Synthetic extremophiles via species-specific formulations improve microbial therapeutics.

Microorganisms typically used to produce food and pharmaceuticals are now being explored as medicines and agricultural supplements. However, maintaining high viability from manufacturing until use remains an important challenge, requiring sophisticated cold chains and packaging. Here we report synthetic extremophiles of industrially relevant gram-negative bacteria (Escherichia coli Nissle 1917, Ensifer meliloti), gram-positive bacteria (Lactobacillus plantarum) and yeast (Saccharomyces boulardii). We develop a high-throughput pipeline to define species-specific materials that enable survival through drying, elevated temperatures, organic solvents and ionizing radiation. Using this pipeline, we enhance the stability of E. coli Nissle 1917 by more than four orders of magnitude over commercial formulations and demonstrate its capacity to remain viable while undergoing tableting and pharmaceutical processing. We further show, in live animals and plants, that synthetic extremophiles remain functional against enteric pathogens and as nitrogen-fixing plant supplements even after exposure to elevated temperatures. This synthetic, material-based stabilization enhances our capacity to apply microorganisms in extreme environments on Earth and potentially during exploratory space travel.

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

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.