Hazard-Screening Methods and Tools for ... - ACS Publications

Subscriber access provided by UNIV OF OREGON

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Sustainable Chemistry & Engineering is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 25 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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

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

1

ACS Paragon Plus Environment

C:\ProgramData\activePDF\DC_ENT\Tmp\4b9a58\2572817_File000006_45510954.docx

ACS Sustainable Chemistry & Engineering 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

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 61 62 63 64 65 66

Page 2 of 25

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

2



Page 3 of 25 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

67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117


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




118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168

Page 4 of 25

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



Page 5 of 25 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

169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219


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




220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270

Page 6 of 25

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

9




423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473

Page 10 of 25

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



Page 11 of 25 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

474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514


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

11




Page 12 of 25

515 516 517

References 1. Hansson SO, Ruden C. Towards a theory of tiered testing. Regul Toxicol Pharmacol 2007, 48:3544.

518 519

2. Knight A, Bailey J, Balcombe J. Animal carcinogenicity studies: 1. poor human predictivity. Altern Lab Anim 2006a, 34:19-27.

520 521

3. Manuppello J, Willett C. Longer rodent bioassay fails to address 2-year bioassay's flaws (letter). Environ Health Perspect 2008, 116:A516-A517, doi: 10.1289/ehp.11964

522 523 524

4. Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH, LaLone CA, et al. Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci 2014, 142:312-320, doi: 10.1093/toxsci/kfu199.

525 526

5. Knight A, Bailey J, Balcombe J. Animal carcinogenicity studies: 3. alternatives to the bioassay. Altern Lab Anim 2006b, 34:39-48.

527 528 529 530

6. Adeleye Y, Andersen M, Clewell R, Davies M, Dent M, Edwards S, et al.Implementing Toxicity testing in the 21st century (TT21C): making safety decisions using toxicity pathways, and progress in a prototype risk assessment. Toxicology 2015, 332:102-111, doi: 10.1016/j.tox.2014.02.007.

531 532 533

7. Safe S. Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs), and related compounds: environmental and mechanistic considerations which support the development of toxic equivalency factors (TEFs). Crit Rev Toxicol 1990, 21:51-88.

534 535

8. Blackburn K, Stuard SB. A framework to facilitate consistent characterization of read-across uncertainty. Regul Toxicol Pharmacol 2014, 68:353-362, doi: 10.1016/j.yrtph.2014.01.004.

536 537 538

9. Schultz TW, Amcoff P, Berggren E, Gautier F, Klaric M, Knight DJ, et al. A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol 2015, 72:586-601, doi: 10.1016/j.yrtph.2015.05.016.

539 540

10. Ball N, Cronin MTD, Shen J, Adenuga MD, Blackburn K, Booth ED, et al. Toward good readacross practice (GRAP) guidance. ALTEX 2016, 33:149-66, doi: 10.14573/altex.1601251.

541 542 543 544 545 546 547

11. Guidance on grouping of chemicals, second edition. OECD (Organisation for Economic Cooperation and Development), Environment Directorate, Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. ENV/JM/MONO(2014)4; OECD Environment Health and Safety Publications Series on Testing and Assessment; Paris, 2014, No. 194. 141p. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono%282014 %294&doclanguage=en [23 December 2014].

548 549 550

12. Read-across assessment framework (RAAF); ECHA (European Chemicals Agency). Helsinki, Finland, 2017, doi: 10.2823/619212. ECHA-17-R-01-EN. http://www.echa.europa.eu/documents/10162/13628/raaf_en.pdf [16 February 2017].

551 552 553 554

13. Category approaches, read-across, (Q)SAR. ECETOC (European Centre for Ecotoxicology and Toxicology of Chemicals), Technical Report No. 116. Brussels, 2012, 192p. http://www.ecetoc.org/wp-content/uploads/2014/08/ECETOC-TR-116-Category-approachesRead-across-QSAR.pdf

555 556 557 558

14. Use of "Read-Across" for Chemical Safety Assessment under REACH: Workshop Report. ECHA (European Chemicals Agency), Cefic - European Chemical Industry Council, Long-range Research Initiative (LRI). 2012, http://cefic-lri.org/wp-content/uploads/2014/03/ECHA-CeficLRI-Read-across-Workshop-Report_171211-FINAL.pdf 12



Page 13 of 25 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


559 560 561

15. Cao Y, Jiang T, Girke T. A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics 2008, 24:i366-i374, doi: 10.1093/bioinformatics/btn186.

