* Laboratory of Molecular Modeling and Design, M/C-781, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 606127231; and
The Procter & Gamble Company, Miami Valley Research Laboratories, P.O. Box 538707, Cincinnati, Ohio 452538707
Received October 2, 2001; accepted December 14, 2001
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ABSTRACT |
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Key Words: QSAR; membrane interaction; skin irritation.
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INTRODUCTION |
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The emergence of animal protection advocacy has driven a search for alternatives to in vivo skin irritation studies. By the end of the 1980`s laws were in place worldwide, which put legal and moral obligations on all concerned to seek to replace, reduce, and/or refine laboratory animal experimentation (Balls, 1999). The alternative approaches to in vivo testing for skin irritation fall into two general classes, in vitro skin irritation screens and computational approaches. One class of in vitro studies utilizes dead human/animal skin for the evaluation of skin properties. Though in vitro studies do not have the advantages of working with living human skin, these studies are easier to perform and have achieved considerable success in predicting the skin irritation potential of individual chemical compounds and mixtures. In vitro tests have been developed using human or animal skin, three-dimensional skin "equivalent" culture systems derived from human skin cells, or noncellular "biobarrier" systems. Human skin can be difficult to obtain in sufficient quantities. Though animal skin and the skin equivalent cultures are more permeable to chemicals than human (Bronaugh et al., 1985), these systems have been more amenable to in vitro methods development as denoted by their recent successful validation (Fentum et al., 1998
; Liebsch et al., 2000
).
The principal computational approach to predicting skin irritation has been to use quantitative structure activity relationship (QSAR) methodology. The QSAR methodology is based on the evaluation of physicochemical properties of chemical compounds and trying to relate these properties to their biological activities. QSAR analyses have achieved considerable success with the prediction of biological activities of molecules when their mechanism of action is known. However, the major limitation of applying QSAR methods to estimating skin irritation is the same limitation that plagues the applicability of the QSAR paradigm to toxicological problems in general. QSAR methods are effectively limited to dealing with analog structure-activity training sets while most structure-toxicity data sets consist of structurally diverse compounds.
Recently, we have developed a methodology that combines structure-based design techniques with the QSAR paradigm (Kulkarni and Hopfinger, 1999; Kulkarni et al., 2001
). The resultant methodology permits structurally diverse training sets to be investigated. The operational key to this approach is to assume that the phospholipid-rich regions of cellular membranes constitute the effective "receptor" for the compounds of the training set. The significance and utility of this approach rest upon the validity of the compound-membrane interaction being a crucial event in the expression of the toxicity. Because of the importance of the membrane in this form of QSAR, we have termed this formalism membrane-interaction (MI)-QSAR analysis.
MI-QSAR analysis has been successfully employed (Kulkarni and Hopfinger, 1999; Kulkarni et al., 2001
) in the prediction of eye irritation potential of chemical compounds. In this article the results are presented from performing a MI-QSAR analysis on a training set of 22 diverse hydroxy alcohol compounds for which Draize skin irritation potencies had been previously determined (RTK web site, 2001). Part of this study includes the comparison of MI-QSAR skin irritation predictions to corresponding predictions made using "traditional" intramolecular 2D-QSAR analyses. This comparison and evaluation demonstrates that MI-QSAR analysis provides more information to estimating skin irritation potency than is realized from traditional intramolecular 2D-QSAR approaches.
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MATERIALS AND METHODS |
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These grades are averaged across all animals and grading time points, and then the averages are combined to derive a primary index (PII). Test materials producing PII values less than 2 are considered mildly irritating, 2 to 5 are moderately irritating, and greater than 5 are severely irritating. Variations around this basic method have formed the regulatory classification of skin corrosion and skin irritation worldwide.
