Design of paclitaxel-loaded PLGA nanoparticles assisted by compatibility modeling

Many anticancer active pharmaceutical ingredients (APIs), such as paclitaxel (PTX), exhibit poor water solubility, which limits their bioavailability and necessitates the use of excipients. While biodegradable polymeric excipients combined with nanotechnology offer promising solutions, the high cost of polymers and APIs, along with the vast number of potential API–polymer combinations, poses significant challenges in developing effective drug delivery systems (DDS). This study explores the potential of API–polymer phase behavior modeling as part of the design of nanoparticle (NP)-based DDS for PTX using poly(lactide-co-glycolide) (PLGA) and poly(lactide-co-glycolide)-b-poly(ethylene glycol) (PLGA-PEG) with varying molecular weights. The phase behavior of PTX–PLGA/PLGA-PEG systems, which reflects the compatibility of PTX with polymeric excipients, was predicted using the Conductor-like Screening Model for Real Solvents (COSMO-RS). To investigate the correlation between the predictions and experimental observations, PTX–PLGA and PEGylated PLGA NPs were prepared via an emulsion-solvent evaporation method with varying initial PTX amounts. The predicted trends in PTX solubility in polymeric excipients were then compared with key NP characteristics, such as drug loading, solid-state properties, and cytotoxicity in HeLa, SKOV-3, and MRC-5 cells. COSMO-RS predictions indicated limited PTX solubility in PLGA, which aligns with experimental observations, where the maximum amorphous PTX loading did not exceed 2 wt%, regardless of the polymer molecular weight. COSMO-RS modeling predicted higher compatibility of PTX with PEG, suggesting that incorporating PEG would enhance PTX loading in PEGylated NPs. This trend was corroborated by experimental findings, which showed increased drug loading capacity and slower PTX release from PEGylated NPs during cytotoxicity studies. These results highlight the potential of API–polymer modeling as a tool for tailoring polymeric carriers and optimizing API consumption in NP-based DDS development.