1. Computationally guided high-throughput design of self-assembling drug nanoparticles.
- Author
-
Reker D, Rybakova Y, Kirtane AR, Cao R, Yang JW, Navamajiti N, Gardner A, Zhang RM, Esfandiary T, L'Heureux J, von Erlach T, Smekalova EM, Leboeuf D, Hess K, Lopes A, Rogner J, Collins J, Tamang SM, Ishida K, Chamberlain P, Yun D, Lytton-Jean A, Soule CK, Cheah JH, Hayward AM, Langer R, and Traverso G
- Subjects
- Animals, Candida albicans drug effects, Computer Simulation, Drug Carriers chemical synthesis, Drug Design, Drug Evaluation, Preclinical methods, Dynamic Light Scattering, Excipients chemistry, Female, Glycyrrhizic Acid chemistry, Humans, Machine Learning, Mice, Inbred Strains, Skin Absorption, Sorafenib chemistry, Sorafenib pharmacokinetics, Taurocholic Acid chemistry, Terbinafine chemistry, Tissue Distribution, Xenograft Model Antitumor Assays, Mice, Drug Carriers chemistry, High-Throughput Screening Assays methods, Nanoparticles chemistry, Sorafenib pharmacology, Terbinafine pharmacology
- 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.
- Published
- 2021
- Full Text
- View/download PDF