* Laboratory of Molecular Modeling and Design (M/C 781), College of Pharmacy, The University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 606127231;
The Procter & Gamble Company, Miami Valley Laboratories, P.O. Box 538707, Cincinnati, Ohio 452538707; and
Procter & Gamble Technical Centre, Ltd., Lovett House, Lovett Road, Stains, Middlesex TW18 3AZ, England
Received May 30, 2000; accepted October 13, 2000
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ABSTRACT |
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Key Words: eye irritation; quantitative structure-activity relationships (QSAR); membrane models; rabbits; animal alternatives.
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INTRODUCTION |
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The in vivo rabbit eye-irritation test has frequently been criticized on animal welfare grounds (Rowan, 1984). Many laboratories have been working to develop in vitro alternatives to this test (Balls et al., 1999
; Brantom et al., 1997
). At the present time, the in vitro alternatives may have a role as screens or adjuncts to the Draize rabbit-eye test, but none are sufficiently well validated to replace the test completely (Balls et al., 1999
). International agencies have proposed and adopted step-wise approaches for eye-irritation assessments with the goal of reducing the need for animal eye-irritation tests (OECD 1996
). Although structure-activity and structure-property analyses are recommended as early steps in the assessment process, a systematic approach for these analyses has not yet been widely accepted. The current work is directed toward this need.
Quantitative structure-activity relationship (QSAR) analysis provides a tool to relate the magnitude of a particular property, such as an eye-irritation score, to one or more physicochemical and/or structural parameters of a molecule. Hence, QSAR analysis can be used to estimate eye irritation. Traditional QSAR methods are normally limited in application to series of chemical analogs for which the dependent property (eye irritation) is derived from a set of intramolecular descriptors based upon an assumed common mechanism of action. However, eye-irritation assessments are normally sought for structurally diverse compounds. Thus, QSAR analysis is relatively limited in utility in applications that estimate eye irritation for diverse classes of chemicals.
The European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) established a "standard" data set for chemicals whose Draize rabbit eye-irritation scores have been measured according to OECD Guideline 405 (1987). The ECETOC data set has come to be used as a standard in the evaluation of in vitro and QSAR methods to estimate eye irritation. A history of the applications of QSAR and molecular modeling to eye irritation in general, and the ECETOC data set in particular, has been given (Kulkarni and Hopfinger, 1999). Several QSAR, data clustering, and molecular modeling studies have been performed using the ECETOC data set. However, all of these studies only employed intramolecular physicochemical properties of the compounds of the training set as correlation descriptors (Barratt, 1995
). These previous studies were based on the then prevalent views on the application of QSAR and modeling methods to preclinical drug discovery. It has been generally assumed that predicting eye irritation is methodology-equivalent to designing an active pharmaceutical agent. None of the previous studies were successful in developing a significant statistical QSAR model spanning all the compounds of the ECETOC data set, because this data set is composed of structurally diverse chemicals.
In principle, progress might be made in the QSAR analysis of any chemically diverse data set, including the ECETOC eye-irritation data set, if the "receptor" linked to the eye-irritation response is known and included in constructing QSAR models. This receptor-based approach to molecular design has been successfully used in building high-affinity ligands and is generally called structure-based design (Kubinyi, 1993). In the case of eye irritation, uptake and diffusion of an irritant into the keratocytes of the corneal epithelium may be a significant event. That is, each test molecule placed in the eye must diffuse through the cell membrane of the keratocytes comprising the outer 7 or so layers of the corneal epithelium of the eye. We have thus hypothesized that interactions of test molecules with cell membranes are at least partly, responsible for eye irritation. Moreover, the phospholipid-rich regions of a membrane bilayer of the cell might comprise the "general receptor" for eye irritation.
In order to test this hypothesis, we simulated the uptake and interaction of each of the ECETOC (solute) molecules with a model phospholipid membrane, as a part of our QSAR analysis of the ECETOC eye-irritation data set. In these simulations, the estimated membrane-solute interaction properties from the molecular simulations are added to the intramolecular physicochemical property descriptors to provide an extended set of trial descriptors for building eye-irritation QSAR models. This overall methodology is called membrane-interaction QSAR (MI-QSAR) analysis. The results indicate that MI-QSAR is a promising approach for development of predictive models for eye irritation.
