A predictive mechanistic model of drug release from surface eroding polymeric nanoparticles.

Area of Science:

• Biomaterials Science
• Polymer Chemistry
• Drug Delivery Systems

Background:

• Effective drug delivery necessitates targeted dosing and minimized side effects.
• Polymeric nanoparticles (NPs) are utilized as drug delivery vehicles.
• Understanding drug release kinetics from NPs is crucial for therapeutic efficacy.

Purpose of the Study:

• To evaluate in vitro drug release from surface-eroding polymeric NPs.
• To develop and validate a mechanistic mathematical model for drug release.
• To compare the model's performance against conventional drug release models.

Main Methods:

• Drug release studies were conducted in vitro using paclitaxel, rapamycin, resiquimod, or doxorubicin loaded NPs.
• NPs were fabricated from FDA-approved polyanhydride or acetalated dextran (Ace-DEX) with tunable degradation.
• A diffusion-erosion mathematical model was constructed and machine learning was applied for parameter estimation.

Main Results:

• Distinct drug release profiles were achieved by varying drug, pH, and polymer composition.
• The developed diffusion-erosion model accurately described drug release from various surface-eroding NPs.
• The diffusion-erosion model demonstrated superior fit compared to conventional models for Ace-DEX NPs.
• Machine learning enabled accurate prediction of drug release for specific Ace-DEX formulations.

Conclusions:

• The mechanistic diffusion-erosion model effectively predicts drug release from surface-eroding polymeric NPs.
• This predictive modeling approach can guide the design of future Ace-DEX formulations for optimized therapeutic outcomes.
• Accurate prediction of drug release kinetics is essential for developing effective drug delivery systems.