Expanding the Toolbox: Hazard-Screening Methods and Tools for

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Expanding the toolbox: Hazard-screening methods and tools for identifying safer chemicals in green product design Joel M Cohen, James William Rice, and Thomas A. Lewandowski ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03368 • Publication Date (Web): 05 Dec 2017 Downloaded from http://pubs.acs.org on December 24, 2017

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ACS Sustainable Chemistry & Engineering

Expanding the toolbox: hazard-screening methods and tools for identifying safer chemicals in green product design Joel M. Cohen1, James W. Rice1, Thomas A. Lewandowski2*

1. Gradient, 20 University Rd, Cambridge, MA, USA 2. Gradient, 600 Stewart Street, Suite 1900, Seattle, WA, USA *corresponding author, 600 Stewart Street, [email protected], 206-267-2920

Suite

1900,

Seattle,

WA

98101,

Running title: Expanding the toolbox Keywords: read-across; skin sensitization; in silico; green product design; safer chemicals

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ACS Paragon Plus Environment

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Abstract A key focus of green product design is to reduce the product's inherent chemical hazard. Various alternative assessment methodologies may be used to compare the hazard properties of possible candidate chemicals. However, only a small fraction of the chemicals currently in commercial use are adequately characterized in terms of toxicological effects. This limitation can hamper the study of safer chemical alternatives and increase the likelihood of regrettable substitutions. Approaches for addressing such data gaps include read-across, in silico programs and high throughput in chemico and in silico assays. Each of these show considerable promise although a consensus on how to use them for hazard evaluation of data poor chemicals is lacking. The limitations of such tools, which attempt to simplify complex biology into key predictive factors, is also often underestimated. To evaluate currently available approaches for addressing data gaps, we established three test sets of chemicals, each with structural similarity to a target chemical (target chemical 1: 4-phenylenediamine, target chemical 2: hydroxyethylacrylate, target chemical 3: methylisothiazolone). We first compared results from the in silico programs Toxtree and Derek Nexus™ with animal test data obtained using standard assays. We then compared chemical similarity scores calculated by two computational tools Toxmatch and ChemMine. Lastly, we refined our test sets by applying a series of exclusion criteria, including in silico analysis and physicochemical data relevant for skin sensitization (e.g. molecular weight, water solubility, and vapor pressure). The in silico programs in combination exhibited a sensitivity of 92%, and specificity of 88%. Toxmatch and ChemMine demonstrated good agreement in their similarity score rankings across the three test sets (TS1: W=0.74, p=0.014; TS2: W=0.72, p=0.067; TS3: W=0.87, p=0.095). Narrowing our chemical test sets using physical chemical properties and in silico evaluation improved the overall accuracy of our read-across approach compared with the initial unrefined test sets (TS1: 56% improved to 100%; TS2: 54% to 100%; TS3: 50% to 100%). Our findings support the development of robust readacross approaches incorporating available data-gap filling tools to help conduct screening level alternatives assessments and identify safer chemicals as part of green product design.

Introduction Green product design aims to focus on developing products with minimal impact to human health or the environment. A key focus of this practice is to reduce the product's inherent chemical hazard. Various alternative assessment methodologies may be used to compare the hazard properties of possible candidate chemicals. However, only a fraction of the chemicals currently in commercial use have complete toxicological data packages.1,2 At the substance or product design stage, newly developed chemicals and materials will lack toxicological test data, making it difficult to compare potential hazards with existing alternatives. Furthermore, at this stage there is rarely a sufficient amount of high purity chemical available to conduct adequate toxicity testing across a range of doses and hazard endpoints. Additional challenges in obtaining complete data arise from the cost and time required for testing. For example, conducting a chronic (i.e. 2-year) exposure rat study by the oral route (far easier and cheaper than by inhalation) can cost 2 to 4 million dollars and will take up to several years to plan, carry out, evaluate tissues and finalize the report.3 Efforts are underway to develop faster, less costly but more sophisticated test methods based on improved understanding of the disease process, as characterized by the Adverse Outcome Pathway (AOP).4 As AOPs are developed and validated for various toxicological pathways, toxicologists illuminate the molecular initiating events, key events, and key event relationships that underlie biological processes resulting in clinically relevant adverse health outcomes. Such approaches may offer a reliable alternatives according to the 3R concept of replace, reduce and refine, as related to animal testing. Even so, the process of getting these new test methods accepted has proven remarkably slow.5,6 In addition, while it is appropriate to commission a full data package for chemicals

