Department Seminar Series: Prashanta Dutta, Ph.D., FASME

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Drug delivery to the brain is a major challenge for the treatment of neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease due to the presence of the blood-brain barrier (BBB), a highly selective barricade formed by microvascular endothelial cells. In recent years, nanoparticles have received a significant amount of interest for targeted drug delivery across the BBB. Experimental studies have revealed that selective NPs can transport drug molecules from microvascular blood vessels to brain parenchyma in an efficient and non-invasive way. However, current methods for finding optimum nanoparticle-based drug carriers are through experimental trial and error, which are not only time-consuming but also expensive. In this talk, we will demonstrate the role of machine learning in drug design and delivery. Machine learning has revolutionized computer vision for the detection of objects. Although machine learning started its journey for the characterization of objects, scientists now started to use it in other research areas. Biological and biomedical research fields can take advantage of this emerging tool due to the availability of a large set of image-based experimental data. In this talk, a machine learning framework will be presented to determine kinetic rate parameters for nanoparticle transport through BBB endothelial cells, where experimental training data are obtained from a transwell (in-vitro) BBB constructed with mouse endothelial cells. Experimental techniques used to synthesize and characterize those biodegradable nanoparticles will also be discussed. Furthermore, applications of machine learning in drug design will be addressed.