Sci· 2025Q1· Review
A Review of Artificial Intelligence (AI)-Driven Smart and Sustainable Drug Delivery Systems: A Dual-Framework Roadmap for the Next Pharmaceutical Paradigm
- 28citations
- Q1SCImago
- 2025year
Short summary
This review integrates AI-driven formulation design, smart drug delivery systems (DDS), and sustainable pharmaceutics, proposing a dual-framework roadmap for future pharmaceutical development.
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Key points
- AI techniques (ML, DL, Bayesian optimization, RL, digital twins) enable precise prediction of drug delivery attributes and generative discovery of excipients.
- AI models complex behaviors in polymeric and nano-based systems, facilitating the design of stimuli-responsive DDS for real-time therapeutic adaptation.
- AI supports green pharmaceutics by guiding biodegradable material selection, energy-efficient process design, and life-cycle optimization.
- The review proposes a dual-framework roadmap integrating AI-enabled DDS across formulation, smart delivery, and sustainability within a life-cycle perspective.
- Challenges include data scarcity, model interpretability, regulatory uncertainty, and high computational costs, requiring FAIR data principles and physics-informed modeling.
AI-generated from the title and abstract; the full text is not read.
Abstract
Artificial intelligence (AI) is transforming pharmaceutical science by shifting drug delivery research from empirical experimentation toward predictive, data-driven innovation. This review critically examines the integration of AI across formulation design, smart drug delivery systems (DDSs), and sustainable pharmaceutics, emphasizing its role in accelerating development, enhancing personalization, and promoting environmental responsibility. AI techniques—including machine learning, deep learning, Bayesian optimization, reinforcement learning, and digital twins—enable precise prediction of critical quality attributes, generative discovery of excipients, and closed-loop optimization with minimal experimental input. These tools have demonstrated particular value in polymeric and nano-based systems through their ability to model complex behaviors and to design stimuli-responsive DDS capable of real-time therapeutic adaptation. Furthermore, AI facilitates the transition toward green pharmaceutics by supporting biodegradable material selection, energy-efficient process design, and life-cycle optimization, thereby aligning drug delivery strategies with global sustainability goals. However, challenges persist, including limited data availability, lack of model interpretability, regulatory uncertainty, and the high computational cost of AI systems. Addressing these limitations requires the implementation of FAIR data principles, physics-informed modeling, and ethically grounded regulatory frameworks. Overall, AI serves not as a replacement for human expertise but as a transformative enabler, redefining DDS as intelligent, adaptive, and sustainable platforms for future pharmaceutical development. Compared with previous reviews that have considered AI-based formulation design, smart DDS, and green pharmaceutics separately, this article integrates these strands and proposes a dual-framework roadmap that situates current AI-enabled DDS within a structured life-cycle perspective and highlights key translational gaps.
The authors' abstract, as published at the source. Sci, 2025 · DOI ↗
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Field: Pharmaceutical Science
Pharmaceutical SciencePharmacology, Toxicology and Pharmaceutics