Data Mining as a Guide for the Construction of Cross-Linked Nanoparticles with Low Immunotoxicity via Control of Polymer Chemistry and Supramolecular Assembly.

The potential immunotoxicity of nanoparticles that are currently being approved, in different phases of clinical trials, or undergoing rigorous in vitro and in vivo characterizations in several laboratories has recently raised special attention. Products with no apparent in vitro or in vivo toxicity may still trigger various components of the immune system unintentionally and lead to serious adverse reactions. Cytokines are one of the useful biomarkers for predicting the effect of biotherapeutics on modulation of the immune system and for screening the immunotoxicity of nanoparticles both in vitro and in vivo, and they were recently found to partially predict the in vivo pharmacokinetics and biodistribution of nanomaterials. Control of polymer chemistry and supramolecular assembly provides a great opportunity for the construction of biocompatible nanoparticles for biomedical clinical applications. However, the sources of data collected regarding immunotoxicities of nanomaterials are diverse, and experiments are usually conducted using different assays under specific conditions. As a result, making direct comparisons nearly impossible, and thus, tailoring the properties of nanomaterials on the basis of the available data is challenging. In this Account, the effects of chemical structure, cross-linking, degradability, morphology, concentration, and surface chemistry on the immunotoxicity of an expansive array of polymeric nanomaterials will be highlighted, with a focus on assays conducted using the same in vitro and in vivo models and experimental conditions. Furthermore, numerical descriptive values have been utilized uniquely to stand for induction of cytokines by nanoparticles. This treatment of available data provides a simple way to compare the immunotoxicities of various nanomaterials, and the values were found to correlate well with published data. On the basis of the polymeric systems investigated in this study, valuable information has been collected that will aid in the future design of nanomaterials for biomedical applications, including the following: (a) the immunotoxicity of nanomaterials is concentration- and dose-dependent; (b) the synthesis of degradable nanoparticles is essential to decrease toxicity; (c) cross-linking minimizes the release of free polymeric chains and maintains high stability of the nanoparticles, thereby lowering their immunotoxicity; (d) lowering the amine density for cationic polymers that are being utilized for delivery of nucleic acids lowers the toxicity of the nanoparticles; (e) among neutral, zwitterionic, anionic, and cationic nanomaterials, neutral and cationic nanoparticles usually have the lowest and highest immunotoxicities, respectively; and (f) morphology, dimension, and surface chemistry have a great influence on the ability of nanomaterials to interact with the various components of the biological system and to modulate the immune system.