Disinfection By-Products in Drinking Water - American Chemical Society

Chapter 4

Downloaded by NANYANG TECHNOLOGICAL UNIV on August 24, 2015 | http://pubs.acs.org Publication Date: August 5, 2008 | doi: 10.1021/bk-2008-0995.ch004

Use of Chemical Models and Structure-Activity Relationships to Identify Novel Disinfection ByProducts of Potential Toxicological Concern 1

2

Richard J . Bull and David. A. Reckhow 1

MoBull Conculting, 1928 Meadows Drive North, Richland, WA 99352 University of Massachusetts at Amherst, 16C Marston Hall, Box 35205, Amherst, MA 01003-5305

2

Chlorinated drinking water has been epidemiologicaliy associated with increased risk of cancer, especially in the bladder, and adverse reproductive outcomes. A large number of chemicals have been identified as disinfection by-products (DBPs). Several by-product classes, such as the trihalo-methanes and haloacetic acids, have received substantial toxicological study. Although many of these have been found carcinogenic in animals, the combination of low potency as carcinogens and/or low concentrations in drinking water make them unlikely causes of the cancer risk of the magnitude suggested by epidemiology studies. To provide some direction for the resolution of this discrepancy, a strategy was developed to predict formation of novel by-products through reactions of chlorine with substructures found in natural organic matter (NOM). These potential disinfection by-products (DBPs) were examined using a quantitative structure toxicity relationship (QSTR) program, TOPKAT®, to identify those chemicals of sufficient toxicologic potency to account for epidemiologic findings, if present. A "weight of evidence" strategy was utilized to rectify the predictions of the multiple cancer models within TOPKAT® to identify those compounds that were most probably potent carcinogens. Potential © 2008 American Chemical Society

In Disinfection By-Products in Drinking Water; Karanfil, T., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2008.

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developmental toxicants were identified with the single model provided in TOPKAT®. A substantial effort was made to verify/deny predictions by literature search; examining both descriptive and mechanistic data to evaluate the prediction. Several haloquinones and related chemicals appear to be the most interesting. It is of note that formation of haloquinones would be favored with chloramine relative to chlorine disinfection.

Introduction Chlorinated drinking water has been epidemiologically associated with increased risk of cancer, especially of the bladder, and with a range of adverse reproductive outcomes (/). The USEPA (7) estimated the population attributable risks (PARs) from a series of published meta analyses. The PARs translate into lifetime risks that range from 2 Χ 10" to as high as 6 X 10 in the U.S. population (Table I). A large number of chemicals have been identified as by products of the disinfection of drinking water. Several by-product classes, such as the trihalomethanes (THMs) and haloacetic acids (HAAs), have received substantial toxicological study. Many of these have been found carcinogenic in animals, but the combination of low potency as carcinogens and/or low concentrations in drinking water make them unlikely causes of risks of the magnitude suggested by epidemiology study (2). There are several possible explanations for the discrepancy between the levels of risk projected from toxicological studies of the major chlorination by products and epidemiological findings: 4

3

Table I. Risks of bladder cancer attributed to chlorinated drinking water Males 39/100,0001

Females 10.1/100,000

Bladder Cancer Incidence Lifetime probability for developing 0.0356$ 0.0113$ bladder cancer Population Attributable Risks to C l H 0§ Cancer risk attributable to C l H 0 0.0007 0.0002 2% 2

2

2

0.006

17%

0.002

t Age adjusted incidence for years 1997-2001 (3) ί Years 1999-2001 (3) § (7)


2

53 1. 2. 3.


4.

Lack of occurrence and/or toxicological data for potent carcinogens that are produced by reaction of disinfectant(s) with natural organic matter. Humans are innately more sensitive to chlorination by-products than experimental animals. Chlorinated drinking water is a complex mixture of by-products that act synergistically to produce cancer or other adverse effects. The epidemiological data available overestimate the actual risk.

These same issues may explain similar apparent descrepancies with respect to reproductive effects that have been associated with chlorinated drinking water. These explanations are not exclusive of one another. Therefore, it is necessary to develop an approach for each. The present project was undertaken to evaluate the first potential explanation. The first question addressed was whether identified by-products might be predicted to have sufficient toxicological potency to contribute to some of the adverse health outcomes that have been associated with chlorinated drinking water. A second question was whether by-products may be formed that have yet to be identified that could contribute to such outcomes. Quantitative structure toxicity relationships (QSTR) were used to evaluate the probable general toxicity, carcinogenic and developmental toxicity of DBPs. The formation of novel DBPs was based upon predicted reactions of disinfectants with reactive centers in substructures found in natural organic matter (NOM) using principles of organic chemistry.

