Thorough characterization of the plasma pharmacokinetics (PK) is a critical step in clinical development of novel therapeutics and is routinely performed for small molecules and biologics. However, there is a paucity of even basic characterization of PK for nanoparticle-based drug delivery systems. This has led to untested generalizations about how nanoparticle properties govern PK. Here, we present a meta-analysis of 100 nanoparticle formulations following IV administration in mice to identify any correlations between four PK parameters derived by non-compartmental analysis (NCA) and four cardinal properties of nanoparticles: PEGylation, zeta potential, size, and material. There was a statistically significant difference between the PK of particles stratified by nanoparticle properties. However, single linear regression between these properties and PK parameters showed poor predictability (r2 < 0.10 for all analyses), while multivariate regressions showed improved predictability (r2 > 0.38, except for t1/2). This suggests that no single nanoparticle property alone is even moderately predictive of PK, while the combination of multiple nanoparticle features does provide moderate predictive power. Improved reporting of nanoparticle properties will enable more accurate comparison between nanoformulations and will enhance our ability to predict in vivo behavior and design optimal nanoparticles.
Keywords: Meta-analysis; Nanoparticles; Non-compartmental analysis; Pharmacokinetics.
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