TY - JOUR
AU - Thompson, Edward D.
AB - Abstract Eye irritation potency of a compound or mixture has traditionally been evaluated using the Draize rabbit-eye test (Draize et al., 1944). In order to aid predictions of eye irritation and to explore possible corresponding mechanisms of eye irritation, a methodology termed “membrane–interaction QSAR analysis” (MI-QSAR) has been developed (Kulkarni and Hopfinger 1999). A set of Draize eye-irritation data established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) (Bagley et al., 1992) was used as a structurally diverse training set in an MI-QSAR analysis. Significant QSAR models were constructed based primarily upon aqueous solvation-free energy of the solute and the strength of solute binding to a model phospholipid (DMPC) monolayer. The results demonstrate that inclusion of parameters to model membrane interactions of potentially irritating chemicals provides significantly better predictions of eye irritation for structurally diverse compounds than does modeling based solely on physiochemical properties of chemicals. The specific MI-QSAR models reported here are, in fact, close to the upper limit in both significance and robustness that can be expected for the variability inherent to the eye-irritation scores of the ECETOC training set. The MI-QSAR models can be used with high reliability to classify compounds of low- and high-predicted eye irritation scores. Thus, the models offer the opportunity to reduce animal testing for compounds predicted to fall into these two extreme eye-irritation score sets. The MI-QSAR paradigm may also be applicable to other toxicological endpoints, such as skin irritation, where interactions with cellular membranes are likely. eye irritation, quantitative structure-activity relationships (QSAR), membrane models, rabbits, animal alternatives Eye irritation traditionally has been evaluated using the Draize in vivo rabbit eye-irritation test (Draize et al., 1944). By this method, a 0.1-ml (or weight equivalent) sample of test material is placed into the lower conjunctival cul-de-sac of albino rabbit eyes; responses of the cornea, and conjunctiva and iris are graded at standard times, generally from 1 to 35 days after dosing. The tissue grades are combined into a weighted score; the highest average score across test animals on the various grading days is termed the maximum average score (MAS). Recent work indicates that the extent (area and depth) of injury produced in the cornea is the principle factor determining acute responses and their eventual repair in that tissue (Jester et al., 1998). However, mechanisms of eye irritation are not yet understood on a biochemical level (Bruner et al., 1998). 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. MATERIALS AND METHODS The difference between traditional QSAR methods and MI-QSAR analysis is that the latter modeling considers not only physicochemical parameters associated with the solute molecule, but also with the physicochemical parameters of the receptor (cell membranes in this case) and parameters that describe the physicochemical interactions between the two. Accordingly, it is necessary to characterize physicochemical parameters associated with the solute molecule, the proposed receptor, and the interaction between the two. It is also necessary to define the in vivo endpoint to be predicted in terms relevant for the analysis. These steps are described in detail below. 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 × 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. Building solute molecules and a DMPC monolayer. The 3-dimensional structures of the solute molecules of the ECETOC training set (see Table 1) were built using the Chemlab-II molecular modeling package (Pearlstein, 1988). A single dimyristoylphosphatidylcholine (DMPC) molecule was built using HyperChem (1998) from available crystal structure data (Hauser et al., 1981). The AM1 Hamiltonian in Mopac 6.0 (Mopac, 1990) was used for the estimation of partial atomic charges on all molecules. 