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QSAR methods in public health practice
Toxicology and Applied Pharmacology 254 (2011) 192–197
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Toxicology and Applied Pharmacology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y t a a p
SAR/QSAR methods in public health practice Eugene Demchuk ⁎, Patricia Ruiz, Selene Chou, Bruce A. Fowler Agency for Toxic Substances and Disease Registry (ATSDR), Division of Toxicology and Environmental Medicine, Atlanta, GA 30333, USA
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Article history: Received 8 June 2009 Revised 14 April 2010 Accepted 24 October 2010 Available online 27 October 2010 Keywords: Structure–activity relationships SAR/QSAR Modeling Computational toxicology Public health practice
a b s t r a c t Methods of (Quantitative) Structure–Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice. Published by Elsevier Inc.
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . (Q)SAR software for hazard identification and hazard characterization Weight-of-evidence approach . . . . . . . . . . . . . . . . . . . Case study I: Hurricane Katrina disaster assessment . . . . . . . . Case study II: Molecular epidemiology . . . . . . . . . . . . . . . (Q)SAR Chemical Space . . . . . . . . . . . . . . . . . . . . . . Case study III: Addressing substance-specific priority data needs . . . Case study IV: Health consultation for USCG . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest disclosure statement . . . . . . . . . . . . . . Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction Environmental exposures to a diverse list of man-made chemicals are on the rise. Millions of tons of toxic and potentially hazardous xenobiotics have been released in the environment since World War II (Hu et al., 2007). Chemicals, such as mercury, lead, 4,4'dichlorodiphenyltrichloroethane (DDT), polychlorinated biphenyls ⁎ Corresponding author. Agency for Toxic Substances and Disease Registry (F62), 1600 Clifton Road, Atlanta, GA 30333, USA. Fax: +1 404 248 4142. E-mail address: [email protected] (E. Demchuk). 0041-008X/$ – see front matter. Published by Elsevier Inc. doi:10.1016/j.taap.2010.10.017
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(PCBs), pesticides, and other environmental pollutants, even though nonexistent or scant before the industrial revolution, are ubiquitous nowadays (CDC, 2005). Although biopersistent pollutants can be found in living organisms as far as Antarctica (Focardi et al., 1992; Geisz et al., 2008), the potential for human exposures is greater at the sites of chemical release and interment. The Agency for Toxic Substances and Disease Registry (ATSDR) serves the public by preventing and mitigating exposures, adverse human health effects, and diminished quality of life associated with exposures to chemical substances from hazardous waste sites (HWS), unplanned releases, and other sources of environmental contamination.
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Mandated by the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) [42 U.S.C. 9604 et seq.], as amended by the Superfund Amendments and Reauthorization Act (SARA) [Pub. L. 99 499], ATSDR promotes optimal decisions in public health practice by using the best science and providing trusted health information. This is achieved through a number of Agency products, including toxicological profiles for hazardous substances found at National Priority List (NPL) sites (ATSDR, 2009), emergency responses, public health assessments, chemical-specific health consultations, priority health conditions, health advisories, environmental alerts, environmental health education, and others. The foundation for these public health actions is laid by minimal risk levels (MRLs) developed by the Agency. An MRL is an estimate of the daily human exposure to a hazardous substance that is likely to be without appreciable risk of adverse (non-carcinogenic) health effects over a specified route and duration of exposure. An MRL is a health guidance value (HGV), which is used as a screening level intended for health professionals and other responders to select potential contaminants of concern and to identify the potential health effects of contaminants that may be a concern at HWS or other contaminated areas (Chou et al., 1998). An MRL is usually derived by applying science-based uncertainty factors (UFs) to a point of departure (POD) for a toxicity endpoint observed in a laboratory animal bioassay or a human epidemiological study. Since appropriate human data are often nonexistent, most MRLs have been derived using animal-to-human extrapolation. In general MRL derivation is a complex time-consuming process that involves the development of a comprehensive multi-page document known as a toxicological profile of the substance. The toxicological profiles include an examination, summary, and interpretation of available toxicological information and epidemiologic evaluations of a hazardous substance. During the development of toxicological profiles, MRLs are derived when ATSDR determines that reliable, sufficient, and scientifically credible information exists to identify the target organ(s) for the health effect (or the most sensitive health effects) for a specific duration and for a given route of exposure to the substance. MRLs are derived for acute (1–14 days), intermediate (15–364 days), and chronic (365 days and longer) exposure durations. MRLs are generally derived using a no observed adverse effect level (NOAEL) or the lowest observed adverse effect level (LOAEL) as a POD. When adequate information is available, physiologically based toxicokinetic (PBTK) modeling, chemical-specific adjustment factor (CSAF) modeling, and benchmark dose (BMD) modeling are used as an adjunct to the NOAEL/LOAEL approach in MRL derivation (Demchuk et al., 2008). To date, 392 MRLs for 175 hazardous agents, including eight MRLs for external radiation have been developed. Although these MRLs comprise most important hazardous substances on the ATSDR priority chemical list for which a vast majority of completed exposure pathways at HWS have been reported, nevertheless a large number of hazardous substances to which the U.S. population is exposed remains underrepresented (Demchuk et al., 2006). A recent analysis of the Third National Report on Human Exposures to Environmental Chemicals (CDC, 2005) conducted by the National Academy of Sciences (NRC, 2006) suggests that in excess of 2/3 of chemicals found in biosamples across the nation have no HGVs yet developed, and only for about 3% of the chemicals is toxicity well-characterized. Similar estimates have been obtained upon the analysis of ATSDR Hazardous Substance Release/Health Effects (HazDat) database (ATSDR, 2008). The analysis also suggests that only 160 (4%) of approximately 4000 toxic chemicals found at the U.S. HWS have developed MRLs (Demchuk et al., 2006). The time and resources required for developing new HGVs using the traditional peer-review approach used in the toxicological profiles can be immense. Many chemicals that still lack HGVs are found at only a few HWS, each presenting a potential danger to a limited population. It is unlikely that these chemicals will top the priority chemical list any time soon and therefore that respective MRLs will
