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<p>This study presents the development and validation of a Deep Q-Learning framework for optimizing drug release profiles of doxorubicin-loaded smart nanoparticles, addressing key challenges in precision drug delivery. By formulating the optimization problem as a Markov Decision Process (MDP), the framework uses reinforcement learning to dynamically adapt drug release under varying physiological conditions, including tumor microenvironments (pH ~ 6.5) and healthy tissues (pH ~ 7.4). The optimized release profiles followed a dual-phase mechanism: an initial burst release of 35% ± 2.1% in six hours, followed by a sustained release of 60% ± 3.4% over 48&#xa0;h. This approach demonstrated computationally inferred improvements in therapeutic performance, reflected by optimized release profiles that reduced simulated off-target drug exposure by 18.7% under physiological pH conditions. dThe framework achieved high computational efficiency, with training times of 2.4 ± 0.1&#xa0;min per 100 episodes and prediction times averaging 0.35 ± 0.02&#xa0;s per profile. Sensitivity analysis confirmed robustness, with cumulative release deviations below 6.2% under varying pH and degradation conditions. Error metrics, including a root mean squared error (RMSE) of 1.4% and mean absolute error (MAE) of 1.1%, validated the predictive accuracy. Compared with heuristic (GA, PSO) and classical RL methods (SARSA, Q-Learning), the Deep Q-Learning framework demonstrated faster convergence, lower release error (RMSE 1.4%), and improved adaptability to environmental fluctuationsOverall, this adaptive AI-driven platform demonstrates a scalable and precise approach for real-time control of drug release, offering a pathway toward intelligent, patient-specific nanomedicine design.</p>

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Deep Q-Learning framework for adaptive drug release control in smart nanoparticles: a computational study

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Dilpreet Singh

摘要

This study presents the development and validation of a Deep Q-Learning framework for optimizing drug release profiles of doxorubicin-loaded smart nanoparticles, addressing key challenges in precision drug delivery. By formulating the optimization problem as a Markov Decision Process (MDP), the framework uses reinforcement learning to dynamically adapt drug release under varying physiological conditions, including tumor microenvironments (pH ~ 6.5) and healthy tissues (pH ~ 7.4). The optimized release profiles followed a dual-phase mechanism: an initial burst release of 35% ± 2.1% in six hours, followed by a sustained release of 60% ± 3.4% over 48 h. This approach demonstrated computationally inferred improvements in therapeutic performance, reflected by optimized release profiles that reduced simulated off-target drug exposure by 18.7% under physiological pH conditions. dThe framework achieved high computational efficiency, with training times of 2.4 ± 0.1 min per 100 episodes and prediction times averaging 0.35 ± 0.02 s per profile. Sensitivity analysis confirmed robustness, with cumulative release deviations below 6.2% under varying pH and degradation conditions. Error metrics, including a root mean squared error (RMSE) of 1.4% and mean absolute error (MAE) of 1.1%, validated the predictive accuracy. Compared with heuristic (GA, PSO) and classical RL methods (SARSA, Q-Learning), the Deep Q-Learning framework demonstrated faster convergence, lower release error (RMSE 1.4%), and improved adaptability to environmental fluctuationsOverall, this adaptive AI-driven platform demonstrates a scalable and precise approach for real-time control of drug release, offering a pathway toward intelligent, patient-specific nanomedicine design.