Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools

Area of Science:

• Nanomedicine
• Materials Science
• Computational Chemistry

Background:

• Colloidal drug aggregates are amorphous nanoparticles formed by self-assembling hydrophobic drugs.
• They offer potential as drug-rich formulations for enhanced drug delivery.
• Predicting drug aggregation, stabilizer efficacy, and in vivo fate remains challenging.

Purpose of the Study:

• To explore the use of computational tools, including machine learning and molecular dynamics simulations, to address challenges in colloidal drug aggregate formulation.
• To identify predictive methods for drug aggregation, stabilizer selection, and nanoparticle behavior.
• To advance the design of colloidal drug aggregates for nanomedicine applications.

Main Methods:

• Utilizing molecular dynamics simulations to understand intermolecular forces governing aggregate assembly.
• Employing computational analyses to predict drug-stabilizer compatibility and identify effective excipients.
• Reviewing existing predictive tools for aggregator identification and their limitations in formulation design.

Main Results:

• Molecular dynamics simulations provide insights into the self-assembly of colloidal drug aggregates.
• Computational methods can predict stabilizer effectiveness and identify suitable drug-stabilizer pairs.
• Existing tools primarily focus on eliminating aggregators, not designing formulations.

Conclusions:

• Computational strategies, particularly AI and machine learning, are crucial for overcoming formulation challenges with colloidal drug aggregates.
• Predictive modeling can guide the design of stable, effective colloidal drug aggregate formulations for applications like nanomedicine and sustained release.
• Leveraging computational tools will unlock the full potential of this high drug-loading delivery platform.