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. Author manuscript; available in PMC: 2022 Oct 24.
Published in final edited form as: Toxicol In Vitro. 2022 Jan 13;80:105313. doi: 10.1016/j.tiv.2022.105313

Ascorbic Acid Specifically Reduces the Misclassification of Nonirritating Reactive Chemicals in the OptiSafe™ Macromolecular Eye Irritation Test

Stewart J Lebrun 1, Sara Chavez 1, Roxanne Chan 1, Linda Nguyen 1, James V Jester 2
PMCID: PMC9590652  NIHMSID: NIHMS1839804  PMID: 35033652

Abstract

Recently, we showed that the addition of physiological concentrations of ascorbic acid, a tear antioxidant, to the OptiSafe™ macromolecular eye irritation test reduced the OptiSafe irritation scores of false-positive (FP) chemicals that had reactive chemistries leading to formation of reactive oxygen species (ROS) and molecular crosslinking. The purpose of the current study was to 1) increase the number of chemicals tested to comprehensibly determine whether the antioxidant-associated reduction in optical density (OD) is specific to FP chemicals associated with ROS chemistries, and 2) determine whether the addition of antioxidants interferes with the detection of true positive (TP) and true negative (TN) ocular irritants. We report that when ascorbic acid is added to the test reagents, retesting of FP chemicals with reactive chemistries show significantly reduced OD values (P<0.05). Importantly, ascorbic acid had no significant effect on the OD values of TP or TN chemicals regardless of chemical reactivity. These findings suggest that supplementation of ascorbic acid in alternative ocular irritation test may help improve the detection of TN for those commonly misclassified reactive chemicals.

Keywords: Ocular Irritation, Antioxidant, Validation

Introduction

The in vivo rabbit eye test (Draize method) uses a clinical scoring system to assess the severity and duration of the ocular irritation response between 1 and 21 days after exposure to a test substance (Draize et al., 1944). The clinical scores and durations that result from the Draize test are then applied to either the United Nations Globally Harmonized System of classification and labeling (GHS; UN, 2011) or the U.S. Environmental Protection Agency (EPA) methods of eye irritation classification (ICCVAM, 2010). Since the use of live animals for routine product testing raises serious ethical and animal cruelty concerns, there is a movement toward the adoption of nonanimal, in vitro tests for the classification of eye area products and chemicals. While these alternative tests are accepted for the identification of the most severe level of ocular irritation (ocular corrosives, GHS Category 1) and the least irritating level [GHS Not Classified (NC) as an ocular irritant], detection of reversible irritants (GHS Category 2B and 2A) has been problematic because of the high false-positive (FP) and false-negative (FN) rate for detection of non-irritants and ocular corrosives leading to inaccurate predictions for the middle classification (reversible irritants) (Lebrun et al., 2019).

Recently, we reviewed the FP and FN rates for the currently accepted alternative eye irritation tests, including Bovine Corneal Opacity and Permeability, EpiOcular™, Isolated Chicken Eye, Ocular Irritection®, and OptiSafe™ tests. In this study, we identified that most if not all tests miss predicted the same group of chemicals as FP (GHS NC overpredicted as GHS Category 2 or 1), suggesting that current in vitro tests do not fully model the in vivo eye (Lebrun et al., 2020). To understand this deficiency, we evaluated the chemical properties of common FP chemicals by searching publication databases and identified that many exhibited chemistries that covalently bind molecules via electron transfer and redox cycling that can lead to the generation of reactive oxygen species (ROS) (van Amsterdam et al., 2001; Kovacic et al., 2002) or act as a chemical crosslinker (CL). We further noted that the eye contains high levels of antioxidants in the tear film, the first barrier to chemicals interacting with the eye. In a recent study, we found that the tear antioxidant, ascorbic acid, significantly reduced the OD for the macromolecular OptiSafe eye irritation test that were generated by several commonly misclassified FP chemicals (Patent Application No. 17/203467, 2021; Lebrun et al., 2021). In our study, five tear-related antioxidants were individually added to the OptiSafe™ formulation, and the effects on OD measurements used for irritant classification were determined. Ascorbic acid, the most abundant water-soluble antioxidant found in tears (Chen et al., 2009), was the most effective tear antioxidant that reduced both the OD and, consequently, the FP classification rate compared to the other tear antioxidants tested. Titration curves showed that this reduction occurred at physiologic tear concentrations for ascorbic acid and appeared specific for chemicals identified as producing ROS or acting as a CL. The purpose of this study was to expand upon these encouraging results by 1) increasing the number of chemicals tested to include prior chemicals used in the recently published OptiSafe validation study and determining whether the effect of ascorbic acid was specific to FP chemicals associated with reactive chemistries, and 2) establishing whether ascorbic acid interferes with the detection of true-positive (TP) and true-negative (TN) ocular irritants.

