Alternatives to statistical decision trees in regulatory (eco-)toxicological bioassays.

Szczegóły
Abstrakt

Tytuł:: Alternatives to statistical decision trees in regulatory (eco-)toxicological bioassays.
Autorzy:: Kluxen FM; ADAMA Deutschland GmbH, Edmund-Rumpler-Strasse 6, 51149, Cologne, Germany. .
Hothorn LA; Lauenau, Germany.
Źródło:: Archives of toxicology [Arch Toxicol] 2020 Apr; Vol. 94 (4), pp. 1135-1149. Date of Electronic Publication: 2020 Mar 19.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: Berlin, New York, Springer-Verlag.
MeSH Terms:: Biological Assay*
Decision Trees*
Toxicity Tests/*methods
Research Design ; Risk Assessment
References:: Altman DG, Bland JM (1995) Statistics notes: absence of evidence is not evidence of absence. BMJ 311(7003):485. https://doi.org/10.1136/bmj.311.7003.485. (PMID: 10.1136/bmj.311.7003.48576476442550545)
Amrhein V, Korner-Nievergelt F, Roth T (2017) The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research. PeerJ 5:e3544. https://doi.org/10.7717/peerj.3544. (PMID: 10.7717/peerj.3544286988255502092)
Amrhein V, Greenland S, McShane B (2019) Retire statistical significance. Nature 567:305–307. https://doi.org/10.1038/d41586-019-00857-9. (PMID: 10.1038/d41586-019-00857-930894741)
Anderson TW, Darling DA (1952) Asymptotic theory of certain "goodness of fit" criteria based on stochastic processes. Ann Math Statist 23(2):193–212. https://doi.org/10.1214/aoms/1177729437. (PMID: 10.1214/aoms/1177729437)
Anscombe FJ (1973) Graphs in statistical analysis. Am Stat 27(1):17–21. https://doi.org/10.1080/00031305.1973.10478966. (PMID: 10.1080/00031305.1973.10478966)
Bartlett MS (1937) Properties of sufficiency and statistical tests. Proc R Soc Lond A Math Phys Sci 160(901):268–282. https://doi.org/10.1098/rspa.1937.0109. (PMID: 10.1098/rspa.1937.0109)
Bland JM, Altman DG (2009) Analysis of continuous data from small samples. BMJ 338:a3166. https://doi.org/10.1136/bmj.a3166. (PMID: 10.1136/bmj.a316619349342)
Box G, Cox D (1964) An analysis of transformations. Proc R Soc Lond A Math Phys Sci 26:211–252.
Cleveland WS (1993) Visualizing data. At & T Bell Laboratories, Murray Hill.
Conover WJ, Johnson ME, Johnson MM (1981) A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23(4):351–361. https://doi.org/10.2307/1268225. (PMID: 10.2307/1268225)
Cook RD (1977) Detection of influential observation in linear regression. Technometrics 19(1):15–18. https://doi.org/10.2307/1268249. (PMID: 10.2307/1268249)
Cumming G (2014) The new statistics: why and how. Psychol Sci 25(1):7–29. https://doi.org/10.1177/0956797613504966. (PMID: 10.1177/095679761350496624220629)
Dallal GE, Wilkinson L (1986) An analytic approximation to the distribution of Lilliefors's test statistic for normality. Am Stat 40(4):294–296. https://doi.org/10.1080/00031305.1986.10475419. (PMID: 10.1080/00031305.1986.10475419)
Dean RB, Dixon WJ (1951) Simplified statistics for small numbers of observations. Anal Chem 23(4):636–638. https://doi.org/10.1021/ac60052a025. (PMID: 10.1021/ac60052a025)
Delignette-Muller ML, Forfait C, Billoir E, Charles S (2011) A new perspective on the Dunnett procedure: filling the gap between NOEC/LOEC and ECx concepts. Environ Toxicol Chem 30(12):2888–2891. https://doi.org/10.1002/etc.686. (PMID: 10.1002/etc.68621932292)
Dilba G, Bretz F, Guiard V, Hothorn LA (2004) Simultaneous confidence intervals for ratios with applications to the comparison of several treatments with a control. Methods Inf Med 43(5):465–469. (PMID: 10.1055/s-0038-1633899)
Drezner Z, Turel O, Zerom D (2010) A modified Kolmogorov–Smirnov test for normality. Commun Stat Simul Comput 39(4):693–704. https://doi.org/10.1080/03610911003615816. (PMID: 10.1080/03610911003615816)
Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56:52–64. https://doi.org/10.1080/01621459.1961.10482090. (PMID: 10.1080/01621459.1961.10482090)
Dunnett CW (1955) A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50(272):1096–1121. https://doi.org/10.2307/2281208. (PMID: 10.2307/2281208)
Ekstrøm CT (2014) Teaching ‘instant experience’ with graphical model validation techniques. Teach Stat 36(1):23–26. https://doi.org/10.1111/test.12027. (PMID: 10.1111/test.12027)
European Commission (2013) COMMISSION REGULATION (EU) No 283/2013 of 1 March 2013 setting out the data requirements for active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market. OJ L 93/1.
