Pratik Bhabad*
Krushi Pradhan
Janvi Patil
Drug delivery systems form the foundation of modern pharmaceutical therapy, as they determine how effectively a drug reaches its target site within the body. The primary goal of any delivery system is to ensure that the active pharmaceutical ingredient reaches the intended site of action at the right concentration and duration while minimizing systemic side effects [1]. Over the years, drug delivery technology has evolved from conventional systems such as tablets, capsules, and injections to more advanced approaches including liposomes, nanoparticles, microspheres, and implantable devices [2]. Despite significant progress, challenges such as poor solubility, rapid metabolism, variable absorption, and limited bioavailability continue to limit therapeutic efficiency [3]. Artificial Intelligence (AI) has recently emerged as a powerful tool capable of transforming pharmaceutical sciences, including the field of drug delivery. AI refers to computational systems that mimic human cognitive functions such as learning, reasoning, and decision-making [4]. When applied to drug delivery, AI facilitates the design, analysis, and optimization of complex formulations and delivery mechanisms. It allows researchers to manage large datasets, identify hidden relationships, and predict performance outcomes that are difficult to derive through conventional statistical or experimental methods [5]. The integration of AI into drug delivery research has been made possible by the rapid growth of experimental and clinical data. Advanced analytical technologies, high-throughput screening methods, and digital data storage platforms produce massive datasets that can be analyzed using AI algorithms [6]. Machine learning and deep learning models can interpret these datasets to uncover nonlinear relationships between formulation parameters, process variables, and therapeutic outcomes [7]. This capability enables scientists to optimize drug formulations, predict in vivo performance, and accelerate product development while reducing time and cost [8]. In formulation design, AI is now used to predict critical quality attributes such as particle size, dissolution rate, stability, and encapsulation efficiency [9]. Algorithms like artificial neural networks, support vector machines, and random forest models can be trained on experimental data to identify the best combination of excipients and manufacturing conditions [10]. This data-driven approach enhances accuracy, reproducibility, and efficiency in pharmaceutical research, reducing reliance on trial-and-error experimentation [11]. AI also plays a transformative role in personalized medicine. By analyzing individual patient data such as genomic profiles, metabolic rates, and disease progression, AI systems can tailor drug delivery strategies to meet specific therapeutic needs [12]. This individualized approach maximizes efficacy, reduces adverse effects, and improves overall patient outcomes. The integration of AI with biosensors, wearable devices, and mobile health applications has led to the emergence of smart drug delivery systems capable of real-time monitoring and automatic dose adjustment [13]. Moreover, AI contributes to pharmaceutical manufacturing through automation, process optimization, and quality control. Combined with robotics and continuous monitoring, AI enhances production efficiency and ensures product consistency within regulatory standards [14]. These AI-based systems support the Quality by Design (QbD) paradigm, helping industries predict process variations and maintain control over product quality [15]. Despite its advantages, the adoption of AI in drug delivery systems faces multiple challenges. Data quality, algorithm interpretability, and model validation remain key barriers to widespread implementation [16]. The reliability of AI predictions depends heavily on the size and quality of available datasets [17]. Ethical considerations such as patient data privacy, security, and potential algorithmic bias must also be addressed to ensure responsible and transparent AI applications in healthcare [18]. Furthermore, the absence of clear regulatory frameworks for AI-based pharmaceutical systems continues to hinder clinical translation [19]. Nonetheless, the potential of AI to revolutionize drug delivery is undeniable. As the pharmaceutical field becomes more digital and data-driven, AI is expected to play an increasingly central role in enhancing therapeutic precision and patient adherence [20]. When combined with emerging technologies such as nanotechnology, 3D printing, and the Internet of Medical Things (IoMT), AI can create adaptive, intelligent, and responsive drug delivery platforms capable of dynamically adjusting to patient needs [21]. The objective of this review is to explore the role of Artificial Intelligence in the development, design, and optimization of drug delivery systems. It aims to provide a comprehensive overview of fundamental concepts, key applications, technological advancements, challenges, and future perspectives [22]. Through this review, it becomes evident that AI is not merely a computational tool but a transformative force that is redefining the future of pharmaceutical formulation and personalized therapeutics [23].
10.5281/zenodo.17611150