STRUCTURAL ALERTS

All molecules that contain an arylacetic acid group can undergo acyl glucuronidation, a major metabolic conjugation pathway in mammals for chemicals that contain these groups. Acyl glucuronides have been implicated—although it is not definitively proved—as a cause of adverse effects in humans because they form protein adducts (Shipkova et al. 2003) (see Figure D-1). Common risk concerns are liver injury and hypersensitivity reactions (Regan et al. 2010). The relative reactivity and half-life of the acyl glucuronide has been suggested as a differentiating factor between chemicals that cause adverse events and ones that are of less concern. Other researchers suggest that arylacetic acids can undergo coenzyme A (CoA) conjugation, and interference of the CoA conjugates with lipid metabolism and other cellular processes can lead to the observed toxicity (Darnell and Weidolf 2013). The metabolic scheme might need to be confirmed experimentally to reduce uncertainty (Patlewicz et al. 2015).

FIGURE D-1

Metabolism of 1-O-β-glucuronide. Source: Stepan et al. 2011.

IN VITRO DATA

To ensure data consistency among the chemicals in question, in vitro data were gathered only from the ToxCast website, and they are summarized in Table D-2 (EPA 2016). Only assays that yielded activity below a 10 µM threshold are considered because they constitute 20% of the observed assay activity for diclofenac and would most likely be the cause of the toxicity used to set assay doses. It is important to note that the assays used in the ToxCast program do not represent the entire spectrum of biological processes that might be relevant to human health (that is, all possible adverse effects of exposure to chemicals); therefore, there are likely to be gaps in knowledge of how the three chemicals would interact in a biological system. To give some context to the values in Table D-2, diclofenac was tested in a zebrafish toxicity screen and had a lowest effect level of 64 μM (Truong et al. 2014).

TABLE D-2

Data from In Vitro Assay in Which Chemicals Had Activity Below 10 µM.

Data on ibufenac are not available, but given its structural similarity to ibuprofen and comparable physicochemical properties, one would expect ibufenac to have an in vitro activity profile similar to that of ibuprofen.

HAZARD IDENTIFICATION

On the basis of the available in vitro data, structural comparisons, and knowledge of structural alerts, a key safety concern about all three chemicals would be liver injury through the formation of reactive acyl glucuronides or acyl coenzyme A conjugates that would cause tissue damage and impaired organ function. Relative reactivity of the acyl conjugates would play an important role in determining the risk of liver injury. Chemicals that have alkyl substitutions at the α-carbon atom have been shown to have lower reactivity with protein nucleophiles; this suggests that inherent electronic and steric effects affect the overall rate of acyl glucuronide rearrangement (Stepan et al. 2011) and so could have a profound effect on the reactivity of the conjugates in the case of ibuprofen (Wang et al. 2004; Walker et al. 2007; Baba and Yoshioka 2009). The risk of liver injury could be increased by induction of cytochrome P-450s through activation of PXR and by lipid dysfunction as a result of activation of PPAR-γ.

Cardiovascular toxicity in the form of increased blood pressure and increased clotting times and renal damage or gastrointestinal bleeding caused by the suppression of prostaglandin secretion are also of concern with diclofenac and ibuprofen and by inference, ibufenac.

As discussed in Chapter 3, for inhibitors of G-protein–coupled receptors, the anticipated pharmacological response is often observed in vivo at plasma concentrations up to 3 times the measured IC₅₀ of the chemical in question (McGinnity et al. 2007). As a general rule of thumb, a 100-fold difference between the measured IC₅₀ or the inhibition constant in a cell-free assay and the circulating plasma C_max free¹ concentration could be considered to be adequate to pose minimal risk of toxicity from a pharmacological interaction. It is worth noting that for more phenotypic cellular responses, such as those measured by the BioSeek platform, more research is required to establish an appropriate translation from in vitro to in vivo.

