PNNL

Abstract

Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we developed an in vitro hemolytic assay protocol for measuring the nanoparticle-dependent complement activity of serum samples and applied this protocol to several nanoparticle formulations that differed in size, surface charge, and surface chemistry; quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework. The robustness and predictability of the model can be improved by training the model with additional data points that are uniformly distributed in the RHA/physicochemical descriptor space and by incorporating instability effects on nanoparticle physicochemical properties into the model.