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soon be developed. However, combined altogether these sites and chemicals impact a sizable human population with an as yet pending response to their health guidance needs. The present review describes how methods of computational toxicology are used to address these needs and other needs of the Agency and its stakeholders. (Q)SAR software for hazard identification and hazard characterization There is a severe disparity between the number of chemicals needing health guidance and those for which ample toxicological information exists for development of HGVs as mandated to ATSDR by Congress. Other government agencies face a similar challenge. For instance, the U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition and Center for Drug Evaluation and Research and U.S. Environmental Protection Agency Office of Pollution Prevention and Toxics regulate chemicals of environmental, nutritional and pharmacologic significance, respectively, under the Pollution Prevention Act (42 U.S.C. 6601–6610), Toxic Substances Control Act (15 U.S.C. 2601–2692), Food Quality Protection Act (7 U.S.C. 136 et seq.), and other legislative authorities. As the number and diversity of new chemicals continues to rise (Nabholz et al., 1997), these agencies have approached the challenge by introducing (Quantitative) Structure–Activity Relationship ((Q)SAR) based guidance and decision support to chemical risk assessment (Auer et al., 1990; Klopman et al., 2005; Zeeman et al., 1995). The process of scientific risk assessment involves four steps, two of which, hazard identification and hazard characterization, can be approached by (Q) SAR. A large part of regulatory decision making is based on chemical hazard identification, which does not necessarily require quantification of a toxic effect, that is for which SAR modeling of the categorical response is sufficient. However, many public health actions taken by ATSDR and public health service (PHS) are impossible without credible chemical hazard characterization of environmental exposures, which requires QSAR modeling. Therefore, SAR models developed for the regulatory arm of the Government are often insufficient in meeting objectives of the PHS arm. Hazard identification is carried out using comparative SAR analysis, expert systems, categorical QSAR, and various kinds of profiling. Because of the relative simplicity and preceding role of hazard identification in the risk assessment process these methods are proliferating. Examples include HazardExpert (Compudrug Inc.), MC4PC (MultiCASE, Inc.; Klopman, 1984), Derek for Windows (LHASA, Ltd.; Sanderson and Earnshaw, 1991), OncoLogic™ (Woo and Lai, 2005), ToxCast™ (Dix et al., 2007), and many others. At present quantitative hazard characterization models, which utilize QSAR, play a relatively minor role in the regulatory arm of the government. These include QSAR models for aquatic life toxicity in ECOSAR and OASIS (OECD, 2006), a few models within the MultiCASE, Inc. line of products, and human/mammal models of the TOPKAT® software suite marketed by Accelrys, Inc. There is an increasing trend to public domain and open source SAR/QSAR software development. Software developed on government funded projects, which do not involve cost-recovery mechanisms, is usually free. Examples include OncoLogic, ToxCast. ECOSAR, OASIS. Many computational toxicology software vendors and academics provide public access to their resources, including ADME/Tox Boxes from Pharma Algorithms, Inc. (Lanevskij et al., 2009), OSIRIS Property Explorer from Actelion Pharmaceuticals Ltd. (Sander et al., 2009), Bioclipse from the Uppsala University (Spjuth et al., 2007), and others (Villoutreix et al., 2007). Human environmental risk assessment adds an additional constraint, which is focusing at the low-dose region of dose-response curve. NOAEL and LOAEL are typical endpoints of the low-dose region. QSAR models applicable to the low-dose region include a human Maximum Recommended Daily Dose (MRDD) model of MC4PC (Contreras et al., 2004) and rat oral chronic LOAEL model of TOPKAT®
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(Mumtaz et al., 1995). However, at present, further improvements are needed to increase user confidence in QSAR models (Moudgal et al., 2007; Venkatapathy et al., 2004; Venkatapathy et al., 2009); it is not anticipated that such models will replace the traditional ATSDR MRL approach any time soon. Weight-of-evidence approach At present, applications of (Q)SAR in public health practice are limited to the weight-of-evidence (WOE) approach, which allows most effective engaging of both available QSAR and SAR models in the integrated chemical hazard identification/characterization process. The ATSDR Computational Toxicology Laboratory (CTMDL) routinely applies these methods to assist the Agency and its customers with express analysis of emerging and existing chemical hazards. (Q)SAR methods are used to assist the Agency with prioritization of chemicals and to provide guidance in laboratory testing, to assess physicalchemical properties of environmental pollutants, including calculation of parameters for PBTK modeling. In chemical-specific health consultations for certain organic chemicals, lacking credible toxicity information, (Q)SAR methods are used to formulate hazard hypotheses directly from the chemical structure and to estimate missing values of major parameters involved in risk assessment. These methods are also used for hazard identification of acute toxicity, carcinogenicity, reproductive and developmental toxicity, irritation and sensitization. (Q)SAR methods also occupy an important place in understanding mechanisms of toxic action of hazardous substances with scant laboratory data, a spectrum of their toxic effects, chemical safety, and anticipated health risks. QSAR estimates are often used in the binary WOE approach to elucidate health effects of chemical mixture components (Demchuk et al., 2008). The greatest practical impact of (Q)SAR methods on public health is, perhaps, in emergency response situations. In cases of emergencies, such as accidental releases of untested chemical substances, natural or technologic disasters when chemical releases become a risk, and casual lifethreatening exposures to hazardous substances, timing of appropriate public health actions becomes a critically sensitive element of public health response. Case study I: Hurricane Katrina disaster assessment In 2005 hurricane Katrina affected a large number of households in the Gulf of Mexico, an area rich with chemical manufacturing and storage facilities. During the hurricane and subsequent torrential flooding there was a tangible danger of unplanned releases and hazardous contamination from industrial and HWS facilities. Potentially harmful chemical exposures put at risk both the local population already weakened by the disaster and remediation personnel working at sites. ATSDR together with sister public health agencies were challenged with prompt and effective actions concerning relocation and lifestyle restrictions impacting millions. In response to the public health challenge posed by the hurricane, ATSDR CTMDL conducted an express (Q)SAR analysis of permissible exposures to hazardous materials found at NPL HWS and U.S. EPA Toxics Release Inventory (TRI) Program operational sites in Louisiana and Mississippi. Approximately 300 chemicals of concern were identified. The initial screening at a number of levels suggested a core group of 132 chemicals without comparison values to which (Q)SAR analysis was applied. The analysis yielded provisional LC50 values and LOAEL estimates for 121 and 89 chemicals without HGVs, respectively. Of these, 18 volatile organic compounds were reported to the emergency response center. The recommendation of CTMDL was that the analyzed chemicals were not expected to cause significant hazard, unless high-concentration exposures occurred. Therefore, no immediate public health actions with respect to inhalation chemical
hazards were recommended. Subsequent developments confirmed these conclusions. Case study II: Molecular epidemiology On another occasion (Q)SAR analysis was used in the ATSDR public health response at the request of the New Jersey Department of Health and Senior Services in connection with a childhood cancer cluster in the Toms River section of Dover Township, NJ. In the mid-1990s an increased incidence rate of leukemia, malignant cancers, brain and central nervous system cancers in children was first identified in that area. The community was concerned about health impacts of local public water supply contamination caused by chemicals leaching from the Reich Farm Superfund site. Completed exposure pathways through a local aquifer were identified for TCE, PCE and other byproducts of Union Carbide Corporation for which experimental toxicity estimates were unavailable. The chemicals included tetrachlorophthalic acid, tetrachlorophthalic anhydride, chlorendic anhydride, chlorendic acid, o-chlorostyrene, mchlorostyrene, p-chlorostyrene, α,β-dichlorostyrene, bis (4-chlorophenyl) sulfone, triallyl isocyanurate, 1,2-diphenylhydrazine diphenylamine, Nethyl-p-toluenesulfonamide, N-methyl-ptoluenesulfonamide, and styrene-acrylonitrile (SAN) trimer. ATSDR used (Q)SAR analysis to evaluate health effects of these chemicals. The evaluated