Transforming Drug Delivery: AI and Machine Learning in Design, Optimization, and Personalization

About the Project

This research explores the transformative role of digital technologies, particularly machine learning, microfluidics, and bioprinting, in advancing pharmaceutical design and therapeutic delivery. By integrating data-driven modeling with precision fabrication techniques, the project aims to develop smart drug delivery systems tailored to individual patient needs. Machine learning will be used to optimize formulation parameters and predict therapeutic outcomes, while microfluidic platforms and bioprinting will enable scalable, customizable production of drug carriers and tissue scaffolds. The objective of this PhD is to develop and validate Artificial Intelligence / Machine Learning - driven models that optimize drug delivery systems for personalised therapeutic outcomes. The outcome will contribute to the development of next-generation personalized medicine, bridging the gap between digital innovation and clinical application.

Training that will be provided through the research project

The project will encompass a comprehensive workflow that includes data collection and processing, followed by the development of machine learning models to predict drug release profiles, optimize formulation parameters, and tailor patient-specific dosing strategies. It will integrate cutting-edge microfluidic and bioprinting technologies to fabricate advanced drug delivery systems, which will be validated through in vitro experiments using cell cultures or tissue models. Drug release studies will be conducted to assess performance, and a feedback loop will be implemented to continuously refine predictions and fabrication processes. In addition to technical training, the project will support the development of key transferrable skills, including research management, personal effectiveness, communication, networking, teamwork, and career planning.

Expected impact activities

The PhD student would be encouraged to engage in a variety of impact activities, disseminate the research project findings through public talks, and participate in QUB showcase events. Examples of impact activities includes Blogs or web articles, Magazine articles, public lectures, School visits, oral & poster Presentations (at local, national and international conferences), and Publication of scientific papers in peer reviewed journals.

Funding Notes

This project is not funded; applications are welcome from self-funding candidates.

References

Artificial Intelligence, Machine Learning, Drug Delivery Systems, Personalised Medicine, Pharmaceutical, Digital Health Technologies