Methods

OptiSafe Eye Irritation Test

Details about the method and protocol used to perform the macromolecular OptiSafe eye irritation test have been previously published (Choksi et al., 2020; U.S. Patent No. 20160290982 A1, 2018). Briefly, OptiSafe measures the damage, that can be measured by the change in OD, to a solution of purified macromolecules in a test reagent mixture after interaction with test chemicals. The specific exposure of test chemicals to the reagent mix is determined using a defined approach (DA) wherein different physiochemical properties of the test chemical are measured, and the approach is modified accordingly. The change in OD is then measured and compared to a standard curve generated by known ocular irritants to give a final score that is then applied to a prediction model for irritation classification. To add ascorbic acid, the OptiSafe test reagent mix was placed in a beaker with a magnetic stir bar, and 0.1 mg/mL ascorbic acid (Sigma Aldrich, Milwaukee, WI; catalog number: A5960) was added and allowed to mix until the pH was stable (approximately 10 minutes). The pH was then adjusted following the OptiSafe procedure (see Choksi et al., 2020).

Test Chemicals

Test chemicals were selected from our prior validation test chemicals as previously reported (Choksi et al., 2020). Since we have made changes in the DA for testing some chemicals, only 62 of the original 78 chemicals used in the validation study were used in the current testing strategy and are listed in Table 1. All 62 chemicals were retested with the addition of ascorbic acid to the reagent mix and the OD measured. Chemicals included solids (powders and crystalline solids), liquids (viscous and nonviscous) and semisolids. Chemicals were identified as ROS or CL by searching the published literature from PubMed or Google Scholar. The name, CASRN, GHS classification, EPA classification, physical state, supplier, catalog number, and purity for chemicals tested are shown in Table 1. Of these, there were 36 GHS NC, 8 GHS Category 2B, 11 GHS Category 2A, and 7 GHS Category 1 chemicals.

Table 1.

