A broad overview of the achievements and emerging areas in the field of Green Chemistry.

In silico toxicity models are critical in addressing experimental aquatic toxicity data gaps and prioritizing chemicals for further assessment. Currently, a number of predictive in silico models for aquatic toxicity are available, but most models are challenged to produce accurate predictions across a wide variety of functional chemical classes. Appropriate model selection must be informed by the models’ applicability domain and performance within the chemical space of interest. Herein we assess five predictive models for acute aquatic toxicity to fish (ADMET Predictor™, Computer-Aided Discovery and REdesign for Aquatic Toxicity (CADRE-AT), Ecological Structure Activity Relationships (ECOSAR) v1.11, KAshinhou Tool for Ecotoxicity (KATE) on PAS 2011, and Toxicity Estimation Software Tool (TEST) v.4). The test data set was carefully constructed to include 83 structurally diverse chemicals distinct from the training data sets of the assessed models. The acute aquatic toxicity models that rely on properties related to chemicals’ bioavailability or reactivity performed better than purely statistical algorithms trained on large sets of chemical properties and structural descriptors. Most models showed a marked decrease in performance when assessing insoluble and ionized chemicals. In addition to comparing tool accuracy and, this analysis provides insights that can guide selection of modeling tools for specific chemical classes and help inform future model development for improved accuracy.

Given the increased utility and lack of consensus regarding carbon nanotube (CNT) environmental and human health hazards, there is a growing demand for guidelines that inform safer CNT design. In this study, the zebrafish (Danio rerio) model is utilized as a stable, sensitive biological system to evaluate the bioactivity of systematically modified and comprehensively characterized multi-walled carbon nanotubes (MWNTs). MWNTs were treated with strong acid to introduce oxygen functional groups, which were then systematically thermally reduced and removed using an inert temperature treatment. While 25 phenotypic endpoints were evaluated at 24 and 120 hours post-fertilization (hpf), high mortality at 24 hpf prevented further resolution of the mode of toxicity leading to mortality. Advanced multivariate statistical methods are employed to establish a model that identifies those MWNT physicochemical properties that best estimate the probability of observing an adverse outcome. The physicochemical properties considered in this study include surface charge, percent surface oxygen, dispersed aggregate size and morphology and electrochemical activity. Of the five physicochemical properties, surface charge, quantified as the point of zero charge (PZC), was determined as the best predictor of mortality at 24 hpf. From a design perspective, the identification of this property–hazard relationship establishes a foundation for the development of design guidelines for MWNTs with reduced hazard.

Toxicity is a concern with many chemicals currently in commerce, and with new chemicals that are introduced each year. The standard approach to testing chemicals is to run studies in laboratory animals (e.g. rats, mice, dogs), but because of the expense of these studies and concerns for animal welfare, few chemicals besides pharmaceuticals and pesticides are fully tested. Over the last decade there have been significant developments in the field of computational toxicology which combines in vitro tests and computational models. The ultimate goal of this field is to test all chemicals in a rapid, cost effective manner with minimal use of animals. One of the simplest measures of toxicity is provided by high-throughput in vitro cytotoxicity assays, which measure the concentration of a chemical that kills particular types of cells. Chemicals that are cytotoxic at low concentrations tend to be more toxic to animals than chemicals that are less cytotoxic. We employed molecular characteristics derived from density functional theory (DFT) and predicted values of log(octanol–water partition coefficient) (log P) to construct a design variable space, and built a predictive model for cytotoxicity based on U.S. EPA Toxicity ForeCaster (ToxCast) data tested up to 100 μM using a Näive Bayesian algorithm. External evaluation showed that the area under the curve (AUC) for the receiver operating characteristic (ROC) of the model to be 0.81. Using this model, we provide probabilistic design rules to help synthetic chemists minimize the chance that a newly synthesized chemical will be cytotoxic.