Poly(DL-lactide-co-glycolic acid) nanoparticle design and payload prediction: a molecular descriptor based study.

Polymer nanoparticles are veritable tools for pharmacokinetic and therapeutic modifications of bioactive compounds. Nanoparticle technology development and scaling up are however often constrained due to poor payload and improper particle dissolution. This work was aimed to develop descriptor based computational models as prior art tools for optimal payload in polymeric nanoparticles. Loading optimization experiments were carried out both in vitro and in-silico. Molecular descriptors generated in three different platforms DRAGON, molecular operating environment (MOE) and VolSurf+ were used. Multiple linear regression analysis (MLR) provided computation models which were further validated based on goodness of fit statistics and correlation coefficients (DRAGON, R(2)=0.889, Q(2)=0.657, R(2)(pred)=0.616; MOE, R(2)=0.826, Q(2)=0.572, R(2)(pred)=0.601; and VolSurf+, R(2)=0.818, Q(2)=0.573, R(2)(pred)=0.653). Pharmacophore space modeling studies were carried out in order to understand the fundamental molecular interactions necessary for drug loading in poly(DL-lactide-co-glycolic acid). The space modeling study (R(2)=0.882, Q(2)=0.662, R(2)(pred)=0.725, Δ(cost)=108.931) indicated that hydrogen bond acceptors and ring aromatic features are of primary significance for nanoparticle drug loading. Results of in vitro experiments have also confirmed the fact as a viable prognosis in case of nanoparticle payload. Polymeric nanoparticles payload prediction can therefore be a useful tool for wider benefits at the preformulation stages itself.