endpoints included mutagenicity, carcinogenicity, developmental toxicity, rat oral LD50, and octanol water partition coefficient. The (Q)SAR analysis suggested that 9 of the 15 chemicals could be potential carcinogens, 6 had a potential for developmental toxicity, and 6 could cause genetic mutations. Based on the results of (Q)SAR analyses presented at the interagency workgroup, SAN trimer, which is formed by the condensation of two moles of acrylonitrile and one mole of styrene, has been nominated for toxicity testing by the National Toxicology Program (NTP). NTP genotoxicity tests confirmed the lack of mutagenicity of SAN trimer devised using (Q)SAR analysis, while a 2-year NTP carcinogenicity study is still under way (NTP, 2007). In both examples above, the (Q)SAR analyses were used to guide site specific environmental health assessments by providing rational prioritization of anticipated chemical hazards at that site. The (Q)SAR analysis was used because appropriate experimental toxicological information was unavailable. Otherwise, if the toxicological information is available and summarized in ATSDR toxicological profiles, which MRLs are used to screen multiple chemical hazards at a HWS to focus the health assessment on those chemicals that pose the greatest health risk at the site. For instance, if multiple completed exposure pathways are identified at the site, a screening assessment using MRLs can help focus the assessment on those pathways associated with the greatest health risk. This way (Q)SAR analysis is used as an adjunct to MRLs in public health practice. A question as to whether a specialized QSAR model, such as an HGV, LOAEL, or NOAEL QSAR model, can be developed that would provide a provisional MRL, rather than WOE (Q)SAR evaluation, remains debatable. ATSDR conducts active research in this area. Sketch calculations suggest the utility of QSAR estimates for both oral and inhalation MRL modeling (Demchuk et al., 2006). In these calculations approximately 70% correlation between the actual and estimated MRLs was observed using the TOPKAT® rat oral chronic LOAEL model as an engine for MRL modeling. Subsequent QSAR modeling of HGVs and rat LOAELs using high-quality toxicological information yielded even better results with correlation greater than 80% in best models (manuscript in preparation). However, the predictive power, and therefore the practical utility of these models, is a subject of suitably dense coverage of environmental chemical space with tested compounds within the applicability domain of the model. (Q)SAR Chemical Space The concept of chemical space is of prime importance in modern (Q)SAR modeling and in the analysis of results (Oprea and Gottfries,
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2001). It helps to define confidence in a (Q)SAR assessment and to formulate plausible hypotheses about toxicity. The concept of chemical space starts with a definition of similarity/dissimilarity measure, ideally, a metric distance. Chemical similarity can be defined using either purely structural features (exemplified by the JaccardTanimoto similarity coefficient), three-dimensional molecular fields (e.g., Carbo index), or using molecular descriptors. ATSDR experience and reports of others (Martin et al., 2002; Steffen et al., 2009) suggest that with respect to correlations with toxicological endpoints, the descriptor similarity usually outperforms purely structural or molecular field measures. A descriptor similarity distance implemented in TOPKAT® is one of similarity measures commonly used at the ATSDR. A TOPKAT® set of descriptors includes Kier and Hall electrotopological states (e-states) (Kier and Hall, 1986), shape, symmetry, molecular weight, and the octanol-water partition coefficient. Thus, the TOPKAT® similarity is a structure-based property-sensitive similarity measure. It reflects similarity of descriptors between two molecules with respect to a specific property or endpoint (Accelrys, 2004). TOPKAT® can be utilized to establish a degree of structureproperty similarity between a query chemical and compounds in the database. Once a valid toxicity assessment is obtained a “similarity search” may be conducted which determines database chemicals in vicinity of the query chemical in the chemical space (optimal prediction space in the TOPKAT® terminology) (Fig. 1). Database chemicals within a user defined distance of the query chemical are considered as potential surrogates, unless a TOPKAT® QSAR model falls short of estimating the actual toxicity of the database chemical (Accelrys, 2004). If the query chemical does not satisfy all model considerations and, thus, a valid prediction is not obtained, no surrogates are suggested. For the similarity distance between two chemicals (otherwise vectors in the chemical space), the smaller the distance, the greater the similarity. A TOPKAT® similarity distance of less than 0.25 has been suggested to be toxicologically representative (HDI, 1997). In other words, if a distance between two compounds is less than 0.25, then there is a good chance that the modeled toxicity endpoint takes on a similar value for these two compounds. Each compound within 0.25 distance of the query compound is suggested to be similar and can be used to support or refute the hypothesis. Case study III: Addressing substance-specific priority data needs To illustrate the concept of similarity in chemical space, let us consider a (Q)SAR assessment of carcinogenicity of p-cresol carried
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out by ATSDR as a part of Agency guidance to the EPA/ATSDR Test Rule and/or Voluntary Research Program. The initial (Q)SAR assessment suggested a 0.027 probability of p-cresol being a male rat carcinogen (negative). A similarity search on complete model database revealed 25 compounds within 0.25 normalized distance of p-cresol in the TOPKAT® chemical space. Four of them within 0.1 vicinity of p-cresol are shown in Fig. 1. They are resorcinol, 1,2-dichlorobenzene, phenol and 1,2-dichloropropane. Of 25 database compounds within the 0.25 vicinity, 22 are non-carcinogenic (NEG in the bottom left corner in Fig. 1) and three carcinogenic (POS in the bottom left corner) in male rats. TOPKAT® model was able to correctly determine the lack of carcinogenicity of 20 non-carcinogenic database compounds (NEG in the bottom right corner). For the two remaining database compounds TOPKAT® assessment was indefinite (IND in bottom right corner). Of the three male rat carcinogens (POS in the bottom left corner), two were incorrectly deemed by TOPKAT® as non-carcinogens (NEG in bottom right corner), but apparently they have not been used to train the model (NO in upper right corner). The third male rat carcinogen has been included in the training set of the model, but its assessment by TOPKAT® was indefinite. Thus, 80% of database compounds within the 0.25 distance of p-cresol and 100% of compounds within the 0.1 distance were correctly assessed by the model. According to the decision-analytic flowchart (Fig. 2) described in details in another ATSDR publication (El-Masri et al., 2002), these results suggest with very high confidence that p-cresol is non-carcinogenic in male rats. Case study IV: Health consultation for USCG More recently (Q)SAR similarity analysis was used to support a chemical-specific health consultation prepared by the ATSDR Division of Health Assessment and Consultation per request of the U.S. Coast Guard. USCG was interested in risk assessment of low-dose exposures to several halogenated benzenes. They included chloroethylbenzenes, chlorodimethylbenzenes, bromodimethylbenzenes and dichlorodimethylbenzenes. None of them had an established oral HGV, but ATSDR had developed oral MRLs for a number of similar compounds. A group of suspected analogs included: benzene, chlorobenzene, 1,3dichlorobenzene, 1,2-dichlorobenzene, 1,4-dichlorobenzene, hexachlorobenzene, ethylbenzene and dimethylbenzenes. Therefore, a question was how appropriate would it be to use information from the toxicological profiles for any of these compounds in health assessments of aforementioned halogenated benzene derivatives? To assess the degree of similarity we conducted (Q)SAR analysis using rat oral LD50 models of TOPKAT®. The chemical space of this
Fig. 1. A fragment of similarity search analysis using TOPKAT®.