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MATERIALS AND METHODS |
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Molar eye-irritation scores.
The dependent variable used in the MI-QSAR analysis was the molar-adjusted eye score (MES), as calculated from Draize rabbit eye-irritation test MAS values. This adjustment of the standard Draize score was made since activities used in QSAR studies are normally expressed as molar concentrations producing a fixed response. Thus, the MES was determined as follows: The molarity of the each solute solution tested in vivo was calculated using molarity = (density x 1000)/relative molecular mass of the test chemical. Molar-adjusted eye scores were then calculated as the Draize MAS values divided by the molarity of the solution. Table 1 contains the MES values for the compounds of the ECETOC training set. Ionizable molecules were not included in the training set because it is not clear if they are neutral or charged when at, or in, the membrane. Both the neutral and charged forms of these molecules could be considered in MI-QSAR analysis, but that was not done in this application.
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The phospholipid DMPC was selected as the model phospholipid in this study and was used to build a membrane monolayer which serves as the receptor for the eye-irritation response in the MI-QSAR analysis. 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 the model membrane monolayer. The size of the monolayer simulation system was selected based on the work done by van der Ploeg and Berendsen (1982). Additional information regarding construction of the model monolayer used in this MI-QSAR analysis is given in (Kulkarni and Hopfinger, 1999
).
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The lowest energy geometry of the solute molecule in the monolayer was sought using each of the 3 trial solute positions. The 3 different initial MDS positions of styrene (one of the test solute molecules) are shown in Figure 2a to illustrate this modeling procedure. The energetically most favorable geometry of styrene in the model DMPC monolayer is shown in Figure 2b
.
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An initial MDS on the model membrane, without a solute molecule present, was carried out to allow for structural relaxation and distribution of the kinetic energy over the monolayer. Each of the solute molecules was placed at each of the 3 different positions in the monolayer, as described above, oriented with the most polar portion of the solute toward the head-group region. The overall simulation scheme is shown in Figure 3, and additional details of the membrane-solute MDS can be found in (Kulkarni and Hopfinger 1999
).
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Only 2 of the descriptors from Table 2, HOMO and LUMO, were found to exhibit any type of correlation relationship to MES over the population of QSAR models sampled in this study.
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A smoothing factor of 1.8 and 100,000 crossover operations were used to optimize the MI-QSAR models using WOLF. Optimization of a QSAR model was considered to be realized when descriptor usage became constant and independent of increasing crossover operations. A crossover operation is the "birth" of a child model from its parent models. Both partial least-squares regression (PLS) and multi-dimensional linear regression (MLR) can be used in WOLF to establish functional data fits. MLR was used in this MI-QSAR eye-irritation study.
In order to test and validate the MI-QSAR models, the dependent variable, MES, was randomly "scrambled" (Waterbeemd, 1995) with respect to the set of independent variables (descriptor set) of the compounds, to see if meaningful correlations (QSARs) could be found among the scrambled data sets. The absence of any significant correlation for each of the scrambled data sets is taken as evidence of the significance of the MI-QSAR models with respect to the original, non-scrambled data set. Covariance among the descriptors in the optimized MI-QSAR models was evaluated by constructing the linear cross-correlation matrix of the descriptors, and by comparing relative descriptor usage in the crossover optimization process of the GFA analysis. Figure 5
describes the flow chart for MI-QSAR analysis.
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RESULTS |
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| (1) |
| (2) |
F(H2O) is the aqueous solvation-free energy of the test solute molecule, as defined earlier, and E(chg) is the electrostatic solute-membrane interaction energy, see Table 3. The number of compounds used to construct the correlation equation is given by n, r2 is the coefficient of determination, xv-r2 is the leave-one-out-cross-validation coefficient and LSE is the least-square error of fit.