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). We equilibrated the monolayer structure by performing molecular dynamic simulations (MDS) at 311 degrees Kelvin (°K) for 50 pico s (ps). In order to prevent unfavorable 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 test solute molecules of the ECETOC data set was inserted at 3 different positions (depths) in the DMPC monolayer with the most polar group of the solute molecule “facing” toward the head group region of the monolayer. Three corresponding MDS models were generated for each solute molecule with regard to the trial positions of the solute molecule in the monolayer. The 3 trial positions were, (1) solute molecule in the head group region, (2) solute molecule between the head-group region and the aliphatic chains, and (3) solute molecule in the tail region of the aliphatic chains. 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. Molecular dynamic simulations (MDS). MDS were carried out using the Molsim package with an extended MM2 force field (Doherty, 1994). A simulation temperature of 311°K was selected, since it is body temperature and is also above the DMPC phase transition temperature. Temperature was held constant in the MDS by coupling the system to an external fixed-temperature bath (Berendsen et al., 1984). The trajectory step size was 0.001 ps over a total simulation time of 20 ps for each test compound. Two-dimensional periodic boundary conditions (PBC) corresponding to the “surface plane” of the monolayer were employed (a = 32 A, b = 32A, c = 80 A, and γ = 96.0°) for the DMPC molecules of the monolayer model, but not the test solute molecule. By using periodic boundary condition it is possible to simulate an infinite system. Also by using PBC simulations can be performed on relatively smaller system in such a way as if the system experience forces in bulk fluid. The angle γ is the angle an extended DMPC molecule makes with the “planar surface” of the monolayer. Only a single solute molecule was explicitly considered in each MDS. 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). Calculation of descriptors. Both intramolecular physicochemical properties and features of the solute molecules, and intermolecular solute-membrane interaction properties were calculated. “Properties” and “features” will both be referred to as descriptors from this point forward, as they constitute the trial pool of independent variables used to build the QSAR models. 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. Some of the intermolecular solute-membrane interaction descriptors were extracted directly from the MDS trajectories and are listed in Table 3. These intermolecular descriptors were calculated using the most stable (lowest total potential energy) solute-membrane geometry realized from MDS sampling of the 3 initial positions (see Fig. 2a) for each of the solutes. Figure 4 shows a plot of the total potential energy vs. MDS trajectory, from which the most energetically favorable position of styrene is identified. It can also be seen in Figure 4 that the membrane-styrene system attains energy equilibrium after about 6 ps of MDS. That is, the energy vs. time plot is, on average, “flat” after 6 ps. The most energetically favorable position for styrene is in the middle region of the monolayer (see Fig. 2b) where the energy is minimum after the equilibration phase. The intermolecular descriptors extracted from MDS trajectories are given in Table 3. Details regarding the methods and algorithms used to compute these descriptors can be found in (Kulkarni and Hopfinger, 1999). F(H2O) was, by far, the most significant MI-QSAR correlation descriptor. Aqueous solvation free energies were calculated using a group additive version of the hydration shell model (Hopfinger, 1973). Construction and testing of MI-QSAR models. MI-QSAR models were constructed using the genetic function approximation, GFA (Rogers and Hopfinger, 