Computer-Aided Molecular Modeling: A Predictive Approach in the Design of Nanoparticulate Drug Delivery System

The purpose of the present work was to investigate Computer-Aided Molecular Modeling (CAMM) as a predictive approach in the design of polymeric nanoparticulate drug delivery systems. Three-model drugs doxorubicin (water soluble), silymarin (sparingly water soluble) and gliclazide (water insoluble) and six polymers with varied functional groups namely, alginic acid, sodium alginate, chitosan, methyl-vinyl-ether-co-malic acid (Gantrez AN119) and the acrylic polymers (Eudragit L100 and Eudragit RSPO) were selected for the CAMM study. Drug loaded polymeric nanoparticles were prepared at a drug:polymer ratio of 1:2 and experimental encapsulation efficiency values were determined. The structures of the drugs and polymers were built individually and their minimum energy conformations determined first using Molecular mechanics force field followed by AM1 Semi-Empirical Quantum mechanics and finally by ab initio method with minimal basis set. Various structural descriptors were evaluated. Cluster analysis, Non linear regression, and Artificial Neural Network (ANN) models were used to predict encapsulation efficiency (EE). CAMM study suggested that EE directly correlated with interaction energy of the drug and polymer and inversely with the hydration energy of the drug. Conclusive results were not obtained using cluster analysis. Hence, various a non-linear regression models were derived to predict the EE. Finally, the following model was derived to predict encapsulation efficiency: EE = -153.1 + 16.99 * IntEn-0.97 * HydEn-0.32 * IntEn(2), where R-2, R-adj(2), R-pre(2), and F ratio were found to be 0.76, 0.7, 0.6, and 45 respectively. Further, a 2-2-1 ANN model revealed a good model fit indicating potential utility of the model in the design of nanoparticulate DDS. This present model based on CAMM study could provide a screening interface in the amalgamation of Drug Discovery with possible drug delivery strategies in the discovery stage by permitting evaluation with small drug quantities and minimum experimental runs.