A QSAR model is usually considered significant if it has a coefficient of determination (r2) greater than 0.7. The two MI-QSAR models, Equations 1 and 2, have r2 values less than 0.2. Thus, these MI-QSAR models are not significant. Figure 6 shows a plot of MES versus F(H2O) for the ECETOC compounds. From an inspection of this plot, it is clear that it is not possible to build a good linear MI-QSAR model for predicting eye-irritation potential using F(H2O) as a descriptor. The MES vs. F(H2O) relationship expressed in Figure 6
is parabolic. This apparent non-linear relationship prompted the exploration of MI-QSAR models using a parabolic dependence of MES on F(H2O). Still, it is noted that the number of compounds with highly negative F(H2O) values is only two. One of these two compounds, propylene glycol [F(H2O) = 16.9 kcal/mole], as already noted, was found as an outlier in the previous MI-QSAR study. The top non-linear MI-QSAR model for all 38 solute molecules in the ECETOC data set is,
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| (3) |
The constant within each square term in Equation 3 defines the value of the corresponding descriptor that yields its maximum, or minimum, contribution to MES. The values in parenthesis following the regression coefficients are the 95% confidence limits. The Fisher F-statistic, F, is also reported for Equation 3.
The observed versus predicted MES values, using Equation 3, are shown in Figure 7, and given as part of Table 6
. By convention, if a predicted MES value is less than zero, that is, outside the lower base line defined by the test, the MES value is set to zero in Table 6
and Figure 7
. F(H2O) and F(OCT) are determined for each test compound using a group-additive model analogous to computing Log P from
constants using Chemlab-II (Pearlstein, 1988
). The AM1 form of Mopac 6.0 in the Cerius2 (MSI, 1997
) was employed to estimate the LUMO values of each molecule in the training set. MDS of the DMPC model membrane monolayer with a single test molecule was used to compute E(vdw) and E(chg).
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Previous attempts to construct QSAR models for the ECETOC data set are discussed in the first paper reporting the MI-QSAR method (Kulkarni and Hopfinger, 1999). Significant QSAR models (r2 > 0.7), only employing intramolecular test molecule descriptors, could be built for small analog subsets of the ECETOC data set. However, all QSAR models reported for the composite ECETOC data set have r2 in the range of 0.1 to 0.3. Thus, Equation 3 is judged to be quite significant on the basis of its superiority in r2 value as compared to other reported QSAR models.
An inspection of Figure 6 reveals that there are only two solute molecules (propylene glycol and glycerol) that are non-irritants, even though they have extremely negative F(H2O) values. If these two solute molecules are removed from the training set, good linear MI-QSAR models can be constructed. Thus, propylene glycol and glycerol were removed from the training set and linear MI-QSAR models were constructed. The top linear MI-QSAR model identified in the GFA analysis is,
| (4) |
The values for the descriptors used in MI-QSAR models, Equations 3 and 4, for the ECETOC data set are given as part of Table 1. The linear cross-correlation matrix for the descriptors in Equations 3 and 4 and the MES is in Table 4
. The cross-correlation matrix for the residuals of fit of the MI-QSAR models (Equations 3 and 4) is given in Table 5
.
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DISCUSSION |
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Aqueous solubility has long been qualitatively identified as influencing the toxic spectrum of a compound. However, there has not been a general computational tool to compute aqueous solubility, or free energy of aqueous solvation, until recently. Hence, the work reported here might be one of the first predictive toxicity studies employing a measure of solute aqueous solubility. The aqueous solvation free energy [FH2O] is roughly proportional to the aqueous solubility of the molecule (Kulkarni and Hopfinger, 1999). Increasingly negative F(H2O) values correspond to increasing aqueous solubility of a solute. Similarly, increasingly negative F(OCT) values correspond to increasing organic solubility of a solute in a nonpolar (1-octanol) medium. In Equation 4, it is seen that aqueous solvation-free energy is negatively correlated with MES. This relationship suggests that water-soluble compounds have a greater propensity to be eye irritants than hydrophobic compounds. However MES and F(H2O) are parabolically related in Equation 3. Compounds that have very negative F(H2O) values have low MES values. Thus, compounds that have very high aqueous solubility are non-irritants to the eye.
The solute-membrane interaction energy descriptors in Equations 3 and 4 are also negatively correlated with the MES. Thus, as the "binding energy" of a solute molecule to the phospholipid regions of a membrane increases (a more negative descriptor value), its irritation potency is predicted to increase. F(OCT) in Equation 3 is conceptually viewed as a psuedo-solute-membrane interaction descriptor, which aids in incorporating all the ECETOC compounds into a single significant MI-QSAR model.