1994), which is a multi-dimensional optimization method based on the genetic algorithm paradigm. The GFA algorithm is coded in the program WOLF (Rogers, 1994). Statistical significance in the optimization of a QSAR model using WOLF is based on Friedman's lack of fit (LOF) measure (Friedman, 1988). The LOF measure is designed to resist overfitting, which is a problem often encountered in constructing statistical models. Since the number of descriptors available in MI-QSAR analysis normally exceeds the number of observations (test compounds), the ability to prevent overfitting using GFA is critical to the successful construction of a statistically significant MI-QSAR model. 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. RESULTS In our previous study (Kulkarni and Hopfinger, 1999), we found one major outlier (propylene glycol) in constructing an MI-QSAR model for a subset of the ECETOC data set. Nevertheless, an attempt was made in this study to build a MI-QSAR model for all the compounds in the ECETOC data set, including the outliers of the earlier subset study. The two best MI-QSAR models for all 38 molecules of the ECETOC data set, including the outliers from the previous study, using only linear representations of the independent variable terms are,     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,   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 π 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). A model with a correlation coefficient squared (r2) of 0.78 is often considered not particularly good. A low r2 value can be due to 2 causes: (1) variability (uncertainty) in the dependent variable measures of the training set and/or (2) a limited and/or poor selection of the independent variables for constructing the model. In the case of the ECETOC training set, and eye irritation in general, there is a considerable variability in the eye-irritation scoring measures that are the dependent variables. Thus, the low r2 values from the eye-irritation MI-QSAR models come predominantly from “noise” in the eye-irritation scoring measures, and not necessarily from a poor selection of descriptors. This assertion has been tested for the ECETOC data set. The raw, multiple eye-irritation scores for each of the compounds in the ECETOC data set are available (Bagley et al., 1992). An analysis of the raw data permits the computation of a mean value (MV) for each compound, which is the MES value used in the MI-QSAR analysis and given in Table 1. It is also possible to compute the standard deviation, SD, from the mean for the raw scores of each compound. Using the MV and SD of each compound, and assuming a random distribution of scoring about the MV, it is possible to perform simulation-scoring experiments (Hopfiner, 1999). In turn, it is then possible to determine the average correlation coefficient of any simulated eye-irritation scoring set for all the compounds in the data set to the set of MV scores. By repeating the simulation eye-irritation scoring experiments, an average correlation coefficient of the fit to the MV scores can be made. This average correlation coefficient can be considered the upper value that is possible from a QSAR analysis. For the ECETOC data set, this average correlation coefficient is 0.79. Thus, the r2 of Equation 3 is quite reasonable when taken relative to the estimated intrinsic noise in the dependent-variable measure. 