562 563

16. Bajusz D, Racz A, Heberger K. Why is Tanimoto index an appropriate choice for fingerprintbased similarity calculations? J Cheminform 2015, 7:20, doi: 10.1186/s13321-015-0069-3.

564 565 566 567

17. Wu S, Blackburn K, Amburgey J, Jaworska J, Federle T. A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments. Regul Toxicol Pharmacol 2010, 56:67-81, doi: 10.1016/j.yrtph.2009.09.006.

568 569

18. Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar biological activity? J Med Chem 2002, 45:4350-4358, doi: 10.1021/jm020155c.

570 571 572

19. Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. ALTEX 2016, 33:95-109, doi: 10.14573/altex.1510052.

573 574 575 576 577 578 579

20. The adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins. Part 1: scientific evidence. Environment Directorate, Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. ENV/JM/MONO(2012)10/PART1; OECD Environment Health and Safety Publications Series on Testing and Assessment Paris, 2012 No. 168. 59p. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2012)10/ part1&doclanguage=en

580 581 582

21. Auer CM, Nabholz JV, Baetcke KP. Mode of action and the assessment of chemical hazards in the presence of limited data: use of structure-activity relationships (SAR) under TSCA, Section 5. Environ Health Perspect 1990, 87:183-197.

583 584 585

22. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 2003, 111:1361-1375, doi: 10.1289/ehp.5758.

586 587 588

23. Teubner W, Mehling A, Schuster PX, Guth K, Worth A, Burton J, et al. Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance. Regul Toxicol Pharmacol 2013, 67:468-485, doi: 10.1016/j.yrtph.2013.09.007.

589 590 591 592

24. Mangiatordi GF, Alberga D, Altomare CD, Carotti A, Catto M, Cellamare S, Gadaleta D, Lattanzi G, Leonetti F, Pisani L, Stefanachi A, Trisciuzzi D, Nicolotti O. Mind the gap! A journey towards computational toxicology. Mol Inform. 2016, 35(8-9): 294-308. doi:

593 594 595

25. Patlewicz G, Ball N, Becker RA, Booth ED, Cronin MT, Kroese D, et al. Read-across approaches - misconceptions, promises and challenges ahead. ALTEX 2014a, 31:387-396, doi: 10.14573/altex.1410071.

596 597 598

26. Miller MA, Kasting GB. 2010. Toward a better understanding of pesticide dermal absorption: diffusion model analysis of parathion absorption in vitro and in vivo. J Toxicol Environ Health A 73:284-300, doi: 10.1080/15287390903249230.

599 600

27. Riviere JE, Monteiro-Riviere NA. Eds. Dermal absorption models in toxicology and pharmacology. CRC Press: Boca Raton, FL. 2006.

601 602

28. NICEATM LLNA database. Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM). National Toxicology Program (NTP), Washington D.C., 2013;

10.1002/minf.201501017

13




603 604

http://ntp.niehs.nih.gov/iccvam/methods/immunotox/niceatm-llnadatabase-23dec2013.xls July 2014].

Page 14 of 25

[25

605 606 607

29. Natsch A, Gfeller H, Haupt T, Brunner G. Chemical reactivity and skin sensitization potential for benzaldehydes: can Schiff base formation explain everything? Chem Res Toxicol 2012, 25:22032215, doi: 10.1021/tx300278t.

608 609 610

30. Natsch A, Emter R, Gfeller H, Haupt T, Ellis G. Predicting skin sensitizer potency based on in vitro data from KeratinoSens and kinetic peptide binding: global versus domain-based assessment. Toxicol Sci 2015, 143:319-332, doi: 10.1093/toxsci/kfu229.

611 612

31. ECHA advanced search for chemicals. ECHA (European Chemicals Agency). 2017, https://echa.europa.eu/home [21 November 2017].

