How AI will reshape future of smart and sustainable drug delivery systems

The study introduces a three-circle convergence framework, AI, smart functionality and sustainability, demonstrating that the intersection of these forces yields next-generation intelligent green drug delivery systems. These systems aim not only to deliver drugs more effectively but also reduce environmental harm and support long-term global health sustainability goals.

A new scientific review reveals that the coming decade will mark the beginning of a new pharmaceutical paradigm driven by AI-enabled smart delivery platforms and sustainability-focused design principles capable of transforming how medicines are discovered, formulated, administered and monitored.

The findings are presented in “A Review of Artificial Intelligence (AI)-Driven Smart and Sustainable Drug Delivery Systems: A Dual-Framework Roadmap for the Next Pharmaceutical Paradigm,” published in Sci. The paper builds an integrated roadmap for the next generation of intelligent and environmentally conscious drug delivery systems.

AI becomes the engine of modern drug delivery design and optimization

According to the study, artificial intelligence is now embedded across the full lifecycle of drug delivery systems, from early molecular modelling to large-scale manufacturing and real-time therapeutic control. The author shows that AI has moved far beyond the simple prediction of solubility, dissolution or release profiles; it now enables iterative design loops in which computational models continuously update based on experimental data, driving faster and more accurate formulation development.

Tree-based ensemble models such as Random Forest, Gradient Boosting and XGBoost remain central tools because they deliver strong accuracy on small and medium datasets while preserving interpretability, a requirement for regulatory compliance. Deep neural networks and graph-based architectures, meanwhile, are gaining traction for polymer design, polymer-drug compatibility prediction, and structure–property relationships where data volumes are larger and chemical complexity is high. The review also highlights that transformers and graph neural networks are starting to influence polymer informatics, enabling researchers to better predict behavior inside advanced delivery systems such as hydrogels, microneedles and nanocarriers.

The study stresses that AI excels when it is integrated into design–test–learn cycles. Models generate candidate formulations or material combinations; experiments validate them; the data returns to the model to refine predictions. This closed-loop workflow reduces the need for time-consuming experiments and accelerates the path to optimized formulations.

The review further identifies emerging advances in generative design, where machine-learning models propose new molecules, excipient blends or structural features of drug delivery systems based on desired therapeutic profiles. Reinforcement learning and Bayesian optimization approaches are now used to fine-tune microneedle array parameters, hydrogel stiffness, release kinetics and excipient ratios while minimizing experimental overhead. However, the study notes that generative tools remain in early developmental stages, especially in areas where limited datasets and complex release mechanisms restrict generalization.

AI cannot be evaluated in isolation from data quality. Small, fragmented, biased datasets remain a major obstacle to robust performance. To overcome these issues, the author emphasizes the importance of FAIR data principles and standardized vocabularies across formulations research, allowing researchers to share, compare and validate models more effectively.

Smart, responsive and eco-conscious drug delivery systems begin to converge

The paper establishes a link between AI, smart drug delivery and sustainability, three areas that have traditionally been treated separately but are increasingly interdependent.

Smart drug delivery systems are defined as materials or devices that respond dynamically to their biological environment. the author describes how AI supports the development of pH-responsive hydrogels, temperature-sensitive nanocarriers, enzyme-triggered release systems, biosensor-equipped microneedles and adaptive wearable patches that adjust dosing based on real-time physiological signals. These technologies show promise in chronic disease management, precision oncology, vaccination strategies, and personalized medicine. AI plays a central role by predicting how materials respond to biological triggers, optimizing sensor thresholds, and managing closed-loop dosing decisions.

The paper notes that smart wearable delivery systems, especially microneedle-based patches integrated with biosensors, are now evolving into autonomous therapeutic platforms. AI algorithms analyze physiological feedback and adjust the release rate to achieve precise therapeutic effects. These systems represent a shift toward real-time, patient-specific drug administration that could significantly reduce dosing errors, minimize adverse reactions and improve health outcomes.

At the same time, sustainability has become a central concern as pharmaceutical manufacturing faces mounting pressure to reduce carbon emissions, eliminate toxic solvents and minimize waste. the author explains that AI can significantly accelerate the transition to environmentally responsible drug delivery by identifying greener solvents, optimizing low-energy processes, predicting environmental degradation pathways and evaluating the ecological footprint of excipients and materials.