Table 1 contains the PII values for the training set of 22 hydroxy organic compounds (RTK web site, 2001) for which MI-QSAR models were sought. Some of the multiple RTK descriptions of irritation for a compound in the training set are not consistent. For example, 2-tert-butylphenol is not rated as a severe irritant, but one report in its RTK file indicates this chemical produced necrosis. The low irritation score for 2-tert-butylphenol is based on it not producing edema. Overall, we have accepted and used the reported PII values given in the RTK files for the compounds of our training set.
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The cross-correlation matrix of the 4 irritation measures is given in Table 2. For this particular training set there are high cross-correlations between PII and PII (molar) [0.89] and between MES and MAS [0.94]. Such high cross-correlations among molecular weight and molar concentration irritation measures cannot be expected for arbitrary sets of organic compounds. However, the high PII and PII (molar) cross-correlation is an important finding since the range in the PII (molar) values is only 0.00 to 1.94. The narrow range and the relatively high uncertainty in the PII (molar) scores combine to limit the applicability of constructing reliable QSAR models using the PII (molar) data. Basically, the average difference among PII (molar) measures is likely smaller than the average error in making a PII (molar) scoring. However, the PII measures, which span a range of 0.00 to 8.00, can used to construct significant MI-QSAR models. Even though these models are molecular weight dependent, the high correlation between PII and PII (molar) measures guarantees the QSAR descriptor terms, and corresponding inferred mechanism of action, should be meaningful.
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Construction of DMPC monolayer.
The selection of the 1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine (DMPC) monolayer was based on the MI-QSAR studies on eye irritation (Kulkarni and Hopfinger, 1999; Kulkarni et al., 2001
). The assumption has been made that both skin and eye irritation do not have specific "irritation receptors" in the membranes, but solute uptake into the phospholipid rich regions of a membrane and resulting disruption of the membrane are related to "irritation." A DMPC monolayer was selected to model a "skin cellular membrane." The single DMPC molecule was built using HyperChem software (HyperChem, 1998
) from the available crystal data (Hauser et al., 1981
). The aliphatic chains of DMPC molecule were assigned to the trans-planar, local minimum energy conformation. The AM1 Hamiltonian in Mopac 6.0 (Mopac, 1990
) was used for the estimation of partial atomic charges on all molecules.
The structure of a DMPC molecule is shown in Figure 1. An assembly of 16 DMPC molecules (4*4*1) in (x, y, z) directions, respectively, was used as a model membrane monolayer. The size of the monolayer simulation system was selected based on the work done by van der Ploeg and Berendsen (Ploeg and Berendsen, 1982
). A representation of the model monolayer prior to molecular dynamics simulation modeling is shown in Figure 2
.
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Molecular dynamics simulations.
The molecular dynamics simulations (MDS) were carried out using the MDS package Molsim (Doherty, 1994), using a molecular mechanics force with an extended MM2 parameterization (Doherty, 1994
). The selection of the simulation temperature was based upon the gel to liquid crystalline phase transition for DMPC that occurs at a temperature of 297°K (Bloom et al., 1991
). The simulation temperature of 311°K was chosen for 2 reasons: (1) It is above the phase transition for DMPC and (2) it is above body temperature. Simulation temperature was held constant by coupling the system to an external constant temperature bath (Berendsen et al., 1984
). A dielectric constant of 3.5 was used since it reproduces known membrane structural/dynamic features (Jin and Hopfinger, 1996
).
The model monolayer, without a solute molecule, was slowly heated starting at 20°K, then at 50°K, and from that point in increments of 50°K until a final step was used to achieve the simulation temperature of 311°K. At each temperature increment, 4 picos (ps) of MDS was carried out to allow for structural relaxation and the distribution of the kinetic energy throughout the simulation model. When 311°K was reached, a 50 ps MDS production run was performed to equilibrate the monolayer.
Docking of solute molecules in a DMPC monolayer.
