Quantitative modelling of Quantitative modelling of
This content explores the quantitative modelling of human potency through regression analysis and correlation studies. It discusses the relationship between experimental human and murine skin sensitization induction thresholds, providing insights on predicting human potency and the use of EC3 to define NOEL. The analysis delves into the interpretation of regression equations and the direct modelling of human potency from chemistry data. Additionally, it categorizes aliphatic aldehydes based on their skin sensitizing potency.
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Quantitative modelling of Quantitative modelling of human potency human potency D W Roberts* A M Api and T W Schultz With acknowledgement to Nora Aptula
How well does LLNA model human potency? Api, Lalko and Basketter, 2015. Correlation between experimental human and murine skin sensitization induction thresholds Cutaneous and Ocular Toxicology 34 (4) 298-302 Overall good agreement between EC3 and NOEL, with some outliers Group 1 good agreement 42 cases Group 2 LLNA underpredicts NOEL 7 cases Group 3 LLNA overpredicts NOEL 7 cases
For regression analysis For regression analysis Group 1 (good agreement) Remove cases where EC3 or NOEL is given as <x or >x Remove cases footnoted No sensitization was observed in human predictive studies. Doses reported reflect the highest concentration tested, not necessarily the highest achievable NOEL This leaves 31 cases
Regression analysis Regression analysis logNOEL vs logEC3 5 4.5 4 3.5 3 y = 1.0404x - 0.0917 R = 0.7862 2.5 2 2 2.5 3 3.5 4 4.5 logNOEL = 1.04( 0.10)logEC3 0.09(( 0.34) n = 31, R2 = 0.786, AdjR2 = 0.779, s = 0.24, F = 107
Interpretation of regression equation Interpretation of regression equation logNOEL = 1.04( 0.10)logEC3 0.09(( 0.34) n = 31, R2 = 0.786, AdjR2 = 0.779, s = 0.24, F = 107 s (standard deviation of residuals) = 0.24 corresponds to 95% confidence limits of a factor of 3 on the NOEL as predicted from the EC3 Slope and intercept not significantly different from 1 and 0 respectively, i.e. . EC3 can be used directly to define NOEL If outliers can be recognised
Can we model human potency directly from chemistry? Can we model human potency directly from chemistry? 20 Aliphatic aldehydes* with human NOEL data: -Methyl-phenylacetaldehyde; 1,2,3,4,5,6,7,8-Octahydro-8,8-dimethyl-2-naphthaldehyde; Phenylacetaldehyde; Citral; Cuminyl acetaldehyde; Bourgeonal; p-Methylhydrocinnamic aldehyde; p-Isobutyl- -methyl hydrocinnamaldehyde; Hydroxycitronellal; Lilial; Landolal (Lyral); Methoxy dicyclopentadiene carboxaldehyde; Triplal; 2-Methyl-3-(p-methoxyphenyl)propanal; Cyclamen aldehyde; Heptanal, 6-methoxy- 2,6-dimethyl-; 3-Phenylbutanal; Citronellal; Isocyclocitral; -Methyl-1,3-benzodioxole-5-propionaldehyde From Basketter et al., . (2014) Categorization of chemicals according to their relative human skin sensitizing potency. Dermatitis 25 (1), 11-21 and IFRA 2015. IFRA standard 48th amendment * Aliphatic aldehydes are defined as those not having an aromatic carbon bonded directly to the C=O group
Modelling parameters Modelling parameters Reactivity Taft substituent constants for groups bonded to carbonyl, * Hydrophobicity Calculated (Leo and Hansch method) logP (octanol/water)
QMM plot QMM plot -0.20 pNOELobs -0.40 Outlier -0.60 -0.80 -1.00 -1.20 -1.40 -1.60 pNOEL = 2.34 + 0.19 logP - 2.62 -1.80 -2.00 -2.00 -1.80 -1.60 -1.40 -1.20 -1.00 -0.80 -0.60 -0.40 pNOELcalc -0.20
QMM equation QMM equation pNOEL = 2.34( 0.33) * + 0.19( 0.07) logP - 2.62( 0.22) n = 19, R2 = 0.770, R2(adj) = 0.741, s = 0.20, F = 27 Error limits (from s value) correspond to a factor of <2.2 between observed and calculated NOEL levels similar to variability of LLNA Except for one outlier, ca 8 times as potent as calculated
Outlier has 6 allylic Hydrogens able to give Outlier has 6 allylic Hydrogens able to give tert tert- -allylic hydroperoxides allylic hydroperoxides
Conclusions Conclusions LLNA EC3 predicts NOEL directly for most chemicals Underpredictions of potency can be attributed to and anticipated for: Aromatic Schiff base electrophiles Chemicals likely to contain impurities/by-products from synthesis Pro-/pre-haptens with complex activation pathways Overpredictions of potency can be attributed to and anticipated for: Chemicals readily susceptible to autoxidation under LLNA conditions Physical-organic chemistry principles underlying LLNA potency also apply to human potency Other reaction mechanistic domains need to investigated similarly
Outliers: potency Outliers: potency underpredicted underpredicted by LLNA by LLNA EC3 ( g/cm2) NOEL ( g/cm2) Chemical Benzaldehyde > 6250 590 Vanillin > 1250 1100 Trans-2-Hexenal 1012 24 6-Methyl-3,5-heptadiene-2-one 1250 110 2-Methoxy-4-methylphenol 1450 118 Methyl 2-nonynoate 625 24 Treemoss absolute > 5000 700 Treemoss absolute not considered further potency variable depending on composition, particularly atranol and chloratranol content