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intended for widespread production and use, this is rarely practical for initial screening of possible chemical candidates in the context of an alternative assessment for green product design. Read-across One approach for addressing data gaps is read-across. It is based on the idea that similar chemicals are likely to behave similarly in the body. It aims to extrapolate (either qualitatively or quantitatively) from toxicity data obtained for well studied chemicals (source chemical) to make predictions for similar chemicals where data are lacking (target chemicals). The idea behind read-across is well established in toxicology and risk assessment, dating back to at least the development of toxicity equivalence factors (TEFs) for dioxins and polycyclic aromatic hydrocarbons (PAHs).7 As this method has seen increased use, various best practice guidance has been proposed to standardize read-across and ensure that decisions are made with at least a basic level of validity.8-10 Regulatory and governmental research bodies have also issued technical guidance documents aiming to make regulatory submissions based on read-across more consistent and robust.11-13 Recent EU guidance has suggested that for chemical registration purposes, full chemical characterization and metabolic studies are required.12,14 This may be suitable when the goal is to register a new chemical to permit widespread use but this clearly will be problematic for different decision making contexts (i.e. evaluation of chemical alternatives for green product design). In light of the above, general consensus is still lacking on the extent and type of evidence sufficient to support read-across for a given endpoint. A critical element of read-across involves the selection of possible source chemicals. In decades past this has been a very informal process conducted by visual examination of chemical structures. With the expanding use of read-across it has become clear that a more robust selection and justification process for source chemicals is required. Computational approaches for chemical similarity Traditionally, risk assessors have judged chemical similarity based on visual inspection of chemical structures and professional judgment. However, quantitative approaches for estimating chemical structural similarity are increasingly seen as more objective and transparent tools for comparing the suitability of various source chemicals for toxicological read-across. Correlation-like similarity indices can range from 0 (completely dissimilar) to 1 (completely similar). One such approach identifies the maximum common substructure (MCS) shared between a pair of chemicals. The size of the two chemicals overall relative to the size of the MCS is then used to calculate a structural similarity coefficient.15 Another approach calculates a Tanimoto coefficient (T), defined as the ratio: T= c / (a + b + c)

(1)

where "a" is the number of structural features present in the target chemical but not in the source chemical, "b" is the number of features present in the source chemical but not the target, and "c" is the number of structural features common to both the target and the source chemical.16 Various computational programs can easily calculate either the MCS or Tanimoto coefficient, to help selection of suitable source chemicals (Toxmatch, ChemMine, etc.). Not all chemicals with similar structures will behave similarly in biological systems and slight changes in structure may engender differences in physical/chemical properties that may be quite significant for exposure or toxicity.17 Along these lines, there is much discussion regarding the possibility of a Tanimoto score cutoff, below which structural similarity is not likely to correspond with biological reactivity or toxicity. However, analysis of 19,533 compounds tested across one or more of 115 high throughput bioactivity assays concluded that there is only a 30% chance that two chemicals with a T>0.85 will exhibit similar bioactivity.18 Structural similarity scores must therefore be considered within the context of a weight-of-evidence evaluation where other relevant properties (e.g. factors that might affect the ability of a chemical to reach its biological target) are considered. 3