LUMO, which appears in both Equations 3 and 4, measures the electrophilicity of a molecule which, in turn, is interpreted as a measure of molecular reactivity and stability. As LUMO increases (relative to other molecules), the molecule is more stable and less reactive. MES is predicted to increase as LUMO increases in both Equations 3 and 4, although the relationship is parabolic in Equation 3, but linear in Equation 4. The linear versus parabolic dependence of MES on LUMO is a consequence of regression fitting for slightly different data sets with Equation 3, based on all 38 ECETOC compounds, and Equation 4, derived by removing 2 outlier compounds. LUMO is also associated with the ability of a compound to produce color, that is, to act as a dye in solution. Hence, LUMO might also reflect color changes observed in eye-irritation scoring not necessarily related to irritation.
Combining the interpretations of the aqueous solvation and solute-membrane interaction energy descriptors in Equations 3 and 4 leads to the following points. If a solute molecule is water-soluble, it possesses some polar moieties. These polar groups can also have favorable binding interactions with the phospholipid regions of a membrane, probably involving the phospholipid head groups. Polar alcohols are known to disturb phospholipid membrane structure (McKarns et al., 1997), which is consistent with this picture. The eye-irritation MI-QSAR models given by Equations 3 and 4 suggest that the eye-irritation potency of a solute molecule, as measured by the Draize test, is highly dependent on the aqueous solubility of the solute.
The solute-membrane interaction energy terms in Equations 3 and 4 suggest that eye-irritation potency increases with increasing binding of the solute to the phospholipid regions of the membrane. A straightforward interpretation of this type of descriptor term is that disruption of membrane structure, and likely function, resulting from strong interactions between solutes and phospholipids promotes eye irritation.
A mechanistic generalization of eye irritation can be made from the discussion above and Equations 3-4. The F(H2O) descriptor reflects the number solute molecules available on the aqueous/saline surface of the eye that could potentially disrupt membrane structure. That is, F(H2O) is a solute concentration measure. The intermolecular membrane-solute interaction energy descriptors provide measures of the intrinsic membrane disrupting potencies of each of the individual solute molecules. MES is thus controlled by an effective solute concentration coupled to the intrinsic membrane disruption propensity of the solute. This mechanistic interpretation of the MI-QSARs models is similar to the model of Abraham and coworkers (Abraham et al., 1998) in terms of an effective solute concentration. Their model identifies the significance of transferring the solute from its application state (pure organic liquid or solid dispersed into aqueous solution) to "an organic biophase" (the biological structure of the eye). In other words, the concentration of the solute in the organic biophase is crucial to eye-irritation potency.
All attempts to build a good linear MI-QSAR model for the entire ECETOC data set resulted in models having 2 major outliers; propylene glycol and glycerol. Both of these alcohols have extremely negative F(H2O) values. Thus, according to Equation 4, which incorporates a linear dependence between MES and F(H2O), these 2 solutes should be highly irritating to the eye. But experimental data shows that they are non-irritating. One plausible explanation for this apparent dichotomy is that, for a solute molecule to partition significantly between the aqueous phase and an organic phase, a proper balance between its aqueous and organic phase solubility is required. If a solute molecule is highly soluble in the aqueous phase, it won't enter the organic phase and vice versa. Propylene glycol and glycerol are highly soluble in water. They prefer to stay in the aqueous phase and not enter phospholipid regions of the membrane. Hence, the net solute concentration available to disrupt the membrane structure is extremely low. If this hypothesis is true, then MES may indeed have an approximate parabolic relationship to F(H2O), as found in Equation 3, for all the ECETOC compounds. From an inspection of Figure 6, the vertex of the "parabola" corresponds to a F(H2O) value of about 11.3 kcal/mole. This value of F(H2O) maximizes eye irritation (MES). The vertex value for F(H2O) in Equation 3 is 6.5 kcal/mole. The difference in the vertex value in Figure 6
and Equation 3 is mainly due to the influence of the other descriptor terms in the MI-QSAR model given by Equation 3. A parabolic fit of MES to only F(H2O) gives a F(H2O) vertex value of 11.3 kcal/mole.