Farouki RT (2012) The Bernstein polynomial basis: a centennial retrospective. Comput Aided Geom Des 29(6):379–419. https://doi.org/10.1016/j.cagd.2012.03.001. (PMID: 10.1016/j.cagd.2012.03.001)
Festing M (1993) Genetic variation in outbred rats and mice and its implications for toxicological screening. J Exp Anim Sci 35(5–6):210–220. (PMID: 8218436)
Fisher RA (1925) Statistical methods for research workers. Oliver & Boyd, Edinburgh.
Fosang AJ, Colbran RJ (2015) Transparency is the key to quality. J Biol Chem 290(50):29692–29694. https://doi.org/10.1074/jbc.E115.000002. (PMID: 10.1074/jbc.E115.000002266577534705984)
Fox DR, Landis WG (2016) Don't be fooled—a no-observed-effect concentration is no substitute for a poor concentration–response experiment. Environ Toxicol Chem 35(9):2141–2148. https://doi.org/10.1002/etc.3459. (PMID: 10.1002/etc.345927089534)
Gandrud C (2015) Reproducible research with R and R studio. Chapman and Hall/CRC, New York.
Greenland S (2019) Valid P-values behave exactly as they should: some misleading criticisms of P-values and their resolution with S-values. Am Stat 73(sup1):106–114. https://doi.org/10.1080/00031305.2018.1529625. (PMID: 10.1080/00031305.2018.1529625)
Greenland S, Senn SJ, Rothman KJ et al (2016) Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 31(4):337–350. https://doi.org/10.1007/s10654-016-0149-3. (PMID: 10.1007/s10654-016-0149-32720900927209009)
Hahn GJ, Meeker WQ (1991) Statistical intervals—a guide for practitioners. Wiley, New York. (PMID: 10.1002/9780470316771)
Hamada C (2018) Statistical analysis for toxicity studies. J Toxicol Pathol 31(1):15–22. https://doi.org/10.1293/tox.2017-0050. (PMID: 10.1293/tox.2017-005029479136)
Hardy A, Benford D, Halldorsson T et al (2017) Update: use of the benchmark dose approach in risk assessment. EFSA J 15(1):e04658. https://doi.org/10.2903/j.efsa.2017.4658. (PMID: 10.2903/j.efsa.2017.4658)
Hasler M (2016) Heteroscedasticity: multiple degrees of freedom vs sandwich estimation. Stat Pap 57(1):55–68. https://doi.org/10.1007/s00362-014-0640-4. (PMID: 10.1007/s00362-014-0640-4)
Hasler M, Hothorn LA (2008) Multiple contrast tests in the presence of heteroscedasticity. Biom J 50(5):793–800. https://doi.org/10.1002/bimj.200710466. (PMID: 10.1002/bimj.20071046618932141)
Hawkins DM (1980) Identification of outliers. Chapman and Hall, New York. (PMID: 10.1007/978-94-015-3994-4)
Herberich E, Hothorn LA (2012) Statistical evaluation of mortality in long-term carcinogenicity bioassays using a Williams-type procedure. Regul Toxicol Pharmacol 64(1):26–34. https://doi.org/10.1016/j.yrtph.2012.06.014. (PMID: 10.1016/j.yrtph.2012.06.01422749913)
Herberich E, Sikorski J, Hothorn T (2010) A robust procedure for comparing multiple means under heteroscedasticity in unbalanced designs. PLoS ONE 5(3):e9788. https://doi.org/10.1371/journal.pone.0009788. (PMID: 10.1371/journal.pone.0009788203609602847912)