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,   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. The scrambling experiments to ascertain the validity of Equations 3 and 4 lead to models with r2 < 0.12. These low r2 values for the scrambling experiments suggest that both Equations 3 and 4 are significant MI-QSAR models and not the result of chance correlations. DISCUSSION Significant MES MI-QSAR models were obtained for the structurally diverse compounds (solutes) of the ECETOC eye-irritation database by combining intramolecular physicochemical properties of the test solute compounds with corresponding intermolecular solute-membrane and solute-solvent interaction properties. Solute-membrane interaction (binding) energies [E(vdw), E(chg + vdw) and E(chg)] comprise one set of intermolecular interaction properties of the MI-QSAR models. F(H2O) and F(OCT), the aqueous and 1-octanol solvation free energies of the solute, respectively, contribute a second set of intermolecular properties, although they are estimated by a scheme solely based on the chemical structure of the solute. LUMO, the lowest unoccupied molecular orbital energy, is the only true intramolecular solute descriptor of Equations 3 and 4. 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/). TABLE 1 The ECETOC Draize Eye Irritation Data Set Along with the MES and Descriptor Values of the MI-QSAR Models (Equations 3 and 4) of Each Solute Molecule  1,5 Dimethylcyclo-octadieneMESLUMOF(H2O)F(OCT)E (chg + vdw)E (vdw)E (chg) Solutes  MES  LUMO  F(H2O)  F(OCT)  E (chg + vdw)  E (vdw)  E (chg)   *Outliers.  Hydrocarbons:    3 Methyl hexane  0.10  6.57  2.75  –2.60  –17.31  –4.09  –13.22    2 Methyl pentane  0.26  6.58  2.54  –2.08  –16.22  –4.07  –12.15    Methylcyclopentane  0.41  6.18  1.36  –2.80  –7.00  3.69  –10.69    1,9-Decadiene  0.37  5.00  1.64  –5.26  –14.36  –1.20  –13.16    Dodecane  0.45  6.33  3.62  –5.52  –24.48  –0.83  –23.65    1,5-hexadiene  0.55  4.70  0.83  –3.18  –9.04  1.45  –10.49    cis-Cyclooctene  0.43  4.62  1.00  –4.48  –14.70  0.26  –14.96    1,5 Dimethylcyclo-octadiene  0.44  3.86  1.20  –5.70  –4.73  0.97  –5.70   Aromatics:    4-Bromophentole  0.19  2.90  –4.20  –10.07  –14.56  –8.92  –5.64    2,4-Difluoronitrobenzene  0.40  1.04  5.20  7.40  –9.20  –4.00  –5.20    3-Ethyltoluene  0.32  3.56  –0.60  –5.54  –15.65  –0.64  –15.01    4-Fluoroaniline  6.62  3.53  –13.68  –15.75  –4.04  6.50  –10.54    Xylene  1.10  3.58  –0.85  –5.06  –12.83  –0.70  –12.13    Toluene  0.96  3.71  –0.93  –4.46  –4.58  0.80  –5.38    Styrene  0.77  2.73  –1.53  –5.39  –9.65  –0.30  –9.35    1-Methylpropylbenzene  0.31  3.92  –0.26  –5.78  –5.36  –0.36  –5.00    1,3-Disopropylbenzene  0.38  3.91  0.09  –6.66  –7.80  –7.48  –0.32   Ketones:    Methyl amyl ketone  2.26  3.67  1.75  –0.73  –9.47  3.65  –13.12    Methyl isobutyl ketone  0.59  3.96  1.80  0.11  –3.92  –0.51  –3.41    Methyl ethyl ketone  4.48  6.67  –7.30  –8.68  –0.67  6.74  –7.41    Acetone  4.83  3.66  –5.50  1.35  –2.06  –0.60  –1.46   Alcohols:    n-Butanol  5.47  6.53  –7.45  –9.00  –18.37  –9.73  –8.64    Isobutanol  6.44  7.00  –7.19  –8.68  –6.67  –0.26  –6.41    Isopropanol  2.34  6.87  –7.51  –8.16  –1.69  3.94  –5.63    Propylene glycol*  0.10  6.81  –20.35  –15.80  –15.04  –7.13  –7.91    2-Ethyl-1-hexanol  7.82  6.51  –6.49  –10.76  –9.16  –3.23  –5.93    Glycerol*  0.12  6.70  –26.39  –23.44  –8.17  0.49  –8.66    Hexanol  8.13  6.36  –7.04  –10.04  –9.04  1.45  –10.49    Butyl cellsolve  8.99  6.51  –11.01  –11.85  –18.30  0.15  –18.45    Cyclohexanol  8.29  6.34  –8.08  –10.44  –10.74  0.76  –11.5   Acetates:    Ethyl acetate  1.47  4.28  –2.82  –0.98  6.59  17.76  –11.17    Methyl acetate  3.14  4.18  –3.02  –0.46  –15.79  –6.82  –8.97    Methyl trimethyl acetate  0.36  4.67  –2.16  –1.70  –3.31  1.57  –4.88    Ethyl trimethyl acetate  0.63  4.76  –1.96  –2.22  –14.31  –6.75  –7.56    Cellosolve acetate  2.03  4.21  –6.83  –3.83  –11.95  –3.49  –8.46    n-Butyl acetate  0.99  4.29  –2.42  –2.02  –27.29  –10.74  –16.55    Ethyl-2-methylaceto-acetate  2.55  3.34  –2.92  –0.03  –5.59  –6.77  1.18   Acids:    2,2Dimethylbutanoic acid  5.59  4.66  –5.46  –7.72  –3.84  –2.69  –1.15  Solutes  MES  LUMO  F(H2O)  F(OCT)  E (chg + vdw)  E (vdw)  E (chg)   *Outliers.  