The integration of artificial intelligence (AI) with nanoparticle design has revolutionized targeted drug delivery and controlled release systems, particularly in the realm of precision medicine. This chapter explores the role of AI-guided nanoparticle design in enhancing the specificity, efficiency, and personalization of drug delivery systems for the treatment of various diseases, with a focus on cancer and chronic conditions. AI models, particularly machine learning (ML) and reinforcement learning (RL), are employed to optimize nanoparticle properties such as size, shape, surface charge, and drug release kinetics, ensuring that therapeutic agents are delivered accurately to the intended targets while minimizing systemic side effects. Multiscale modeling, a key component in AI-driven design, allows for the simulation of nanoparticle behavior across molecular, cellular, and organismal levels, providing invaluable insights into drug release mechanisms and biological interactions. Despite challenges related to data integration, computational costs, and model validation, the potential for AI to accelerate the development of personalized, smart nanoparticle-based therapies is immense. Case studies of AI-enhanced targeted drug delivery systems in oncology illustrate the transformative impact of these technologies on cancer treatment. As computational power increases and data availability expands, AI-guided nanoparticle design promises to reshape the future of drug delivery, paving the way for safer, more effective treatments. This chapter highlights the current state of research, challenges, and future directions in the application of AI for nanoparticle-based drug delivery systems, emphasizing their potential to revolutionize modern therapeutics.
The rapid advancement of nanotechnology has significantly transformed the landscape of drug delivery systems, offering new avenues for precision medicine [1]. Nanoparticles, owing to their unique size and surface properties, have the potential to enhance the delivery of therapeutic agents by improving their solubility, stability, and targeted delivery to disease sites [2]. These nanoparticles can be engineered to cross biological barriers such as the blood-brain barrier or tumor vasculature, ensuring that drugs are delivered directly to the intended site of action [3]. Despite these advantages, one of the key challenges in developing effective nanoparticle-based drug delivery systems lies in achieving precise control over drug release [4]. The ability to release drugs at a controlled rate, in a targeted manner, and for a prolonged period, remains a significant hurdle in the design of these systems. In this context, artificial intelligence (AI) has emerged as a powerful tool to enhance nanoparticle design and optimize drug delivery, providing solutions that were previously unattainable through traditional methods [5].
AI's integration with nanotechnology has unlocked new possibilities in the design and optimization of nanoparticles for drug delivery [6]. Machine learning (ML) algorithms, particularly deep learning (DL) models, are able to process large datasets and identify complex patterns that inform the design of nanoparticles with specific properties [7]. These models analyze various parameters, such as nanoparticle size, shape, surface charge, and composition, and predict how these factors influence drug encapsulation, release kinetics, and cellular uptake [8]. By harnessing the power of AI, researchers can accelerate the design process, bypassing the trial-and-error approach that has traditionally been used in nanoparticle development [9]. AI models can predict nanoparticle behavior in vivo, offering valuable insights into how these particles interact with biological systems, and allowing for the refinement of nanoparticle properties to achieve optimal therapeutic outcomes [10].
One of the key advantages of AI-guided nanoparticle design lies in the ability to create systems that release their drug payloads in response to specific stimuli [11]. Stimuli-responsive nanoparticles are designed to release their contents only under certain conditions, such as changes in pH, temperature, or the presence of specific enzymes [12]. This targeted release is particularly useful in the treatment of diseases like cancer, where the tumor microenvironment offers unique conditions that can be exploited to trigger drug release precisely at the site of action [13]. AI algorithms can be used to simulate how nanoparticles will respond to these environmental changes, ensuring that drug release is both controlled and localized [14]. By predicting how nanoparticles will behave in response to specific triggers, AI models can optimize drug release profiles, improving the efficacy of the treatment while reducing systemic toxicity [15].
