Dr. D. Y. Patil College of Pharmacy Akurdi, Pune.
The administration of medicinal drugs has been completely transformed by Novel Drug Delivery Systems (NDDS), which are designed to increase patient compliance, safety, and efficacy. A revolutionary method for drug formulation, optimization, and customized treatment is the use of Artificial Intelligence (AI) and Machine Learning (ML) into NDDS. With an emphasis on formulation design, nanotechnology, predictive modeling, and smart drug delivery platforms, this review focuses on the present uses, approaches, difficulties, and potential future developments of AI and ML in NDDS.
Novel Drug Delivery Systems (NDDS) represent a paradigm shift in pharmaceutical research, aiming to overcome the limitations of conventional dosage forms by enhancing therapeutic efficacy, improving patient compliance, and enabling site-specific drug targeting. NDDS technologies such as nanoparticles, liposomes, dendrimers, transdermal patches, microneedles, and implantable devices have significantly contributed to the advancement of modern therapeutics [1,2]. However, their development is often hindered by challenges including complex formulation variables, high development costs, time-intensive optimization, and unpredictable clinical outcomes [3]. The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has opened new opportunities for accelerating NDDS development. AI refers to computational systems capable of mimicking human intelligence, while ML is a subset of AI focused on training algorithms to learn patterns from data and make predictions or decisions [4]. The pharmaceutical industry has increasingly adopted AI/ML for drug discovery, clinical trials, biomarker identification, and pharmacovigilance [5,6]. Their application in NDDS is particularly promising, as they enable predictive modelling of formulation parameters, optimization of excipient selection, real-time monitoring of drug release, and patient-specific therapy design [7].
Figure 1: Role of AI & ML in Novel Drug Delivery Systems (NDDS)
Recent advances in deep learning and neural networks have further strengthened the role of AI in NDDS. For instance, deep learning models have been applied to predict nanoparticle size, drug encapsulation efficiency, and release kinetics with higher accuracy compared to traditional statistical methods [8]. Similarly, ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and Gradient Boosting have been successfully implemented for predicting transdermal permeability, oral bioavailability, and stability profiles of NDDS [9]. Furthermore, AI-based biosensors and smart delivery platforms are emerging as next-generation tools for stimuliresponsive and personalized drug delivery [10]. Therefore, integrating AI and ML into NDDS not only reduces time and cost of formulation development but also provides safer, more efficient, and patient-centric therapies. This review highlights the role of AI and ML in NDDS, with an emphasis on their applications, advantages, limitations, and future perspectives.
Role of AI and ML in Novel Drug Delivery Systems (NDDS) Drug Formulation and Design
AI and ML algorithms are increasingly being applied to drug formulation, where they can predict optimal drug–excipient ratios, enhance stability, and reduce formulation time. ML tools such as decision trees and support vector machines (SVM) have been used to model the relationships between input formulation variables and encapsulation efficiency in nanoparticles and liposomes [11]. AI-based optimization has also enabled screening of excipients (polymers, surfactants, and lipids) to design safer and more effective delivery carriers [12].
Figure 2: AI & ML in Drug Formulation and Design
Nanomedicine Development
Nanomedicine development requires optimization of nanoparticle size, surface charge, and biocompatibility. AI systems help predict these critical parameters, ensuring reproducibility and improved therapeutic performance [13]. Deep learning models analyze nano–bio interactions, including endocytosis and immune responses, which are difficult to capture through experimental methods alone [14]. Furthermore, ML-guided nanocarrier design has accelerated the development of safer and more efficient tumor-targeted nanomedicines [15].
Figure 3: Importance of nanomedicine development
Pharmacokinetics and Pharmacodynamics (PK/PD)
AI models are increasingly used to predict ADME (Absorption, Distribution, Metabolism, Excretion) processes for NDDS formulations. Physiologically based pharmacokinetic (PBPK) models integrated with ML approaches provide insights into drug clearance and systemic distribution [6]. Personalized PK/PD modeling using patient datasets supports individualized therapy and dose adjustments, minimizing adverse effects [17].
Figure 4: Pharmacokinetics and Pharmacodynamics (PK/PD)
Smart and Targeted Drug Delivery
Smart NDDS platforms integrate AI-driven biosensors for real-time monitoring of drug release and therapeutic response. These biosensors, coupled with ML algorithms, allow adaptive drug release depending on physiological triggers such as pH, temperature, and enzymatic activity [8]. Stimuliresponsive systems designed using AI ensure better targeting and reduced toxicity, particularly in cancer and neurological disorders [19,20].
Figure 5 : Smart and Targeted Drug Delivery
Current Applications of AI and ML in NDDS
The application of AI and ML in NDDS has expanded from formulation design to clinical applications. Several case studies and experimental models highlight their growing significance:
AI in Liposomal Formulation
AI algorithms have been applied to optimize liposomal drug formulations by predicting critical parameters such as vesicle size, surface charge, and drug release profiles. ML tools, including Random Forests and Artificial Neural Networks (ANN), have shown high predictive accuracy in encapsulation efficiency and release kinetics, thereby reducing the number of experimental trials required [21,22].
Figure 6: Tree Diagram Representing Applications of AI in Liposomal Formulation
Deep Learning in Transdermal Delivery
Deep learning models are increasingly used to predict skin permeability based on drug physicochemical properties and excipient composition. Convolutional neural networks (CNNs) have been reported to outperform traditional regression models in forecasting transdermal drug absorption, aiding in the rational design of patches and gels [23].
ML in Microneedle Patches
Microneedle-based NDDS is gaining popularity for painless, self-administrable drug delivery. ML algorithms have been successfully implemented to predict mechanical strength, insertion depth, and drug loading efficiency of microneedles fabricated from biodegradable polymers. These predictive models enhance the scalability and reproducibility of microneedle technologies [24].
