Environ. Sci. Technol. 1991, 25, 695-702
A New Strategy for Ranking Chemical Hazards. Framework and Application Maria Livia Tosato,
*mt
Luigi Vigan6,f Bert Skagerberg,§ and Sergio Clementill
Department of Comparative Toxicology and Ecotoxicology, Istituto Superiore di Sanith, Viale Regina Elena 299, 00 161 Rome, Italy, Water Research Institute (IRSAKNR), 20047 Brugherio, Milano, Italy, Research Group for Chemometrics, Ume6 University, S-90187 Ume6, Sweden, and Department of Chemistry, University of Perugia, Via Elce di Sotto 10, 06100 Perugia, Italy
This paper outlines a strategy, based on statistical design, for assessing and ranking the hazards of chemicals for which little or no useful data on toxic effects are available. Missing data of series of chemicals are predicted on the basis of test data generated for a minimum number of specific compounds that are adequate representatives of the relevant series: such critical compounds are identified by means of a factorial design. Accordingly, the aquatic toxicities of 100 monosubstituted benzenes were predicted from the experimental values of 8 selected representatives. The reliability of the predictions was checked by comparing the predicted and experimental toxicities of six additional compounds in the series. 1. Introduction A study carried out by the U.S. National Academy of Science pointed out, in quantitative terms, the present low level of our knowledge about the toxic potential of several thousands of chemicals produced industrially ( I 1. The study witnesses that hazard identification is confined to some 10% of the existing commercial chemicals for which a t least more than minimal toxicity information may be found. A number of testing programs are ongoing to produce new data. The selection of chemicals to test with priority is entrusted to expert judgment and is largely based on exposure and toxicity considerations. Testing alone, in this chemical-by-chemical approach, cannot, however, be expected to fill the enormous data gap in a reasonable period of time. It would therefore be desirable to also use other tools to complement, integrate, and possibly optimize the efficiency of testing. It is generally agreed that the use of mathematical models, such as quantitative structure-activity relationships (QSARs), may represent a unique tool for predicting missing data (1, 2 ) . Several approaches to QSAR, either based on the fragments additivity concept, or, more realistically, based on the concept that biological activity may be expressed in terms of structural descriptors have been used. In recent reviews (349, optimistic statements are made concerning the potential value and future use of QSARs, but severe limitations are also mentioned. The often poor predictive power and ill-defined range of application make their use questionable in the management and control of toxic substances. Considering the above, it is therefore legitimate to ask the question whether the scientific development in toxicology, on the one hand, and in chemometrics and related areas, on the other hand, may assist in dealing with the problem of data gaps. To give an answer, the problem should be first formulated in two subproblems to be addressed separately: whether it is possible, (i) to limit the number of toxicity tests for each chemical and yet to be able to make judgment about the likely type and intensity ‘Istituto Superiore di SanitA. t Water Research Institute. 8 Umei University. 11 University of Perugia. 0013-936X/91/0925-0695$02.50/0
of the associated toxic potential and (ii) to reduce substantially the number of chemicals to test and yet to be able, via some kind of model, to predict nontested chemicals. The first subproblem is outside the scope of this paper; however, it can be mentioned that, in some areas, the current knowledge in toxicology is good enough to select reasonably small batteries of short-term tests that may be quite effective in signaling the possible presence or absence of specific hazards. As regards the second subproblem, it is our opinion that present state of the art in statistics and chemometrics, integrated to chemical and toxicological knowledge, may permit dealing with it properly. It is possible, in fact, under certain conditions to reduce substantially, by means of ad hoc optimization procedures, the number of chemicals for which test data are essential for constructing reliable QSAR models. Furthermore, on this basis, it is possible to formulate a strategy for ranking chemical hazards and setting priorities for toxicological testing. In this paper, we will briefly discuss the rationale underlying the need for a critical selection of the chemicals to be used for the training of a QSAR model and introduce statistical designs as the practical means to select optimal training sets. This will lead us to outline the framework of a general strategy, based on statistical design, for a systematic analysis of chemicals, their toxicological screening, and priority setting. We then will present and discuss the results obtained in the application of the strategy to the ranking of monosubstituted derivatives of benzene in relation to their hazard to aquatic organisms. 2. Optimization of t h e Selection of QSAR Training