Test Chemicals

# Name CASRN GHS EPA Phys. State Supplier Catalog No. Purity (%)
1 Cyclopentasiloxane 541-02-6 NC NA L SiAl 444278 97.0
2 Glycerol 56-81-5 NC IV L SiAl G5516 ≥99.0
3 Hexane 110-54-3 NC IV L SiAl 270504 ≥95.0
4 Dodecane 112-40-3 NC III L SiAl 297879 ≥99.0
5 iso-Octyl acrylate 29590-42-9 NC IV L SiAl 437425 ≥90.0
6 Hexamethyldisiloxane 107-46-0 NC IV L SiAl 52630 ≥98.5
7 Hexyl cinnamic aldehyde 101-86-0 NC IV L SiAl W25690 ≥95.0
8 n-Hexyl bromide 111-25-1 NC IV L SiAl B68240 98.0
9 1,6-Dibromohexane 629-03-8 NC IV L SiAl D41007 96.0
10 Di-iso-butyl ketone 108-83-8 NC IV L SiAl 273848 99.0
11 Xylene 1330-20-7 NC II L SiAl 534056 -
12 n-Octyl bromide 111-83-1 NC IV L SiAl 152951 99.0
13 3-Methoxy-1,2-propanediol 623-39-2 NC IV L SiAl 260401 98.0
14 Propylene glycol 57-55-6 NC IV L SiAl 398039 ≥99.5
15 1-Bromo-4-chlorobutane 6940-78-9 NC IV L SiAl B60800 99.0
16 1,2,6-Hexanetriol 106-69-4 NC IV L SiAl T66206 96.0
17 2-Ethylhexylthioglycolate 7659-86-1 NC IV L SiAl 88670 ≥95.0
18 2,4-Pentanediol 625-69-4 NC IV L SiAl 156019 98.0
19 p-Methyl thiobenzaldehyde 3446-89-7 NC IV L SiAl 222771 95.0
20 n,n-Dimethylguanidine sulfate 598-65-2 NC III S SiAl 276669 97.0
21 Ethyl acetate 141-78-6 NC III L SiAl 270989 99.8
22 3-Phenoxybenzyl alcohol 13826-35-2 NC III L SiAl 190284 98.0
23 2,4-Pentanedione 123-54-6 NC III L SiAl P7754 ≥99.0
24 Triphenyl phosphite 101-02-0 NC IV L SiAl T84654 97.0
25 1,4-Dibromobutane 110-52-1 NC III L SiAl 140805 99.0
26 1,5-Hexadiene 592-42-7 NC III L SiAl 128554 97.0
27 iso-Propyl bromide 75-26-3 NC IV L SiAl B78114 99.0
28 Triethylene glycol 112-27-6 NC IV L SiAl T59455 99.0
29 2,2-Dimethyl-3-pentanol 3970-62-5 NC III L SiAl D173622 97.0
30 2-(2-Ethoxyethoxy)ethanol 111-90-0 NC III L SiAl 537616 99.0
31 Potassium tetrafluoroborate 14075-53-7 NC IV S SiAl 278955 96.0
32 1,9-Decadiene 1647-16-1 NC IV L SiAl 118303 97.0
33 Ethylene glycol diethyl ether 629-14-1 NC IV L SiAl 224111 98.0
34 Styrene 100-42-5 NC III L SiAl S4972 ≥99.0
35 1,3-Di-iso-propylbenzene 99-62-7 NC IV L SiAl 113263 96.0
36 2-Ethoxyethyl methacrylate 2370-63-0 NC IV L SiAl 280666 99.0
37 2-Methyl-1-pentanol 105-30-6 2B III L SiAl 214019 99.0
38 Isobutyraldehyde 78-84-2 2B III L SiAl 240788 ≥99.0
39 n,n-Diethyl-m-toluamide 134-62-3 2B III L SiAl D100951 97.0
40 3-Chloropropionitrile 542-76-7 2B III L SiAl C69101 98.0
41 n-Butanal 123-72-8 2B III L SiAl 418102 99.5
42 Ethyl-2-methyl acetoacetate 609-14-3 2B III L SiAl E35400 90.0
43 Maneb (solid) 12427-38-2 2B III S SiAl 45554 90.0
44 6-Methyl purine 2004-03-7 2B I S FiSc 50-496-810 -
45 Ammonium nitrate 6484-52-2 2A III S SiAl A3795 ≥99.5
46 Isobutanol 78-83-1 2A II L SiAl 33064 ≥99.0
47 Propasol solvent P 1569-01-3 2A II L SiAl 484326 ≥98.5
48 Methyl cyanoacetate 105-34-0 2A II L SiAl 108421 99.0
49 Isopropanol 67-63-0 2A III L SiAl I9516 ≥99.5
50 Allyl alcohol 107-18-6 2A III L SiAl 240532 ≥99.0
51 Cyclopentanol 96-41-3 2A II L SiAl C112208 99.0
52 n-Hexanol 111-27-3 2A II L SiAl 471402 ≥99.0
53 gamma-Butyrolactone 96-48-0 2A II L SiAl B103608 ≥99.0
54 n-Octanol 111-87-5 2A II L SiAl 297887 ≥99.0
55 Methyl acetate 79-20-9 2A II L SiAl 296996 99.5
56 n-Butanol 71-36-3 1/2A II L SiAl B7906 ≥99.0
57 3,4-Dichlorophenyl isocyanate 102-36-3 1 I S SiAl 245607 97.0
58 p-Tert-butylphenol 98-54-4 1 I S SiAl B99901 99.0
59 Methylthioglycolate 2365-48-2 1 II L SiAl 108995 95.0
60 Cyclohexanol 108-93-0 1 I L SiAl 105899 99.0
61 Protectol PP 80-54-6 1 I L SiAl 43884 ≥96.0
62 Lauric acid 143-07-7 1 I S SiAl W261408 ≥98.0
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Table 1. CASRN = Chemical Abstracts Service Registry Number; GHS = Globally Harmonized System of classification and labeling of chemicals; EPA = Environmental Protection Agency; NC = Not Classified; Phys. State = Physical State; L = Liquid; S = Solid; SiAl = Sigma Aldrich; FiSc = Fisher Scientific; Catalog No. = Catalog number.