EXPOSURE ASSESSMENT

In this hypothetical case study, the three chemicals of interest have not been used in commercial products; therefore, there are no monitoring data, and there are no emissions and use data on which to formulate a typical risk-based evaluation. However, the available premarket toxicity or bioactivity data identified above can be used to develop parameters for exposure models that can “back-calculate” the rates of chemical use for various scenarios that correspond to specific hazard thresholds. The selected threshold for such simulations could be a concentration from a bioassay in the case of ibuprofen or diclofenac or a read-across value in the case of ibufenac determined from in vivo, in vitro, or computational methods or a no-effect threshold method, such as one that uses a threshold of toxicological concern.

The general exposure-assessment approach outlined here is analogous to the critical-emission-rate concept that has been applied in ecological assessments (Arnot et al. 2006) and to concepts applied in reverse toxicokinetics that is used to calculate intake rates expressed as oral equivalent doses (OEDs;² mg/kg-day) from the in vitro testing data (Rotroff et al. 2010). In the present case, toxicokinetic models are combined with indoor-fate models to back-calculate the rates of chemical use that correspond to illustrative exposure scenarios. The simulations can consider various assumptions and contexts for exposure and chemical or product scenarios. The exposure models used in such simulations can vary in complexity according to the amount of data needed to satisfy all the parameter requirements. In that regard, tiered modeling strategies might be helpful.

In this example, a one-compartment, whole-body toxicokinetic model that considers primary routes of exposure and intake (dermal, ingestion, and inhalation) and routes of elimination (for example, exhalation, renal excretion, biotransformation, egestion, and desquamation) is linked to a representative indoor environment (Arnot et al. 2014b) to back-calculate the rates of chemical use for three hypothetical exposure scenarios:

• Scenario 1. The chemical is released to air in a defined indoor environment. Exposure pathways include inhalation, dermal permeation (from passive diffusion in air), and nondietary ingestion (from hand-to-surface and surface-to-mouth contact).
• Scenario 2. The chemical is applied directly to skin and assumed to be left on indefinitely. Exposure pathways include dermal permeation and inhalation (from volatilization of the chemical from dermal application).
• Scenario 3. The chemical is ingested.

Simplifying assumptions are steady-state calculations and no charged species (that is, no explicit calculation for charged species; only the neutral form is simulated). The latter assumption is similar to recent hazard and risk-based calculations that used ToxCast data in which the potential for chemical dissociation was ignored; that is, acids and bases were treated as nondissociating neutral organics (Rotroff et al. 2010; Wetmore et al. 2012; Shin et al. 2015).

The first step is to translate the in vitro bioassay concentrations (C_{in vitro}) that correspond to the observed bioactivity to in vivo concentrations (C_{in vivo}). Here, the committee uses the same assumptions as in recent applications of ToxCast data for OED calculations: C_in vivo, blood = C_{in vitro} (Rotroff et al. 2010; Wetmore et al. 2012; Shin et al. 2015). However, more explicit calculations should be used to account for differences in the in vitro and in vivo systems; for example, free dissolved concentrations rather than assumed nominal in vitro concentrations could be used (see discussion in Chapter 2). The steady-state volume of distribution is assumed to be 0.5 L/kg (35 L) for the three chemicals to relate blood concentrations to whole-body concentrations. Models for volume of distribution and other methods to address differential concentrations among and within tissues could be considered. The lowest AC₅₀ from the available ToxCast assays is selected as the hazard threshold on which to base parameter values for the exposure model. That value for ibuprofen and ibufenac is 0.002 µM, and the selected threshold for diclofenac is 0.01 µM (see Table D-2).

The second step is to select the parameters needed for the exposure models to calculate chemical fate in various environments. For the sake of illustration, the committee assumes an adult human in a single room, although infants and children have greater breathing rates relative to body weight; the evaluative model requires the following chemical-specific information: K_OW, K_AW, and degradation half-lives in air (see Table D-1). Quantitative structure–activity relationship (QSAR) models are used here to predict whole-body biotransformation half-life data. Half-lives could also be determined by scaling in vitro assay data derived from hepatocytes to liver (see, for example, Rotroff et al. 2010) or whole-body half-life estimates. In addition, hepatic, renal, or other compartment-specific QSAR models could be used to provide parameter values for pharmacokinetic models that are used for exposure assessment. Ideally, multiple lines of evidence (for example, various measured and predicted estimates) will show concordance in key information used in the model simulations (chemical partitioning properties and reaction half-lives), and this concordance will foster confidence in the assessment results. If chemicals are shown to have high environmental persistence, adding far-field human-exposure models to the assessment is warranted to account for possible far-field exposure pathways (chemical dispersed and diffused into food and water).