Hoffman D, Berger M (2011) Statistical considerations for calculation of immunogenicity screening assay cut points. J Immunol Methods 373(1–2):200–208. https://doi.org/10.1016/j.jim.2011.08.019. (PMID: 10.1016/j.jim.2011.08.01921906599)
Hothorn L (1989) Robustness study on Williams- and Shirley-procedure, with application in toxicology. Biom J 31(8):891–903. https://doi.org/10.1002/bimj.4710310802. (PMID: 10.1002/bimj.4710310802)
Hothorn LA (2014) Statistical evaluation of toxicological bioassays—a review. Toxicol Res 3(6):418–432. https://doi.org/10.1039/c4tx00047a. (PMID: 10.1039/c4tx00047a)
Hothorn LA (2016a) Statistics in toxicology using R. CRC Press, Boca Raton. (PMID: 10.1201/b19659)
Hothorn LA (2016b) The two-step approach—a significant ANOVA F-test before Dunnett's comparisons against a control—is not recommended. Commun Stat Theory Methods 45(11):3332–3343. https://doi.org/10.1080/03610926.2014.902225. (PMID: 10.1080/03610926.2014.902225)
Hothorn T (2018) Most likely transformations: the mlt package. J Stat Softw.
Hothorn LA, Hasler M (2008) Proof of hazard and proof of safety in toxicological studies using simultaneous confidence intervals for differences and ratios to control. J Biopharm Stat 18(5):915–933. https://doi.org/10.1080/10543400802287511. (PMID: 10.1080/1054340080228751118781525)
Hothorn LA, Kluxen FM (2019) Robust multiple comparisons against a control group with application in toxicology arXiv.
Hothorn LA, Pirow R (2019) Use compatibility intervals in regulatory toxicology [submitted to Regulatory Toxicology and Pharmacology].
Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biom J 50(3):346–363. https://doi.org/10.1002/bimj.200810425. (PMID: 10.1002/bimj.20081042518481363)
Hothorn T, Möst L, Bühlmann P (2018) Most Likely Transformations. Scand J. Stat 45(1):110–134. https://doi.org/10.1111/sjos.12291. (PMID: 10.1111/sjos.12291)
Igl B-W, Bitsch A, Bringezu F et al (2019) The rat bone marrow micronucleus test: statistical considerations on historical negative control data. Regul Toxicol Pharmacol 102:13–22. https://doi.org/10.1016/j.yrtph.2018.12.009. (PMID: 10.1016/j.yrtph.2018.12.00930572081)
Jaki T, Hothorn LA (2013) Statistical evaluation of toxicological assays: Dunnett or Williams test-take both. Arch Toxicol 87(11):1901–1910. https://doi.org/10.1007/s00204-013-1065-x. (PMID: 10.1007/s00204-013-1065-x23652543)
Jarvis P, Saul J, Aylott M, Bate S, Geys H, Sherington J (2011) An assessment of the statistical methods used to analyse toxicology studies. Pharm Stat 10(6):477–484. https://doi.org/10.1002/pst.527. (PMID: 10.1002/pst.52722140058)
Jensen SM, Kluxen FM, Ritz C (2019) A review of recent advances in benchmark dose methodology. Risk Anal 39(10):2295–2315. (PMID: 10.1111/risa.13324)
Kluxen FM (2019a) "New Statistics” for regulatory toxicology? [submitted, preprint available https://doi.org/10.13140/RG.2.2.14639.48803 ].