Statistical analysis

All results are reported as the mean ± standard error (SE). Differences between groups were assessed by Chi Square and two-way analysis of variance (ANOVA) (Holm-Sidak method) for all pairwise multiple comparisons (Sigma Stat version 4.0, Systat Software Inc, Point Richmond, CA). All measurements were based on triplicate samples, and a P value of less than 0.05 was considered statistically significant.

Results

The averages and SEs for the triplicate OD measurements of the test chemicals with and without ascorbic acid are presented in Table 2. In addition, those chemicals that have been reported to have reactive chemical properties and capable of forming ROS or acting as CLs are identified along with the reporting sources. The average difference (ΔOD) between the measured OD before (OS I) and after (OS II) addition of ascorbic acid to the Optisafe (OS) test are also provided. Overall, of the 62 test chemicals, there were 18 TN chemicals, of which 5 were identified as reactive chemicals; 18 FN chemicals, of which 10 were identified as reactive chemicals; and 26 TP chemicals, of which 5 were identified as reactive chemicals. A Chi Square analysis indicated that there was a significant relationship between classification and chemistry with FP chemicals overly represented by reactive chemicals (P<0.05).

Table 2.

OD Comparison with/without Ascorbic Acid

Chemical Classification ROS/CL OS I OS II ΔOD
Avg. OD SE Avg. OD SE Avg.
1 True Negative No evidence1 226.0 8.9 264.7 11.5 38.7
2 True Negative No evidence 229.7 10.2 257.7 10.1 28.0
3 True Negative Y (Zhang, 2015)2 246.7 6.7 271.7 14.9 25.0
4 True Negative No evidence 253.7 12.3 257.7 14.3 4.0
5 True Negative No evidence 267.0 8.4 261.3 20.2 −5.7
6 True Negative No evidence 268.7 28.5 247.0 8.9 −21.7
7 True Negative No evidence 279.3 17.9 267.0 10.4 −12.3
8 True Negative Y (Lee, 2010) 286.3 15.1 298.0 9.5 11.7
9 True Negative Y (Metelko, 1989; Han, 2014) 291.3 11.3 301.7 15.3 10.3
10 True Negative No evidence 316.0 32.5 302.3 11.1 −13.7
11 True Negative Y (Zhu, 2021) 321.0 23.1 272.7 10.2 −48.3
12 True Negative No evidence 322.0 28.9 271.3 8.1 −50.7
13 True Negative No evidence 343.3 30.3 323.3 7.6 −20.0
14 True Negative No evidence 346.0 10.3 376.7 40.3 30.7
15 True Negative No evidence 346.7 13.6 330.3 32.1 −16.3
16 True Negative Y (Iza, 1998; Divakaran, 2014) 354.3 31.8 331.7 13.7 −22.7
17 True Negative No evidence 350.3 10.4 325.7 15.7 −24.7
18 True Negative No evidence 444.3 14.4 416.3 14.8 −28.0
19 False Positive No evidence 323.3 11.2 354.0 8.5 30.7
20 False Positive No evidence 543.3 4.3 478.7 40.5 −64.7
21 False Positive No evidence 798.0 6.9 674.3 17.3 −123.7
22 False Positive No evidence 1059.7 48.6 939.0 23.8 −120.7
23 False Positive No evidence 1325.0 31.7 1306.0 64.1 −19.0
24 False Positive No evidence 2888.0 20.5 2859.0 42.8 −29.0
25 False Positive Y (Nishide, 1977; Sriram, 2001) 377.7 11.8 292.7 11.3 −85.0