RISK CHARACTERIZATION

The results of the back-calculation simulations are summarized in Table D-3. The calculations yield the indoor air release (Scenario 1), application (Scenario 2), and ingestion (Scenario 3) rates in milligrams per day corresponding to the selected in vitro bioactivity-assay data (assumed threshold values). The results could be used for interim guidance on use scenarios for each chemical and for comparative analyses between the three candidate chemicals. If one assumes that all three chemicals are used in the same quantity, the chemicals and scenarios with the lowest rates correspond to the greatest potential to achieve the in vitro bioactivity threshold. For example, for diffuse release to air in an indoor environment (Scenario 1), diclofenac shows the highest emission or release rate and, so could pose the lowest potential concern of the three chemicals. Of the three exposure scenarios, Scenario 3 results in the lowest use and application rates for all chemicals. Overall, the ranges of values are not large because the chemicals have similar properties for partitioning, reaction, and bioactivity (that is, the same bioactivity value is used for ibuprofen and ibufenac on the basis of structural read-across).

TABLE D-3

Indoor Release (Scenario 1), Application (Scenario 2), and Ingestion (Scenario 3) Rates (mg/d) Corresponding to Selected In Vitro Bioactivity Data.

The values in Table D-3 do not show the uncertainty in the calculations and do not account for interindividual variability in the pharmacokinetics and pharmacodynamics that one would expect in a large diverse population. The results of this example are illustrative, but the general concept can be helpful in determining putative use and release scenarios for premarket chemicals. The application of exposure models to back-calculate emission and use rates corresponding to a toxic threshold or bioactivity can also be useful for evaluating commercial chemicals when emission and use rates are unknown or highly uncertain. Ultimately, confidence in the calculated emission and use rates depends on the confidence in and suitability of the toxicity (threshold) data and the exposure-model estimates. For the three chemicals in this example, measured volumes of distribution are about one-third to one-half the assumed values, and half-lives in adults are one-seventh to one-half the values used in this premarket assessment (Obach et al. 2008). For risk-based decision-making, additional analyses for various life stages and alternative use scenarios should be considered as warranted.

To put the exposure estimates in Table D-3 into context, the typical over-the-counter medicines that contain ibuprofen recommend an oral dose of 200–400 mg every 4 hours with a maximum dose of 1,200 mg in any 24-hour period for persons over 12 years old.³ However, doctors can prescribe ibuprofen to be given orally at up to 3,200 mg/day in doses of up to 800 mg at any one time.⁴ Similarly, ibuprofen has been approved in Europe for administration to children 3 to 6 months of age at a starting dose of 50 mg taken orally three times a day. Ibuprofen is contraindicated in pregnant women in their third trimester, and doctors recommend that women during the first 6 months of pregnancy not take it, if that is possible.

Diclofenac is approved for use by prescription, and the maximum recommended daily oral dose is 150 mg in adults; it is not recommended for use in children under 12 years old. It is also contraindicated for use by pregnant women. Ibufenac was withdrawn from the market because of severe hepatotoxicity and jaundice in patients taking the drug. At the time, the maximum recommended daily oral dose of ibufenac was 750 mg.

In Europe, both ibuprofen and diclofenac were approved for use as a topical gel (ibuprofen, 5% w/w gel; diclofenac, 2.32% w/w gel). A maximum daily application of the diclofenac gel was 8 g, which is equivalent to 160 mg of the active ingredient. Similarly, the recommended application of the ibuprofen gel was up to 125 mg, four times per day, which is equivalent to 25 mg of the active ingredient. However, only 22% of the dose is absorbed through the skin. Compared with the oral route of administration of ibuprofen, the plasma exposure is considered to be much lower and unlikely to cause systemic side effects.