Kluxen FM (2019b) Scatter plotting as a simple tool to analyse relative organ to body weight in toxicological bioassays. Arch Toxicol 93(8):2409–2420. https://doi.org/10.1007/s00204-019-02509-3. (PMID: 10.1007/s00204-019-02509-331263913)
Kobayashi K, Pillai KS, Sakuratani Y, Abe T, Kamata E, Hayashi M (2008) Evaluation of statistical tools used in short-term repeated dose administration toxicity studies with rodents. J Toxicol Sci 33(1):97–104. (PMID: 10.2131/jts.33.97)
Koller M, Stahel WA (2011) Sharpening Wald-type inference in robust regression for small samples. Comput Stat Data Anal 55(8):2504–2515. https://doi.org/10.1016/j.csda.2011.02.014. (PMID: 10.1016/j.csda.2011.02.014)
Konietschke F, Placzek M, Schaarschmidt F, Hothorn LA (2015) nparcomp: an R software package for nonparametric multiple comparisons and simultaneous confidence intervals. J Stat Softw 64(9):17. https://doi.org/10.18637/jss.v064.i09. (PMID: 10.18637/jss.v064.i09)
Kozak M (2009) Analyzing one-way experiments: a piece of cake or pain in the neck? Sci Agric 66(4):556–562. https://doi.org/10.1590/S0103-90162009000400020. (PMID: 10.1590/S0103-90162009000400020)
Kozak M, Piepho HP (2018) What's normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions. J Agron Crop Sci 204(1):86–98. https://doi.org/10.1111/jac.12220. (PMID: 10.1111/jac.12220)
Levene H (1960) Robust tests for equality of variances. In: Olkin I (ed) Contributions to probability and statistics; essays in honor of harold hotelling. Stanford University Press, Palo Alto, pp 278–292.
Lohse T, Rohrmann S, Faeh D, Hothorn T (2017) Continuous outcome logistic regression for analyzing body mass index distributions [version 1; peer review: 3 approved]. F1000Res. https://doi.org/10.12688/f1000research.12934.1. (PMID: 10.12688/f1000research.12934.1292597685721934)
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Statist 18(1):50–60. https://doi.org/10.1214/aoms/1177730491. (PMID: 10.1214/aoms/1177730491)
Matejka J, Fitzmaurice G (2017) Same stats, different graphs. Paper presented at the proceedings of the 2017 CHI conference on human factors in computing systems—CHI '17.
Na J, Yang H, Bae S, Lim K-M (2014) Analysis of statistical methods currently used in toxicology journals. Toxicol Res 30(3):185–192. https://doi.org/10.5487/TR.2014.30.3.185. (PMID: 10.5487/TR.2014.30.3.185253430124206745)
National Toxicology Program (2010) Toxicology and carcinogenesis studies of sodium dichromate dihydrate (CAS No. 7789-12-0) in F344/N rats and B6C3F1 mice (Drinking water studies). Technical report.
Nature methods editorial (2014) Kick the bar chart habit. Nat Methods 11:113. https://doi.org/10.1038/nmeth.2837. (PMID: 10.1038/nmeth.2837)
Nuzzo R (2014) Scientific method: statistical errors – P values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume. Nature 506:150–152. (PMID: 10.1038/506150a)
OECD (1998) Test no. 409: repeated dose 90-day oral toxicity study in non-rodents OECD guidelines for the testing of chemicals, section 4. OECD Publishing, Paris. (PMID: 10.1787/9789264070721-en)
OECD (2008) Test no. 407: repeated dose 28-day oral toxicity study in rodents. OECD Publishing, Paris. (PMID: 10.1787/9789264070684-en)
OECD (2010) Section 4: statistical and dose response analysis, including benchmark dose and linear extrapolation, NOAELS and NOELS, LOAELS and LOELS OECD guidance document for the design and conduct of chronic toxicity and carcinogenicity studies, supporting TG 451, 452 and 453. OECD Publishing, Paris.
OECD (2014a) Current approaches in the statistical analysis of ecotoxicity data. OECD Publishing, Paris. (PMID: 10.1787/9789264085275-en)
OECD (2014b) Guidance document 116 on the conduct and design of chronic toxicity and carcinogenicity studies, supporting test guidelines 451, 452 and 453. OECD Publishing, Paris. (PMID: 10.1787/9789264221475-en)
OECD (2014c) No. 198 report on statistical issues related to OECD test guidelines (tgs) on genotoxicity. OECD Publishing, Paris.