26 False Positive Y (Lou, 2000; Zhao, 2006) 453.3 27.9 253.7 5.0 −199.7
27 False Positive Y (Wu, 2002) 463.3 21.9 307.3 3.0 −156.0
28 False Positive Y (Zhu, 2012; Mikulas, 2018) 543.7 63.4 319.3 23.6 −224.3
29 False Positive No evidence 680.3 14.4 481.3 5.4 −199.0
30 False Positive Y (Adedara, 2014; Bodin, 2003) 734.7 7.3 433.3 2.2 −301.3
31 False Positive No evidence 740.3 3.5 419.0 25.7 −321.3
32 False Positive Y (Palmlof, 2000; Smedburg, 1997) 763.7 61.6 429.7 68.2 −334.0
33 False Positive Y (Di Tommaso, 2011; Clark, 2001) 785.7 18.2 583.0 17.0 −202.7
34 False Positive Y (Zhang, 2017; Belvedere, 1981) 1440.3 176.8 276.0 15.1 −1164.3
35 False Positive Y (Cavalli, 1975; Baj, 1991) 1579.7 25.2 845.7 40.5 −734.0
36 False Positive Y (Chirila, 1991; Garcia, 2002) 2953.0 29.5 377.0 16.3 −2576.0
37 True Positive No evidence 1032.3 20.3 930.0 7.8 −102.3
38 True Positive No evidence 2064.0 15.3 2075.7 46.5 11.7
39 True Positive No evidence 1381.7 21.8 1365.0 66.0 −16.7
40 True Positive No evidence 1409.3 46.8 1261.0 25.2 −148.3
41 True Positive Y (Shrager, 1969; Kuykendalll, 1992) 1840.7 20.7 1473.7 73.7 −367.0
42 True Positive Y (Hong, 2008) 605.7 3.2 551.0 16.9 −54.7
43 True Positive Y (Amara, 2015; Jaballi, 2017) 1954.7 125.5 2412.7 76.3 458.0
44 True Positive No evidence 796.3 49.0 604.0 94.0 −192.3
45 True Positive No evidence 640.7 35.0 630.3 19.6 −10.3
46 True Positive No evidence 1457.3 47.1 1335.7 26.2 −121.7
47 True Positive No evidence 1098.7 9.6 986.7 24.0 −112.0
48 True Positive No evidence 2091.7 11.6 1896.3 39.3 −195.3
49 True Positive No evidence 965.0 34.9 861.3 20.0 −103.7
50 True Positive Y (Buonocore, 2010) 1150.0 22.9 1236.0 20.2 86.0
51 True Positive Y (Brown, 1954) 1701.7 30.9 1608.3 14.3 −93.3
52 True Positive No evidence 990.7 25.9 1053.0 81.5 62.3
53 True Positive No evidence 909.7 12.7 880.0 63.3 −29.7
54 True Positive No evidence 638.0 21.4 614.3 34.9 −23.7
55 True Positive No evidence 840.0 67.9 634.0 10.0 −206.0
56 True Positive No evidence 1566.0 3.8 1420.7 27.7 −145.3
57 True Positive No evidence 2670.7 133.7 2739.7 146.4 69.0
58 True Positive No evidence 2605.7 61.1 2023.3 22.1 −582.3
59 True Positive No evidence 1751.3 37.6 1691.3 58.4 −60.0
60 True Positive No evidence 1429.0 36.5 1386.7 17.7 −42.3
61 True Positive No evidence 1846.7 37.9 1672.7 15.1 −174.0
62 True Positive No evidence 2309.7 128.4 1616.0 125.3 −693.7
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Table 2. ROS = Reactive oxygen species; CL = Crosslinker; OS I = OptiSafe without Ascorbic acid; OS II = OptiSafe with Ascorbic acid; Avg. OD = Average optical density value; SE = Standard error; Avg. = Average; ΔOD = Change in OD value;

1 =

No Evidence was recorded for echemicals where a literature search failed to reveal evidence of the chemical forming ROS or crosslinks;

2 =

Yes (Y) and identifies those chemicals were a literature search identified a report of chemical reactivity to form ROS or crosslinks. Parenthesis identifies the reference source.