The estimated oral and dermal exposures in the present example would be substantially below the therapeutic doses for most populations, including children. However, it should be noted that at therapeutic doses, some side effects and adverse events are observed with various, albeit relatively low, frequencies that might not necessarily be considered tolerable in an environmental or occupational risk assessment in which long-term, low-level exposures of a broad population demographic have to be considered. Similarly, conclusions cannot be drawn at this time about whether the estimated doses would ensure the protection of the most sensitive group—pregnant women.

REFERENCES

• ACD (Advanced Chemistry Development). ACD/Percepta Suite. ACD; Toronto, ON, Canada: 2015.
• Arnot JA, Mackay D, Webster E, Southwood JM. Screening level risk assessment model for chemical fate and effects in the environment. Environ. Sci. Technol. 2006;40(7):2316–2323. [PubMed: 16646468]
• Arnot JA, Brown TN, Wania F. Estimating screening-level organic chemical half-lives in humans. Environ. Sci. Technol. 2014a;48(1):723–730. [PubMed: 24298879]
• Arnot JA, Zhang X, Kircanski I, Hughes L, Armitage J. Develop Sub-module for Direct Human Exposures to Consumer Products. Toronto, Canada: 2014b. (Technical report for the US Environmental Protection Agency, by ARC Arnot Research & Consulting Inc.).
• Baba A, Yoshioka T. Structure-activity relationships for the degradation reaction of 1-beta-O-acyl glucuronides. Part 3: Electronic and steric descriptors predicting the reactivity of aralkyl carboxylic acid 1-beta-O-acyl glucuronides. Chem. Res. Toxicol. 2009;22(12):1998–2008. [PubMed: 19902937]
• Brown TN, Arnot JA, Wania F. Iterative fragment selection: A group contribution approach to predicting fish biotransformation half-lives. Environ. Sci. Technol. 2012;46(15):8253–8260. [PubMed: 22779755]
• Darnell M, Weidolf L. Metabolism of xenobiotic carboxylic acids: Focus on coenzyme a conjugation, reactivity, and interference with lipid metabolism. Chem. Res. Toxicol. 2013;26(8):1139–1155. [PubMed: 23790050]
• DuBois RN, Abramson SB, Crofford L, Gupta RA, Simon LS, Van De Putte LB, Lipsky PE. Cyclooxygenase in biology and disease. FASEB J. 1998;12(12):1063–1073. [PubMed: 9737710]
• EPA (US Environmental Protection Agency). Estimation Programs Interface (EPI) Suite for Microsoft® Windows, Version 4.1. US Environmental Protection Agency; Washington, DC: 2011.
• EPA (US Environmental Protection Agency). Toxicity Forecasting: Advancing the Next Generation of Chemical Evaluations. 2016. [July 26, 2016]. https://www​.epa.gov/chemical-research​/toxicity-forecasting.
• Esmon CT, Esmon NL, Harris KW. Complex formation between thrombin and thrombomodulin inhibits both thrombin-catalyzed fibrin formation and factor V activation. J. Biol. Chem. 1982;257(14):7944–7947. [PubMed: 6282863]
• Ferwana M, Firwana B, Hasan R, Al-Mallah MH, Kim S, Montori VM, Murad MH. Pioglitazone and risk of bladder cancer: A meta-analysis of controlled studies. Diabet. Med. 2013;30(9):1026–1032. [PubMed: 23350856]
• Gerlitz B, Hassell T, Vlahos CJ, Parkinson JF, Bang NU, Grinell BW. Identification of the predominant glycosaminoglycan-attachment site in soluble recombinant human thrombomodulin: Potential regulation of functionality by glycosyltransferase competition for serine474. Biochem. J. 1993;295(Pt 1):131–140. [PMC free article: PMC1134829] [PubMed: 8216207]
