Automated Discovery of Ultra-stabilizers for Self-Assembled Nanoparticles via Drug Aggregation Induced Emission

Introduction:

Dye stabilized drug nanoformulations with high drug loading were previously shown to be effective and predictable based on the molecular drug structure. However, these particular formulations have relatively low long-term stability and can stabilize only a small fraction of the drug space.

Methods:

Identifying drugs with strong aggregation-induced emission (AIE) with spectral scanning. Liquid handling robot for automated dye synthesis and selection. NMR and MALDI-TOF of lead stabilizer R595. Evaluation of NPs in-vivo and in-vitro on KRAS driven colon cancer and lung tumor models.

Results:

We developed an automated system to synthesize and select novel dye stabilizers based on their ability to inhibit drug AIE. We found AIE active hydrophobic drugs and used the AIE properties of kinase inhibitors in the automated discovery workflow to identify a new ultra-stabilizer named R595. We also developed an automated NPs purification method using size exclusion chromatography and showed that R595 is superior to previously published dye stabilizers both in long-term stability and with broader drug range compatibility. Finally, we show that MEKi-R595 NPs are non-toxic, safe and effective both in-vitro and in-vivo studies conducted on KRAS driven HCT116 colon cancer xenografts and in the experimental KPL lung tumor model.

Conclusion:

To conclude, automated synthesis and nano-formulation, combined with drug AIE as the selection readout, can be efficiently used to develop novel NP stabilizers.