613 614 615

32. Hazardous substances data bank (HSDB) record for N-N-dimethylaniline (CAS No. 121-69-7), NLM (National Library of Medicine). 2015. https://toxnet.nlm.nih.gov/cgibin/sis/search/a?dbs+hsdb:@term+@DOCNO+1179

616 617 618

33. Robust summaries & test plans: monocyclic aromatic amines. Monocyclic Aromatic Amines and Nitroaromatics Panel. ACC (American Chemistry Council). Submitted to US EPA, High Production Volume (HPV) Chemical Challenge Program. 2001, AR201-13310B. 507p.

619 620 621 622 623

34. Inventory multi-tiered assessment and prioritisation (IMAP): human health tier ii assessment for 2-hydroxypropyl methacrylate (CAS No. 923-26-2). NICNAS (Australia, Dept. of Health, National Industrial Chemicals Notification and Assessment Scheme). 2017. https://www.nicnas.gov.au/chemical-information/imap-assessments/imap-group-assessmentreport?assessment_id=855 [8 February 2017].

624 625 626

35. Methyl vinyl ketone. doi: 10.1002/3527600418.mb7894e0009. In: The MAK Collection for Occupational Health and Safety. Wiley-VCH Verlag GmbH & Co. KGaA, 1998, Weinheim, Germany. 11p.

627 628 629

36. Golla S, Madihally S, Robinson RL Jr., Gasem KA. Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction. Toxicol In Vitro 2009, 23:454-465, doi: 10.1016/j.tiv.2008.12.025.

630 631 632 633

37. Opinion concerning an initial list of perfumery materials which must not form part of fragrances compounds used in cosmetic products (final). SCCNFP (European Commission Health & Consumer Protection Directorate-General, Scientific Committee on Cosmetic Products and NonFood Products Intended for Consumers). 2000. SCCNFP/0320/00.

634

38. Proposed re-evaluation decision: octhilinone. Health Canada. 2015. PRVD2015-11. 36p.

635 636

39. Toxtree - Toxic hazard estimation by decision tree approach (Version 2.6.13). Ideaconsult Ltd. 2015. http://toxtree.sourceforge.net [19 December 2016].

637 638 639

40. Langton K, Patlewicz GY, Long A, Marchant CA, Basketter DA. Structure-activity relationships for skin sensitization: recent improvements to Derek for Windows. Contact Dermatitis 2006, 55:342-347, doi: 10.1111/j.1600-0536.2006.00969.x.

640 641

41. Toxmatch. Ideaconsult Ltd. 2016. https://eurl-ecvam.jrc.ec.europa.eu/laboratoriesresearch/predictive_toxicology/qsar_tools/toxmatch [17 October 2016].

642 643

42. Backman TW, Cao Y, Girke T. ChemMine tools: An online service for analyzing and clustering small molecules. Nucleic Acids Res 2011, 39:W486-W491, doi: 10.1093/nar/gkr320.

644 645 646

43. Gaddis GM, Gaddis ML. Introduction to biostatistics: part 5, statistical inference techniques for hypothesis testing with nonparametric data. Ann Emerg Med 1990, 19:1054-1059, doi: 10.1016/S0196-0644(05)82571-5. 14



Page 15 of 25 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


647 648 649 650 651

44. Workshop on integrated approaches to testing and assessment. OECD (Organisation for Economic Co-operation and Development). 2008. ENV/JM/MONO(2008)10; OECD Series on Testing and Assessment No. 88. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2008)10 &doclanguage=en [22 November 2016].

652 653 654 655

45. Patlewicz G, Kuseva C, Kesova A, Popova I, Zhechev T, Pavlov T, et al. Towards AOP application - Implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization. Regul Toxicol Pharmacol 2014b, 69:529-545, doi: 10.1016/j.yrtph.2014.06.001.

656 657 658

46. Ezendam J, Braakhuis HM, Vandebriel RJ. State of the art in non-animal approaches for skin sensitization testing: From individual test methods towards testing strategies. Arch Toxicol 2016, 90:2861-2883, doi: 10.1007/s00204-016-1842-4.

659 660

47. Guidance for alternatives assessment and risk reduction (draft). IC2 (Interstate Chemicals Clearinghouse). 2013. 250p.