In order to determine the most favorable solute-membrane interaction, as well as to sample the major interaction states between the solute and the model membrane, a modeling schema was proposed in which the solute molecule is docked in 3 different positions inside the membrane model. These 3 positions are different from each other with respect to their chemical environment. Upper region is the polar head group region of the DMPC membrane; lower region is the highly hydrophobic environment near the tails of the hydrocarbon chains; and middle region is the region near the branching of a DMPC molecule to form the 2 aliphatic chains.
The solute molecule is docked in these 3 positions in such a way that the dipole moment of the solute molecule is in the opposite direction to the model membrane's dipole moment. This modeling scheme makes sure that the solute molecule encounters all the possible environments in the biological system, and thus identifies the most energetically stable position for the solute molecule in the membrane. Three corresponding MDS were carried out for each solute molecule with regard to the trial positions of the solute molecule in the monolayer. The energetically favorable geometry of the solute molecule in the monolayer was sought using each of these trial positions.
In order to prevent unfavorable steric repulsive van der Waals interactions between a solute molecule and the membrane DMPC molecules, one of the "center" DMPC molecules was removed from the equilibrated monolayer and a test solute molecule inserted in the space created by the missing DMPC molecule. Each of the solute molecules was placed at each of the 3 different positions in the monolayer described above with the dipolar portion of the solute "facing" towards the headgroup region. The same heating steps used to equilibrate the isolated monolayer were carried out with solute present. However, a final production run of only 20 ps was performed at 311°K.
Calculation of QSAR descriptors.
The QSAR descriptors are the various properties and features of molecules. A descriptor can be intramolecularan inherent property of a molecule obtained solely from its chemical structure. The other class of descriptors is the set of intermolecular properties and features that depend on the environment in which the molecules are located. These properties and features are normally computed from the interaction between 2 or more molecules.
The intramolecular descriptors of the molecules in the skin irritation training set were calculated using multiple software packages including TSAR (Oxford Molecular), Cerius 2 (MSI, 1997), and Chemlab-II (Pearlstein, 1988
) and are listed in Table 3
. The intramolecular descriptor set used in this study includes the large majority of descriptors generally employed in current intramolecular QSAR analyses. The intermolecular descriptor set is given in Table 4
. Part A of Table 4
lists the membrane-solute intermolecular descriptors computed from the MDS. Part B of Table 4
list the solute-solvent descriptors FH2O, FOCT, and Log P. While these 3 descriptors are intermolecular properties, they have been computed using intramolecular computational algorithms.
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RESULTS |
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Measures of statistical fit were obtained for intramolecular QSAR models with different number of descriptors in order to evaluate the role of the number of descriptors on the significance of the model. The chosen statistical measures of importance in QSAR model construction are the correlation coefficient, r2, and the leave-one-out cross validation coefficient, xv-r2. It was found that the r2 value increases when the model size was increased from 1 descriptor term to 4 descriptor terms. However, r2 values were largely unaffected by a greater (than 4) number of descriptors in an intramolecular QSAR model. However, xv-r2 is maximized for the two-term model, and then decreases for increasing size of the QSAR model. The relatively low values of r2 across the QSAR models, and the low and decreasing values of xv-r2 with increasing model size, suggests these QSAR models are of limited stability and significance.
![]() | ((1)) |
![]() | ((2)) |
![]() | ((3)) |
![]() | ((4)) |
4-tert-Butylphenol and 2-propyl 1-heptanol are outliers in the MI-QSAR models presented below. These 2 compounds were not included in the construction of the intramolecular QSAR models so that the MI-QSAR and intramolecular QSAR models could be meaningfully compared to one another. Thus, the number of compounds used in intramolecular QSAR training set is 20.
MI-QSAR Models
Equations 5through 8
represent the best MI-QSAR models as a function of increasing model size.
