Toward a Comprehensive Molecular Design Framework for Reduced Hazard

Voutchkova, Adelina M.; Osimitz, Thomas G.; Anastas, Paul T.

doi:10.1021/cr9003105

Cited by 95 publications

(77 citation statements)

References 200 publications

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“…It has become increasingly evident that there are significant concerns about the adverse human health and ecosystem impacts resulting from chemical exposure and the challenge associated with predicting and modeling such endpoints (25). Several tools have emerged in this space with a consensus-building effort around USETox (26)(27)(28).…”

mentioning

confidence: 99%

Identifying and designing chemicals with minimal acute aquatic toxicity

Kostal¹,

Voutchkova‐Kostal

Anastas³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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103

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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.green chemistry | safer chemicals | rational design | toxicity prediction I ndustrial ecology and green chemistry are two rigorous scientific disciplines with global scientific communities that empower sustainability science. Sustainability science is the science, technology, and innovation in support of sustainable development-meeting human needs and reducing hunger and poverty while maintaining the life support systems of the planet (1, 2). With a systems view, industrial ecology investigates material and energy flows of coupled human-natural systems and has made significant strides in assessing the impacts of these flows on the environment and human health (3-8). The need for more sustainable products and processes has triggered (further) development of a large number of environmental assessment tools (9), including substance flow analysis (10), chemical/product risk assessment (11), life cycle assessment (LCA) (12-14), and a variety of screening tools (15-19). The knowledge generated by these investigations and assessments provides key information about the chemicals, materials and processes with the most significant adverse impacts throughout the life cycle. We need to understand the inherent nature of these materials to not only quantify their impact on human health and the environment but also to facilitate the design of a more sustainable materials basis of our society. Analogous to the industrial ecology assessment tools, several National Academies of Science reports have identified the need for new green chemist...

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mentioning

confidence: 99%

Identifying and designing chemicals with minimal acute aquatic toxicity

Kostal¹,

Voutchkova‐Kostal

Anastas³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

103

View full text Add to dashboard Cite

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“…This may make them useful in green chemistry [65] (http://www .epa.gov/gcc/pubs/about gc.html) as well as in biomedical research. The ready accessibility of such NR binding predictions from computational models like LASSO will be key in future for both pharmaceutical and environmental applications, and databases like ChemSpider can have an important role in providing them to the public as a predocking criteria, as we have demonstrated in this study.…”

Section: Discussionmentioning

confidence: 99%

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

Ekins

Goldsmith

Simon³

et al. 2013

Journal of Computational Medicine

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Nuclear receptors (NRs) are important biological macromolecular transcription factors that are implicated in multiple biological pathways and may interact with other xenobiotics that are endocrine disruptors present in the environment. Examples of important NRs include the androgen receptor (AR), estrogen receptors (ER), and the pregnane X receptor (PXR). In this study we have utilized the Ligand Activity by Surface Similarity Order (LASSO) method, a ligand-based virtual screening strategy to derive structural (surface/shape) molecular features used to generate predictive models of biomolecular activity for AR, ER, and PXR. For PXR, twentyfive models were built using between 8 to 128 agonists and tested using 3000, 8000, and 24,000 drug-like decoys including PXR inactive compounds ( = 228). Preliminary studies with AR and ER using LASSO suggested the utility of this approach with 2-fold enrichment factors at 20%. We found that models with 64-128 PXR actives provided enrichment factors of 10-fold (10% actives in the top 1% of compounds screened). The LASSO models for AR and ER have been deployed and are freely available online, and they represent a ligand-based prediction method for putative NR activity of compounds in this database.

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“…Again, hazard reductions rely on molecular design, i.e. the core of green chemistry, which harnesses knowledge on physical properties, toxicity data and toxicity mechanisms leading to predictive modeling via, for example, quantitative structure-activity relationships (QSAR methods) [7]. That modeling along with information obtained from proteomics and toxicogenomics will perhaps contribute to much more efficient and safer protocols by pharmaceutical companies, as toxicity data may not be available for a variety of chemicals or intermediates generated during the synthetic operations [5].…”

Section: The Importance Of Being Greenestmentioning

confidence: 99%