Table 3 provides a breakdown of the TN, FP, and TP chemicals into either the nonreactive or reactive (ROS/CL) subgroups and presents the average and SE of the OD measurements before (OS I) and after (OS II) the addition of ascorbic acid as well as the average and SE of the difference (OS II – OS I). A two-way ANOVA identified that ascorbic acid significantly lowered the OD values for FP chemicals with reactive chemistries but had no significant effect on the OD values of TNs or TPs, regardless of chemistry, or FPs with nonreactive chemistries. Further, ascorbic acid significantly lowered the OD values of the reactive FP chemicals compared to the nonreactive FP chemicals. Overall, the effect of ascorbic acid is best demonstrated in the scatter plots shown in Figure 1. Plotting of OD measurements with and without ascorbic acid for TN chemicals showed virtually no effect of ascorbic acid regardless of reactive chemistries (Figure 1A). Similarly, ascorbic acid showed no effect on the OD measurements of TPs, with and without reactive chemistries, and FPs without reactive chemistries (Figure 1B). However, ascorbic acid appeared to specifically lower all FP chemicals with reactive chemistries as shown by the red trendline (Figure 1B).

Table 3.

Effect of Ascorbic Acid by Chemical Classification and Reactivity

Classification Number OS I OS II P-valule1 OS II - OS I P-value2
Avg.OD SE Avg. OD SE Avg. ΔOD SE
True Negative
 Non Reactive 13 307.2 16.9 300.1 14.5 NS −7.1 14.5 NS
 Reactive 5 299.9 18.0 295.1 11.0 NS −4.8 6.0
False Positives
 Non Reactive 8 1044.8 266.2 938.6 277.4 NS −105.8 14.1 <0.05
 Reactive 10 1009.5 251.6 411.8 57.4 <0.05 −597.7 76.9
True Positives
 Non Reactive 21 1452.1 136.0 1318.0 124.2 NS −134.1 40.8 NS
 Reactive 5 1450.5 252.3 1456.3 300.5 NS 5.8 134.8
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Table 3. OS I = OptiSafe without Ascorbic acid; OS II = OptiSafe with Ascorbic acid; Avg. OD = Average optical density value; SE = Standard error; Avg. ΔOD = Average change in OD value;

1 =

Effects of ascorbic acid on measured OD of chemicals;

2 =

Comparison of the effects of ascorbic acid on non-reactive versus reactive chemicals;

NS = Not significant.

Figure 1.

Figure 1.

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A. Scatter plots for true negatives test chemicals without (TN) and with (TN-ROS) reactive chemistry. Note that the trend lines show no effect of ascorbic acid. B. Scatter plots for true positives and false positives without (TP and FP, respectively) and with (TP-ROS and FP-ROS, respectively) ascorbic acid. Note that the trend line for the FP reactive chemicals (FP-ROS) shows a dramatic effect on the OD measurements. OD = Optical density value; TN = True negative; TP = True positive; FP = False positive; ROS = Reactive oxygen species.

Discussion

In our previous studies, we have shown that there is a group of chemicals that are generally misclassified by most, if not all, alternative ocular irritation tests (Lebrun et al., 2020). Analysis of these FP chemicals identified that many were associated with reactive chemistries, particularly those capable of forming ROS and acting as molecular CLs. The cornea is well known to have intracellular and extracellular defense mechanisms that protect against oxidative damage, particularly against UV injury (Chen et al., 2013). In a survey of corneal antioxidants, we identified that the ocular tear film also contains important antioxidants, including but not limited to ascorbic acid, which is in particularly high concentrations in the tears, the cornea, and aqueous humor. Recently, we tested the effects of ascorbic acid on a limited subset of reactive chemicals that showed FP classification using the macromolecular alternative ocular irritation test, OptiSafe (Lebrun et al., 2021). In that study, we showed that ascorbic acid significantly reduced the OptiSafe score for some FP reactive chemicals, while showing little effect on the OptiSafe score for a few chemicals classified as TP irritants or NC chemicals.