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Similarity Analysis of p-cresol
Distance < 0.25
Actual and Predicted Values Match
Actual and Predicted Values do not Match
If 3 out of 4 Match
High Confidence
If 2 out of 4 Match
If less than 2 out of 4 Match
Low Confidence
No Confidence
Fig. 2. A diagram of confidence analysis of (Q)SAR results using TOPKAT® similarity scoring.
model is most densely populated in the area of substituted benzene derivatives, and its training set includes most of the analog chemicals in question. A TOPKAT® similarity analysis showed that the toxicological profile for benzene and benzene MRLs are unlikely to be a good surrogate for toxicological properties of the halogenated benzene derivatives of interest. For α-chloroethylbenzene (Fig. 3), monohalogenated benzenes (ranks 28–41, similarity distances 0.096– 0.1011) came out closer than ethylbenzene (78, 0.119). Similar results were obtained for another chloroethylbenzene isomer, (1-chloroethyl) benzene. For (1-chloroethyl)benzene monohalogenated benzenes (14– 20, 0.035–0.041) were also located closer in the chemical space than ethylbenzene (109, 0.114). Although monohalogenated benzenes and ethylbenzene both were within the similarity cut-off distance of 0.25, extrapolation of toxicological information from monohalogenated benzenes (ATSDR, 1990), as first choice, seem to be more appropriate because of their proximity to chloroethylbenzenes in the chemical space. Presence of ethylbenzene within the cut-off distance suggests that its toxicological information can be used for consensus verification of hypotheses constructed in the process of cross-chemical extrapolation from monohalogenated benzenes. Similarity (Q)SAR analysis applied to the halogenated dimethylbenzenes did not yield conclusive results. For the ortho-, meta-, and parasubstituted dichlorodimethylbenzenes, tetra-halogenated benzenes (ranks 3–5, similarity distance 0.118) were found to have greater property similarity than hexachlorobenzene (25, 0.267), which was the only analog chemical in the database for which a toxicological profile has been developed (ATSDR, 2002). Since the distance to hexachlorobenzene was in excess of cut-off of 0.25, no propositions in support of cross-chemical extrapolation from hexachlorobenzene were deemed worthy. For monochlorinated dimethylbenzenes none of the analog
Fig. 3. Isomers of chloroethylbenzene.
chemicals were found in the vicinity of query compounds. From the results of (Q)SAR analysis it was concluded that toxicological information on dichlorodimethylbenzenes represented a pending PDN, which is large in terms of chemical similarity/diversity. So, that even crosschemical extrapolation would not be productive in application to these chemicals. Conclusions (Q)SAR modeling is an important component of computational toxicology. In public health practice, (Q)SAR modeling helps ATSDR to conduct a scientifically defensible “express” analysis in situations when other laboratory and computational methods either are missing or ineffective, and where prior knowledge is scarce. The universality comes at a premium: the capabilities of (Q)SAR analysis depend on the database of the model, and appropriate confidence limits on the results are often uncertain. (Q)SAR methodology is mechanistic in nature. However, human toxicology is a complex topic involving disparate modes of action and comprised of systems of disparate yet interconnected mechanisms for which not every chemical qualifies for each step on the pathway of mechanisms. Although, SAR analysis is a quite developed method, and a variety of modeling options exist in the area of hazard identification, QSAR modeling in the area of risk characterization, especially in the low-dose region of the dose–response curve, still represents a significant challenge, since endpoints are not as clearly resolved as discrete. (Q)SAR modeling in public health practice is often driven by the need for a rapid response for public health action in a timely manner. Such a rapid response may of necessity occur under constraints of toxicological data gaps and in the presence of unmet Agency PDNs required to protect human populations from exposure to hazardous environmental contaminants. These conditions entail high expectations about reliability of (Q)SAR analysis. At present the reliability is achieved by means of the WOE approach, which utilizes a consensus and knowledge-driven approach towards interpretation of results. Even so, further improvements of (Q)SAR modeling in mammalian toxicology are likely to be driven by both the development of public domain and open source software resources, accumulation of toxicological information for additional chemicals, and careful attribution of these data with respect to quality. Quality attribution of the underlying data (LOAELs, NOAELs and BMDs) is expected to focus QSAR modeling at most reliable information and thus translate quality improvements in the training set structure into improved quality of the model. Since at present the database of HGVs is fairly undersized by
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QSAR standards, expansion of the database of high-quality HGVs (such as MRLs and RfDs) represents the major research need in the area of QSAR modeling of mammalian toxicity. Appropriate population and expansion of the environmental-chemistry-relevant chemical space with (Q)SAR-prone data pertinent to low-dose mammalian toxicology represents an important objective of current research that has a potential for significantly improving the level of service in public health practice. Conflict of interest disclosure statement Neither author has a conflict of interest or a financial relationship with a commercial entity that has an interest in the subject of this publication. Disclaimer The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Agency for Toxic Substances and Disease Registry and should not be construed to represent any Agency determination or policy. Mention of trade names or other proprietary information is made for the convenience of the reader and is not an endorsement of any commercial product.
