Data-driven optimization of nanoparticle size using the prediction reliability enhancing parameter (PREP)

Area of Science:

• Biomaterials Science
• Nanotechnology
• Chemical Engineering

Background:

• Nanoparticle size critically influences biodistribution, cellular uptake, and therapeutic efficacy.
• Current experimental methods for nanoparticle size control are often time-consuming and costly due to numerous iterations.
• Precise control over particle size and distribution is essential for effective therapeutic applications.

Purpose of the Study:

• To address the challenge of achieving precise nanoparticle size control.
• To implement and evaluate the Prediction Reliability Enhancing Parameter (PREP) for streamlining nanoparticle design.
• To demonstrate the efficacy of PREP in reducing experimental iterations for targeted nanoparticle properties.

Main Methods:

• Applied the data-driven modeling approach, Prediction Reliability Enhancing Parameter (PREP).
• Utilized PREP to predict and control particle sizes of two distinct nanoparticle types: thermoresponsive microgels and polyelectrolyte complexes.
• Fabricated microgels via precipitation polymerization and polyelectrolyte complexes via charge-driven self-assembly.

Main Results:

• PREP enabled efficient and precise particle size control for both nanoparticle types.
• Target nanoparticle sizes and properties were achieved in only two experimental iterations for each case.
• Demonstrated significant reduction in experimental workload for nanoparticle fabrication.

Conclusions:

• PREP effectively reduces the number of experimental iterations required for nanoparticle size control.
• This data-driven approach streamlines experimental workflows in biomaterials optimization.
• PREP offers a promising strategy for efficient development of nanoparticles with desired characteristics.