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Skin sensitization: a case study for endpoint specific read-across Due to the variety and complexity of biological activity underlying different toxicity mechanisms, readacross is best applied specific to a particular endpoint of interest (i.e. reproductive toxicity, dermal irritation, etc.). A recent review of the European Chemicals Agency (ECHA) REACH dossier submissions determined that of 6,186 unique substances, 20% (1,255 substances) were assigned hazard code H317 "May cause allergic skin reaction," and 70% (4,317 substances) have "conclusive but not sufficient data for classification".19 Skin sensitization is also one of the few toxicological endpoints with a well established AOP.20 In brief, the skin sensitization AOP involves 1) dermal penetration of the chemical through the stratum corneum, followed by 2) covalent interactions between the chemical and skin proteins referred to as haptenization (molecular initiating event), 3) activation, maturation and mobilization of epidermal dendritic cells (i.e. Langerhans cells) towards the lymph node, as well as activation of keratinocytes and induction of the cyto-protective gene pathway (cellular response), 4) antigen presentation and proliferation of T lymphocytes in the lymph node (organ response), and 5) upon re-exposure (elicitation), inflammation, erythema and edema (organism response) (see Figure 1).

Computational in silico tools for chemical hazard assessment The use of in silico computational programs to make inferences about the properties of chemicals is well established in toxicology and related fields.21 The basic concept is that particular molecular components or structures will confer similar properties when present in otherwise different molecules. Thus for example, hydroxyl (-OH) groups may increase a chemical's reactivity with biological molecules and may also increase the rate of elimination. In its simplest form, a in silico programs make a qualitative prediction that a chemical with a particular substructure will produce a biological effect. Quantitative structure-activity relationships (QSARs) take this process further and express that prediction in numerical terms (i.e. probability). This allows the user to compare multiple chemicals by their relative probabilities to have an adverse effect or to set cut off limits beyond which the risk is considered excessive (an approach used frequently in drug development). In silico programs may have a list of chemical substructures that are considered potentially problematic and evaluate new chemicals to see if these are present (or could be present with metabolism). Alternatively they may have a 'training set' of chemicals that are known to exhibit a particular toxic effect (e.g. developmental toxicity) and then make quantitative comparisons of the chemical of interest to the members of the training set. Hybrids of these two approaches are also in use. While some in silico programs are sold under licensing agreements (e.g. Derek NexusTM, TIMES-SS), others are freely available (Toxtree, CAESAR). As with any predictive tool, there is a need with in silico programs to minimize both false positives (identifying a chemical as a problem when it isn't) and false negatives (identifying a chemical as not a problem when in fact it is). The goal for in silico is to be as predictive as in vivo testing (which itself generates some false positives and negatives when compared to human experience). Predictive ability does show improvement by using multiple in silico programs in combination.22,23 In silico programs have been most successfully applied for specific health concerns such as toxicity to mutagenicity, genotoxicity, or allergic skin sensitization. Their use for other endpoints (e.g. carcinogenicity, developmental toxicity) has been more limited.24 In silico programs may also be used as a contributing element supporting a readacross analysis. The current opinion is that in silico predictions are not suitable as standalone replacements for animal or human data, especially for complex regulatory endpoints such as developmental toxicity or sensitization.25 Read-across approaches for skin sensitization Despite advancements in sensitization hazard assessment, currently available testing strategies do not address situations where one needs an affordable evaluation right away (e.g. cases of product adulteration, emergency response or accidental release scenarios, or first pass screening for chemical alternatives 4



