Fjodor Melnikov

Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals

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.

Toward safer multi-walled carbon nanotube design: Establishing a statistical model that relates surface charge and embryonic zebrafish mortality

Abstract 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.

The Green ChemisTREE: 20 years after taking root with the 12 principles

The field of Green Chemistry has seen many scientific discoveries and inventions during the 20 years since the 12 Principles were first published. Inspired by tree diagrams that illustrate diversity of products stemming from raw materials, we present here the Green ChemisTREE as a showcase for the diversity of research and achievements stemming from Green Chemistry. Each branch of the Green ChemisTREE represents one of the 12 Principles, and the leaves represent areas of inquiry and development relevant to that Principle (branch). As such, in this ‘meta-review’, we aim to describe the history and current status of the field of Green Chemistry: by exploring activity within each Principle, by summarizing the benefits of Green Chemistry through robust examples, by discussing tools and metrics available to measure progress towards Green Chemistry, and by outlining knowledge gaps, opportunities, and future challenges for the field.

Toward the Design of Less Hazardous Chemicals: Exploring Comparative Oxidative Stress in Two Common Animal Models.

Sustainable molecular design of less hazardous chemicals presents a potentially transformative approach to protect public health and the environment. Relationships between molecular descriptors and toxicity thresholds previously identified the octanol-water distribution coefficient, log D, and the HOMO-LUMO energy gap, ΔE, as two useful properties in the identification of reduced aquatic toxicity. To determine whether these two property-based guidelines are applicable to sublethal oxidative stress (OS) responses, two common aquatic in vivo models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio), were employed to examine traditional biochemical biomarkers (lipid peroxidation, DNA damage, and total glutathione) and antioxidant gene activation following exposure to eight structurally diverse industrial chemicals (bisphenol A, cumene hydroperoxide, dinoseb, hydroquinone, indene, perfluorooctanoic acid, R-(-)-carvone, and tert-butyl hydroperoxide). Bisphenol A, cumene hydroperoxide, dinoseb, and hydroquinone were consistent inducers of OS. Glutathione was the most consistently affected biomarker, suggesting its utility as a sensitivity response to support the design of less hazardous chemicals. Antioxidant gene expression (changes in nrf2, gclc, gst, and sod) was most significantly (p < 0.05) altered by R-(-)-carvone, cumene hydroperoxide, and bisphenol A. Results from the present study indicate that metabolism of parent chemicals and the role of their metabolites in molecular initiating events should be considered during the design of less hazardous chemicals. Current empirical and computational findings identify the need for future derivation of sustainable molecular design guidelines for electrophilic reactive chemicals (e.g., SN2 nucleophilic substitution and Michael addition reactivity) to reduce OS related adverse outcomes in vivo.

Probabilistic diagram for designing chemicals with reduced potency to incur cytotoxicity

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 Naive 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.

Coupled molecular design diagrams to guide safer chemical design with reduced likelihood of perturbing the NRF2-ARE antioxidant pathway and inducing cytotoxicity

The NRF2-ARE antioxidant pathway is an important biological sensing and regulating system that responds to xenochemicals. NRF2 senses chemically-caused production of reactive oxygen species (ROS) and electrophilic interactions with chemical species. Upon NRF2 activation, the expression of a wide array of genes will be upregulated to counteract oxidative or electrophilic insults. However, when the external disruption exceeds the inherent resilience of the biological system, cellular damage can occur, eventually leading to cytotoxicity. Induced NRF2 activity in in vitro assays is therefore a signal that a man-made chemical may cause unwanted cellular activity. This was the motivation to derive a chemical design strategy to minimize the risk that new chemicals would perturb this pathway. We constructed a logistic regression model using design variables derived from density functional theory (DFT) calculations and physical properties. The model showed excellent predictive power to distinguish between NRF2-active and inactive chemicals based on the EPA ToxCast high throughput screen (HTS) assay data (tested in the concentration range of 10−3–102 μM). External evaluation showed that the area under the curve (AUC) for the receiver operating characteristic (ROC) of the model is 0.81 and the precision is 0.90. Combining this model with a previously developed cytotoxicity model, we developed a probabilistic design diagram to guide chemical design with the twin goals of minimizing NRF2 antioxidant pathway activity and cytotoxicity. This work initiated a simultaneous design strategy against two toxicity pathways of interest in molecular design research.

Multifunctional photoactive and selective adsorbent for arsenite and arsenate: Evaluation of nano titanium dioxide-enabled chitosan cross-linked with copper

A novel multifunctional sorbent material of nano-titanium dioxide-enabled chitosan beads cross-linked with copper (CuTICB) is capable of photo-oxidation of As(III) to the less-toxic and more easily adsorbed As(V) in UV light and selective adsorption of arsenite (As(III)) and arsenate (As(V)) in the presence of phosphate, a strong adsorptive competitor and inhibitor of arsenic removal performance. CuTICB is an attractive sorbent as simultaneous photo-oxidation and adsorption reduces treatment time and cost while selective adsorption improves removal efficiency of arsenic in typical environmental conditions where competitive ions are predominant. In CuTICB, nano-titanium dioxide (n-TiO2) anatase photo-oxidizes As(III) to As(V) through generation of reactive oxygen species. Additionally, Cu-chitosan bidentate crosslinkers form through Lewis acid-base coordinate bonding between Cu(II) and chitosan amine groups resulting in cationic behavior that electrostatically favors As(V) chelation even when phosphate concentrations are orders of magnitude higher. The influence of copper and n-TiO2 loading on arsenic photo-oxidation and selective removal over phosphate was explored to optimize CuTICB design using batch experiments under varying systems conditions. For a system requiring both photo-oxidation and selective adsorption, it was found that copper and n-TiO2 act non-linearly and synergistically, where maximum loadings of both does not yield the optimal selectivity or removal efficacy.

The safer chemical design game. Gamification of green chemistry and safer chemical design concepts for high school and undergraduate students

ABSTRACT Green chemistry can strongly attract students to chemistry. We, therefore, developed a green chemistry educational game that motivates students at the undergraduate and advanced high school levels to consider green chemistry and sustainability concerns as they design a hypothetical, chemical product. The game is intended for incorporation into any chemistry course for majors and non-majors that teaches sustainability and/or the Principles of Green Chemistry at the undergraduate level. The game is free of charge and encourages students to think like professional chemical designers and to develop a chemical product with respect to function and improved human and environmental health. This computer simulation has been assessed by educators and can be seamlessly integrated into an existing curriculum. GRAPHICAL ABSTRACT