Quantitative self-assembly prediction yields targeted nanomedicines.

Area of Science:

• Nanotechnology
• Drug Delivery Systems
• Computational Chemistry

Background:

• Developing targeted nanoparticle drug carriers typically involves complex, unpredictable synthesis methods.
• Current methods for supramolecular self-assembly and chemical modification of drug carriers lack predictability and control.

Purpose of the Study:

• To describe a novel targeted drug delivery system with predictable nanoparticle self-assembly.
• To enable the computational design of nanomedicines based on quantitative structure-nanoparticle assembly prediction (QSNAP) models.

Main Methods:

• Utilized precursor drug molecules and sulfated indocyanines for nanoparticle self-assembly.
• Developed and validated Quantitative Structure-Nanoparticle Assembly Prediction (QSNAP) models.
• Employed electrotopological molecular descriptors to predict nano-assembly and nanoparticle size.

Main Results:

• Achieved ultrahigh drug loadings of up to 90% in self-assembled nanoparticles.
• Demonstrated selective targeting of kinase inhibitors to caveolin-1-expressing human colon and liver cancer models.
• Observed significant therapeutic effects in cancer models while avoiding off-target inhibition in healthy tissues.

Conclusions:

• The developed system allows for accurate and quantitative prediction of nanoparticle self-assembly based on molecular structure.
• This approach facilitates the computational design of nanomedicines, optimizing drug payload selection for targeted delivery.
• The findings pave the way for more efficient and predictable development of targeted cancer therapies.