Hydrocarbons:    3 Methyl hexane  0.10  6.57  2.75  –2.60  –17.31  –4.09  –13.22    2 Methyl pentane  0.26  6.58  2.54  –2.08  –16.22  –4.07  –12.15    Methylcyclopentane  0.41  6.18  1.36  –2.80  –7.00  3.69  –10.69    1,9-Decadiene  0.37  5.00  1.64  –5.26  –14.36  –1.20  –13.16    Dodecane  0.45  6.33  3.62  –5.52  –24.48  –0.83  –23.65    1,5-hexadiene  0.55  4.70  0.83  –3.18  –9.04  1.45  –10.49    cis-Cyclooctene  0.43  4.62  1.00  –4.48  –14.70  0.26  –14.96    1,5 Dimethylcyclo-octadiene  0.44  3.86  1.20  –5.70  –4.73  0.97  –5.70   Aromatics:    4-Bromophentole  0.19  2.90  –4.20  –10.07  –14.56  –8.92  –5.64    2,4-Difluoronitrobenzene  0.40  1.04  5.20  7.40  –9.20  –4.00  –5.20    3-Ethyltoluene  0.32  3.56  –0.60  –5.54  –15.65  –0.64  –15.01    4-Fluoroaniline  6.62  3.53  –13.68  –15.75  –4.04  6.50  –10.54    Xylene  1.10  3.58  –0.85  –5.06  –12.83  –0.70  –12.13    Toluene  0.96  3.71  –0.93  –4.46  –4.58  0.80  –5.38    Styrene  0.77  2.73  –1.53  –5.39  –9.65  –0.30  –9.35    1-Methylpropylbenzene  0.31  3.92  –0.26  –5.78  –5.36  –0.36  –5.00    1,3-Disopropylbenzene  0.38  3.91  0.09  –6.66  –7.80  –7.48  –0.32   Ketones:    Methyl amyl ketone  2.26  3.67  1.75  –0.73  –9.47  3.65  –13.12    Methyl isobutyl ketone  0.59  3.96  1.80  0.11  –3.92  –0.51  –3.41    Methyl ethyl ketone  4.48  6.67  –7.30  –8.68  –0.67  6.74  –7.41    Acetone  4.83  3.66  –5.50  1.35  –2.06  –0.60  –1.46   Alcohols:    n-Butanol  5.47  6.53  –7.45  –9.00  –18.37  –9.73  –8.64    Isobutanol  6.44  7.00  –7.19  –8.68  –6.67  –0.26  –6.41    Isopropanol  2.34  6.87  –7.51  –8.16  –1.69  3.94  –5.63    Propylene glycol*  0.10  6.81  –20.35  –15.80  –15.04  –7.13  –7.91    2-Ethyl-1-hexanol  7.82  6.51  –6.49  –10.76  –9.16  –3.23  –5.93    Glycerol*  0.12  6.70  –26.39  –23.44  –8.17  0.49  –8.66    Hexanol  8.13  6.36  –7.04  –10.04  –9.04  1.45  –10.49    Butyl cellsolve  8.99  6.51  –11.01  –11.85  –18.30  0.15  –18.45    Cyclohexanol  8.29  6.34  –8.08  –10.44  –10.74  0.76  –11.5   Acetates:    Ethyl acetate  1.47  4.28  –2.82  –0.98  6.59  17.76  –11.17    Methyl acetate  3.14  4.18  –3.02  –0.46  –15.79  –6.82  –8.97    Methyl trimethyl acetate  0.36  4.67  –2.16  –1.70  –3.31  1.57  –4.88    Ethyl trimethyl acetate  0.63  4.76  –1.96  –2.22  –14.31  –6.75  –7.56    Cellosolve acetate  2.03  4.21  –6.83  –3.83  –11.95  –3.49  –8.46    n-Butyl acetate  0.99  4.29  –2.42  –2.02  –27.29  –10.74  –16.55    Ethyl-2-methylaceto-acetate  2.55  3.34  –2.92  –0.03  –5.59  –6.77  1.18   Acids:    2,2Dimethylbutanoic acid  5.59  4.66  –5.46  –7.72  –3.84  –2.69  –1.15  View Large TABLE 2 Intramolecular Solute Descriptors Considered in the Trial QSAR Descriptor Set Intramolecular descriptors  Source   Note. The sources are indicated as follows: a, computed using Cerius2 (MSI, 1997); b, calculated using, MOPAC 6.0 (Mopac, 1990); and c, calculated using Chemlab II (Pearlstein, 1988).  Kappa-2-AM (topological descriptors)  a   HOMO (highest occupied molecular orbital energy)  a   LUMO (lowest occupied molecular orbital energy)  a   Dipole moment  b   Molecular volume  c   SA (molecular surface area)  c   Density  a   Molecular weight  b   Molecular refractivity  a   Number of hydrogen bond acceptors   a   Number of hydrogen bond donors  a   Number of rotatable bonds  a   Jurs-Stanton CSPA (charged partial surface-area) descriptors  a   Kappa descriptors (topological descriptors)  a   Radius of gyration  a   PM (principle moment of inertia)  c  Intramolecular descriptors  Source   Note. The sources are indicated as follows: a, computed using Cerius2 (MSI, 1997); b, calculated using, MOPAC 6.0 (Mopac, 1990); and c, calculated using Chemlab II (Pearlstein, 1988).  Kappa-2-AM (topological descriptors)  a   HOMO (highest occupied molecular orbital energy)  a   LUMO (lowest occupied molecular orbital energy)  a   Dipole moment  b   Molecular volume  c   SA (molecular surface area)  c   Density  a   Molecular weight  b   Molecular refractivity  a   Number of hydrogen bond acceptors   a   Number of hydrogen bond donors  a   Number of rotatable bonds  a   Jurs-Stanton CSPA (charged partial surface-area) descriptors  a   Kappa descriptors (topological descriptors)  a   Radius of gyration  a   PM (principle moment of inertia)  c  View Large TABLE 3 Intermolecular Solute-Membrane Interaction Descriptors Considered in the Trial QSAR Symbol  Description of the descriptor  Source   Note. The sources are indicated as follows: a, computed directly from MDS energy files using molsim simulation package, Molsim (Doherty, 1994); and b, calculated using Chemlab–II (Pearlstein 1988).  