History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments.

Chemical property estimation is a key component in many industrial, academic, and regulatory activities, including in the risk assessment associated with the approximately 1000 new chemical pre-manufacture notices the United States Environmental Protection Agency (US EPA) receives annually. The US EPA evaluates fate, exposure and toxicity under the 1976 Toxic Substances Control Act (amended by the 2016 Frank R. Lautenberg Chemical Safety for the 21st Century Act), which does not require test data with new chemical applications. Though the submission of data is not required, the US EPA has, over the past 40 years, occasionally received chemical-specific data with pre-manufacture notices. The US EPA has been actively using this and publicly available data to develop and refine predictive computerized models, most of which are housed in EPI Suite™, to estimate chemical properties used in the risk assessment of new chemicals. The US EPA develops and uses models based on (quantitative) structure-activity relationships ([Q]SARs) to estimate critical parameters. As in any evolving field, (Q)SARs have experienced successes, suffered failures, and responded to emerging trends. Correlations of a chemical structure with its properties or biological activity were first demonstrated in the late 19th century and today have been encapsulated in a myriad of quantitative and qualitative SARs. The development and proliferation of the personal computer in the late 20th century gave rise to a quickly increasing number of property estimation models, and continually improved computing power and connectivity among researchers via the internet are enabling the development of increasingly complex models.

[1]  Robert S. Boethling,et al.  Handbook of Property Estimation Methods for Chemicals : Environmental Health Sciences , 2000 .

[2]  Marianthi G. Ierapetritou,et al.  A comparative assessment of efficient uncertainty analysis techniques for environmental fate and transport models: application to the FACT model , 2005 .

[3]  W. Meylan,et al.  How accurate are physical property estimation programs for organosilicon compounds? , 2013, Environmental toxicology and chemistry.

[4]  S. Free,et al.  A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.

[5]  Alan C. Lloyd,et al.  A chemical mechanism for use in long‐range transport/acid deposition computer modeling , 1986 .

[6]  M. Shukla,et al.  In silico kinetics of alkaline hydrolysis of 1,3,5-trinitro-1,3,5-triazinane (RDX): M06-2X investigation. , 2017, Environmental science. Processes & impacts.

[7]  A. Leo,et al.  Comparison of parameters currently used in the study of structure-activity relationships. , 1969, Journal of medicinal chemistry.

[8]  G. Kovačević,et al.  Atmospheric oxidation of halogenated aromatics: comparative analysis of reaction mechanisms and reaction kinetics. , 2017, Environmental science. Processes & impacts.

[9]  N. Singhal,et al.  Omics in mechanistic and predictive toxicology , 2010, Toxicology mechanisms and methods.

[10]  M. Protic,et al.  Relationship between molecular connectivity indices and soil sorption coefficients of polycyclic aromatic hydrocarbons , 1982, Bulletin of environmental contamination and toxicology.

[11]  Antony J. Williams,et al.  ChemSpider:: An Online Chemical Information Resource , 2010 .

[12]  Alexander Tropsha,et al.  Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research , 2010, J. Chem. Inf. Model..

[13]  S. Endo,et al.  Determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for established and alternative flame retardants. , 2013, Environmental science & technology.

[14]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[15]  F. Wania,et al.  Quantifying the equilibrium partitioning of substituted polycyclic aromatic hydrocarbons in aerosols and clouds using COSMOtherm. , 2017, Environmental science. Processes & impacts.

[16]  Aleksandar Sabljic,et al.  On the prediction of soil sorption coefficients of organic pollutants from molecular structure: application of molecular topology model. , 1987, Environmental science & technology.

[17]  Harpreet S. Chadha,et al.  Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the delta log P parameter of seiler. , 1994, Journal of pharmaceutical sciences.

[18]  Eileen D Kuempel,et al.  Risk assessment and risk management of nanomaterials in the workplace: translating research to practice. , 2012, The Annals of occupational hygiene.

[19]  Elizabeth A. Casman,et al.  Modeling nanomaterial environmental fate in aquatic systems. , 2015, Environmental science & technology.

[20]  Eugene N Muratov,et al.  Existing and Developing Approaches for QSAR Analysis of Mixtures , 2012, Molecular informatics.

