Artificial intelligence and machine learning in smart vaginal formulation development

Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative potential, the application of AI and ML in vaginal drug delivery remains limited. This review addresses this gap by examining the emerging role of AI/ML in the design, optimization, and development of next-generation vaginal formulations, including mucoadhesive, mucus-penetrating, bio-responsive, and controlled-release systems. Key factors influencing formulation performance, such as vaginal microbiota composition, are discussed, and ethical, regulatory, and technical challenges relevant to integrating AI/ML into vaginal drug delivery are also considered. Although research is in its early stages, existing studies indicate that AI and ML integration can enhance predictability, efficiency, and reproducibility in vaginal formulation development. Finally, we highlight the potential of developing personalized medicines through the integration of AI/ML and digital health monitoring devices to create patient-centered vaginal drug delivery systems, thereby advancing therapeutic outcomes and transforming women's health.