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assessment). Based on the high number of known chemical sensitizers and the well characterized biological processes underlying skin sensitization, read-across is a particularly useful tool for weight-ofevidence hazard assessment of data-poor chemicals. Specific physical-chemical parameters related to the well characterized AOP may help inform assessment of skin sensitization hazard potential as well as identify source chemicals suitable for read-across. The first step of the skin sensitization AOP involves the ability of a substance to penetrate through the stratum corneum. For example, skin penetration is expected to be largely based on the hydrophilicity (often characterized as the log of the octanol-water partition coefficient log Kow or the water solubility) as well as the size of a compound (often characterized by molecular weight) .26 Vapor pressure may also impact the length of time a chemical will remain on the epidermis before either penetrating the stratum corneum or volatilizing away from the skin surface. It is important to note the physical state of a given substance (e.g. solid vs. liquid) can also affect dermal penetration,27 though for the sake of this study we focus on assessing the sensitization potential of chemicals in liquid form at room temperature. A robust read-across approach should include consideration of such physicochemical properties and the resulting impacts on skin permeability. Objectives This study aimed to evaluate the utility of various data gap filling tools (i.e. in silico programs) and approaches (i.e. read-across) for rapid hazard screening, a critical step in any alternatives assessment or green product design program. We first evaluated the predictive accuracy of two in silico programs (Toxtree, Derek Nexus™) used separately or in a complimentary fashion for comparison against the animal toxicity data for a set of 94 structurally distinct chemicals, all with well characterized skin sensitization hazard data (either human patch test data or guideline toxicity studies in guinea pigs or mice). We next conducted three read-across case studies looking at the dermal sensitization potential of three target chemicals with substantially different structures (target chemical 1: 4-phenylenediamine, target chemical 2: hydroxyethylacrylate, target chemical 3: methylisothiazolone, see Figure 2), and each a well-known and well-characterized skin sensitizer. For the three target chemicals, we generated a broad set of possible source chemical chemicals that also possessed in vivo data on sensitizing potential. We then evaluated the suitability of two freely available computational tools for quantitatively estimating chemical structural similarity (Toxmatch and ChemMine), an important first step in selecting suitable source chemicals for read-across. To evaluate the validity of various read-across approaches we posed the hypothetical scenario where the three target chemicals did not have any sensitization hazard data and relied instead on read-across data from sets of seemingly suitable source chemicals. We defined readacross accuracy as the percentage of chemical source chemicals within a specific test set for which animal toxicity data (i.e. skin sensitizer vs. non-sensitizer) matched the animal toxicity data for the respective target chemical. Lastly, we investigated whether narrowing the source chemical test sets based on readily obtainable physicochemical criteria (i.e. quantitative similarity scores, structural alerts, relevant chemical properties) would improve read-across accuracy. Methods Case studies: Three distinct chemical test sets To evaluate the suitability of various read-across tools and approaches we selected three chemically distinct target chemicals with comprehensive physicochemical and sensitization hazard characterization data: 4-phenylenediamine (target chemical 1, TC1), hydroxyethylacrylate (target chemical 2, TC2), and methylisothiazolone (target chemical 3, TC3). We then established three corresponding test sets for TC1, TC2 and TC3. Each test set was selected from chemicals listed in readily available databases of skin sensitization hazard data (e.g. the National Toxicology Program NICEATM LLNA database, among others).28-38 Most chemicals within each test set exhibit broadly similar structural features to the respective target, and varying apparent suitability for read-across. A few seemingly less suitable source chemicals were included in each test set to see how our method dealt with poor source chemical choices. However, in an effort to eliminate objectively poor read-across candidates with low similarity to the target 5




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chemical, we excluded chemicals with low similarity scores (ChemMine score 500 or Kow500 and MW 0.4, applying exclusion rules 1-4 to TS1 would be reduced to only 4 viable source chemicals, all of which match the skin sensitization hazard of the target chemical. If the criteria were modified to exclude chemicals with molecular weight >1.5x the CoC, applying exclusion rules 1-4 to TS1 would be reduced