Underpredicted Underpredicted benzaldehyde benzaldehyde and vanillin and vanillin Vanilin Benzaldehyde O O OMe OH Schiff base electrophiles, aromatic Most aromatic aldehydes are weak or NS in LLNA, weaker than predicted by the QMM for SB: pEC3 = 1.12 * + 0.42logP 0.62 (Roberts et al 2007), developed from data on aliphatics
QMM prediction for QMM prediction for benzaldehyde benzaldehyde and vanillin and vanillin Although aromatic aldehydes are outside the applicability domain The QMM predicts (assuming NOEL = predicted EC3): Benzaldehyde NOEL 1078 (actual 590) factor of 1.8 Vanillin NOEL 3935 (actual 1181) factor of 3.3 Within/close to 95% confidence limits of logNOEL vs logEC3 regression
Underpredicted Underpredicted potency: 6 6- -methyl methyl- -3,5 potency: trans 3,5- -heptadienal heptadienal trans- -2 2- -hexenal and hexenal and Michael acceptors, volatile but NOEL potency < predicted from Michael acceptor QMM, so volatility alone cannot explain the large underpredictions Impurities (eg from aldol dimerisation) in samples tested in HRIPT may be responsible
Underpredicted Underpredicted potency potency 2 2- -methoxy methoxy- -4 4- -methylphenol methylphenol O OH O Quinone OH OMe OH Quinone methide OH O Pro- or pre-electrophile, activated by oxidation, either after metabolic demethylation or directly to quinone methide. Free radical mechanisms also possible. Variety of possible mechanisms makes inter-species variation more likely
Underpredicted Underpredicted potency potency 2 2- -methoxy methoxy- -4 4- -methylphenol methylphenol O OH O Quinone OH OMe OH Quinone methide OH O Pro- or pre-electrophile, activated by oxidation, either after metabolic demethylation or directly to quinone methide. Free radical mechanisms also possible. Variety of possible mechanisms makes inter-species variation more likely
Underpredicted Underpredicted potency potency methyl 2 methyl 2- -nonynoate nonynoate EC3 NOEL MA QMM prediction Me 2-nonynoate 625 24 450 Me 2-octynoate <125 118 412 Simplest interpretation: The EC3 value of 625 for methyl 2-nonynoate is correct The NOEL value of 24 for methyl 2-nonynoate is anomalous The EC3 value of <125 for methyl 2-octynoate is anomalous The NOEL of 118 for methyl 2-octynoate is anomalous This pattern suggests that the recorded potency values are influenced by potent impurities present in the samples tested, except for methyl 2-nonynoate (LLNA) which must have contained only insignificant levels. 20th century literature says that potency of these ynoates is low when freshly synthesised but increases with age (English and Rycroft 1988) consistent with the impurity interpretation QMM: pEC3 (mol%) = 0.24logk + 2.11 Roberts, D.W., Natsch, A. (2009). High throughput kinetic profiling approach for covalent binding to peptides: application to skin sensitization potency of Michael acceptor electrophiles. Chemical Research in Toxicology 22, 592-603. English JS and Rycroft RJ.1988. Allergic contact Dermatitis from methyl octine and methyl heptine carbonates. Contact Dermtitis 18: 174-175
Outliers: potency Outliers: potency overpredicted overpredicted by LLNA by LLNA EC3 ( g/cm2) NOEL ( g/cm2) Chemical -Amyl cinnamal -Hexyl cinnamal 2420 23600 2372 23600 Benzyl salicylate 725 17700 Hexyl salicylate 45 35400 Isocyclocitral -iso-Methylionone 1825 7000 5450 70000 OTNE 6825 47200 Benzyl and hexyl salicylate EC3 values are anomalous compared to other salicylates (weak or NS). By-products from synthesis suspected. The other 5 may be explained by autoxidation being favoured under LLNA open application conditions
Overpredicted Overpredicted by LLNA by LLNA Amyl Amyl- - and hexyl and hexyl- -cinnamal cinnamal Ph OOH Ph O2 O O R Peracid, reactive epoxidising agent R OH Ph Ph O O O R R Highly electrophilic benzylic epoxide Extent of this reaction, and hence degree of sensitization, depends on accessibility to oxygen
Overpredicted Overpredicted by LLNA by LLNA hydroperoxide hydroperoxide precursors precursors O O O O O2 HOO HOO O2 Isocylcocitral, 1335-66-6 One of 3 tertiary allylic hydroperoxides that can result from autoxidation a-iso-Methylionone, 127-51-5 Doubly allylic tertiary hydroperoxide that can result from autoxidation OOH O2 O OTNE, 54464-57-2 One of 3 tertiary allylic hydroperoxides that can result from autoxidation
Conclusions Conclusions LLNA EC3 predicts NOEL directly for most chemicals Underpredictions of potency can be attributed to and anticipated for: Aromatic Schiff base electrophiles Chemicals likely to contain impurities/by-products from synthesis Pro-/pre-haptens with complex activation pathways Overpredictions of potency can be attributed to and anticipated for: Chemicals readily susceptible to autoxidation under LLNA conditions (Consistent with earlier findings for LLNA false positives )