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Contents lists available at ScienceDirect
Toxicology and Applied Pharmacology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y t a a p
SAR/QSAR methods in public health practice Eugene Demchuk ⁎, Patricia Ruiz, Selene Chou, Bruce A. Fowler Agency for Toxic Substances and Disease Registry (ATSDR), Division of Toxicology and Environmental Medicine, Atlanta, GA 30333, USA
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Article history: Received 8 June 2009 Revised 14 April 2010 Accepted 24 October 2010 Available online 27 October 2010 Keywords: Structure–activity relationships SAR/QSAR Modeling Computational toxicology Public health practice
a b s t r a c t Methods of (Quantitative) Structure–Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice. Published by Elsevier Inc.
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . (Q)SAR software for hazard identification and hazard characterization Weight-of-evidence approach . . . . . . . . . . . . . . . . . . . Case study I: Hurricane Katrina disaster assessment . . . . . . . . Case study II: Molecular epidemiology . . . . . . . . . . . . . . . (Q)SAR Chemical Space . . . . . . . . . . . . . . . . . . . . . . Case study III: Addressing substance-specific priority data needs . . . Case study IV: Health consultation for USCG . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest disclosure statement . . . . . . . . . . . . . . Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction Environmental exposures to a diverse list of man-made chemicals are on the rise. Millions of tons of toxic and potentially hazardous xenobiotics have been released in the environment since World War II (Hu et al., 2007). Chemicals, such as mercury, lead, 4,4'dichlorodiphenyltrichloroethane (DDT), polychlorinated biphenyls ⁎ Corresponding author. Agency for Toxic Substances and Disease Registry (F62), 1600 Clifton Road, Atlanta, GA 30333, USA. Fax: +1 404 248 4142. E-mail address: [email protected] (E. Demchuk). 0041-008X/$ – see front matter. Published by Elsevier Inc. doi:10.1016/j.taap.2010.10.017
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(PCBs), pesticides, and other environmental pollutants, even though nonexistent or scant before the industrial revolution, are ubiquitous nowadays (CDC, 2005). Although biopersistent pollutants can be found in living organisms as far as Antarctica (Focardi et al., 1992; Geisz et al., 2008), the potential for human exposures is greater at the sites of chemical release and interment. The Agency for Toxic Substances and Disease Registry (ATSDR) serves the public by preventing and mitigating exposures, adverse human health effects, and diminished quality of life associated with exposures to chemical substances from hazardous waste sites (HWS), unplanned releases, and other sources of environmental contamination.
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Mandated by the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) [42 U.S.C. 9604 et seq.], as amended by the Superfund Amendments and Reauthorization Act (SARA) [Pub. L. 99 499], ATSDR promotes optimal decisions in public health practice by using the best science and providing trusted health information. This is achieved through a number of Agency products, including toxicological profiles for hazardous substances found at National Priority List (NPL) sites (ATSDR, 2009), emergency responses, public health assessments, chemical-specific health consultations, priority health conditions, health advisories, environmental alerts, environmental health education, and others. The foundation for these public health actions is laid by minimal risk levels (MRLs) developed by the Agency. An MRL is an estimate of the daily human exposure to a hazardous substance that is likely to be without appreciable risk of adverse (non-carcinogenic) health effects over a specified route and duration of exposure. An MRL is a health guidance value (HGV), which is used as a screening level intended for health professionals and other responders to select potential contaminants of concern and to identify the potential health effects of contaminants that may be a concern at HWS or other contaminated areas (Chou et al., 1998). An MRL is usually derived by applying science-based uncertainty factors (UFs) to a point of departure (POD) for a toxicity endpoint observed in a laboratory animal bioassay or a human epidemiological study. Since appropriate human data are often nonexistent, most MRLs have been derived using animal-to-human extrapolation. In general MRL derivation is a complex time-consuming process that involves the development of a comprehensive multi-page document known as a toxicological profile of the substance. The toxicological profiles include an examination, summary, and interpretation of available toxicological information and epidemiologic evaluations of a hazardous substance. During the development of toxicological profiles, MRLs are derived when ATSDR determines that reliable, sufficient, and scientifically credible information exists to identify the target organ(s) for the health effect (or the most sensitive health effects) for a specific duration and for a given route of exposure to the substance. MRLs are derived for acute (1–14 days), intermediate (15–364 days), and chronic (365 days and longer) exposure durations. MRLs are generally derived using a no observed adverse effect level (NOAEL) or the lowest observed adverse effect level (LOAEL) as a POD. When adequate information is available, physiologically based toxicokinetic (PBTK) modeling, chemical-specific adjustment factor (CSAF) modeling, and benchmark dose (BMD) modeling are used as an adjunct to the NOAEL/LOAEL approach in MRL derivation (Demchuk et al., 2008). To date, 392 MRLs for 175 hazardous agents, including eight MRLs for external radiation have been developed. Although these MRLs comprise most important hazardous substances on the ATSDR priority chemical list for which a vast majority of completed exposure pathways at HWS have been reported, nevertheless a large number of hazardous substances to which the U.S. population is exposed remains underrepresented (Demchuk et al., 2006). A recent analysis of the Third National Report on Human Exposures to Environmental Chemicals (CDC, 2005) conducted by the National Academy of Sciences (NRC, 2006) suggests that in excess of 2/3 of chemicals found in biosamples across the nation have no HGVs yet developed, and only for about 3% of the chemicals is toxicity well-characterized. Similar estimates have been obtained upon the analysis of ATSDR Hazardous Substance Release/Health Effects (HazDat) database (ATSDR, 2008). The analysis also suggests that only 160 (4%) of approximately 4000 toxic chemicals found at the U.S. HWS have developed MRLs (Demchuk et al., 2006). The time and resources required for developing new HGVs using the traditional peer-review approach used in the toxicological profiles can be immense. Many chemicals that still lack HGVs are found at only a few HWS, each presenting a potential danger to a limited population. It is unlikely that these chemicals will top the priority chemical list any time soon and therefore that respective MRLs will