E (total)  Average total interaction energy of the solute and membrane (kcal/mole)  a   Einter (total)  Interaction energy between the solute and the membrane at the total system minimum potential energy (sum of electrostatic, H-bonding, and vdw energies) (kcal/mole)  a   E (chg)  Electrostatic interaction energy between the solute and the membrane at total system minimum potential energy (kcal/mole)  a   E (vdw)  Van der Waals interaction energy between the solute and the membrane at total system minimum potential energy (kcal/mole)  a   E (chg + vdw)  Electrostatic plus van der Waals interaction energy between the solute and the membrane at the total system minimum potential energy (kcal/mole)  a   F(H2O)  The aqueous solvation free energy computed using a hydration shell model (Hopfinger, 1973)  b   F(OCT)  The 1- octanol solvation free energy computed using a hydration shell model (Hopfinger, 1973)  b   Log P  Logarithm of the 1-octanyl/water partition coefficient  b  Symbol  Description of the descriptor  Source   Note. The sources are indicated as follows: a, computed directly from MDS energy files using molsim simulation package, Molsim (Doherty, 1994); and b, calculated using Chemlab–II (Pearlstein 1988).  E (total)  Average total interaction energy of the solute and membrane (kcal/mole)  a   Einter (total)  Interaction energy between the solute and the membrane at the total system minimum potential energy (sum of electrostatic, H-bonding, and vdw energies) (kcal/mole)  a   E (chg)  Electrostatic interaction energy between the solute and the membrane at total system minimum potential energy (kcal/mole)  a   E (vdw)  Van der Waals interaction energy between the solute and the membrane at total system minimum potential energy (kcal/mole)  a   E (chg + vdw)  Electrostatic plus van der Waals interaction energy between the solute and the membrane at the total system minimum potential energy (kcal/mole)  a   F(H2O)  The aqueous solvation free energy computed using a hydration shell model (Hopfinger, 1973)  b   F(OCT)  The 1- octanol solvation free energy computed using a hydration shell model (Hopfinger, 1973)  b   Log P  Logarithm of the 1-octanyl/water partition coefficient  b  View Large TABLE 4 The Linear Cross-Correlation Matrix for the Reported MES and the MI-QSAR Descriptors Used in Equations 3 and 4   MES  LUMO  F(H2O)  F(OCT)  E (chg + vdw)  E (vdw)  E (chg)   MES  1.00              LUMO  0.36  1.00            F(H2O)  –0.39  –0.43  1.00          E (chg + vdw)  0.15  –0.23  0.12    1.00      F(OCT)  –0.48  –0.51  0.66  1.00  0.11      E (vdw)  0.13  0.07  –0.03  –0.04  0.62  1.00    E (chg)  0.02  –0.29  –0.10  0.13  0.61  –0.15  1.00    MES  LUMO  F(H2O)  F(OCT)  E (chg + vdw)  E (vdw)  E (chg)   MES  1.00              LUMO  0.36  1.00            F(H2O)  –0.39  –0.43  1.00          E (chg + vdw)  0.15  –0.23  0.12    1.00      F(OCT)  –0.48  –0.51  0.66  1.00  0.11      E (vdw)  0.13  0.07  –0.03  –0.04  0.62  1.00    E (chg)  0.02  –0.29  –0.10  0.13  0.61  –0.15  1.00  View Large TABLE 5 The Cross-Correlation Matrix for the Residuals of Fit of the MI-QSAR Models, Equations 3 and 4   Equation 3  Equation 4   Equation 3  1    Equation 4  0.34  1    Equation 3  Equation 4   Equation 3  1    Equation 4  0.34  1  View Large TABLE 6 The Observed and Predicted, Using Equation 3, MES and MAS Scores for the ECETOC Data Set   MES  MAS   Compounds  Observed  Predicted  Residual  Observed  Predicted  Residual   Note. If an actual predicted MES value was less than zero, both the MES and MAS predicted values were set to zero in the table.  