AI for Controlled Release Tablets
Controlled release systems are complex and often require extensive optimization. Neural networks have been used to model drug release kinetics and dissolution profiles of sustained-release tablets. Such AI-driven models provide rapid in silico simulations, reducing experimental workload and enabling fine-tuning of tablet matrices [25].
Nanoparticle Toxicity Prediction
AI is also used to evaluate the potential toxicity of nanoparticles by analyzing high-dimensional datasets of physicochemical properties. Decision-tree and clustering algorithms identify patterns associated with cytotoxicity, hemolysis, and immunogenicity, allowing for safer NDDS design [26].
AI in Oral Bioavailability Enhancement
AI-based predictive tools help optimize oral formulations of poorly soluble drugs. By modeling solubility, permeability, and excipient interactions, ML approaches assist in designing NDDS that improve bioavailability, such as solid dispersions, nanocrystals, and lipid-based carriers [27].
Table No. 1. AI/ML Applications in Novel Drug Delivery Systems
|
NDDS |
AI/ML Model |
Focus |
Benefit |
|
Transdermal |
CNNs |
Skin permeability |
Accurate prediction, better patch/ gel design |
|
Microneedles |
ML algorithms |
Strength, insertion, drug load |
Scalable, reproducible fabrication |
|
Controlled Release |
Neural Networks |
Release kinetics, dissolution |
Quick in silico optimization |
|
Nanoparticles |
Decision Trees, Clustering |
Toxicity profiling |
Safer nanocarrier design |
|
Oral Systems |
Predictive ML tools |
Solubility, permeability |
Improved bioavailability |
Advantages of AI/ML in NDDS [28,29]
The integration of artificial intelligence (AI) and machine learning (ML) into novel drug delivery systems (NDDS) offers multiple benefits that enhance both formulation development and patient outcomes.
Reduction in formulation time and cost:
AI/ML models minimize the need for repetitive experimental trials, reducing resource consumption and accelerating formulation timelines.
Enhanced accuracy in prediction models:
By analyzing large datasets of excipients, polymers, and drug properties, ML algorithms provide highly accurate predictions of encapsulation efficiency, release kinetics, and stability.
Improved patient-specific therapies:
Personalized dosing and tailored NDDS design are facilitated by AI, enabling therapies aligned with patient-specific genetic, metabolic, and disease profiles.
Facilitates green and sustainable pharmaceutical approaches:
AI-driven optimization reduces the use of hazardous solvents and excipients, thereby contributing to eco-friendly pharmaceutical practices
Limitations and Challenges of AI/ML in NDDS
Requirement of large, high-quality datasets:
AI/ML models need well-curated, representative datasets. But often in pharmaceutical / drug delivery contexts, datasets are scarce, heterogenous, or biased. [31]
Black-box nature of AI models (lack of interpretability):
Many deep learning and complex ML models are opaque. Their internal decision-making isn’t transparent, which leads to difficulty in interpreting predictions, and hence lowers trust among clinicians and regulators.[32]
Ethical issues in patient data usage:
Use of patient data raises concerns over privacy, consent, bias, and fairness. When training data is not sufficiently anonymized or representative, there’s risk of misuse or biased predictions. [33]
Regulatory challenges in AI-based NDDS approval:
Regulatory frameworks often lag behind technological advances. Challenges include absence of standard guidelines for validating AI/ML models, lack of clarity in liability, lack of harmonization between regions, and concerns over model validation and reproducibility. [34]
Future Perspectives of AI/ML in NDDS
The next decade is expected to witness transformative integration of AI and ML technologies in NDDS, moving beyond laboratory optimization into clinical and regulatory translation. Key directions include:
Integration of AI with digital twins for real-time simulation:
Digital twin technology, when combined with AI, can provide patient-specific simulations of NDDS performance, enabling real-time adjustments to therapy.
AI-driven personalized nanomedicine:
Using patient-specific genomic, proteomic, and metabolic data, AI can design nanocarriers tailored for individualized therapy, improving safety and efficacy.
Application of generative AI in excipient and carrier design:
Generative AI algorithms (e.g., GANs, reinforcement learning) may accelerate the discovery of novel polymers, lipids, and surfactants, facilitating the development of next-generation NDDS.
Wider adoption in clinical decision support systems (CDSS):
AI-integrated NDDS can be linked with hospital CDSS platforms to guide clinicians in therapy selection, dosing strategies, and monitoring patient outcomes.
CONCLUSION
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the field of Novel Drug Delivery Systems (NDDS) by enabling data-driven design, optimization, and personalized therapy. These technologies allow researchers to predict drug–excipient interactions, optimize formulations, and simulate drug release profiles with higher accuracy and efficiency, reducing the time and cost of experimental work. AI and ML also enhance safety assessments and support precision medicine by tailoring drug delivery to individual patient needs.Despite their promising applications, challenges such as limited high-quality datasets, model interpretability, and regulatory hurdles remain. With continued advancements in computational algorithms, integration with real-world clinical data, and improved standardization, AI and ML are poised to become indispensable tools in the development of safer, more effective, and personalized drug delivery systems, ultimately transforming pharmaceutical research and patient care.
Conflicts of Interest
The authors declare no conflicts of interest.
REFERENCES
Shyam Manza*, Shweta Galande, Srushti Bansode, Sushma Waghmare, Sonai Antapure, Priyatama Powar, Artificial Intelligence and Machine Learning in Novel Drug Delivery Systems (NDDS): A Review, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 11, 112-120 https://doi.org/10.5281/zenodo.17501641
10.5281/zenodo.17501641
