Gaussian processes modeling for the prediction of polymeric nanoparticle formulation design to enhance encapsulation efficiency and therapeutic efficacy.

Abstract

Conventional drugs have been facing various drug delivery obstacles, including first-pass metabolism for oral medications, drug degradation by cellular enzymes, off-target effects, and cytotoxicity of healthy cells. Nanoparticles (NP) application in drug delivery can compensate for these drawbacks to a great extent. NPs can be fabricated using different materials and structures to achieve desired therapeutic effects. For each type of NP material, its physicochemical properties determine compatibility with specific drugs and other supplemental compositions. The optimized material selection becomes prominent in NP development to improve NP performances. Due to the nature of NP fabrication, the process is long and expensive. To accelerate NP composition optimization, machine learning (ML) techniques are among the most promising methods for efficient data predictions and optimizations.As a proof-of concept, we created Gaussian Process (GP) models to make predictions for drug encapsulation efficiency (EE%) and therapeutic efficacy of 32 poly (lactic-co-glycolic acid) (PLGA) NPs that are formed with materials with different physicochemical properties. Two model drugs, doxorubicin (DOX) and docetaxel (DTX) were loaded separately. The IC50 values for the various NPs formulations were evaluated using the OVCAR3 epithelial ovarian cancer cell line. EE% GP model has the highest prediction accuracy with the lowest normalized root-mean-squared-error (RMSE) of 0.187. The DOX and DTX IC50 GP models have normalized RMSEs of 0.296 and 0.206, respectively, which are higher than that of the EE% GP model.