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soon be developed. However, combined altogether these sites and chemicals impact a sizable human population with an as yet pending response to their health guidance needs. The present review describes how methods of computational toxicology are used to address these needs and other needs of the Agency and its stakeholders. (Q)SAR software for hazard identification and hazard characterization There is a severe disparity between the number of chemicals needing health guidance and those for which ample toxicological information exists for development of HGVs as mandated to ATSDR by Congress. Other government agencies face a similar challenge. For instance, the U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition and Center for Drug Evaluation and Research and U.S. Environmental Protection Agency Office of Pollution Prevention and Toxics regulate chemicals of environmental, nutritional and pharmacologic significance, respectively, under the Pollution Prevention Act (42 U.S.C. 6601–6610), Toxic Substances Control Act (15 U.S.C. 2601–2692), Food Quality Protection Act (7 U.S.C. 136 et seq.), and other legislative authorities. As the number and diversity of new chemicals continues to rise (Nabholz et al., 1997), these agencies have approached the challenge by introducing (Quantitative) Structure–Activity Relationship ((Q)SAR) based guidance and decision support to chemical risk assessment (Auer et al., 1990; Klopman et al., 2005; Zeeman et al., 1995). The process of scientific risk assessment involves four steps, two of which, hazard identification and hazard characterization, can be approached by (Q) SAR. A large part of regulatory decision making is based on chemical hazard identification, which does not necessarily require quantification of a toxic effect, that is for which SAR modeling of the categorical response is sufficient. However, many public health actions taken by ATSDR and public health service (PHS) are impossible without credible chemical hazard characterization of environmental exposures, which requires QSAR modeling. Therefore, SAR models developed for the regulatory arm of the Government are often insufficient in meeting objectives of the PHS arm. Hazard identification is carried out using comparative SAR analysis, expert systems, categorical QSAR, and various kinds of profiling. Because of the relative simplicity and preceding role of hazard identification in the risk assessment process these methods are proliferating. Examples include HazardExpert (Compudrug Inc.), MC4PC (MultiCASE, Inc.; Klopman, 1984), Derek for Windows (LHASA, Ltd.; Sanderson and Earnshaw, 1991), OncoLogic™ (Woo and Lai, 2005), ToxCast™ (Dix et al., 2007), and many others. At present quantitative hazard characterization models, which utilize QSAR, play a relatively minor role in the regulatory arm of the government. These include QSAR models for aquatic life toxicity in ECOSAR and OASIS (OECD, 2006), a few models within the MultiCASE, Inc. line of products, and human/mammal models of the TOPKAT® software suite marketed by Accelrys, Inc. There is an increasing trend to public domain and open source SAR/QSAR software development. Software developed on government funded projects, which do not involve cost-recovery mechanisms, is usually free. Examples include OncoLogic, ToxCast. ECOSAR, OASIS. Many computational toxicology software vendors and academics provide public access to their resources, including ADME/Tox Boxes from Pharma Algorithms, Inc. (Lanevskij et al., 2009), OSIRIS Property Explorer from Actelion Pharmaceuticals Ltd. (Sander et al., 2009), Bioclipse from the Uppsala University (Spjuth et al., 2007), and others (Villoutreix et al., 2007). Human environmental risk assessment adds an additional constraint, which is focusing at the low-dose region of dose-response curve. NOAEL and LOAEL are typical endpoints of the low-dose region. QSAR models applicable to the low-dose region include a human Maximum Recommended Daily Dose (MRDD) model of MC4PC (Contreras et al., 2004) and rat oral chronic LOAEL model of TOPKAT®
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(Mumtaz et al., 1995). However, at present, further improvements are needed to increase user confidence in QSAR models (Moudgal et al., 2007; Venkatapathy et al., 2004; Venkatapathy et al., 2009); it is not anticipated that such models will replace the traditional ATSDR MRL approach any time soon. Weight-of-evidence approach At present, applications of (Q)SAR in public health practice are limited to the weight-of-evidence (WOE) approach, which allows most effective engaging of both available QSAR and SAR models in the integrated chemical hazard identification/characterization process. The ATSDR Computational Toxicology Laboratory (CTMDL) routinely applies these methods to assist the Agency and its customers with express analysis of emerging and existing chemical hazards. (Q)SAR methods are used to assist the Agency with prioritization of chemicals and to provide guidance in laboratory testing, to assess physicalchemical properties of environmental pollutants, including calculation of parameters for PBTK modeling. In chemical-specific health consultations for certain organic chemicals, lacking credible toxicity information, (Q)SAR methods are used to formulate hazard hypotheses directly from the chemical structure and to estimate missing values of major parameters involved in risk assessment. These methods are also used for hazard identification of acute toxicity, carcinogenicity, reproductive and developmental toxicity, irritation and sensitization. (Q)SAR methods also occupy an important place in understanding mechanisms of toxic action of hazardous substances with scant laboratory data, a spectrum of their toxic effects, chemical safety, and anticipated health risks. QSAR estimates are often used in the binary WOE approach to elucidate health effects of chemical mixture components (Demchuk et al., 2008). The greatest practical impact of (Q)SAR methods on public health is, perhaps, in emergency response situations. In cases of emergencies, such as accidental releases of untested chemical substances, natural or technologic disasters when chemical releases become a risk, and casual lifethreatening exposures to hazardous substances, timing of appropriate public health actions becomes a critically sensitive element of public health response. Case study I: Hurricane Katrina disaster assessment In 2005 hurricane Katrina affected a large number of households in the Gulf of Mexico, an area rich with chemical manufacturing and storage facilities. During the hurricane and subsequent torrential flooding there was a tangible danger of unplanned releases and hazardous contamination from industrial and HWS facilities. Potentially harmful chemical exposures put at risk both the local population already weakened by the disaster and remediation personnel working at sites. ATSDR together with sister public health agencies were challenged with prompt and effective actions concerning relocation and lifestyle restrictions impacting millions. In response to the public health challenge posed by the hurricane, ATSDR CTMDL conducted an express (Q)SAR analysis of permissible exposures to hazardous materials found at NPL HWS and U.S. EPA Toxics Release Inventory (TRI) Program operational sites in Louisiana and Mississippi. Approximately 300 chemicals of concern were identified. The initial screening at a number of levels suggested a core group of 132 chemicals without comparison values to which (Q)SAR analysis was applied. The analysis yielded provisional LC50 values and LOAEL estimates for 121 and 89 chemicals without HGVs, respectively. Of these, 18 volatile organic compounds were reported to the emergency response center. The recommendation of CTMDL was that the analyzed chemicals were not expected to cause significant hazard, unless high-concentration exposures occurred. Therefore, no immediate public health actions with respect to inhalation chemical
hazards were recommended. Subsequent developments confirmed these conclusions. Case study II: Molecular epidemiology On another occasion (Q)SAR analysis was used in the ATSDR public health response at the request of the New Jersey Department of Health and Senior Services in connection with a childhood cancer cluster in the Toms River section of Dover Township, NJ. In the mid-1990s an increased incidence rate of leukemia, malignant cancers, brain and central nervous system cancers in children was first identified in that area. The community was concerned about health impacts of local public water supply contamination caused by chemicals leaching from the Reich Farm Superfund site. Completed exposure pathways through a local aquifer were identified for TCE, PCE and other byproducts of Union Carbide Corporation for which experimental toxicity estimates were unavailable. The chemicals included tetrachlorophthalic acid, tetrachlorophthalic anhydride, chlorendic anhydride, chlorendic acid, o-chlorostyrene, mchlorostyrene, p-chlorostyrene, α,β-dichlorostyrene, bis (4-chlorophenyl) sulfone, triallyl isocyanurate, 1,2-diphenylhydrazine diphenylamine, Nethyl-p-toluenesulfonamide, N-methyl-ptoluenesulfonamide, and styrene-acrylonitrile (SAN) trimer. ATSDR used (Q)SAR analysis to evaluate health effects of these chemicals. The evaluated endpoints included