![]() | ((5)) |
Two descriptor model
![]() | ((6)) |
![]() | ((7)) |
![]() | ((8)) |
The one-term MI-QSAR model, Equation 5, is identical to the one-term intramolecular QSAR model, Equation 1
. This observation suggests there is no intermolecular descriptor that, by itself, correlates more significantly with PII than LogP. The high r2 value of the four-term MI-QSAR model, Equation 8
, suggests that this may be an overfit model. The average experimental error in the eye irritation scores, MES (eye irritation potency scores), is at least 20% (Kulkarni et al., 2001
). It is a reasonable to assume that the average experimental error of the PII measures is as large as that of the MES endpoint. An average error of 20% in the dependent variable corresponds to an upper value in r2 of 0.75 to 0.80 (Kulkarni et al., 2001
). Thus, the three-term MI-QSAR model is probably the most meaningful QSAR model of all given by Equations 18
.
Both r2 and xv-r2 of the MI-QSAR models increase with increasing number of descriptor terms, and are generally larger than the corresponding values of the intramolecular QSAR models. Overall, a comparison of the statistics of intramolecular QSAR and MI-QSAR models for the same number of descriptors demonstrates that the MI-QSAR models are more robust and predictive. Figure 3 is a plot that compares r2 for the intramolecular QSAR and MI-QSAR models having the same number of descriptors. The correlation coefficients of the MI-QSAR models are larger than the corresponding intramolecular QSAR models when the number of descriptor terms is 3 and 4. The r2 values are essentially constant for models with 4 or more descriptor terms (see Fig. 3
for both intramolecular and MI-QSAR models).
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2-Propyl 1-heptanol, the other outlier, has a PII of 7.2, which is the highest skin irritation potency for the aliphatic alcohols in the training set. The average skin irritation potency of aliphatic alcohols in the training set is 3.14 (n = 11). The high potency of 2-propyl 1-heptanol, when compared to other alcohols in the training set, may result from a different skin irritation mechanism as compared to other hydroxy organic compounds in the training set. More insight about the behavior of this molecule might be obtained from a larger training set, or a training set having molecules of greater similarity to 2-propyl 1-heptanol. The 22 hydroxy compounds used in this study were selected on the basis of available PII values that covered a reasonable range of measured irritation and were considered to be reliable measures.
The 22 hydroxy organic compounds in the training set can be classified into the following 3 groups: (1) aliphatic alcohols, (2) alicyclic alcohols, or (3) phenols. The number of compounds in the alicyclic alcohol group is only 3. Hence, the alicyclic alcohols have been grouped with the aliphatic alcohols, and individual MI-QSAR models have been constructed for the aliphatic alcohols and for the phenols. The best three-term MI-QSAR model for the aliphatic alcohols is given by Equation 9 and the best three-term MI-QSAR model for the phenol group of the training set is represented by Equation 10
.
Three-term models were constructed for the two classes of alcohols of the training set to permit direct comparison to the three-term MI-QSAR model, Equation 7, of the entire training set. The best three-term MI-QSAR model for the aliphatic alcohols is
![]() | ((9)) |
The best three-term MI-QSAR model for the phenol analogs is
![]() | ((10)) |
EHB is the intramolecular hydrogen bonding energy of the solute when in the membrane, and is defined in Table 4, Part A, while SA is the surface area of the solute and defined in Table 3
. The composite set of PII predictions using Equations 9 and 10
, as well as the observed PII values, are reported in Table 9
. The corresponding PII plots of the data reported in Table 9
are given in Figure 5
. The observed PII of 2-propyl 1-heptanol is predicted with better accuracy using Equation 9
than by Equation 7
. Likewise, the PII of 4-tert-butylphenol is better predicted by Equation 10
than by Equation 7
. Moreover, Equations 9 and 10
, in composite, better fit the training set data than does Equation 7
. However, the better fitting of the outliers of Equation 7
using Equations 9 and 10
may also be due to partial overfitting that is required in order to meaningfully compare Equations 9 and 10
to Equation 7
.
