Machine learning based characterisation of nanoparticles synthesized using microfluidics
The application of machine learning (ML) to microfluidics is currently gaining significant momentum within the research community particularly for applications in material science, nanomedicine and biologics. Microfluidics is the miniaturisation and automation of laboratory processes resulting in improved quality of data generated with reduced reagent and support costs. While most widely applied to diagnostic testing (e.g. pregnancy tests and COVID-19 testing). Microfluidics is also applied to high-throughput applications such as bioprocess monitoring, cell analysis, genetic screening, and drug discovery. These high-throughput applications involve large data sets of complex biological processes – thereby, making them ideally suited to control and optimisation through ML.
An emerging application of high-throughput microfluidics, particularly for drug discovery and in the development of drug delivery systems, is the use of microfluidics for nanoparticle synthesis. Furthermore, these same microfluidic platforms can also be used to investigate the interaction of nanoparticles with cells. These platforms can help optimise drug design and uptake, and therefore potentially improve patient outcomes for many chronic diseases (e.g., various types of cancer, HIV and diabetes).
The premise of the proposed research is based on ML application to microfluidic nanoparticle synthesis to optimise their desired properties (i.e., particle size and charge, polydispersity index, drug loading capacity and cell encapsulation). Then application of similar algorithms to optimise the nanoparticle interaction with cells for efficacious drug delivery and therapeutic response. In this project, the specific aim will be to implement different passive microfluidic techniques (e.g., hydrodynamic flow focusing, microvortices, chaotic advection and droplets generation) for efficient fluid mixing leading towards optimal nanoparticle synthesis. ML will be implemented through the development of at-line monitoring technology to measure the physical properties of the nanoparticles within the microfluidic channel. The coupling of this multimodal monitoring to ML is novel and can lead to innovative advances in the field of nanomedicine, biologics and organ-on-a-chip.