AI tools now support solvent selection guided by life-cycle assessment metrics, enabling researchers to pick options that balance manufacturability, safety and environmental impact. Machine learning also lowers energy consumption during granulation, drying and tableting by optimizing temperature, pressure and processing durations. Multi-objective optimization techniques allow drug developers to simultaneously consider quality, cost, performance and environmental footprint during system design.

The study introduces a three-circle convergence framework, AI, smart functionality and sustainability, demonstrating that the intersection of these forces yields next-generation intelligent green drug delivery systems. These systems aim not only to deliver drugs more effectively but also reduce environmental harm and support long-term global health sustainability goals.

A dual-framework roadmap outlines the future of AI-enabled drug delivery

To guide the next decade of innovation, the author presents a dual-framework roadmap that outlines how AI-driven drug delivery systems can progress from computational concept to global pharmaceutical infrastructure.

The first framework is a four-stage lifecycle model:

Stage I: Computational design and prediction. This stage relies on machine learning to screen materials, predict pharmacokinetic behavior, identify ideal excipients, model release kinetics and highlight chemical incompatibilities before any experimental work begins.

Stage II: Experimental integration and optimization. AI interacts with laboratory experimentation through active learning, automated data collection and iterative model refinement. Here, predictive power increases, prototype formulations become more reliable, and traditional design-of-experiments approaches are supplanted by closed-loop optimization.

Stage III: Smart and autonomous delivery systems. Drug delivery platforms incorporate biosensors, wearable modules, or environmental triggers, allowing real-time feedback control. AI becomes involved not only in design but also in digital therapeutics, providing responsive dosing systems tailored to patient signals.

Stage IV: Regulatory translation and sustainable deployment. The final stage addresses how smart, AI-enabled systems reach clinical testing, market authorization and real-world use. the author argues that regulatory bodies will increasingly require model documentation, interpretability, uncertainty quantification and sustainability metrics. Environmental considerations may become part of future regulatory filings as industries adopt circular pharmaceutics principles.

The second framework uses the technology readiness level (TRL) scale to map where these systems currently stand. According to the review, many AI-enabled formulation tools sit in low to mid-TRL stages due to data limitations, while smart drug delivery platforms incorporating sensors are climbing toward mid- and late-stage readiness. Sustainable design integration is still early, but the study predicts rapid progress as global pharmaceutical firms adopt ESG-aligned practices.

The author explains that successful adoption requires investment not only in AI models but also in infrastructure such as automated labs, digital inventory systems, sensor-integrated manufacturing lines and secure data pipelines. The paper warns that technological inequality between well-resourced laboratories and emerging research regions could widen unless global collaboration and open scientific ecosystems are prioritized.

Challenges and ethical considerations define the path forward

The rapid expansion of AI-driven drug delivery brings new challenges in ethics, regulation, environment and workforce skills. the author outlines several risks that must be addressed.

First, data scarcity and fragmentation undermine the robustness of AI models. Many drug delivery datasets contain small sample sizes, narrow parameter ranges or unreported negative results. Without richer datasets, models risk overfitting and poor real-world performance.

Second, black-box models challenge transparency requirements in pharmaceutical regulation. Without clear model explainability, it becomes difficult for regulators to trust predictions related to safety-critical attributes such as stability, toxicity, degradation or therapeutic release. The study highlights the need for interpretable or physics-informed machine learning architectures.

Third, infrastructure gaps limit the real-world implementation of autonomous or smart delivery systems. Deploying wearable biosensor-linked drug platforms requires reliable electronics, secure cloud connectivity, and integrated patient monitoring environments, which remain uneven across global healthcare systems.

Fourth, the energy footprint of large AI models raises sustainability concerns. While AI supports green pharmaceutics, its own computational demands may conflict with environmental goals unless balanced through efficient modelling, low-power algorithms and renewable energy integration.

The study notes the risk of over-automation, where human pharmaceutical expertise is diminished. AI must supplement, not replace, the interpretive, ethical, and contextual reasoning skills of scientists, pharmacists and clinicians.