[21]  Sean Ekins,et al.  Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation. , 2012, Drug discovery today.

[22]  Eric Scerri The discovery of the periodic table as a case of simultaneous discovery , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Jingwen Chen,et al.  Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals. , 2017, Environmental science. Processes & impacts.

[24]  Heather C. Dantzker,et al.  Building a Robust 21st Century Chemical Testing Program at the U.S. Environmental Protection Agency: Recommendations for Strengthening Scientific Engagement , 2014, Environmental health perspectives.

[25]  Xue Z. Wang,et al.  (Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review , 2015 .

[26]  Mark T. D. Cronin,et al.  Predicting Chemical Toxicity and Fate , 2004 .

[27]  Andrew Worth,et al.  Regulatory assessment of chemical mixtures: Requirements, current approaches and future perspectives. , 2016, Regulatory toxicology and pharmacology : RTP.

[28]  W. Arnold,et al.  QSARs for phenols and phenolates: oxidation potential as a predictor of reaction rate constants with photochemically produced oxidants. , 2017, Environmental science. Processes & impacts.

[29]  S. Friend,et al.  Crowdsourcing biomedical research: leveraging communities as innovation engines , 2016, Nature Reviews Genetics.

[30]  J P Doucet,et al.  Application of topological descriptors in QSAR and drug design: history and new trends. , 2002, Current drug targets. Infectious disorders.

[31]  W. F. Reehl,et al.  Handbook of Chemical Property Estimation Methods: Environmental Behavior of Organic Compounds , 1982 .

[32]  David A Winkler,et al.  Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. , 2016, Toxicology and applied pharmacology.

[33]  Sulfate radical oxidation of aromatic contaminants: a detailed assessment of density functional theory and high-level quantum chemical methods. , 2017, Environmental science. Processes & impacts.

[34]  C. Hansch Quantitative approach to biochemical structure-activity relationships , 1969 .

[35]  Lorenz C. Blum,et al.  A computer-based prediction platform for the reaction of ozone with organic compounds in aqueous solution: kinetics and mechanisms. , 2017, Environmental science. Processes & impacts.

[36]  Terje Aven,et al.  Models and model uncertainty in the context of risk analysis , 2003, Reliab. Eng. Syst. Saf..

[37]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[38]  P. Ge,et al.  The degradation mechanism of sulfamethoxazole under ozonation: a DFT study. , 2017, Environmental science. Processes & impacts.

[39]  Tomasz Arodz,et al.  Computational methods in developing quantitative structure-activity relationships (QSAR): a review. , 2006, Combinatorial chemistry & high throughput screening.

[40]  E. Barriuso,et al.  Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review , 2015, Critical reviews in environmental science and technology.

[41]  M. Nendza,et al.  Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies. , 2017, Environmental science. Processes & impacts.

[42]  M. C. Newman,et al.  The practice of structure activity relationships (SAR) in toxicology. , 2000, Toxicological sciences : an official journal of the Society of Toxicology.

[43]  Alexander Tropsha,et al.  Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation , 2016, J. Chem. Inf. Model..

[44]  A M Richard,et al.  An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling$ , 2016, SAR and QSAR in environmental research.

[45]  M. Halsey,et al.  Recent molecular theories of general anaesthesia. , 1979, British journal of anaesthesia.

[46]  S. Endo,et al.  3D-QSAR predictions for bovine serum albumin-water partition coefficients of organic anions using quantum mechanically based descriptors. , 2017, Environmental science. Processes & impacts.

[47]  Yang-hsin Shih,et al.  Linear free energy relationships for the adsorption of volatile organic compounds onto multiwalled carbon nanotubes at different relative humidities: comparison with organoclays and activated carbon. , 2017, Environmental science. Processes & impacts.

[48]  S. Endo,et al.  Experimental determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for pesticides and other contaminants: new measurements and recommendations. , 2013, Environmental science & technology.

[49]  Hugo Kubinyi,et al.  From Narcosis to Hyperspace: The History of QSAR , 2002 .

[50]  Andrew P. Worth,et al.  QSAR modeling of nanomaterials. , 2011, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.

[51]  Johann Gasteiger,et al.  Chemoinformatics: Achievements and Challenges, a Personal View , 2016, Molecules.