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Tamayo-Mendoza, T.

Publications and source records attributed to Tamayo-Mendoza, T..

2 recordsLinked to original sources

In Vivo Production of a Protein via the Delivery of Plasmid DNA by Computationally Designed Polymer Nanoparticles (PNP)

While monoclonal antibodies (mAbs) are a prominent class of pharmaceutical products there remain significant issues of cost, complexity, and especially delivery: concepts that overcome the need to infuse antibodies frequently are a desirable objective. An attractive approach is to deliver non-integrating DNA directly to muscle tissue, allowing the patient to act as their own so-called "protein factory." Demonstrations of this concept have been made using lipid nanoparticles (LNPs) and viral vectors, but these delivery methods face significant challenges, including poor extrahepatic delivery, cargo compatibility, safety, redosability, and cost. Polymer nanoparticles (PNPs) offer a solution to these problems, but face their own challenges, such as the vast number of possible polymer structures, and polyplex formulation conditions. However, advances in machine learning, materials informatics, and high-throughput chemical synthesis techniques provide a foundation for addressing these challenges by efficiently exploring the polymer design space. Our SAYERTM platform utilizes a large computational dataset of plasmid DNA (pDNA) - polymer interactions to facilitate targeting agent discovery and refinement via deep learning, and to drive discovery of novel PNPs for a wide variety of target tissues. In this work, we demonstrate our ability to design PNPs that can deliver pDNA encoding for PGT121, a broadly neutralizing anti-HIV antibody that targets a V3 glycan-dependent epitope site on the HIV-1 envelope glycoprotein. SAYER-designed polymers formed small and stable PNPs with PGT121 plasmids. Intravenous delivery demonstrated strong serum PGT121 protein levels at 1-day post-transfection and higher levels of protein expression when compared to other state-of-the-art DNA-delivery vehicles. More importantly, intramuscular delivery of Nanite PNPs enabled greater than 1.0 {micro}g/mL peak protein expression levels, with meaningful, durable expression levels at > 56 days post-injection. Further, we showed that we can increase antibody levels and durability by redosing. A general trend of lower dose and lower N/P ratio providing higher antibody levels is seen when PNPs were delivered intramuscularly. These parameters, separate from polymer structure, provide different mechanisms to optimize PNP in vivo delivery performance using machine learning techniques. Extension of the concept to the continuous production of other antibodies, proteins or enzymes is possible, suggesting the broad applicability of pDNA depoting via PNPs as a therapeutic modality. Finally, we emphasize that this strategy of delivering a DNA-encoded secreted proteins via a safe and effective PNP in vivo could be applicable to a wide range of other disease modalities.

bioengineering↗

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM Reference FormatAbigail Dommer1{dagger}, Lorenzo Casalino1{dagger}, Fiona Kearns1{dagger}, Mia Rosenfeld1, Nicholas Wauer1, Surl-Hee Ahn1, John Russo,2 Sofia Oliveira3, Clare Morris1, AnthonyBogetti4, AndaTrifan5,6, Alexander Brace5,7, TerraSztain1,8, Austin Clyde5,7, Heng Ma5, Chakra Chennubhotla4, Hyungro Lee9, Matteo Turilli9, Syma Khalid10, Teresa Tamayo-Mendoza11, Matthew Welborn11, Anders Christensen11, Daniel G. A. Smith11, Zhuoran Qiao12, Sai Krishna Sirumalla11, Michael OConnor11, Frederick Manby11, Anima Anandkumar12,13, David Hardy6, James Phillips6, Abraham Stern13, Josh Romero13, David Clark13, Mitchell Dorrell14, Tom Maiden14, Lei Huang15, John McCalpin15, Christo- pherWoods3, Alan Gray13, MattWilliams3, Bryan Barker16, HarindaRajapaksha16, Richard Pitts16, Tom Gibbs13, John Stone6, Daniel Zuckerman2*, Adrian Mulholland3*, Thomas MillerIII11,12*, ShantenuJha9*, Arvind Ramanathan5*, Lillian Chong4*, Rommie Amaro1*. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing 21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

biophysics↗