Computational design of self-assembling drug-excipient nanoparticles with Daniel Reker, Ph.D.

The Center for Nanotechnology in Drug Delivery and the Carolina Cancer Nanotechnology Training Program are pleased to welcome:

Daniel Reker, Ph.D.
Assistant Professor
Biomedical Engineering
Duke University

“Computational design of self-assembling drug-excipient nanoparticles”
Monday February 21, 2022
12:00 – 1:00 PM

*Please e-mail Amy Fry at amy_fry@unc.edu to register.  Zoom link will be provided.

Abstract:

Machine learning is becoming a pervasive tool to accelerate and optimize various processes in research and development. In drug delivery, successful applications of machine learning are still sparse given that drug delivery research commonly deals with data based on composite delivery systems tested in complex ex vivo and in vivo models to create small datasets that aim to describe complex processes. We follow a bottom-up approach and use machine learning to identify biological and physical effects of small molecular excipients, which has enabled us to study adverse and beneficial effects of formulations [1-3]. In particular, we have recently designed a novel platform that combines high-throughput experimentation, molecular simulations, and machine learning to rapidly design novel self-assembling drug-excipient nanoparticles with drug loading capacities of up to 95% [3]. Using our platform, we identified 100 novel self-assembling drug nanoparticles from 2.1 million pairings. Each pairing included one of 788 candidate drugs, with a variety of therapeutic uses, and one of 2686 approved excipients, which included well-known food additives, vitamins, or drugs. We further characterized two novel nanoparticles, sorafenib-glycyrrhizin and terbinafine-taurocholic acid, both ex vivo and in vivo and show improved targeting through these novel formulations. We anticipate that our platform can accelerate the development of safer and more efficacious nanoformulations with high drug loading capacities for a wide range of therapeutics [3].

1. Reker et al. Sci. Transl. Med. 11, eaau6753 (2019).
2. Reker et al. Cell Rep. 30, 3710-3716.e4 (2020).
3. Reker et al. Nat. Nanotechnol. 16, 725–733 (2021).