A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish.

An evaluated database of whole body in vivo biotransformation rate estimates in fish was used to develop a model for predicting the primary biotransformation half-lives of organic chemicals. The estimated biotransformation rates were converted to half-lives and divided into a model development set (n=421) and an external validation set (n=211) to test the model. The model uses molecular substructures similar to those of other biodegradation models. The biotransformation half-life predictions were calculated based on multiple linear regressions of development set data against counts of 57 molecular substructures, the octanol-water partition coefficient, and molar mass. The coefficient of determination (r2) for the development set was 0.82, the cross-validation (leave-one-out coefficient of determination, q2) was 0.75, and the mean absolute error (MAE) was 0.38 log units (factor of 2.4). Results for the external validation of the model using an independent test set were r2 = 0.73 and MAE = 0.45 log units (factor of 2.8). For the development set, 68 and 95% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. For the test (or validation) set, 63 and 90% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. Reasons for discrepancies between model predictions and expected values are discussed and recommendations are made for improving the model. This model can predict biotransformation rate constants from chemical structure for screening level bioaccumulation hazard assessments, exposure and risk assessments, comparisons with other in vivo and in vitro estimates, and as a contribution to testing strategies that reduce animal usage.

Workgroup report: review of fish bioaccumulation databases used to identify persistent, bioaccumulative, toxic substances.

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota-sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure-activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

Biodegradation of US premanufacture notice chemicals in OECD tests.

Biodegradation testing of commercial chemicals other than pesticides is generally performed using test guidelines of the Organization for Economic Cooperation and Development (OECD). We used test data submitted with US Premanufacture Notifications (PMNs) received from 1995 through 2005 to study performance of OECD biodegradation tests, as well as the overall testing strategy and guidance. Among the findings are that (1) ready biodegradation (RB) tests gave fairly consistent results relative to the pass/fail outcome, but not necessarily percent degradation; (2) the Zahn-Wellens test worked well in providing a quick measure of sorption potential, but aside from this, provided little useful information for the investigated chemicals beyond what was already available from RB tests; (3) the SCAS test sometimes gives lower % removal than continuous-feed simulation tests like OECD 303A; and (4) OECD 306 (marine biodegradation test) appeared less conservative than ordinary RB tests. Overall, the PMN data lend support to new OECD guidance that endorses the primary role of RB tests, but emphasizes simulation rather than inherent biodegradation tests as the next step.

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

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.

Estimating biodegradation half-lives for use in chemical screening.

Biodegradation half-lives are needed for many applications in chemical screening, but these data are not available for most chemicals. To address this, in phase one of this work we correlated the much more abundant ready and inherent biodegradation test data with measured half-lives for water and soil. In phase two, we explored the utility of the BIOWIN models (in EPI Suite) and molecular fragments for predicting half-lives. BIOWIN model output was correlated directly with measured half-lives, and new models were developed by re-regressing the BIOWIN fragments against the half-lives. All of these approaches gave the best results when used for binary (fast/slow) classification of half-lives, with accuracy generally in the 70-80% range. In the last phase, we used the collected half-life data to examine the default half-lives assigned by EPI Suite and the PBT Profiler for use as input to their level III multimedia models. It is concluded that estimated half-lives should not be used for purposes other than binning or prioritizing chemicals unless accuracy improves significantly.

Modeling ready biodegradability of fragrance materials.

In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials.

Finding and estimating chemical property data for environmental assessment.

The ability to predict the behavior of a chemical substance in a biological or environmental system largely depends on knowledge of the physicochemical properties and reactivity of that substance. We focus here on properties, with the objective of providing practical guidance for finding measured values and using estimation methods when necessary. Because currently available computer software often makes it more convenient to estimate than to retrieve measured values, we try to discourage irrational exuberance for these tools by including comprehensive lists of Internet and hard-copy data resources. Guidance for assessors is presented in the form of a process to obtain data that includes establishment of chemical identity, identification of data sources, assessment of accuracy and reliability, substructure searching for analogs when experimental data are unavailable, and estimation from chemical structure. Regarding property estimation, we cover estimation from close structural analogs in addition to broadly applicable methods requiring only the chemical structure. For the latter, we list and briefly discuss the most widely used methods. Concluding thoughts are offered concerning appropriate directions for future work on estimation methods, again with an emphasis on practical applications.

Predicting ready biodegradability of premanufacture notice chemicals.

