Integrating artificial intelligence into drug delivery systems: Formulation development and current challenges
- 1Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Coimbra Chemistry Centre, Institute of Molecular Sciences - IMS, Department of Chemistry, University of Coimbra 3000-535 Coimbra, Portugal
- 2Faculty of Pharmacy, University of Coimbra, Pólo das Ciências da Saúde, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal; Coimbra Chemistry Centre, Institute of Molecular Sciences - IMS, Department of Chemistry, University of Coimbra 3000-535 Coimbra, Portugal. Electronic address: [email protected]
Abstract
Artificial intelligence (AI) is increasingly being explored as a supportive tool to address persistent challenges in drug delivery research, particularly those associated with formulation complexity, optimization efficiency, and controlled drug release. While AI applications in drug discovery are well established, their role in drug delivery systems remains less structured and is primarily focused on supporting formulation design, optimization, and performance prediction within experimentally constrained environments. Conventional trial-and-error approaches are resource-intensive and often struggle to capture the multidimensional relationships between formulation variables, carrier properties, and biological performance. In this context, AI-driven methods offer opportunities to enhance data interpretation and guide formulation development in a more systematic and predictive manner. This review examines the application of AI across key stages of drug delivery development, including formulation design, nanocarrier optimization, and smart and controlled release systems. Machine learning (ML) and deep learning (DL) approaches applied to pre-formulation analysis, excipient selection, particle engineering, and release-profile optimization are discussed, highlighting their role as decision-support tools rather than autonomous systems. The integration of AI within Quality by Design (QbD) frameworks is also critically assessed, with particular attention to its potential to support the identification of critical formulation variables and design-space exploration. Despite these advances, translation into routine pharmaceutical practice remains limited by challenges related to data quality, experimental validation, model interpretability, and biological complexity. Overall, this review positions AI as a complementary and decision-support layer in drug delivery development and highlights the key barriers that must be addressed for its reliable, scalable, and regulatory-aligned implementation.
Related
- Challenges and barriers to integrating artificial intelligence in medical education and healthcare: a qualitative exploration
- Strategies for integrating artificial intelligence and cognitive assessment to predict disability progression in relapsing-remitting multiple sclerosis: A model development study
- Toward Precision Colorectal Cancer Screening: Integrating Clinical Risk Stratification, Molecular Biomarkers, and Artificial Intelligence
- Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges
- A Tale of Two Countries: Challenges in the Adoption of Artificial Intelligence in Medical Imaging in India and Singapore.
BibTeX
@article{lucas2026integrating,
title = {{Integrating artificial intelligence into drug delivery systems: Formulation development and current challenges}},
author = {Inês Lucas and João Sousa and Carla Vitorino},
journal = {European Journal of Pharmaceutics and Biopharmaceutics},
year = {2026},
month = jun,
doi = {10.1016/j.ejpb.2026.115133},
url = {https://doi.org/10.1016/j.ejpb.2026.115133},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating artificial intelligence into drug delivery systems: Formulation development and current challenges
AU - Inês Lucas
AU - João Sousa
AU - Carla Vitorino
PY - 2026
DA - 2026/06/04
JO - European Journal of Pharmaceutics and Biopharmaceutics
DO - 10.1016/j.ejpb.2026.115133
UR - https://doi.org/10.1016/j.ejpb.2026.115133
AB - Artificial intelligence (AI) is increasingly being explored as a supportive tool to address persistent challenges in drug delivery research, particularly those associated with formulation complexity, optimization efficiency, and controlled drug release. While AI applications in drug discovery are well established, their role in drug delivery systems remains less structured and is primarily focused on supporting formulation design, optimization, and performance prediction within experimentally constrained environments. Conventional trial-and-error approaches are resource-intensive and often struggle to capture the multidimensional relationships between formulation variables, carrier properties, and biological performance. In this context, AI-driven methods offer opportunities to enhance data interpretation and guide formulation development in a more systematic and predictive manner. This review examines the application of AI across key stages of drug delivery development, including formulation design, nanocarrier optimization, and smart and controlled release systems. Machine learning (ML) and deep learning (DL) approaches applied to pre-formulation analysis, excipient selection, particle engineering, and release-profile optimization are discussed, highlighting their role as decision-support tools rather than autonomous systems. The integration of AI within Quality by Design (QbD) frameworks is also critically assessed, with particular attention to its potential to support the identification of critical formulation variables and design-space exploration. Despite these advances, translation into routine pharmaceutical practice remains limited by challenges related to data quality, experimental validation, model interpretability, and biological complexity. Overall, this review positions AI as a complementary and decision-support layer in drug delivery development and highlights the key barriers that must be addressed for its reliable, scalable, and regulatory-aligned implementation.
SN - 0939-6411
SN - 1873-3441
ER -