Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Nat Nanotechnol. 2021 Jun;16(6):725-733. doi: 10.1038/s41565-021-00870-y. Epub 2021 Mar 25.
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.
治疗药物的纳米制剂正在改变我们有效传递和治疗各种疾病的能力。然而，它们通常生产复杂，药物载药量低，除了通过药物和小分子染料共组装形成的纳米颗粒，其载药量高达 95%。目前还不清楚数以百万计的小分子组合中哪些可以形成这些纳米颗粒。在这里，我们报告了机器学习与高通量实验的整合，以实现这种纳米制剂的快速和大规模鉴定。我们从 210 万对组合中鉴定出 100 种自组装药物纳米颗粒，每种组合包含 788 种候选药物之一和 2686 种批准的赋形剂之一。我们进一步对两种纳米颗粒，索拉非尼-甘草酸和特比萘芬-牛磺胆酸进行了离体和体内的特性研究。我们预计我们的平台可以加速开发更安全、更有效的纳米制剂，这些纳米制剂具有高载药量，适用于广泛的治疗药物。