• Hedges JC, Singer CA, Gerthoffer WT. Mitogen-activated protein kinases regulate cytokine gene expression in human airway myocytes. Am. J. Respir. Cell Mol. Biol. 2000;23(1):86–94. [PubMed: 10873157]
• Hobbs HH, Brown MS, Goldstein JL. Molecular genetics of the LDL receptor gene in familial hypercholesterolemia. Hum. Mutat. 1992;1(6):445–466. [PubMed: 1301956]
• Johnsen SP, Larsson H, Tarone RE, McLaughlin JK, Nørgård B, Friis S, Sørensen HT. Risk of hospitalization for myocardial infarction among users of rofecoxib, celecoxib, and other NSAIDs: A population-based case-control study. Arch. Intern. Med. 2005;165(9):978–984. [PubMed: 15883235]
• Kliewer A. The nuclear pregnane X receptor regulates xenobiotic detoxification. J. Nutr. 2003;133(7 Suppl.):2444S2447S. [PubMed: 12840222]
• Lu NZ, Wardell SE, Burnstein KL, Defranco D, Fuller PJ, Giguere V, Hochberg RB, McKay L, Renoir JM, Weigel NL, Wilson EM, McDonnell DP, Cidlowski JA. International union of pharmacology. LXV. The pharmacology and classification of the nuclear receptor superfamily: Glucocorticoid, mineralocorti-coid, progesterone, and androgen receptors. Pharmacol. Rev. 2006;58(4):782–797. [PubMed: 17132855]
• McGinnity DF, Collington J, Austin RP, Riley RJ. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr. Drug Metab. 2007;8(5):463–479. [PubMed: 17584017]
• Morita I. Distinct functions of COX-1 and COX-2. Prostag. Oth. Lipid M. 2002;68-69:165–175. [PubMed: 12432916]
• Mukaida N, Harada A, Matsushima K. Interleukin-8 (IL-8) and monocyte chemotactic and activating factor (MCAF/MCP-1), chemokines essentially involved in inflammatory and immune reactions. Cytokine Growth Factor Rev. 1998;9(1):9–23. [PubMed: 9720753]
• Noreen Y, Ringbom T, Perera P, Danielson H, Bohlin L. Development of a radiochemical cyclooxygenase-1 and -2 in vitro assay for identification of natural products as inhibitors of prostaglandin biosynthesis. J. Nat. Prod. 1998;61(1):2–7. [PubMed: 9461646]
• Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab. Dispos. 2008;36(7):1385–1405. [PubMed: 18426954]
• Patlewicz G, Ball N, Boogaard PJ, Becker RA, Hubesch B. Building scientific confidence in the development and evaluation of read-across. Regul. Toxicol. Pharmacol. 2015;72(1):117–133. [PubMed: 25857293]
• Rangwala SM, Lazar MA. Peroxisome proliferator-activated receptor gamma in diabetes and metabolism. Trends Pharmacol. Sci. 2004;25(6):331–336. [PubMed: 15165749]
• Regan SL, Maggs JL, Hammond TG, Lambert C, Williams DP, Park BK. Acyl glucuronides: The good, the bad and the ugly. Biopharm. Drug Dispos. 2010;31(7):367–395. [PubMed: 20830700]
• Rhen T, Cidlowski JA. Antiinflammatory action of glucocorticoids- new mechanisms for old drugs. N. Engl. J. Med. 2005;353(16):1711–1723. [PubMed: 16236742]
• Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, Lecluyse EL, Andersen ME, Judson RS, Smith CM, Sochaski MA, Kavlock RJ, Boellmann F, Martin MT, Reif DM, Wambaugh JF, Thomas RS. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicol. Sci. 2010;117(2):348–358. [PMC free article: PMC6280841] [PubMed: 20639261]
• Schöbitz B, de Kloet ER, Holsboer F. Gene expression and function of interleukin I, interleukin 6 and tumor necrosis factor in the brain. Prog. Neurobiol. 1994;44(4):397–432. [PubMed: 7886232]