Chemical substances other than pesticides, drugs, and food additives are regulated by the U.S. Environmental Protection Agency (U.S. EPA) under the Toxic Substances Control Act (TSCA), but the United States does not require that new substances be tested automatically for such critical properties as biodegradability. The resulting lack of submitted data has fostered the development of estimation methods, and the BioWIN models for predicting biodegradability from chemical structure have played a prominent role in premanufacture notice (PMN) review. Until now, validation efforts have used only the Japanese Ministry of International Trade and Industry (MITI) test data and have not included all models. To assess BioWIN performance with PMN substances, we assembled a database of PMNs for which ready biodegradation data had been submitted over the period 1995 through 2001. The 305 PMN structures are highly varied and pose major challenges to chemical property estimation. Despite the variability of ready biodegradation tests, the use of at least six different test methods, and widely varying quality of submitted data, accuracy of four of six BioWIN models (MITI linear, MITI nonlinear, survey ultimate, survey primary) was in the 80+% range for predicting ready biodegradability. Greater accuracy (>90%) can be achieved by using model estimates only when the four models agree (true for 3/4 of the PMNs). The BioWIN linear and nonlinear probability models did not perform as well even when classification criteria were optimized. The results suggest that the MITI and survey BioWIN models are suitable for use in screening-level applications.

Recent developments in broadly applicable structure-biodegradability relationships.

Biodegradation is one of the most important processes influencing concentration of a chemical substance after its release to the environment. It is the main process for removal of many chemicals from the environment and therefore is an important factor in risk assessments. This article reviews available methods and models for predicting biodegradability of organic chemicals from structure. The first section of the article briefly discusses current needs for biodegradability estimation methods related to new and existing chemicals and in the context of multimedia exposure models. Following sections include biodegradation test methods and endpoints used in modeling, with special attention given to the Japanese Ministry of International Trade and Industry test; a primer on modeling, describing the various approaches that have been used in the structure/biodegradability relationship work, and contrasting statistical and mechanistic approaches; and recent developments in structure/biodegradability relationships, divided into group contribution, chemometric, and artificial intelligence approaches.

Using Biowin, Bayes, and batteries to predict ready biodegradability.

Whether or not a given chemical substance is readily biodegradable is an important piece of information in risk screening for both new and existing chemicals. Despite the relatively low cost of Organization for Economic Cooperation and Development tests, data are often unavailable and biodegradability must be estimated. In this paper, we focus on the predictive value of selected Biowin models and model batteries using Bayesian analysis. Posterior probabilities, calculated based on performance with the model training sets using Bayes' theorem, were closely matched by actual performance with an expanded set of 374 premanufacture notice (PMN) substances. Further analysis suggested that a simple battery consisting of Biowin3 (survey ultimate biodegradation model) and Biowin5 (Ministry of International Trade and Industry [MITI] linear model) would have enhanced predictive power in comparison to individual models. Application of the battery to PMN substances showed that performance matched expectation. This approach significantly reduced both false positives for ready biodegradability and the overall misclassification rate. Similar results were obtained for a set of 63 pharmaceuticals using a battery consisting of Biowin3 and Biowin6 (MITI nonlinear model). Biodegradation data for PMNs tested in multiple ready tests or both inherent and ready biodegradation tests yielded additional insights that may be useful in risk screening.

Comparison of ready biodegradation estimation methods for fragrance materials.

Biodegradability is fundamental to the assessment of environmental exposure and risk from organic chemicals. Predictive models can be used to pursue both regulatory and chemical design (green chemistry) objectives, which are most effectively met when models are easy to use and available free of charge. The objective of this work was to evaluate no-cost estimation programs with respect to prediction of ready biodegradability. Fragrance materials, which are structurally diverse and have significant exposure potential, were used for this purpose. Using a database of 222 fragrance compounds with measured ready biodegradability, 10 models were compared on the basis of overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC), a measure of quality for binary classification. The 10 models were VEGA© Non-Interactive Client, START (Toxtree©), Biowin©1-6, and two models based on inductive machine learning. Applicability domain (AD) was also considered. Overall accuracy was ca. 70% and varied little over all models, but sensitivity, specificity and MCC showed wider variation. Based on MCC, the best models for fragrance compounds were Biowin6, VEGA and Biowin3. VEGA performance was slightly better for the <50% of the compounds it identified as having "high reliability" predictions (AD index >0.8). However, removing compounds with one and only one quaternary carbon yielded similar improvement in predictivity for VEGA, START, and Biowin3/6, with a smaller penalty in reduced coverage. Of the nine compounds for which the eight models (VEGA, START, Biowin1-6) all disagreed with the measured value, measured analog data were available for seven, and all supported the predicted value. VEGA, Biowin3 and Biowin6 are judged suitable for ready biodegradability screening of fragrance compounds.