661 662 663

48. Fitzpatrick JM, Roberts DW, Patlewicz G. Is skin penetration a determining factor in skin sensitization potential and potency? Refuting the notion of a LogKow threshold for skin sensitization. J Appl Toxicol 2017a, 37:117-127, doi: 10.1002/jat.3354.

664 665 666

49. Fitzpatrick JM, Roberts DW, Patlewicz G. What determines skin sensitization potency: Myths, maybes and realities. The 500 molecular weight cut-off: an updated analysis. J Appl Toxicol 2017b, 37:105-116, doi: 10.1002/jat.3348.

667 668 669

50. Roberts DW, Patlewicz G. Chemistry based nonanimal predictive modeling for skin sensitization. doi: 10.1007/978-1-4419-0197-2_3. In: Ecotoxicology Modeling. (Ed.: Devillers J), Springer US, New York, NY. p61-83. 2009.

670 671 672

51. Scholes EW, Basketter DA, Sarll AE, Kimber I, Evans CD, Miller K, Robbins MC, Harrison PT, Waite SJ. The local lymph node assay: Results of a final inter-laboratory validation under field conditions. J Appl Toxicol 1992, 12(3):217-222. doi: 10.1002/jat.2550120312.

673 674 675

52. Verheyen GR, Braeken E, Van Deun K, Van Miert S. Evaluation of in silico tools to predict the skin sensitization potential of chemicals. SAR QSAR Environ Res 2017, 28:59-73, doi: 10.1080/1062936X.2017.1278617.

676 677 678

53. Enoch SJ, Madden JC, Cronin MTD. Identification of mechanisms of toxic action for skin sensitization using a SMARTS pattern based approach. SAR QSAR Environ Res 2008, 19(56):555-578. doi: 10.1080/10629360802348985

679 680 681

54. Safford RJ, Aptula AO, Gilmour N. Refinement of the dermal sensitisation threshold (DST) approach using a larger dataset and incorporating mechanistic chemistry domains. Regul Toxicol Pharmacol 2011, 60:218-224, doi: 10.1016/j.yrtph.2011.03.009.

682 683 684

55. Natsch A, Emter R. Reaction chemistry to characterize the molecular initiating event in skin sensitization: a journey to be continued. Chem Res Toxicol 2017, 30:315-331, doi: 10.1021/acs.chemrestox.6b00365.

685 686 687 688

56. OECD guideline for the testing of chemicals: in chemico skin sensitisation: direct peptide reactivity assay (DPRA). OECD (Organisation for Economic Co-operation and Development). 2015a. OECD/OCDE TG 442C. http://www.oecd-ilibrary.org/environment/test-no-442c-inchemico-skin-sensitisation_9789264229709-en [accessed 22 November 2016].

689 690

57. OECD guideline for the testing of chemicals: in vitro skin sensitisation: ARE-Nrf2 luciferase test method. OECD (Organisation for Economic Co-operation and Development). 2015b.

15




Page 16 of 25

691 692

OECD/OCDE TG 442D. 20p. http://www.oecd-ilibrary.org/environment/test-no-442d-in-vitroskin-sensitisation_9789264229822-en [22 November 2016].

693 694 695 696

58. OECD guideline for the testing of chemicals: in vitro skin sensitisation: human cell line activation test (h-CLAT). OECD (Organisation for Economic Co-operation and Development). 2016. OECD/OCDE 442E. http://www.oecd-ilibrary.org/environment/test-no-442e-in-vitro-skinsensitisation_9789264264359-en [22 November 2016].

697 698 699

59. Skin sensitisation [list of test guidelines]. ECHA (European Chemicals Agency). 2015. https://echa.europa.eu/documents/10162/21650280/oecd_test_guidelines_skin_sensitisation_en.p df [22 November 2016].

700 701 702

60. Collaborative Adverse Outcome Pathway Wiki (AOP-Wiki). EC JRC and US EPA (European Commission Joint Research Centre; US EPA). 2016. https://aopwiki.org/wiki/index.php/Main_Page [accessed 22 November 2016].

703

16



Page 17 of 25 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

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



Page 19 of 25 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

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



Page 21 of 25 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

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



Page 23 of 25 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

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

Page 24 of 25

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



Page 25 of 25 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

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



Hazard-Screening Methods and Tools for ... - ACS Publications

Recommend Documents