Adelina Voutchkova-Kostal

Identifying and designing chemicals with minimal acute aquatic toxicity

Significance Two of the rigorous disciplines that have emerged over the last 20 y to empower sustainability science are industrial ecology and green chemistry. Robust assessment tools of industrial ecology identify the greatest opportunities to mitigate human health and environmental impacts resulting from human activity. Green chemistry designs and develops chemicals, materials, and processes that, throughout the life cycle, minimize hazard and maximize efficiency. This process often entails synthesizing new molecules while maintaining function and minimizing adverse outcomes, particularly toxicity. There is an urgent need to develop accurate and economical screening tools that predict potential toxicity and inform the design of safer alternatives. A computational approach is presented for the rational design of molecules for reduced acute aquatic toxicity. Industrial ecology has revolutionized our understanding of material stocks and flows in our economy and society. For this important discipline to have even deeper impact, we must understand the inherent nature of these materials in terms of human health and the environment. This paper focuses on methods to design synthetic chemicals to reduce their intrinsic ability to cause adverse consequence to the biosphere. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or the environment. The approach presented herein builds on the important work in quantitative structure–activity relationships by linking toxicological and chemical mechanistic insights to the identification of critical physical–chemical properties needed to be modified. This in silico approach yields design guidelines using boundary values for physiochemical properties. Acute aquatic toxicity serves as a model endpoint in this study. Defining value ranges for properties related to bioavailability and reactivity eliminates 99% of the chemicals in the highest concern for acute aquatic toxicity category. This approach and its future implementations are expected to yield very powerful tools for life cycle assessment practitioners and molecular designers that allow rapid assessment of multiple environmental and human health endpoints and inform modifications to minimize hazard.

Towards rational molecular design for reduced chronic aquatic toxicity

The routine rational design of commercial chemicals with minimal toxicological hazard to humans and the environment is a key goal of green chemistry. The development of such a design strategy requires an understanding of the interrelationships between physical–chemical properties, structure, mechanisms and modes of action. This study develops property-based guidelines for the design of chemicals with reduced chronic aquatic toxicity to multiple standardized species and endpoints by exploring properties associated with bioavailability, narcotic toxicity and reactive modes of action, such as electrophilic interactions. Two simple properties emerge as key parameters that distinguish chemicals in the Low EPA level of concern to three aquatic species from those in the High level of concern – octanol–water partition coefficient, (log Po–w) and ΔE (LUMO–HOMO energy gap). Physicochemical properties were predicted using Schrodingers QikProp, while frontier orbital energies were determined based on AM1 and DFT calculations using Gaussian03. Experimental toxicity data used consisted of chronic toxicity thresholds (NOEC) for Daphnia magna reproduction (317 compounds, 504 h-assay) and Oryzias latipes (Japanese medaka, 122 compounds in 336, 504 and 672 h assays) survival, and Pseudokirchneriella subcapitata, a green algae model (392 compounds). Results indicate that 92% of compounds of Low chronic concern have log Po–w values 9 eV. Chronically safe compounds to P. subcapitata meet similar criteria – 80% have log Po–w values < 3 and ΔE greater than 9 eV. Our work proposes design guidelines that can be used to significantly increase the probability that a chemical will have low chronic toxicity, based on the endpoints evaluated, to the three diverse aquatic species studied, and potentially to other aquatic species.

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.

Recyclable hydrotalcite catalysts for alcohol imination via acceptorless dehydrogenation

Here we report that hydrotalcite-like materials (HTs) are active heterogeneous catalysts for alcohol imination, which proceeds through acceptorless alcohol dehydrogenation. The catalytic activity of a series of Mg–Al hydrotalcites doped with Fe3+, Zn2+, Ni2+, Cr3+ and Cu2+ is dependent on their composition, and Fe : Mg : Al HT yields up to 92% imine under mild conditions. Impregnation of Fe : Mg : Al HT with Pd0 resulted in an enhancement of activity for acceptorless dehydrogenation, but a decrease in the isolated yield of imine in a loading-dependent manner. This is attributed to the Pd loading-dependent retention of imine and aldehyde on the catalysts. The substrate scope for alcohol imination and recyclability of the catalysts is discussed.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

A free energy approach to the prediction of olefin and epoxide mutagenicity and carcinogenicity.