Methods A schematic of the study design is provided in Figure 1. The study included all established chlorination by-products [detailed references in (5)]. In addition, formation of novel by-products were predicted based upon standard reactions of chlorine or chloramines with substructures that are found in natural organic matter (NOM). Several dozen probable DBPs were predicted as likely reaction products. The precursor structures under consideration included lignin monomers, nucleic acids, amino acids, terpenoids, simple aromatics and many others. General pathways for the formation and degradation of chlorination byproducts were proposed for each precursor type. These were derived from published studies of similar compounds, and known reaction pathways and products from the attack of chlorine on a wide range of reactive centers. The nature of the precursors is described in detail in Bull et al. (5). These probable disinfection by-products (DBPs) were examined using a QSTR program, TOPKAT® (4), supplemented by literature review, to identify those chemicals with a high probability of being carcinogens or developmental toxicants of



54 sufficient toxicological potency to be candidate causes for epidemiologic findings, if they are present. By-products that were obviously of no toxicological importance (e.g. fatty acids) were not evaluated by QSTR. A total of 489 chemicals were evaluated by QSTR. A complete explanation of the desposition of each DBP evaluated is provided in Bull et al. (5). QSTR relationships were first evaluated through the use of TOPKAT® (Accelrys). Second, the available toxicological literature on related compounds was evaluated. This was particularly important when the training set of the models employed were inadequate to support a prediction. QSTR programs, and TOPKAT® in particular, have limitations. TOPKAT® makes predictions based on the electrotopological characteristics of the molecule. Such predictions

Figure 1. Outline of the prioritization scheme used.



55 may be within the optimum prediction space (OPS) of the model even though the training set does not include a close structural analog. Each model within TOPKAT® provides a listing of the chemicals in its training set, indicates which compounds were used in making the prediction, the structure of each chemical in the training set, identifies the weight assigned to each functional groups within the molecule, and an indication (defined here as the similarity index) of how closely related the chemical in the training set is related to the query structure. With the chemical diversity of DBPs, the simplest summary statistic that described the adequacy of the training set was the similarity index. An effort was made to verify or deny predictions by literature search. Both descriptive and mechanistic data for related compounds were examined to determine if an independent rationale could be developed for the prediction. As with model predictions, the ability to perform this analysis depends very heavily on the availability of data in the open scientific literature. For example, predictions of carcinogenicity depend upon there being at least one related compound for which in vivo carcinogenic activity has been assessed. The first criteria applied was to identify DBPs which had predicted chronic lowest observed adverse effect levels (LOAELs) < 1 mg/kg day" . This criterion was applied because chemicals of this toxicological potency would be of interest regardless of the actual health outcome. It was reasoned that chemicals of lesser potency would not contribute significantly to risk of the magnitude suggested by epidemiological studies. The regulated DBPs typically have chronic LOAELs in a range of 10 -100 mg/kg day" . For chemicals that were predicted to be carcinogens or developmental toxicants, the potency criteria was raised to 10 mg/kg day' . This adjustment was made to account for the finding that the chronic LOAEL was approximately an order of magnitude higher relative to the dose that produces a 50% increase in tumor incidence in a lifetime (TD ) for potent carcinogens (Figure 2). The relationship between actual LOAELs in the TOPKAT® training set with TD s for the same compounds from the Cancer Potency Data Base (6) was based upon the correlations that are presented in Figure 2. Chemicals were considered potential carcinogens only if a positive prediction was obtained for two species that were both within the acceptable limits of the optimum predictive space (OPS) of the respective model. The chemicals also had to be sufficiently similar to chemicals within the training set of the model from which the prediction was derived. This was judged if the similarity for at least one compound in the training set fell below 0.2 (0.00 = identity, 1.0 = no similarity). Within TOPKAT®, similarity is judged based on the electrotopological relationships amoung pairs of atoms within the molecule. The ability of each of the 8 TOPKAT® carcinogen models to predict whether a chemical was a carcinogen was very dependent upon the chemicals in their respective training sets. The training sets were independently derived from 1

1

1

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56 1000

CO "Ο

Ι* Downloaded by NANYANG TECHNOLOGICAL UNIV on August 24, 2015 | http://pubs.acs.org Publication Date: August 5, 2008 | doi: 10.1021/bk-2008-0995.ch004

σ> Ε Ο)

—j LU

s (Ό

I 1

10

100

1000

10000

Rat TD (mg/kg day) 50

Figure 2. Relationship of the TD s in rats to the actual chronic LOAELs for the same compounds within the TOPKAT" chronic LOAEL model training set. TD s obtainedfrom Gold et al. (6). 50

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male and female rat and mouse studies in the National Toxicology Program (NTP) or the Food and Drug Administration (FDA) databases. Therefore, a chemical was judged a probable carcinogen if it met the criteria in the prior paragraph in at least one sex of two species in either the NTP or FDA databases. Negative predictions arefrequentlyobserved for well known carcinogens in one or the other of these models if there were no suitable structural analogs in the training set. While the carcinogen models were used to identify carcinogens, the ranking of carcinogens for potency depended upon the predicted chronic LOAEL and prediction of Ames' mutagenic activity as indicated above. In these cases, the similarity index was provided, but the chemical was not removed from consideration unless the value was outside of the OPS of the model or if there data in the literature that indicated that the prediction erred > 10X. In the prediction of potential developmental toxicity (DTP), the criteria of a probability of > 0.7 and a similarity index of < 0.2 was required. In this case there is only one model in TOPKAT®.

