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Session: Technical Session IV

JCR Special Session

Wednesday, July 10, 2024
2:30 PM – 4:30 PM CET
Location: Sala Italia
The formulation of therapeutic agents in advanced drug delivery systems such as nanoparticles and microparticles can significantly improve their safety and efficacy. However, the design and development of advanced formulations remains expensive, labour-intensive and time consuming with a heavy reliance on the expertise of the formulation development team and composition of formulations that have been approved to date. In the design of these systems, there are a plethora of parameters that must be considered in relation to the drug, material(s) or excipient(s) as well as processing variables. Experimental evaluation of every combination is intractable and at this time it is not possible to predict the performance of specific formulations a priori. As a result, it is likely that some of the formulation candidates that have moved forward to clinical development are not optimal but rather the best that could be achieved with the time and resources available. 
Machine learning (ML) has led to significant advances in various fields, such as drug discovery and materials science. In recent years, we have explored integration of ML to discern the relationships between composition, property and performance with a goal towards fast-tracking innovative drug formulation development.  In this work, we have identified a lack of robust datasets in the published literature to apply data-driven methods. This has led us to consider strategies such as experimental automation, and more recently to the concept of a materials acceleration platform (MAP), or self-driving laboratory (SDL), that combines automated experimentation with ML-guided experiment planning for the design of advanced drug delivery systems. The integration of such technological advancements in the pharmaceutical sciences has the potential to fast-track preclinical research, improve efficiency in drug development pipelines and thus improve patient access to effective medicines.