Hydrocarbons:       3 Methyl hexane  0.10  0  0.10  0.67  0  0.67       2 Methyl pentane  0.26  0  0.26  2.00  0  2.00    Methylcyclopentane  0.41  1.39  –0.98  3.67  12.40  –8.73       1,9-Decadiene  0.37  0.33  0.04  2.00  1.77  0.23       Dodecane  0.45  0.32  0.13  2.00  1.40  0.60       1,5-hexadiene  0.55  0.93  –0.38  4.67  7.93  –3.26    cis-Cyclooctene  0.43  1.15  –0.72  3.33  8.89  –5.56       1,5-Dimethylcyclooctadiene  0.44  0  0.44  2.83  0  2.83   Aromatics:       4-Bromophentole  0.19  1.94  –1.75  1.33  13.55  –12.22       2,4-Difluoronitrobenzene  0.40  0.75  –0.35  3.67  6.88  –3.21       3-Ethyltoluene  0.32  2.00  –1.68  2.33  14.58  –12.25       4-Fluoroaniline  6.62  7.24  –0.62  69.83  76.39  –6.56       Xylene  1.10  1.75  –0.65  9.00  14.34  –5.34       Toluene  0.96  1.10  –0.14  9.00  10.30  –1.30       Styrene  0.77  1.64  –0.87  6.75  14.39  –7.64       1-Methylpropylbenzene  0.31  0.61  –0.30  2.00  3.95  –1.95       1,3-Disopropylbenzene  0.38  0  0.38  2.00  0  2.00   Ketones:    Methyl amyl ketone  2.26  0.73  1.53  16.25  5.28  10.97    Methyl isobutyl ketone  0.59  0  0.59  4.75  0  4.75    Methyl ethyl ketone  4.48  5.65  –1.17  50.00  63.10  –13.10       Acetone  4.83  4.04  0.79  65.75  55.05  10.70   Alcohols:       n-Butanol  5.47  4.12  1.35  60.75  37.46  23.29       Isobutanol  6.44  5.62  0.82  60.25  52.56  7.69       Isopropanol  2.34  5.42  –3.08  30.50  70.64  –40.14       Propylene glycol  0.10  0  0.10  1.33  0  1.33       2-Ethyl-1-hexanol  7.82  5.95  1.87  50.00  38.05  11.95       Glycerol  0.12  0  0.12  1.67  0  1.67    Hexanol  8.13  6.62  1.51  64.75  52.75  12.00       Butyl cellsolve  8.99  7.68  1.31  68.67  58.66  10.01       Cyclohexanol  8.29  6.80  1.49  79.75  65.43  14.32   Acetates:       Ethyl acetate  1.47  1.25  0.22  15.00  12.74  2.26    Methyl acetate  3.14  2.29  0.85  39.50  28.81  10.69    Methyl trimethyl acetate  0.36  2.63  –2.27  2.67  19.51  –16.84       Ethyl trimethyl acetate  0.63  1.27  –0.64  4.17  8.40  –4.23    Cellosolve acetate  2.03  2.98  –0.95  15.00  22.00  –7.00       n-Butyl acetate  0.99  2.00  –1.01  7.50  8.63  –1.13       Ethyl-2-methyl-acetoacetate  2.55  0.80  1.75  18.00  5.66  12.34   Acids:       2,2Dimethylbutanoic acid  5.59  2.83  2.76  44.67  22.60  22.07    MES  MAS   Compounds  Observed  Predicted  Residual  Observed  Predicted  Residual   Note. If an actual predicted MES value was less than zero, both the MES and MAS predicted values were set to zero in the table.  Hydrocarbons:       3 Methyl hexane  0.10  0  0.10  0.67  0  0.67       2 Methyl pentane  0.26  0  0.26  2.00  0  2.00    Methylcyclopentane  0.41  1.39  –0.98  3.67  12.40  –8.73       1,9-Decadiene  0.37  0.33  0.04  2.00  1.77  0.23       Dodecane  0.45  0.32  0.13  2.00  1.40  0.60       1,5-hexadiene  0.55  0.93  –0.38  4.67  7.93  –3.26    cis-Cyclooctene  0.43  1.15  –0.72  3.33  8.89  –5.56       1,5-Dimethylcyclooctadiene  0.44  0  0.44  2.83  0  2.83   Aromatics:       4-Bromophentole  0.19  1.94  –1.75  1.33  13.55  –12.22       2,4-Difluoronitrobenzene  0.40  0.75  –0.35  3.67  6.88  –3.21       3-Ethyltoluene  0.32  2.00  –1.68  2.33  14.58  –12.25       4-Fluoroaniline  6.62  7.24  –0.62  69.83  76.39  –6.56       Xylene  1.10  1.75  –0.65  9.00  14.34  –5.34       Toluene  0.96  1.10  –0.14  9.00  10.30  –1.30       Styrene  0.77  1.64  –0.87  6.75  14.39  –7.64       1-Methylpropylbenzene  0.31  0.61  –0.30  2.00  3.95  –1.95       1,3-Disopropylbenzene  0.38  0  0.38  2.00  0  2.00   Ketones:    Methyl amyl ketone  2.26  0.73  1.53  16.25  5.28  10.97    Methyl isobutyl ketone  0.59  0  0.59  4.75  0  4.75    Methyl ethyl ketone  4.48  5.65  –1.17  50.00  63.10  –13.10       Acetone  4.83  4.04  0.79  65.75  55.05  10.70   Alcohols:       n-Butanol  5.47  4.12  1.35  60.75  37.46  23.29       Isobutanol  6.44  5.62  0.82  60.25  52.56  7.69       Isopropanol  2.34  5.42  –3.08  30.50  70.64  –40.14       Propylene glycol  0.10  0  0.10  1.33  0  1.33       2-Ethyl-1-hexanol  7.82  5.95  1.87  50.00  38.05  11.95       Glycerol  0.12  0  0.12  1.67  0  1.67    Hexanol  8.13  