RESUMEN
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.
Asunto(s)
Portadores de Fármacos/química , Ensayos Analíticos de Alto Rendimiento/métodos , Nanopartículas/química , Sorafenib/farmacología , Terbinafina/farmacología , Animales , Candida albicans/efectos de los fármacos , Simulación por Computador , Portadores de Fármacos/síntesis química , Diseño de Fármacos , Evaluación Preclínica de Medicamentos/métodos , Dispersión Dinámica de Luz , Excipientes/química , Femenino , Ácido Glicirrínico/química , Humanos , Aprendizaje Automático , Ratones Endogámicos , Absorción Cutánea , Sorafenib/química , Sorafenib/farmacocinética , Ácido Taurocólico/química , Terbinafina/química , Distribución Tisular , Ensayos Antitumor por Modelo de XenoinjertoRESUMEN
The study of amyotrophic lateral sclerosis (ALS) and potential interventions would be facilitated if motor axon degeneration could be more readily visualized. Here we demonstrate that stimulated Raman scattering (SRS) microscopy could be used to sensitively monitor peripheral nerve degeneration in ALS mouse models and ALS autopsy materials. Three-dimensional imaging of pre-symptomatic SOD1 mouse models and data processing by a correlation-based algorithm revealed that significant degeneration of peripheral nerves could be detected coincidentally with the earliest detectable signs of muscle denervation and preceded physiologically measurable motor function decline. We also found that peripheral degeneration was an early event in FUS as well as C9ORF72 repeat expansion models of ALS, and that serial imaging allowed long-term observation of disease progression and drug effects in living animals. Our study demonstrates that SRS imaging is a sensitive and quantitative means of measuring disease progression, greatly facilitating future studies of disease mechanisms and candidate therapeutics.