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DISCUSSION |
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![]() | ((11)) |
MES is the Draize molar eye irritation score and has the same range of values as PII of the hydroxyl organic compound training set. The three-term MI-QSAR model for skin irritation given by Equation 7 can be reexpressed as
![]() | ((12)) |
![]() | ((13)) |
The form and descriptors of Equation 11 are remarkably similar to those of Equation 12
for PII. In both MI-QSAR models irritation is predicted to increase as E(chg + vdw) becomes more negative, that is as solute-membrane binding becomes stronger. Skin irritation appears to possess a more complex dependence on the interaction of a solute and membrane than eye irritation. The regression coefficient for E(chg + vdw) in Equation 12
is larger in magnitude than in Equation 11
, suggesting solute-membrane binding increases PII to a greater extent than MES. However, Equation 12
also includes the FOCT term that is taken to represent increasing membrane solubility of a solute as FOCT becomes more negative. The positive regression coefficient for FOCT in Equation 12
indicates that PII decreases as membrane solubility increases. Thus, FOCT appears to modulate the role of E(chg + vdw) on PII in Equation 12
, and these 2 terms are, consequently, grouped together as membrane-solute interaction terms.
An increase in aqueous solubility, as measured by FH2O becoming more negative in value, is postulated to increase both types of irritation. The regression coefficients for the FH2O terms in both MI-QSAR models, Equations 11 12, are quite similar suggesting both types of irritation are about equally dependent upon the aqueous solubility of the solute.
Equation 11 has LUMO as a descriptor while Equation 12
contains HOMO suggesting that the chemical reactivity of a solute plays a role in the expression of irritation potency. It is not possible to discern why 1 of these 2 electronic measures is selectively preferred in a given MI-QSAR model. However, it is noted that HOMO and LUMO are highly correlated [r = 0.79] to one another over the combined skin irritation and ECETOC eye irritation training sets. Thus, the preferred selection of LUMO, or HOMO, in a MI-QSAR model for a given training set of compounds, may simply be a consequence of a slightly better statistical fit of the data. A reviewer of this manuscript has suggested that the phenols might be metabolized to reactive oxidative species (free radical or catechol, hydroquinone) and that alcohols are metabolized to aldehydes or ketones (e.g., cyclohexanol to cyclohexanone) and these metabolites may be chemically reactive. The HOMO and LUMO descriptors may reflect the propensity for the corresponding metabolic activations.
A very significant observation can be made from the skin irritation and eye irritation MI-QSAR models as well as the discussion in the preceding paragraphs. A common molecular mechanism of irritant action seems to exist for these 2 types of toxic endpoints. This mechanism of action involves 3 factors. First, irritation increases as the available concentration of irritant for membrane uptake from an aqueous medium increases. FH2O and Log P are 2 descriptors that model this concentration factor. Next, the strength of interaction of the irritant with the phospholipid-rich regions of the cellular membrane is directly proportional to irritation potency. This irritation factor is modeled in the skin and eye irritation MI-QSAR models by E(chg + vdw). Lastly, both skin and eye irritations are seemingly dependent upon on the chemical reactivity of the irritant. It is not possible to pinpoint the specific type of chemical reaction(s) involved for a given class of irritation and/or chemical class of irritant. However, LUMO and HOMO repeatedly show up in the MI-QSAR models as representations for chemical reactivity and postulated metabolic pathways have been suggested above. Future MI-QSAR studies of toxic endpoints will be extremely interesting to analyze in terms of seeing the extent to which this proposed molecular mechanism of action for irritation endpoints can be further generalized. It is certainly possible that other factors, explicitly involving, for example, the acidity of the phenols and/or the nonacidity of the aliphatic alcohols, may be involved in skin irritation. Acidity is included in the MI-QSAR methodology only in terms of solvation and solute-membrane interactions.