Using the vertex value of F(H2O) as a reference, if the F(H2O) values increase, the solute molecules becomes less water soluble and the net number of solute molecules available for transfer into the membrane decreases. If F(H2O) becomes more negative, the solute molecules are highly water-soluble and do not transfer into the membrane. In both these cases, the number of solute molecules realized within the membrane decreases and, correspondingly, so does eye irritation (MES).
It is important to point out 2 biochemical factors not considered in the MI-QSAR formalism. First, possible interactions of a solute with membrane proteins are not considered. If this class of interactions is important to the expression of eye irritation of a solute, MI-QSAR analysis is not applicable and will fail to provide a meaningful prediction of an MES. Based on the consistently accurate estimation of eye irritation from the MI-QSAR models (e.g., Fig. 7), however, it does not appear that direct protein interactions play any substantial role for these chemicals.
Secondly, at the current stage of development of MI-QSAR analysis, cellular membrane specificity, in terms of specific phospholipids, has not been considered. The MI-QSAR models are based solely on DMPC monolayer "receptor" models. However, there is no reason that other phospholipid membrane models cannot be considered in a MI-QSAR analysis. A library of membrane "receptor" models could be constructed and employed in extended MI-QSAR investigations to determine model sensitivity to membrane composition and structure. Such multiple phospholipid membrane modeling could examine tissue-specific membrane lipid compositions such as in the cornea and conjunctiva, and adjunct structures such as tear film.
Figure 7 and Table 6
report the predicted vs. experimental MES values using Equation 3. It is clear that the predicted MES values track closely to those actually determined by Draize eye-irritation tests. These results indicate that inclusion of solute-membrane interaction properties in the MI-QSAR analysis provide a better prediction (and description) of eye irritation across chemical classes than can be obtained on the basis of the QSAR analysis of the physicochemical parameters of the test chemicals alone, e.g., Barratt (1995). It is proposed that a MI-QSAR approach be used to develop "standard" QSAR analyses for eye irritation, which would then be incorporated into risk assessment processes for eye irritation, such as the process proposed by the OECD (1998).
Table 6 also contains the observed MAS values, and the predicted MAS values based on the predicted MES values from Equation 3, and the estimated solute densities determined as described in the Materials and Methods section. When the MES value is predicted to be less than zero, a value of zero is assigned for both the predicted MES and MAS scores. Where there are large differences between observed and predicted MAS values, the corresponding observed MAS values are often large, that is, the compounds are highly irritating. The major source of the "magnification" of difference between predicted and observed MAS values, relative to MES values, resides in the estimations of the solute densities. A small change in the density for propylene glycol by 0.2 units results in a change of the predicted MAS value by 12 units. Thus, a small change in estimated solute density often results in a magnified change in the predicted MAS value. Work is currently underway in our laboratory to find a suitable approach to estimating the "effective" density of a test solute molecule.
Overall, the most significant feature of this study is the successful treatment of a representative set of structurally diverse compounds from the ECETOC eye-irritation training set by including interactions of these compounds with membrane models. This approach, membrane-interaction (MI)-QSAR analysis, may be a breakthrough method to reduce animal testing in several areas of toxicology. In addition to eye irritation, other areas that may involve membrane interactions in their biochemical mechanisms, and therefore, could be meaningfully investigated using MI-QSAR analysis include skin sensitization and irritation, aquatic toxicity, drug-membrane receptor interactions, and general modeling of bioavailability. Additional work and applications of MI-QSAR analysis continue in our laboratory with the hope of learning more about both the reliability and general utility of the method.
This study may be emblematic of the progress made in the quantitative prediction of toxicological endpoints. The predictive toxicity models developed for eye irritation appear to be sufficiently robust to be used to reduce animal testing by eliminating compounds of predicted high and low eye-irritation scores from the pool of animal test compounds. However, other areas of toxicology, such as chemical carcinogenicity, remain less tractable to computational prediction methods. Still, the promise of success seems within reach, and in fact, a competition is now being held to see which computational methods work best in predicting chemical carcinogenicity (see The Predictive Toxicology Challenge, http://www.methods.informatik.uni-freiburg.de/).
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ACKNOWLEDGMENTS |
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NOTES |
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