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to only 17 viable source chemicals, all of which match the skin sensitization hazard of the target chemical. One limitation of this approach is the assumption that there are potential source chemicals with reliable toxicological data readily available for many or most chemicals of interest. This may not always be the case for unusual, large or complex compounds, in which case generation of data via guideline animal testing assays may be necessary. It is also important to note here that LLNA and guinea pig data can sometimes offer conflicting results, even for the same chemical. One important consideration in weighing conflicting results is the specific exposure concentrations tested. For example, a study with a negative result may have only tested a few relatively low doses, without testing concentrations high enough to induce a sensitization response. Another factor to consider is the biological realism of a specific assay. For example, the GPMT protocol is highly aggressive in that it includes a step that primes the immune system by injecting adjuvant and test item into animals, a step that is absent in human exposure situations. As a result, the GPMT offers a more conservative sensitization assessment than the Buehler guinea pig assay. It may also be better at detecting weak sensitizers than the LLNA51 although again at the risk of potentially overpredicting human responses. Considering some of these study details in instances where data give conflicting results is therefore important, although this can require additional time. That additional effort can be minimized by maintaining accurate chemical databases that are transparent about their hazard assessment conclusions. Furthermore, the 3 case studies presented here involve target chemicals that are known sensitizers. It may be useful to investigate similar case studies using nonsensitizing chemicals. This however presents the challenge where source chemical suitability would be based on the absence of structural alerts, which is a less specific criterion than two chemicals sharing an in silico alert. Only two in silico programs were investigated here in combination. Future work could investigate additional software options in combination. However, such an approach may prove needlessly expensive as many such programs have licensing fees. Furthermore, other groups have recently evaluated the performance of in silico tools [23, 49].23,52 Most recently, Verheyen et al. evaluated skin sensitization predictions across various in silico tools (Vega, CASE Ultra, OECD Toolbox, Toxtree, Derek Nexus), for a test set of 160 chemicals with experimental data for skin sensitization hazard.52 The authors determined Derek Nexus had the highest predictive accuracy (78%), followed by Case Ultra (73%), the OECD Toolbox (60%), and Vega (48%). Using various combinations of the various models improved the predictive accuracy, further supporting combination approaches such as what we present here. Toxtree identified all three target chemicals as Michael acceptors, a relatively broad class of sensitizers. Note, 1,4-phenylenediamine triggers a Michael acceptor alert in Toxtree because the oxidation reaction product (a quinone diimine) is capable of acting via Michael addition.53 Studies suggest the Michael acceptor alert may be associated with less potent or even non-sensitizing chemicals,54 and that reactivity of Michael acceptors can be greatly influenced by the presence of other functional groups present on the chemical of concern that are in proximity to the electrophilic moiety.55 In light of these limitations, approaches, such as the one we present here, that consider multiple in silico program evaluations and also consider physical chemical properties related to the endpoint of interest, can increase the overall specificity of hazard assessments for data-poor chemicals. Beyond currently available in silico tools, one could also include the results of in chemico or in vitro data. Recently developed and validated in chemico assays such as the direct peptide reactivity assay (DPRA)55 and in vitro cellular models such as the ARE-Nrf2 Luciferase Test Method (KeratinosensTM),57 and the human cell line activation test ((h-CLAT),58 are gaining increased acceptance. Each non-animal testing method also corresponds to specific stages along the AOP, with the h-CLAT and KeratinosensTM measuring isolated cellular responses of LCs and keratinocytes respectively (step 3: cellular response), while the DPRA assay measures haptenization (step 2: molecular initiating event). While such assays are 10



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faster and less expensive than traditional animal testing, they provide only limited supporting evidence of sensitization hazard and no direct link to human exposure concentrations. For regulatory purposes, ECHA states such results should not be used in isolation, but rather used in combination within a Weightof-Evidence approach.59 Hopefully, as alternative testing strategies gain increasing acceptance, in vitro data sets for otherwise untested chemicals will continue to increase and be made publically available. It is important to note that the specific approach proposed here is viable for skin sensitization only. Readacross approaches for different toxicological endpoints (reproductive toxicity, carcinogenicity) will depend largely on their respective AOPs. The AOP-Wiki, a joint effort by the European Commission and US Environmental Protection Agency (EPA), aims to encourage researchers to propose endpoint specific AOPs along with supporting toxicological evidence.60 The toxicological community can then review the proposed AOP and provide feedback through an iterative process towards formally establishing an accepted and peer reviewed AOP. To date only a few AOPs are established and accepted within the toxicological community. We present here three distinct case studies for skin sensitization hazard assessment only. Future efforts will involve evaluating our proposed approach against additional test sets of chemicals that are structurally distinct from those presented here. We also aim to investigate possible approaches for endpoints beyond skin sensitization, based on the best available knowledge regarding mode of action and AOP. Acknowledgments, including grant information: The authors declare that over the past five years they have conducted work for consumer products manufacturers related to skin sensitization quantitative risk assessment. The work presented here was conducted independently of those matters with no external funding. Conflict of interest statement: The authors declare they have no actual or potential competing financial interests. Correspondence address: 600 Stewart Street, Suite 1900, Seattle, WA 98101, [email protected], 206-267-2920 Supporting Information Supplementary Table S1: Chemical Test Set 1 Supplementary Table S2: Chemical Test Set 2 Supplementary Table S3: Chemical Test Set 3