mutagenicity, carcinogenicity, developmental toxicity, rat oral LD50, and octanol water partition coefficient. The (Q)SAR analysis suggested that 9 of the 15 chemicals could be potential carcinogens, 6 had a potential for developmental toxicity, and 6 could cause genetic mutations. Based on the results of (Q)SAR analyses presented at the interagency workgroup, SAN trimer, which is formed by the condensation of two moles of acrylonitrile and one mole of styrene, has been nominated for toxicity testing by the National Toxicology Program (NTP). NTP genotoxicity tests confirmed the lack of mutagenicity of SAN trimer devised using (Q)SAR analysis, while a 2-year NTP carcinogenicity study is still under way (NTP, 2007). In both examples above, the (Q)SAR analyses were used to guide site specific environmental health assessments by providing rational prioritization of anticipated chemical hazards at that site. The (Q)SAR analysis was used because appropriate experimental toxicological information was unavailable. Otherwise, if the toxicological information is available and summarized in ATSDR toxicological profiles, which MRLs are used to screen multiple chemical hazards at a HWS to focus the health assessment on those chemicals that pose the greatest health risk at the site. For instance, if multiple completed exposure pathways are identified at the site, a screening assessment using MRLs can help focus the assessment on those pathways associated with the greatest health risk. This way (Q)SAR analysis is used as an adjunct to MRLs in public health practice. A question as to whether a specialized QSAR model, such as an HGV, LOAEL, or NOAEL QSAR model, can be developed that would provide a provisional MRL, rather than WOE (Q)SAR evaluation, remains debatable. ATSDR conducts active research in this area. Sketch calculations suggest the utility of QSAR estimates for both oral and inhalation MRL modeling (Demchuk et al., 2006). In these calculations approximately 70% correlation between the actual and estimated MRLs was observed using the TOPKAT® rat oral chronic LOAEL model as an engine for MRL modeling. Subsequent QSAR modeling of HGVs and rat LOAELs using high-quality toxicological information yielded even better results with correlation greater than 80% in best models (manuscript in preparation). However, the predictive power, and therefore the practical utility of these models, is a subject of suitably dense coverage of environmental chemical space with tested compounds within the applicability domain of the model. (Q)SAR Chemical Space The concept of chemical space is of prime importance in modern (Q)SAR modeling and in the analysis of results (Oprea and Gottfries,
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2001). It helps to define confidence in a (Q)SAR assessment and to formulate plausible hypotheses about toxicity. The concept of chemical space starts with a definition of similarity/dissimilarity measure, ideally, a metric distance. Chemical similarity can be defined using either purely structural features (exemplified by the JaccardTanimoto similarity coefficient), three-dimensional molecular fields (e.g., Carbo index), or using molecular descriptors. ATSDR experience and reports of others (Martin et al., 2002; Steffen et al., 2009) suggest that with respect to correlations with toxicological endpoints, the descriptor similarity usually outperforms purely structural or molecular field measures. A descriptor similarity distance implemented in TOPKAT® is one of similarity measures commonly used at the ATSDR. A TOPKAT® set of descriptors includes Kier and Hall electrotopological states (e-states) (Kier and Hall, 1986), shape, symmetry, molecular weight, and the octanol-water partition coefficient. Thus, the TOPKAT® similarity is a structure-based property-sensitive similarity measure. It reflects similarity of descriptors between two molecules with respect to a specific property or endpoint (Accelrys, 2004). TOPKAT® can be utilized to establish a degree of structureproperty similarity between a query chemical and compounds in the database. Once a valid toxicity assessment is obtained a “similarity search” may be conducted which determines database chemicals in vicinity of the query chemical in the chemical space (optimal prediction space in the TOPKAT® terminology) (Fig. 1). Database chemicals within a user defined distance of the query chemical are considered as potential surrogates, unless a TOPKAT® QSAR model falls short of estimating the actual toxicity of the database chemical (Accelrys, 2004). If the query chemical does not satisfy all model considerations and, thus, a valid prediction is not obtained, no surrogates are suggested. For the similarity distance between two chemicals (otherwise vectors in the chemical space), the smaller the distance, the greater the similarity. A TOPKAT® similarity distance of less than 0.25 has been suggested to be toxicologically representative (HDI, 1997). In other words, if a distance between two compounds is less than 0.25, then there is a good chance that the modeled toxicity endpoint takes on a similar value for these two compounds. Each compound within 0.25 distance of the query compound is suggested to be similar and can be used to support or refute the hypothesis. Case study III: Addressing substance-specific priority data needs To illustrate the concept of similarity in chemical space, let us consider a (Q)SAR assessment of carcinogenicity of p-cresol carried
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out by ATSDR as a part of Agency guidance to the EPA/ATSDR Test Rule and/or Voluntary Research Program. The initial (Q)SAR assessment suggested a 0.027 probability of p-cresol being a male rat carcinogen (negative). A similarity search on complete model database revealed 25 compounds within 0.25 normalized distance of p-cresol in the TOPKAT® chemical space. Four of them within 0.1 vicinity of p-cresol are shown in Fig. 1. They are resorcinol, 1,2-dichlorobenzene, phenol and 1,2-dichloropropane. Of 25 database compounds within the 0.25 vicinity, 22 are non-carcinogenic (NEG in the bottom left corner in Fig. 1) and three carcinogenic (POS in the bottom left corner) in male rats. TOPKAT® model was able to correctly determine the lack of carcinogenicity of 20 non-carcinogenic database compounds (NEG in the bottom right corner). For the two remaining database compounds TOPKAT® assessment was indefinite (IND in bottom right corner). Of the three male rat carcinogens (POS in the bottom left corner), two were incorrectly deemed by TOPKAT® as non-carcinogens (NEG in bottom right corner), but apparently they have not been used to train the model (NO in upper right corner). The third male rat carcinogen has been included in the training set of the model, but its assessment by TOPKAT® was indefinite. Thus, 80% of database compounds within the 0.25 distance of p-cresol and 100% of compounds within the 0.1 distance were correctly assessed by the model. According to the decision-analytic flowchart (Fig. 2) described in details in another ATSDR publication (El-Masri et al., 2002), these results suggest with very high confidence that p-cresol is non-carcinogenic in male rats. Case study IV: Health consultation for USCG More recently (Q)SAR similarity analysis was used to support a chemical-specific health consultation prepared by the ATSDR Division of Health Assessment and Consultation per request of the U.S. Coast Guard. USCG was interested in risk assessment of low-dose exposures to several halogenated benzenes. They included chloroethylbenzenes, chlorodimethylbenzenes, bromodimethylbenzenes and dichlorodimethylbenzenes. None of them had an established oral HGV, but ATSDR had developed oral MRLs for a number of similar compounds. A group of suspected analogs included: benzene, chlorobenzene, 1,3dichlorobenzene, 1,2-dichlorobenzene, 1,4-dichlorobenzene, hexachlorobenzene, ethylbenzene and dimethylbenzenes. Therefore, a question was how appropriate would it be to use information from the toxicological profiles for any of these compounds in health assessments of aforementioned halogenated benzene derivatives? To assess the degree of similarity we conducted (Q)SAR analysis using rat oral LD50 models of TOPKAT®. The chemical space of this
Fig. 1. A fragment of similarity search analysis using TOPKAT®.