A significant operational finding from this work is that MI-QSAR analysis provides reliable models that can serve as predictive quantitative virtual screens to evaluate skin irritation. The training set used in this study is small, and limited to alcohols. Certainly more, as well as structurally diverse, compounds for the training set would be welcomed to extend the utility of the corresponding MI-QSAR models for skin irritation. However, the similarity of these MI-QSAR models to those models developed for eye irritation, as described above, leads the assumption that these skin irritation models incorporate all the salient features of a general virtual screen for skin irritation.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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Berendsen, H. J. C., Postma, J. P. M., Gunsteren, W. F. V., DiNola, A., and Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 36843690.[ISI]
Bloom, M., Evans, E., and Mouritsen, O. (1991). Physical properties of the fluid lipid-bilayer component of cell membranes. Q. Rev. Biophys. 24, 293397.[ISI][Medline]
Bronaugh, R. L., Stewart, R. F., and Congdon, E. R. (1985). Methods for in vitro percutaneous absorption studies. IV: The flow-through diffusion cell. J. Pharm. Sci. 74, 6468.[ISI][Medline]
Doherty, D. C. (1994). Molsim Version 3.0 User's Guide. Chem21 Group, Chicago.
Draize, J. H., Woodard, G., and Calvery, H. O. (1944). Methods for the study of irritation and toxicity of substances applied to the skin and mucous membranes. J. Pharmacol. Exp. Ther. 82, 377390.
Fentum, J. H., Archer, G. E. B., Balls, M., Botham, P. A., Curren, R. D., Earl, L. K., Esdaile, D. J., Holzhutter, H. G., and Liebsch, M. (1998). The ECVAM international validation study on in vitro tests for skin corrosivity. 2. Results and evaluation by the management team. Toxicol. in Vitro 12, 483524.[ISI]
Hauser, H., Pascher, I., Pearson, R. H., and Sundell, S. (1981). Preferred conformation and molecular packing of phosphatidylethanolamine and phosphatidylcholine. Biochim. Biophys. Acta 650, 2151.[ISI][Medline]
HyperChem (1998). HyperChem, Release 4.5 for MS Windows. Hypercube, Waterloo, Ontario.
Jin, B., and Hopfinger, A. J. (1996). Characterization of lipid membrane dynamic by simulation. Quantitative representation of lipid fluidity. Comput. Theor. Polymer Sci. 6, 95101.[ISI]
Kulkarni, A., and Hopfinger, A. J. (1999). Membrane-interaction QSAR analysis: Application to the estimation of eye irritation by organic compounds. Pharm. Res. 16, 12451253.[ISI][Medline]
Kulkarni, A., Hopfinger, A. J., Osborne, R., Bruner, L. H., and Thompson, E. D. (2001). Prediction of eye irritation from organic compounds using membrane-interaction QSAR analysis. Toxicol. Sci. 59, 335345.
Liebsch, M., Traue, D., Barrabas, C., Spielmann, H., Uphill, P., Wilkins, S., Wiemann, C., Kaufmann, T., Remmele, M., and Holzhutter, H. G. (2000). The ECVAM prevalidation study on the use of EpiDerm for skin corrosivity testing. ALTA 28, 371401.
Mopac (1990). Mopac 6.0 Release Notes. Frank J. Seiler Research Laboratory, United States Air Force Academy, Fort Collins, CO.
MSI (1997). Cerius 2 V.3.0 Users Guide. Molecular Simulations, San Diego.
Pearlstein, R. A. (1988). CHEMLAB-II Users Guide. CHEMLAB, Chicago.
Ploeg, P. V. D., and Berendsen, H. J. C. (1982). Molecular dynamic simulation of a bilayer membrane. J. Chem. Phys. 76, 32713276.[ISI]
Rogers, D., and Hopfinger, A. J. (1994). Applications of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 34, 854866.[ISI]
RTK Web site (2001). Available at http://www.rtk.net/tscatsinputstandard.html. Accessed December 8, 2001.