• Shin HM, Ernstoff A, Arnot JA, Wetmore BA, Csiszar SA, Fantke P, Zhang X, McKone TE, Jolliet O, Bennett DH. Risk-based high-throughput chemical screening and prioritization using exposure models and in vitro bioactivity assays. Environ. Sci. Technol. 2015;49(11):6760–6771. [PubMed: 25932772]
• Shipkova M, Armstrong VW, Oellerich M, Wieland E. Acyl glucuronide drug metabolites: Toxicological and analytical implications. Ther. Drug Monit. 2003;25(1):1–16. [PubMed: 12548138]
• Singh S, Loke YK, Furberg CD. Long-term risk of cardiovascular events with rosiglitazone: A meta-analysis. JAMA. 2007;298(10):1189–1195. [PubMed: 17848653]
• Spiegelman BM. PPAR-gamma: Adipogenic regulator and thiazolidinedione receptor. Diabetes. 1998;47(4):507–514. [PubMed: 9568680]
• Stepan AF, Walker DP, Bauman J, Price DA, Bail-lie TA, Kalgutkar AS, Aleo M. Structural alert/reactive metabolite concepts as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicitiy: A perspectove based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem. Res. Toxicol. 2011;24(9):1345–1410. [PubMed: 21702456]
• Strong CG, Bohr DF. Effects of prostaglandins E1, E2, A1, and F1-alpha on isolated vascular smooth muscle. Am. J. Physiol. 1967;213(3):725–733. [PubMed: 6036791]
• Süleyman H, Demircan B, Karagöz Y. Anti-inflammatory and side effects of cyclooxygenase inhibitors. Pharmacol. Rep. 2007;59(3):247–258. [PubMed: 17652824]
• Truong L, Reif DM, St Mary L, Geier MC, Truong HD, Tanguay RL. Multidimensional in vivo hazard assessment using zebrafish. Toxicol. Sci. 2014;137(1):212–233. [PMC free article: PMC3871932] [PubMed: 24136191]
• Vane JR. Inhibition of prostaglandin synthesis as a mechanism of action for aspirin-like drugs. Nat. New Biol. 1971;231(25):232–235. [PubMed: 5284360]
• Wada T, Gao J, Xie W. PXR and CAR in energy metabolism. Trends Endocrinol. Metab. 2009;20(6):273–279. [PubMed: 19595610]
• Walker GS, Atherton J, Bauman J, Kohl C, Lam W, Reily M, Lou Z, Mutlib A. Determination of degradation pathways and kinetics of acyl glucuronides by NMR spectroscopy. Chem. Res. Toxicol. 2007;20(6):876–886. [PubMed: 17536843]
• Wang J, Davis M, Li F, Azam F, Scatina J, Talaat R. A novel approach for predicting acyl glucuronide reactivity via Schiff base formation: Development of rapidly formed peptide adducts for LC/MS/MS measurements. Chem. Res. Toxicol. 2004;17(9):1206–1216. [PubMed: 15377154]
• Watkins PB. Insight into hepatotoxicity: The troglitazone experience. Hepatology. 2005;41(2):229–230. [PubMed: 15657920]
• Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, Clewell HJ III, Dix DJ, Andersen ME, Houck KA, Allen B, Judson RS, Singh R, Kavlock RJ, Richard AM, Thomas RS. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol. Sci. 2012;125(1):157–174. [PubMed: 21948869]
• Wiemer AJ, Hegde S, Gumperz JE, Huttenlocher A. A live imaging cell motility screen identifies prostaglandin E2 as a T Cell stop signal antagonist. J. Immun. 2011;187(7):3663–3670. [PMC free article: PMC3178752] [PubMed: 21900181]
• Xue J, Chen F, Wang J, Wu S, Zheng M, Zhu H, Liu Y, He J, Chen Z. Emodin protects against concanavalin A-induced hepatitis in mice through inhibiting activation of the p38 MAPK-NF-ĸB signaling pathway. Cell Physiol. Biochem. 2015;35:1557–1570. [PubMed: 25792514]
• Zou Q, Hong W, Zhou Y, Ding Q, Wang J, Jin W, Gao J, Hua G, Xu X. Bone marrow stem cell dysfunction in radiation-induced abscopal bone loss. J. Orthop. Surg. Res. 2016;11:3. [PMC free article: PMC4704383] [PubMed: 26739584]