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704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723


Table legends Table 1. Exclusion Criteria for Identifying Chemicals Suitable for Read-across Table 2. In silico Accuracy for Predicting Sensitization Hazard Table 3. Statistical Analyses of Structural Similarity Rank Orders from Toxmatch vs ChemMine Table 4. Read-across Accuracy for Chemical Test Sets Figure Captions Figure 1. Adverse Outcome Pathway for Skin Sensitization Figure 2. Target Chemical Structures Figure 3. Plot of ChemMine and Toxmatch similarity scores. Data are for TS2, similar results were observed for the other 2 test sets.

17




724

Page 18 of 25

Table 1. Exclusion Criteria for Identifying Chemicals Suitable for Read-across 1

Property Molecular weight

Exclusion Criteria > 2x the target chemical

2

Water solubility

≤ 1 g/L

3

Vapor pressure

≥ 2,000 x the target chemical

4

MCS similarity score

> 0.1

5

In silico alert for sensitization

Alerts reported by Toxtree and Derek Nexus™ similar to the target chemical

Comment Related to a chemical's ability to penetrate the skin and interact with immune cells Related to hydrophilicity and the ability to penetrate the stratum corneum Indication of whether a chemical is likely to volatilize before it can penetrate the skin Structural similarity may relate to biological reactivity Indication of similar mechanism of action for sensitization toxicity

725 726

18



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727


Table 2. In silico Accuracy for Predicting Sensitization Hazard Statistical Measure

Toxtree Derek Nexus™

Toxtree and Toxtree or Derek Nexus™ Derek Nexus™

Sensitivity

85%

87%

79%

92%

Specificity

76%

63%

51%

88%

728 729 730 731

19




732 733

Page 20 of 25

Table 3. Statistical Analyses of Structural Similarity Rank Orders from Toxmatch vs ChemMine Statistical Measure Kendall's Coefficient of Concordance (K) Statistical Significance (p)

TS-1

TS-2

TS-3

n = 55

n = 28

n=8

0.74

0.72

0.87

0.014

0.067

0.095

734 735

20



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736


Table 4. Read-across Accuracy for Chemical Test Sets Read-across Accuracy Full test set Refined test set (exclusion criteria 1-4) Refined test set (exclusion criteria 5) Refined test set (exclusion criteria 1-5)

TS-1

TS-2

TS-3

56% (n = 55) 64% (n=39) 100% (n=11) 100% (n=9)

54% (n = 28) 60% (n=20) 88% (n=16) 100% (n=11)

50% (n = 8) 43% (n=7) 100% (n=4) 100% (n=3)

737 738

21




739 740 741

Page 22 of 25

Figure 1. Adverse Outcome Pathway for Skin Sensitization

742 743

22



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744


Figure 2. Target Chemical Structures

745 746

23




747 748

Figure 3. Plot of ChemMine and Toxmatch similarity scores. Data are for TS2, similar results were observed for the other 2 test sets. 1 0.9 0.8 0.7

ToxMatch Similarity Score

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.1

0.2

0.3

0.4 0.5 0.6 ChemMine Similarity Score

0.7

0.8

0.9

1

749 750

24



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751 752

753 754 755 756 757


For Table of Contents Use Only

Synopsis: Developing and refining approaches and computational tools for addressing chemical hazard data gaps will help to identify safer chemicals for use in green product design.

25



Expanding the Toolbox: Hazard-Screening Methods and Tools for

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