Design of paclitaxel-loaded PLGA nanoparticles assisted by compatibility modeling

Area of Science:

• Materials Science
• Pharmaceutical Sciences
• Computational Chemistry

Background:

• Poor water solubility of anticancer drugs like paclitaxel (PTX) limits bioavailability.
• Nanoparticle-based drug delivery systems (DDS) using biodegradable polymers offer solutions but face challenges in API-polymer selection.
• High costs and numerous combinations necessitate predictive modeling for efficient DDS development.

Purpose of the Study:

• To explore API-polymer phase behavior modeling for designing nanoparticle (NP)-based DDS for PTX.
• To predict the compatibility of PTX with poly(lactide-co-glycolide) (PLGA) and PLGA-PEG using COSMO-RS.
• To correlate modeling predictions with experimental NP characteristics and performance.

Main Methods:

• Utilized Conductor-like Screening Model for Real Solvents (COSMO-RS) to predict PTX-polymer phase behavior.
• Prepared PTX-loaded PLGA and PEGylated PLGA NPs via emulsion-solvent evaporation.
• Compared predicted solubility trends with experimental drug loading, solid-state properties, and cytotoxicity.

Main Results:

• COSMO-RS predicted limited PTX solubility in PLGA, consistent with experimental maximum amorphous PTX loading ≤2 wt%.
• Modeling indicated higher PTX compatibility with PEG, predicting enhanced loading in PEGylated NPs.
• Experimental results confirmed increased drug loading and slower PTX release from PEGylated NPs.

Conclusions:

• API-polymer phase behavior modeling, specifically COSMO-RS, is a valuable tool for optimizing NP-based DDS.
• Predictive modeling aids in tailoring polymeric carriers and improving API utilization in drug delivery.
• This approach can accelerate the development of effective nanoparticle drug delivery systems.