How accurate are physical property estimation programs for organosilicon compounds?

Organosilicon compounds are important in chemistry and commerce, and nearly 10% of new chemical substances for which premanufacture notifications are processed by the US Environmental Protection Agency (USEPA) contain silicon (Si). Yet, remarkably few measured values are submitted for key physical properties, and the accuracy of estimation programs such as the Estimation Programs Interface (EPI) Suite and the SPARC Performs Automated Reasoning in Chemistry (SPARC) system is largely unknown. To address this issue, the authors developed an extensive database of measured property values for organic compounds containing Si and evaluated the performance of no-cost estimation programs for several properties of importance in environmental assessment. These included melting point (mp), boiling point (bp), vapor pressure (vp), water solubility, n-octanol/water partition coefficient (log KOW ), and Henry's law constant. For bp and the larger of 2 vp datasets, SPARC, MPBPWIN, and the USEPA's Toxicity Estimation Software Tool (TEST) had similar accuracy. For log KOW and water solubility, the authors tested 11 and 6 no-cost estimators, respectively. The best performers were Molinspiration and WSKOWWIN, respectively. The TEST's consensus mp method outperformed that of MPBPWIN by a considerable margin. Generally, the best programs estimated the listed properties of diverse organosilicon compounds with accuracy sufficient for chemical screening. The results also highlight areas where improvement is most needed.

Use of the bioaccumulation factor to screen chemicals for bioaccumulation potential.

The fish bioconcentration factor (BCF), as calculated from controlled laboratory tests, is commonly used in chemical management programs to screen chemicals for bioaccumulation potential. The bioaccumulation factor (BAF), as calculated from field-caught fish, is more ecologically relevant because it accounts for dietary, respiratory, and dermal exposures. The BCFBAF™ program in the U.S. Environmental Protection Agency's Estimation Programs Interface Suite (EPI Suite™ Ver 4.10) screening-level tool includes the Arnot-Gobas quantitative structure-activity relationship model to estimate BAFs for organic chemicals in fish. Bioaccumulation factors can be greater than BCFs, suggesting that using the BAF rather than the BCF for screening bioaccumulation potential could have regulatory and resource implications for chemical assessment programs. To evaluate these potential implications, BCFBAF was used to calculate BAFs and BCFs for 6,034 U.S. high- and medium-production volume chemicals. The results indicate no change in the bioaccumulation rating for 86% of these chemicals, with 3% receiving lower and 11% receiving higher bioaccumulation ratings when using the BAF rather than the BCF. All chemicals that received higher bioaccumulation ratings had log K(OW ) values greater than 4.02, in which a chemical's BAF was more representative of field-based bioaccumulation than its BCF. Similar results were obtained for 374 new chemicals. Screening based on BAFs provides ecologically relevant results without a substantial increase in resources needed for assessments or the number of chemicals screened as being of concern for bioaccumulation potential.

Environmental persistence of organic pollutants: guidance for development and review of POP risk profiles.

Environmental persistence is an important property that can enhance the potential of a chemical substance to exert adverse effects and be transported to remote environments. The persistence of organic compounds is governed by the rates at which they are removed by biological and chemical processes, such as biodegradation, hydrolysis, atmospheric oxidation, and photolysis. The persistence workgroup in a recent Society of Environmental Toxicology and Chemistry (SETAC) Pellston workshop (Pensacola, FL, USA, January 2008) focused on evaluating persistence of organic compounds in environmental media (air, water, soil, sediment) in terms of their single-medium degradation half-lives. The primary aim was to provide guidance to authors and reviewers of chemical dossiers for persistent organic pollutants (POPs) and persistent, bioaccumulative, and toxic substances (PBTs) proposed for action. A second objective was to provide a summary of the current state of the science with respect to POP fate assessment. Assessing the persistence of chemical substances in the environment is not straightforward. A common misconception is that, like many chemical properties, environmental persistence is an inherent property of the substance and can be readily measured. In fact, rates of degradation of a substance in the environment are determined by a combination of substance-specific properties and environmental conditions. This article addresses how persistence can be evaluated based on an assortment of supporting information. Special attention is given to several critical issues, including transformation products, nonextractable residues, and treatment of uncertainty and conflicting data as part of a weight-of-evidence assessment.