The mutagenic and carcinogenic effects of strong alkylating agents, such as epoxides, have been attributed to their ability to covalently bind DNA in vivo. Most olefins are readily oxidized to reactive epoxides by CytP450. In an effort to develop predictive models for olefin and epoxide mutagenicity, the ring openings of 15 halogen-, alkyl-, alkenyl-, and aryl-substituted epoxides were modeled by quantum-mechanical transition state calculations using MP2/6-31+G(d,p) in the gas phase and in aqueous solution. Free energies of activation (ΔG(‡)) and free energies of reaction (ΔG(rxn)) were computed for each epoxide in the series. This study finds that an aqueous solution ΔG(rxn) threshold value of approximately -14.7 kcal/mol can be used to discern mutagenic/carcinogenic epoxides (ΔG(rxn) < -14.7 kcal/mol) from nonmutagens/noncarcinogens (ΔG(rxn) > -14.7 kcal/mol). The computed reaction thermodynamics are appropriate regardless of ring-opening mechanism in vivo and are thus proposed as an effective in silico screen and design guideline for decreasing potential mutagenicity and carcinogenicity of olefins and their respective epoxides.

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.

Immobilization of imidazolium ionic liquids on hydrotalcites using silane linkers: retardation of memory effect

We report a new covalent surface immobilization of silane-modified imidazolium ionic liquids on hydrotalcite-like materials (HTs) and provide detailed characterization of the resulting surface chemistry using PXRD, CP-MAS, TGA and FT-IR. We show that this immobilization interferes with the “memory effect” of HTs and explore the stability of the resulting complexes to hydrolysis.

Global Model for Octanol-Water Partition Coefficients from Proton Nuclear Magnetic Resonance Spectra

The ability to estimate chemical and physical properties from experimental spectra is highly desirable, as it eliminates the need for a priori knowledge of exact chemical structure and allows the property estimation of mixtures. Here we report the proof of principle that a predictive method for octanol‐water partition coefficient (logP) based on 1H‐NMR spectra in d3‐chloroform is feasible and can yield accuracy comparable to in silico logP models. The Spectrometric Data‐Activity Relationship (QSDAR) reported predicts logP of neutral organic chemicals using descriptors derived only from 1H‐NMR chemical shifts, integrations and peak widths. Proton NMR spectra of 140 compounds with diverse structures were used to construct a Multiple Linear Regression (MLR) and a Partial Least Squares (PLS) model that predicts logP. The optimized models were internally validated by K‐fold cross validation and leave‐one‐out validation, and externally with a test set of 28 chemicals. The squared regression coefficients of prediction for the MLR and PLS regression models were 0.970 and 0.971 respectively, showing that the method allows accurate prediction of logP values exclusively from predicted 1H NMR spectra.

Comparative behavioral toxicology with two common larval fish models: Exploring relationships among modes of action and locomotor responses

Behavioral responses inform toxicology studies by rapidly and sensitively detecting molecular initiation events that propagate to physiological changes in individuals. These behavioral responses can be unique to chemical specific mechanisms and modes of action (MOA) and thus present diagnostic utility. In an initial effort to explore the use of larval fish behavioral response patterns in screening environmental contaminants for toxicity and to identify behavioral responses associated with common chemical specific MOAs, we employed the two most common fish models, the zebrafish and the fathead minnow, to define toxicant induced swimming activity alterations during interchanging photoperiods. Though the fathead minnow (Pimephales promelas) is a common model for aquatic toxicology research and regulatory toxicology practice, this model has received little attention in behavioral studies compared to the zebrafish, a common biomedical model. We specifically compared behavioral responses among 7 different chemicals (1-heptanol, phenol, R-(-)-carvone, citalopram, diazinon, pentylenetetrazole (PTZ), and xylazine) that were selected and classified based on anticipated MOA (nonpolar narcosis, polar narcosis, electrophile, specific mechanism) according to traditional approaches to examine whether these comparative responses differ among chemicals with various structure-based predicted toxicity. Following standardized experimental guidelines, zebrafish embryos and fathead minnow larvae were exposed for 96 h to each compound then were observed using digital behavioral analysis. Behavioral observations included photomotor responses, distance traveled, and stimulatory, refractory and cruising locomotor activity. Though fathead minnow larvae displayed greater behavioral sensitivity to 1-heptanol, phenol and citalopram, zebrafish were more sensitive to diazinon and R-(-)-carvone. Both fish models were equally sensitive to xylazine and PTZ. Further, the pharmaceuticals citalopram and xylazine significantly affected behavior at therapeutic hazard values, and each of the seven chemicals elicited unique behavioral response profiles. Larval fish behaviors appear useful as early tier diagnostics to identify mechanisms and pathways associated with diverse biological activities for chemicals lacking mechanistic data.