Results DBPs and putative DBPs that met the criteria for predicted lowest observed adverse effect levels (LOAELs) of less than 1 mg/kg day' are presented in 1


57 Table II. Predictions of the chronic LOAEL in the halonitrile group of compounds were retained as being reasonable based upon the range of LOAELs identified with haloacetonitriles that have been studied in some detail in subchronic and/or reproductive toxicology studies (5).

Table II. DBPs and putative DBPs with predicted chronic LOAELs < 1 mg/kg day' listed in order of potency. Downloaded by NANYANG TECHNOLOGICAL UNIV on August 24, 2015 | http://pubs.acs.org Publication Date: August 5, 2008 | doi: 10.1021/bk-2008-0995.ch004

1

Chemical

2,3-Dibromopropionitrile Dichloroacetonitrile 2,3-Dichloropropamide 3,3-Dichloropropionitrile 3-Bromopropionitrile 2,3-Dichloropropenal

Pred. Ames' mut. + + + + + +

Simil. Index 0.121 0.000 0.209 0.109 0.200 0.223

LOAEL (mg/kg day') 0.15 0.29 0.51 0.77 0.89 0.97 t#

f

f

Simil. Index 0.183 0.170 0.183 0.205 0.149 0.164

t Outside the optimal predictive space of the model and exceeds permissible limits # This table corrects a typographical error in reference 5.

Table III adds chemicals to the list of concern that were predicted to be carcinogenic and whose predicted chronic LOAELs fell below 10 mg/kg day" . For perspective, the predicted chronic LOAELs of chloroform and the regulated HAAs are 30 and in a range of 6-60 mg/kg day" , respectively. All chemicals in this list had a similarity < 0.2 in the carcinogenesis models in which they were predicted as probable carcinogens. The similarity index in the chronic LOAEL model for the first five compounds are > 0.2 indicating that they were not well represented in the trainings set and a few compounds had predicted LOAELs outside the OPS of the model. In the case of haloquinone derivatives, a substantial literature on mechanism of quinone action lead credibility to the predictions. In the case of the furanone derivative, 3-chloro-4-(chloromethyl)-5-hydroxy-2-(5H)furanone (CMCF), the predicted LOAEL of M X was also outside the OPS, but approximated the value established experimentally in the study of M X (7), so it was retained. Several halonitriles were identified as likely carcinogens. Dichloro-acetonitrile is well recongnized as a DBP and occurs in the low μg/L range. Two additional halonitriles were identified, 3-bromopropionitrile and 2,3-dibromopropionitrile, but no evidence of their occurrence was found. Dichloroacetaldehyde and 2,3dichloropropenal were also identified as probable carcinogens. The former has been identified in drinking water in the low \x%JL range (8). 1

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Table III. Additional DBPs predicted to be carcinogens that were predicted to have chronic LOAELs < 10 mg/kg day" Downloaded by NANYANG TECHNOLOGICAL UNIV on August 24, 2015 | http://pubs.acs.org Publication Date: August 5, 2008 | doi: 10.1021/bk-2008-0995.ch004

1

Chemical

2,3,6-Trichloro-l,4-benzoquinone

Pred. Simil. Ames' index Mut. 0.333 -

LOAEL Simil. (mg/kg index day ) 0.342 0.033 1

-

0.318

0.029

0.298

+

0.407

0.064*

0.280

2,3,6-Trichloro-1,4-benzoquinone-4-(Nchloro)imine 2,6-Dichloro-3-methyl-1,4-benzoquinone 2,3-Dibromoproprionitrile

-

0.590

0.073*

0.328

+

0.398 0.120

0.079 0.15

0.341 0.183

Dichloroacetonitrile

+

0.000

0.29

3-Chloro-4-(chloromethyl)-5-hydroxy2(5H)furanone 2,3-Dichloro-4,5-diketobenzoprop-2-enol

f

f

0.170

+

3-Bromopropionitrile

+

0.200

0.89

0.149

2,3-Dichloropropenal

+

0.223

1.0

0.164

3-Chloro-4-(dichloromethyl)-2-ruranone

+

0.324

1.7*

0.154

Trichloroacetonitrile

+t

0.494

4.5t

0.166

1,2,3-Trichloropropane

+

0.156

4.7

0.000

Dichloroacetaldehdye

+

0.192

5.8

0.047

3,3,3-Trichloropropionitrile 2-Bromo-3,3-dichloropropionic acid

-

0.212 0.271

3.9t 5.8

0.169 0.068

-

* Prediction is outside the optimal predictive space of the model, but within permissible limits t Prediction is outside the optimal predictive space of the model and exceeds permissible limits $ Prediction in Bull et al. (5) was based on the default acyclic sub-model of TOPKAT®, this prediction is based upon the heteroaromatic sub-model.



59 Some chemicals came very close to meeting the criteria established. For example, both 3,3-dichloro- and 3,3-dibromopropenoic acid had similiarity indices >0.2 but

Disinfection By-Products in Drinking Water - American Chemical Society

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