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Similarity Analysis of p-cresol
Distance < 0.25
Actual and Predicted Values Match
Actual and Predicted Values do not Match
If 3 out of 4 Match
High Confidence
If 2 out of 4 Match
If less than 2 out of 4 Match
Low Confidence
No Confidence
Fig. 2. A diagram of confidence analysis of (Q)SAR results using TOPKAT® similarity scoring.
model is most densely populated in the area of substituted benzene derivatives, and its training set includes most of the analog chemicals in question. A TOPKAT® similarity analysis showed that the toxicological profile for benzene and benzene MRLs are unlikely to be a good surrogate for toxicological properties of the halogenated benzene derivatives of interest. For α-chloroethylbenzene (Fig. 3), monohalogenated benzenes (ranks 28–41, similarity distances 0.096– 0.1011) came out closer than ethylbenzene (78, 0.119). Similar results were obtained for another chloroethylbenzene isomer, (1-chloroethyl) benzene. For (1-chloroethyl)benzene monohalogenated benzenes (14– 20, 0.035–0.041) were also located closer in the chemical space than ethylbenzene (109, 0.114). Although monohalogenated benzenes and ethylbenzene both were within the similarity cut-off distance of 0.25, extrapolation of toxicological information from monohalogenated benzenes (ATSDR, 1990), as first choice, seem to be more appropriate because of their proximity to chloroethylbenzenes in the chemical space. Presence of ethylbenzene within the cut-off distance suggests that its toxicological information can be used for consensus verification of hypotheses constructed in the process of cross-chemical extrapolation from monohalogenated benzenes. Similarity (Q)SAR analysis applied to the halogenated dimethylbenzenes did not yield conclusive results. For the ortho-, meta-, and parasubstituted dichlorodimethylbenzenes, tetra-halogenated benzenes (ranks 3–5, similarity distance 0.118) were found to have greater property similarity than hexachlorobenzene (25, 0.267), which was the only analog chemical in the database for which a toxicological profile has been developed (ATSDR, 2002). Since the distance to hexachlorobenzene was in excess of cut-off of 0.25, no propositions in support of cross-chemical extrapolation from hexachlorobenzene were deemed worthy. For monochlorinated dimethylbenzenes none of the analog
Fig. 3. Isomers of chloroethylbenzene.
chemicals were found in the vicinity of query compounds. From the results of (Q)SAR analysis it was concluded that toxicological information on dichlorodimethylbenzenes represented a pending PDN, which is large in terms of chemical similarity/diversity. So, that even crosschemical extrapolation would not be productive in application to these chemicals. Conclusions (Q)SAR modeling is an important component of computational toxicology. In public health practice, (Q)SAR modeling helps ATSDR to conduct a scientifically defensible “express” analysis in situations when other laboratory and computational methods either are missing or ineffective, and where prior knowledge is scarce. The universality comes at a premium: the capabilities of (Q)SAR analysis depend on the database of the model, and appropriate confidence limits on the results are often uncertain. (Q)SAR methodology is mechanistic in nature. However, human toxicology is a complex topic involving disparate modes of action and comprised of systems of disparate yet interconnected mechanisms for which not every chemical qualifies for each step on the pathway of mechanisms. Although, SAR analysis is a quite developed method, and a variety of modeling options exist in the area of hazard identification, QSAR modeling in the area of risk characterization, especially in the low-dose region of the dose–response curve, still represents a significant challenge, since endpoints are not as clearly resolved as discrete. (Q)SAR modeling in public health practice is often driven by the need for a rapid response for public health action in a timely manner. Such a rapid response may of necessity occur under constraints of toxicological data gaps and in the presence of unmet Agency PDNs required to protect human populations from exposure to hazardous environmental contaminants. These conditions entail high expectations about reliability of (Q)SAR analysis. At present the reliability is achieved by means of the WOE approach, which utilizes a consensus and knowledge-driven approach towards interpretation of results. Even so, further improvements of (Q)SAR modeling in mammalian toxicology are likely to be driven by both the development of public domain and open source software resources, accumulation of toxicological information for additional chemicals, and careful attribution of these data with respect to quality. Quality attribution of the underlying data (LOAELs, NOAELs and BMDs) is expected to focus QSAR modeling at most reliable information and thus translate quality improvements in the training set structure into improved quality of the model. Since at present the database of HGVs is fairly undersized by
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QSAR standards, expansion of the database of high-quality HGVs (such as MRLs and RfDs) represents the major research need in the area of QSAR modeling of mammalian toxicity. Appropriate population and expansion of the environmental-chemistry-relevant chemical space with (Q)SAR-prone data pertinent to low-dose mammalian toxicology represents an important objective of current research that has a potential for significantly improving the level of service in public health practice. Conflict of interest disclosure statement Neither author has a conflict of interest or a financial relationship with a commercial entity that has an interest in the subject of this publication. Disclaimer The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Agency for Toxic Substances and Disease Registry and should not be construed to represent any Agency determination or policy. Mention of trade names or other proprietary information is made for the convenience of the reader and is not an endorsement of any commercial product.
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