6.62  1.51  64.75  52.75  12.00       Butyl cellsolve  8.99  7.68  1.31  68.67  58.66  10.01       Cyclohexanol  8.29  6.80  1.49  79.75  65.43  14.32   Acetates:       Ethyl acetate  1.47  1.25  0.22  15.00  12.74  2.26    Methyl acetate  3.14  2.29  0.85  39.50  28.81  10.69    Methyl trimethyl acetate  0.36  2.63  –2.27  2.67  19.51  –16.84       Ethyl trimethyl acetate  0.63  1.27  –0.64  4.17  8.40  –4.23    Cellosolve acetate  2.03  2.98  –0.95  15.00  22.00  –7.00       n-Butyl acetate  0.99  2.00  –1.01  7.50  8.63  –1.13       Ethyl-2-methyl-acetoacetate  2.55  0.80  1.75  18.00  5.66  12.34   Acids:       2,2Dimethylbutanoic acid  5.59  2.83  2.76  44.67  22.60  22.07  View Large FIG. 1. View largeDownload slide The chemical structure of a DMPC molecule having arbitrary atom number assignments. FIG. 1. View largeDownload slide The chemical structure of a DMPC molecule having arbitrary atom number assignments. FIG. 2. View largeDownload slide (a) A “side” view of a styrene molecule inserted at 3 different positions in the DMPC model monolayer prior to the start of each MDS used in the MI-QSAR analysis; (b) the lowest-energy geometry of a DMPC-styrene complex found in the MDS. FIG. 2. View largeDownload slide (a) A “side” view of a styrene molecule inserted at 3 different positions in the DMPC model monolayer prior to the start of each MDS used in the MI-QSAR analysis; (b) the lowest-energy geometry of a DMPC-styrene complex found in the MDS. FIG. 3. View largeDownload slide The schedule of performing a membrane-solute MDS. See text for details. FIG. 3. View largeDownload slide The schedule of performing a membrane-solute MDS. See text for details. FIG. 4. View largeDownload slide Energy vs. simulation time plot for the 3 different initial geometries sampled for styrene in the DMPC model monolayer as shown in Figure 2. FIG. 4. View largeDownload slide Energy vs. simulation time plot for the 3 different initial geometries sampled for styrene in the DMPC model monolayer as shown in Figure 2. FIG. 5. View largeDownload slide Flow chart for developing predictive MI-QSAR models for Draize rabbit eye irritation test. FIG. 5. View largeDownload slide Flow chart for developing predictive MI-QSAR models for Draize rabbit eye irritation test. FIG. 6. View largeDownload slide A plot of the MES versus F(H2O). FIG. 6. View largeDownload slide A plot of the MES versus F(H2O). FIG. 7. View largeDownload slide Predicted vs. experimental molar-adjusted eye scores as a function of arbitrary solute number for Equation 3. Predicted MES less than zero have been set equal to zero. FIG. 7. View largeDownload slide Predicted vs. experimental molar-adjusted eye scores as a function of arbitrary solute number for Equation 3. Predicted MES less than zero have been set equal to zero. 1 To whom correspondence should be addressed. Fax: 312.413.3479. E-mail: hopfingr@uic.edu. AJH and AK are pleased to acknowledge the financial support of The Procter & Gamble Company. Resources of the Laboratory of Molecular Modeling and Design at UIC, and of The Chem21 Group, Inc. were used in performing this work. REFERENCES Abraham, M. H., Kumarsingh, R., Cometto-Muniz, J. E., and Cain, W. S. ( 1998). Quantitative structure-activity relationship (QSAR) for a Draize eye-irritation database. Toxicol. in Vitro  12, 201–207. Google Scholar Bagley, D. M., Botham, P. A., Gardner, J. R., Holland, G., Kreiling, R., Lewis, R. W., Stringer, D. A., and Walker, A. P. ( 1992). Eye irritation: Reference chemicals data bank. Toxicol. in Vitro  6, 487–491. Google Scholar Balls, M., Berg, N., Bruner, L. 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TI - Prediction of Eye Irritation from Organic Chemicals Using Membrane-Interaction QSAR Analysis
JF - Toxicological Sciences
DO - 10.1093/toxsci/59.2.335
DA - 2001-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/prediction-of-eye-irritation-from-organic-chemicals-using-membrane-uBRcPfmwyo
SP - 335
EP - 345
VL - 59
IS - 2
DP - DeepDyve
ER -