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The use of artificial neural networks to control the concentration of a model drug released acoustically

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Abstract

Liposomes are designed to encapsulate chemotherapy drugs used in cancer treatment. Their small size (nano-scale) allows them to extravagate through the leaky vascular surroundings of the tumor. Ultrasound waves can be used as an external trigger to control drug release from these liposomes. It is essential that the therapeutic dose is released as cancer cells can develop drug resistance, in part due to the concentration levels of the chemotherapeutic agent dipping below therapeutic levels during the treatment. To address this issue, this study proposes a feedback drug release controller based on model predictive control theory (MPC) and neural networks (NN). Our preliminary simulation results suggest that using a feedback controller is capable of keeping drug concentration levels, in the tumor site, at or above therapeutic levels. This is achieved by controlling the amount of acoustic drug release from these lipid-based nanocarriers, thus ensuring a controlled, safe, and effective therapeutic dose.

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Funding

This work was financially supported by the American University of Sharjah Faculty Research Grants (FRGs and eFRGs), Al-Jalila Foundation (AJF 2015555), Al Qasimi Foundation, Patient’s Friends Committee-Sharjah, the Takamul program, the Technology Innovation Pioneer (TIP) program, and Dana Gas Endowed Chair for Chemical Engineering.

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Authors and Affiliations

  1. Department of Electrical Engineering, American University of Sharjah, Sharjah, UAE

    Hesham G. Moussa

  2. Department of Chemical Engineering, American University of Sharjah, Sharjah, UAE

    Ghaleb A. Husseini, Salma E. Ahmad & Nahid Awad

Authors
  1. Hesham G. Moussa
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  2. Ghaleb A. Husseini
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  3. Salma E. Ahmad
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  4. Nahid Awad
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Corresponding author

Correspondence to Ghaleb A. Husseini.

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Moussa, H.G., Husseini, G.A., Ahmad, S.E. et al. The use of artificial neural networks to control the concentration of a model drug released acoustically. emergent mater. 3, 503–513 (2020). https://doi.org/10.1007/s42247-020-00077-2

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  • DOI: https://doi.org/10.1007/s42247-020-00077-2

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