Predicting drug loading in PLA-PEG nanoparticles

Polymer nanoparticles present advantageous physical and biopharmaceutical properties as drug delivery systems compared to conventional liquid formulations. Active pharmaceutical ingredients (APIs) are often hydrophobic, thus not soluble in conventional liquid delivery. Encapsulating the drugs in polymer nanoparticles can improve their pharmacological and bio-distribution properties, preventing rapid clearance from the bloodstream. Such nanoparticles are commonly made of non-toxic amphiphilic self-assembling block copolymers where the core (poly-[d,l-lactic acid] or PLA) serves as a reservoir for the API and the external part (Poly-(Ethylene-Glycol) or PEG) serves as a stealth corona to avoid capture by macrophage. The present study aims to predict the drug affinity for PLA-PEG nanoparticles and their effective drug loading using in silico tools in order to virtually screen potential drugs for non-covalent encapsulation applications. To that end, different simulation methods such as molecular dynamics and Monte-Carlo have been used to estimate the binding of actives on model polymer surfaces. Initially, the methods and models are validated against a series of pigments molecules for which experimental data exist. The drug affinity for the core of the nanoparticles is estimated using a Monte-Carlo "docking" method. Drug miscibility in the polymer matrix, using the Hildebrand solubility parameter (δ), and the solvation free energy of the drug in the PLA polymer model is then estimated. Finally, existing published ALogP quantitative structure-property relationships (QSPR) are compared to this method. Our results demonstrate that adsorption energies modelled by docking atomistic simulations on PLA surfaces correlate well with experimental drug loadings, whereas simpler approaches based on Hildebrand solubility parameters and Flory-Huggins interaction parameters do not. More complex molecular dynamics techniques which use estimation of the solvation free energies both in PLA and in water led to satisfactory predictive models. In addition, experimental drug loadings and Log P are found to correlate well. This work can be used to improve the understanding of drug-polymer interactions, a key component to designing better delivery systems.