In this study, we confirm and extend our previous findings and show that the antioxidant, ascorbic acid, has a very specific effect on reactive chemicals that have been classified falsely as irritants/corrosives in the OptiSafe test. The study goes on to show using a validation test set, assembled by an outside source (NICETAM), that reactive chemicals are significantly more likely to be detected as FP chemicals compared to either TN or TP chemicals in our OptiSafe test. Since we have previously shown that many of these chemicals are also misidentified by other alternative tests, it is likely that this inability to correctly identify this set of chemicals has widespread implications for the design and predictability of alternative testing strategies.

First, our findings support the hypothesis that the ocular surface tear film plays an important role in modifying the properties of chemicals that are exposed to the eye. While in this specific case, ascorbic acid has been shown to significantly reduce the effects of FP reactive chemicals, other yet-to-be-identified tear components may have complementary or contrasting effects. These effects, if not taken into consideration in alternative tests, may explain other common mispredictions, particularly if shown to be consistent for the same chemicals between different alternative tests. To our knowledge, the effects of the tear film on the ocular irritation response has not been taken into consideration in modeling ocular irritation or the development of alternative ocular irritation tests and clearly requires further study.

Second, it was surprising to discover that ascorbic acid has such a specific effect on FP reactive chemicals, but not on other reactive chemicals that were correctly identified. Since ascorbic acid acts as a free radical scavenger, it was expected that ascorbic acid would similarly affect all chemicals with ROS or CL chemistries. Since this is not the apparent case, at least when concentrations of ascorbic acid are used at physiological levels, ascorbic acid must have a unique functional role within the tear film. It will therefore be important to assess the effect of ascorbic acid in other eye irritation testing strategies and determine whether similar effects on reducing the misclassification of reactive chemicals can be realized. This possibility also underscores the importance of the continued refinement of current eye irritation tests and the development of improved physiological models that more accurately recapitulate the intact eye.

Conclusion

The results of this study suggest that the tear-related antioxidant ascorbic acid specifically inactivates reactive molecules not associated with GHS ocular irritation before they damage macromolecules, offering an explanation for why some of these chemicals are FPs when tested with in vitro eye irritation tests.

Highlights:

  • A previous study found that a tear-related antioxidant (ascorbic acid) reduced the false-positive rate of the OptiSafe macromolecular eye irritation test, but only a limited number of chemicals were tested.

  • In the current study, chemicals from a prior validation study were retested with ascorbic acid.

  • Results indicate that the addition of ascorbic acid specifically reduced the false-positive rate.

Acknowledgments

Supported in part by NIEHS Small Business Innovative Research Grant, R44ES025501 (Lebrun), Unrestricted Grant from Research to Prevent Blindness, Inc. RPB-203478, and the Skirball program in Molecular Ophthalmology Research to Prevent Blindness, Inc.

Footnotes

Declaration of interests

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Stewart Lebrun reports financial support was provided by National Institute of Environmental Health Sciences. James V. Jester reports financial support was provided by Research to Prevent Blindness, Inc. and Skirball program in Molecular Ophthalmology Research to Prevent Blindness, Inc. Stewart Lebrun reports a relationship with Lebrun Labs LLC that includes: employment, equity or stocks, and funding grants. James V. Jester reports a relationship with Lebrun Labs LLC that includes: consulting or advisory. Sara Chavez reports a relationship with Lebrun Labs LLC that includes: employment. Roxanne Chan reports a relationship with Lebrun Labs LLC that includes: employment. Linda Nguyen reports a relationship with Lebrun Labs LLC that includes: employment. Stewart Lebrun has patent #“Biochemistry based ocular toxicity assay”. Issued Patent Number US 20160290982 A1 issued to Stewart Lebrun Stewart Lebrun has patent #“Formulations and Methods Related to Eye Irritation”. Patent Application Number 17/203467 pending to Lebrun Labs LLC Lebrun Labs LLC and Stewart Lebrun developed the OptiSafe test, sell the OptiSafe test as a kit and provide testing services for the OptiSafe test. The patent Biochemistry Based Ocular Toxicity Assay, Publication number: 20160290982 that covers the OptiSafe test and patent application Methods and Reagents to Improve the Specificity, Sensitivity and Accuracy of Nonanimal Eye Safety Tests, application number 63048112 that covers the use of antioxidants as described in this publication are owned by Stewart Lebrun.

Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Small Business Innovative Research Grant Award Numbers R44ES025501. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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