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
Monolayers of cancer-derived cell lines are widely used in the modelling of the gastrointestinal (GI) absorption of drugs and in oral drug development. However, they do not generally predict drug absorption in vivo. Here, we report a robotically handled system that uses large porcine GI tissue explants that are functionally maintained for an extended period in culture for the high-throughput interrogation (several thousand samples per day) of whole segments of the GI tract. The automated culture system provided higher predictability of drug absorption in the human GI tract than a Caco-2 Transwell system (Spearman's correlation coefficients of 0.906 and 0.302, respectively). By using the culture system to analyse the intestinal absorption of 2,930 formulations of the peptide drug oxytocin, we discovered an absorption enhancer that resulted in a 11.3-fold increase in the oral bioavailability of oxytocin in pigs in the absence of cellular disruption of the intestinal tissue. The robotically handled whole-tissue culture system should help advance the development of oral drug formulations and might also be useful for drug screening applications.