communication Channel in Presence of a Probabilistic Absorber: Single Receptor Model and Congestion Analysis,” IEEE Transactions on NanoBioscience, vol. 18, no. 1, pp. 8492, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Nafiseh Sohrabi et al., “Analysis of Dynamics Targeting CNT-Based Drug Delivery through Lung Cancer Cells: Design, Simulation, and Computational Approach,” Membranes, vol. 10, no. 10, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Qingying Zhao, Min Li, and Lin Lin, “Release Rate Optimization in Molecular Communication for Local Nanomachine-Based Targeted Drug Delivery,” IEEE Transactions on NanoBioscience, vol. 20, no. 4, pp. 396-405, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yue Sun et al., “A Cooperative Molecular Communication for Targeted Drug Delivery,” Bio-inspired Information and Communication Technologies, pp. 16-26, 2020.  
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tania Islam, Ethungshan Shitiri, and Ho-Shin Cho, “A Molecular Communication-Based Simultaneous Targeted-Drug Delivery Scheme,” IEEE Access, vol. 9, pp. 96658-96670, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Muhammad Javed Iqbal et al., “Targeted Therapy Using Nanocomposite Delivery Systems in Cancer Treatment: Highlighting miR34a Regulation for Clinical Applications,” Cancer Cell International, vol. 23, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Liquan Hong et al., “Nanoparticle-Based Drug Delivery Systems Targeting Cancer Cell Surfaces,” RSC Advances, vol. 13, no. 31, pp. 21365-21382, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Li Wang et al., “Extracellular Vesicles for Drug Delivery in Cancer Treatment,” Biological Procedures Online, vol. 25, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Martin Damrath et al., “Optimization of Extracellular Vesicle Release for Targeted Drug Delivery,” IEEE Transactions on NanoBioscience, vol. 23, no. 1, pp. 109-117, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Maximilian Schäfer et al., “Channel Responses for the Molecule Release from Spherical Homogeneous Matrix Carriers,” IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 8, no. 4, pp. 212-228, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Dhruv Varshney et al., “Employing Drug Delivery Strategies to Overcome Challenges Using TLR7/8 Agonists for Cancer Immunotherapy,” The AAPS Journal, vol. 23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] H. Birkan Yilmaz et al., “A Machine Learning Approach to Model the Received Signal in Molecular Communications,” 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, Turkey, pp. 1-5, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hao Sun et al., “Targeting Toll-Like Receptor 7/8 for Immunotherapy: Recent Advances and Prospectives,” Biomarker Research, vol. 10, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Sepp Hochreiter, and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[CrossRef] [Google Scholar] [Publisher Link]