OECD (2016) Test no. 474: mammalian erythrocyte micronucleus test. OECD Publishing, Paris. (PMID: 10.1787/9789264264762-en)
OECD (2016) Test no.: in vitro mammalian cell micronucleus test 487. OECD Publishing, Paris. (PMID: 10.1787/9789264264861-en)
OECD (2018a) Test no. 408: repeated dose 90-day oral toxicity study in rodents. OECD Publishing, Paris. (PMID: 10.1787/9789264070707-en)
OECD (2018b) Test no. 451: carcinogenicity studies. OECD Publishing, Paris. (PMID: 10.1787/9789264071186-en)
OECD (2018c) Test no. 453: combined chronic toxicity/carcinogenicity studies. OECD Publishing, Paris. (PMID: 10.1787/9789264071223-en)
Pallmann P, Hothorn LA (2016) Boxplots for grouped and clustered data in toxicology. Arch Toxicol 90(7):1631–1638. https://doi.org/10.1007/s00204-015-1608-4. (PMID: 10.1007/s00204-015-1608-426438403)
R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
Ramsey FL, Schafer DW (2002) The statistical sleuth: a course in methods of data analysis. Thomson Learning, Duxbury.
Salsburg D (2002) The lady tasting tea: how statistics revolutionized science in the twentieth century. Freeman, New York.
Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biometr Bull 2(6):110–114. https://doi.org/10.2307/3002019. (PMID: 10.2307/300201920287815)
Schaarschmidt F, Biesheuvel E, Hothorn LA (2009) Asymptotic simultaneous confidence intervals for many-to-one comparisons of binary proportions in randomized clinical trials. J Biopharm Stat 19(2):292–310. https://doi.org/10.1080/10543400802622501. (PMID: 10.1080/1054340080262250119212881)
Schaarschmidt F, Sill M, Hothorn LA (2008) Poly-k-trend tests for survival adjusted analysis of tumor rates formulated as approximate multiple contrast test. J Biopharm Stat 18(5):934–948. https://doi.org/10.1080/10543400802294285. (PMID: 10.1080/1054340080229428518781526)
Schmidt K, Schmidtke J, Kohl C et al (2016) Enhancing the interpretation of statistical P values in toxicology studies: implementation of linear mixed models (LMMs) and standardized effect sizes (SESs). Arch Toxicol 90(3):731–751. https://doi.org/10.1007/s00204-015-1487-8. (PMID: 10.1007/s00204-015-1487-825724152)
Schucany WR, Tony Ng HK (2006) Preliminary goodness-of-fit tests for normality do not validate the one-sample student t. Commun Stat Theory Methods 35(12):2275–2286. https://doi.org/10.1080/03610920600853308. (PMID: 10.1080/03610920600853308)
Shaffer JP (1995) Multiple hypothesis testing. Annu Rev Psychol 46(1):561–584. https://doi.org/10.1146/annurev.ps.46.020195.003021. (PMID: 10.1146/annurev.ps.46.020195.003021)
Steel RGD (1959) A multiple comparison rank sum test: treatments versus control. Biometrics 15(4):560–572. https://doi.org/10.2307/2527654. (PMID: 10.2307/2527654)
Student (1908) The probable error of the mean. Biometrika 6(1):1–25. https://doi.org/10.1093/biomet/6.1.1. (PMID: 10.1093/biomet/6.1.1)
Szocs E, Schafer RB (2015) Ecotoxicology is not normal: a comparison of statistical approaches for analysis of count and proportion data in ecotoxicology. Environ Sci Pollut Res Int 22(18):13990–13999. https://doi.org/10.1007/s11356-015-4579-3. (PMID: 10.1007/s11356-015-4579-325953608)
Tukey JW (1977) Exploratory data analysis. Addison-Wesley Pub. Co, Reading.
U.S. Food and Drug Administration (2001) Guidance for industry: statistical approaches to establishing bioequivalence.
