Computationally guided high-throughput design of self-assembling drug nanoparticles

Abstract

Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug-loading capacities of up to 95%. There is currently no understanding of which of the millions of small-molecule combinations can result in the formation of these nanoparticles. Here we report the integration of machine learning with high-throughput experimentation to enable the rapid and large-scale identification of such nanoformulations. We identified 100 self-assembling drug nanoparticles from 2.1 million pairings, each including one of 788 candidate drugs and one of 2,686 approved excipients. We further characterized two nanoparticles, sorafenib–glycyrrhizin and terbinafine–taurocholic acid both ex vivo and in vivo. 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. Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models.