The distribution of nanoparticles (NPs) in the human body is associated with the development of nano-medicines and nanotoxicity. Physiologically based pharmacokinetic (PBPK) tools can simulate the distribution and elimination of NPs but are primarily dependent on experimental data. Models involving NP size and surface coating as parameters for estimating the clearance of NPs from blood are beneficial to the extension application of PBPK models. To this end, we first collected intravenous kinetic data on the blood distribution of 19 types of NPs for model parameterization and then collected 20 types of NPs for validation. Rate constants for clearance from blood were obtained by fitting the collected data to one- and two-compartment kinetics. A generic model (NP size-based) for estimation of rate constants was developed based on collision and diffusion behavior driven by NP size. NPs with a hydrodynamic diameter of 20 nm have the highest clearance rate constant via penetration and phagocytosis pathways. An extended model (NP size- and surface coating-based) was built to estimate rate constants of various NPs by calculating van der Waals energy between NPs and macrophages. Nearly 3/4 of the validation data are within 95% confidence intervals, indicating that our generic and extended models can be applied to NPs with different sizes and surface modifications.

After use and disposal of chemical products, many types of polymer particles end up in the aquatic environment with potential toxic effects to primary producers like green algae. In this study, we have developed Quantitative Structure-Activity Relationships (QSARs) for a set of highly structural diverse polymers which are capable to estimate green algae growth inhibition (EC50). The model (N = 43, R2 = 0.73, RMSE = 0.28) is a regression-based decision tree using one structural descriptor for each of three polymer classes separated based on charge. The QSAR is applicable to linear homo polymers as well as copolymers and does not require information on the size of the polymer particle or underlying core material. Highly branched polymers, non-nitrogen cationic polymers and polymeric surfactants are not included in the model and thus cannot be evaluated. The model works best for cationic and non-ionic polymers for which cellular adsorption, disruption of the cell wall and photosynthesis inhibition were the mechanisms of action. For anionic polymers, specific properties of the polymer and test characteristics need to be known for detailed assessment. The data and QSAR results for anionic polymers, when combined with molecular dynamics simulations indicated that nutrient depletion is likely the dominant mode of toxicity. Nutrient depletion in turn, is determined by the non-linear interplay between polymer charge density and backbone flexibility.

QSPR prediction models for chemical fate and exposure are critically reviewed so that knowledge gaps may be filled in subsequent study.

The reaction between superoxide (O₂·⁻) and organic chemicals is of interest in many scientific disciplines including biology and synthetic chemistry, as well as for the evaluation of chemical fate in the environment. Due to limited data and lack of congeneric modeling, the involvement of superoxide in many complex processes cannot be adequately evaluated. In this study, we developed new Quantitative Structure-Property Relationship (QSPR) models for the prediction of the aqueous-phase rate constant for the reaction between superoxide and a wide variety of organic chemicals reacting via one-electron oxidation, reduction and hydrogen-transfer. It is shown that the relative importance of these pathways is related to frontier molecular orbital (FMO) interaction and to pH. The class-specific QSPRs developed have good statistics (0.84 ≤ R² ≤ 0.92). For noncongeneric chemicals it is demonstrated that the reactivity towards superoxide can be described by applying explicit descriptions for competition kinetics and speciation. Therefore, the relationships developed in this study are useful as a starting point to evaluate more complex molecules having eg multiple reactive functional groups, labile H bonds, or delocalized cationic charges. However, additional kinetic data and more rigorous computation are needed to evaluate such molecules.