Wasserstein RL, Lazar NA (2016) The ASA's Statement on p-values: context, process, and purpose. Am Stat 70(2):129–133. https://doi.org/10.1080/00031305.2016.1154108. (PMID: 10.1080/00031305.2016.1154108)
Wasserstein RL, Schirm AL, Lazar NA (2019) Moving to a world beyond p < 0.05. Am Stat 73(1):1–19. https://doi.org/10.1080/00031305.2019.1583913. (PMID: 10.1080/00031305.2019.1583913)
Weissgerber TL, Winham SJ, Heinzen EP et al (2019) Reveal, don't conceal: transforming data visualization to improve transparency. Circulation 140(18):1506–1518. https://doi.org/10.1161/CIRCULATIONAHA.118.037777. (PMID: 10.1161/CIRCULATIONAHA.118.03777731657957)
Welch BL (1947) The generalization of `student's' problem when several different population variances are involved. Biometrika 34(1/2):28–35. https://doi.org/10.2307/2332510. (PMID: 10.2307/23325102028781920287819)
Wheeler J (2019) Historical control data for the interpretation of ecotoxicity data: are we missing a trick? Ecotoxicology. https://doi.org/10.1007/s10646-019-02128-9. (PMID: 10.1007/s10646-019-02128-9316964456872505)
Wheeler MW, Bailer AJ (2007) Properties of model-averaged BMDLs: a study of model averaging in dichotomous response risk estimation. Risk Anal 27(3):659–670. https://doi.org/10.1111/j.1539-6924.2007.00920.x. (PMID: 10.1111/j.1539-6924.2007.00920.x17640214)
Wickham H, Stryjewski L (2011) 40 years of boxplots. hadconz.
Wilcox RR (2012) Introduction to robust estimation and hypothesis testing. Academic Press, Amsterdam.
Wilk MB, Shapiro SS (1965) An analysis of variance test for normality (complete samples)†. Biometrika 52(3–4):591–611. https://doi.org/10.1093/biomet/52.3-4.591. (PMID: 10.1093/biomet/52.3-4.591)
Williams DA (1971) A test for differences between treatment means when several dose levels are compared with a zero dose control. Biometrics 27(1):103–117. https://doi.org/10.2307/2528930. (PMID: 10.2307/25289305547548)
Zeileis A (2006) Object-oriented computation of sandwich estimators. J Stat Softw 16(9):16. https://doi.org/10.18637/jss.v016.i09. (PMID: 10.18637/jss.v016.i09)
Zimmerman DW (1996) A note on homogeneity of variance of scores and ranks. J Exp Educ 64(4):351–362. (PMID: 10.1080/00220973.1996.10806603)
Zimmerman DW (2004) A note on preliminary tests of equality of variances. Br J Math Stat Psychol 57(1):173–181. https://doi.org/10.1348/000711004849222. (PMID: 10.1348/00071100484922215171807)
Contributed Indexing:: Keywords: Assumption tests; Hazard characterization; Hazard identification; Pre-tests; Regulatory toxicology; Robust statistics
Entry Date(s):: Date Created: 20200321 Date Completed: 20210308 Latest Revision: 20220304
Update Code:: 20240105
DOI:: 10.1007/s00204-020-02690-w
PMID:: 32193567
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The goal of (eco-) toxicological testing is to experimentally establish a dose or concentration-response and to identify a threshold with a biologically relevant and probably non-random deviation from "normal". Statistical tests aid this process. Most statistical tests have distributional assumptions that need to be satisfied for reliable performance. Therefore, most statistical analyses used in (eco-)toxicological bioassays use subsequent pre- or assumption-tests to identify the most appropriate main test, so-called statistical decision trees. There are however several deficiencies with the approach, based on study design, type of tests used and subsequent statistical testing in general. When multiple comparisons are used to identify a non-random change against negative control, we propose to use robust testing, which can be generically applied without the need of decision trees. Visualization techniques and reference ranges also offer advantages over the current pre-testing approaches. We aim to promulgate the concepts in the (eco-) toxicological community and initiate a discussion for regulatory acceptance.

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