is a graduate student of Chemical Engineering at the University of Toledo. He earned a B.S. degree from the University of Benin, Nigeria in chemical engineering. His current research involves the reverse engineering online videos as well as rheology of complex fluids.

Understanding the endocytosis and intracellular trafficking of short interfering RNA (siRNA) delivery vehicle complexes remains a critical bottleneck in designing siRNA delivery vehicles for highly active RNA interference (RNAi)-based therapeutics. In this study, we show that dextran functionalization of silica nanoparticles enhanced uptake and intracellular delivery of siRNAs in cultured cells. Using pharmacological inhibitors for endocytotic pathways, we determined that our complexes are endocytosed via a previously unreported mechanism for siRNA delivery in which dextran initiates scavenger receptor-mediated endocytosis through a clathrin/caveolin-independent process. Our findings suggest that siRNA delivery efficiency could be enhanced by incorporating dextran into existing delivery platforms to activate scavenger receptor activity across a variety of target cell types.

In the development of RNA interference (RNAi) therapeutics, merely selecting short, interfering RNA (siRNA) sequences that are complementary to the messenger RNA (mRNA) target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stability of the two ends of the siRNA. However, we have previously shown that highly-active siRNA sequences are likely to have particular nucleotides at each 5’-end, independent of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted based on training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly- and highly-active sequences for enhanced green fluorescent protein (EGFP) and protein kinase R (PKR). Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.

is a Professor of Chemical Engineering at the University of Toledo. He earned a B.S. degree from the University of Illinois at Chicago and M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign, all in chemical engineering. His current research involves the rheology of complex fluids as well as active learning, reverse engineering online videos, and interactive textbooks.

The development of nanoscale delivery vehicles for siRNAs is a current topic of considerable importance. However, little is understood about the exact trafficking mechanisms for siRNA-vehicle complexes across the plasma membrane and into the cytoplasm. While some information can be gleaned from studies on delivery of plasmid DNA, the different delivery requirements for these two vehicles makes drawing specific conclusions a challenge. However, using chemical inhibitors of different endocytosis pathways, studies on which endocytotic pathways are advantageous and deleterious for the delivery of nucleic acid drugs are emerging. Using this information as a guide, it is expected that the future development of effective siRNA delivery vehicles and therapeutics will be greatly improved.