Sets Rationale. The various reasons why often QSARs do not work properly have been reviewed in detail and conditions for sound modeling described (6-10). It is here worth remembering that QSARs, owing to their theoretical foundation, are locally valid models and, therefore, applicable within series of chemicals that have closely related structures and are biologically similar. More important, in the present context, is to reemphasize that t,he predictive power and range of applicability of a QSAR is strictly dependent on, and defined by, the chemicals used to calibrate them. A crucial point in the construction of a QSAR is therefore to be able to supervise the selection of the training set and to direct it toward the most informative compounds that effectively “represent” all the compounds of the series. Statistical designs provide appropriate means to optimize the selection and thus ensure, as far as possible, that test data collected for these, usually few, compounds in each series will form an adequate data basis to construct well-balanced models with, possibly, good predictive power for many similar compounds. Statistical Designs. Statistical designs are suitable techniques for the optimization of experiments in general (11,12)by an appropriate setting of the variables involved. For example, they may be used for identifying optimal
0 1991 American Chemical Society
Environ. Sci. Technol., Vol. 25, No. 4, 1991 695
Table I. Substituent Constants, x ,-xg, Observed and Calculated/Predicted Toxicity for the Training and Validation set of MBs“ YCdC
1
2 3 4 5
6 7 8
9 10
a
Training Set 0.78 3.44 0.45 5.96 0.00 0.00 2.06 0.10 -0.32 6.99 8.88 0.39 0.23 3.83 30.33 0.34 0.43 4.57 5.65 -0.07 -0.17 3.00 19.61 -0.08 -0.16 6.17 sum of squared errors = 0.16 SS
4.79 1.90 1.95 1.95 5.98 3.11 2.04 1.90 1.52 4.42 1.90 = 4.24 SD = 0.14
Validation Set 4.30 -0.15 4.11 0.23 3.52 -0.16 -0.66 2.93 -0.07 -0.15 4.11 -0.07 -0.13 5.05 sum of squared errors = 0.08 SS
1.70 3.26 1.90 1.52 2.97 1.90 1.80 1.80 1.80 1.50 1.50 1.84 2.04 2.76 3.16 1.52 3.49 1.90 = 1.12 SD = 0.12
NO2 C02C2HS H OC4Hg Br COC~HS CH, n-CdHg
-0.28 0.51 0.00 1.55 0.86 1.05 0.56 2.13
7.36 17.47 1.03 21.66
SCH, CH,CH,
0.61 1.02 0.71 -1.23 1.53 1.55
13.82 10.30 6.03 5.42 14.96 14.96
11
c1
12 13 14
NH, CHMe, C,H7
0.71 0.37
0.15 -0.07 0.37
0.00
1.70 1.90
1.70 1.90
2.44 2.36
1.00
1.00
1.00
1.35 1.95 2.36 1.52
Sum of squared errors, sum of squares (SS),and standard deviation (SD) are reported for each set.
conditions-say, temperature, pH, substrate concentration conditions-for maximizing the yield of organic syntheses. Recently, these techniques have been adapted to optimize the selection of small sets (training sets) of chemicals whose biological data are essential for calibrating QSARs ( 1 3 , 1 4 ) . In this area, the objective of design is to identify-within series of chemicals-a set of compounds that together maximize the information about the range of properties covered by the considered series. A design-aided selection of a training set requires primarily a definition of the “design variables” that must be spanned in the selection. Secondly, the use of these variables in a good statistical design permits identification of a set of compounds that, together, are able to map the design variables space in a well-balanced manner. The design variables should reflect important and independent structural properties. For the selection of the training compounds, several types of Statistical designs are available. Factorial designs and fractional factorial designs are examples of techniques that are effective and both easy to construct and to interpret. Principles and methods of statistical design are described in detail in textbooks (see refs l l and 12). Applications to the QSAR area are reported, for example, in refs 10 and 13-15. In this paper, the concept of statistical design is utilized in a broader perspective, as the pillar of a strategy to deal systematically with existing chemicals, their screening, and priority setting. 3. A Strategy for Ranking Chemical Hazards
The strategy, presented earlier on a number of occasions (16-19), applies to any random universe of organics. Its
final goal is to rank the organics, on a class-by-class basis, according to the concern related to their measured or estimated properties-both toxicity and environmental fate related properties. The effectiveness of the strategy is entrusted to QSAR based on statistical design. The strategy may be formulated in six major steps. 1. Chemicals in any list of interest (e.g., an inventory of commercial chemicals) are grouped together to form series of compounds with close similarity in structure. Each series is then separately processed through steps 24. 2. The compounds are parametrized by a consistent ensemble of several structural descriptors that provides the X-data table. Design variables are derived thereof. 898
Environ. Sci. Technol., Vol. 25, No. 4, 1991
2.44 4.29 1.00 1.90 1.95 3.11 1.90 1.90
-1.97 -2.03 -2.36 -1.15 -0.19 --1.88 -0.57
--1.91 -2.09 -2.36 -1.04 -1.26 -0.07 --1.69 -0.74
1.90 1.90
-1.38 -1.32 -1.58 --2.39 -1.06 -1.22
-1.30’ -1.38’ -1.45‘ -2.47’ -0.89‘ --1.09*
1.80
1.84 3.16 1.90
-1.00
Ypred.
3. The design variables are used in a statistical design leading to the selection of a training set of representative compounds of the class. 4. The training set compounds are tested to develop (when not available) homogeneous data for the toxic end points of interest. The testing provides the Y-data table. 5. A multivariate statistical analysis of the X and Y tables for the training set will give QSAR models together with some measure of their predictive power, range of application, and means for interpretation. 6. The models are finally validated by comparing measured and predicted toxicity data for a reasonable number of compounds outside the training set. Experimentally validated models can then be used for predictions of all the nontested compounds in the class and thus define their ranking and priorities for further studies. The effectiveness of the strategy for handling existing chemicals has been tested in a number of investigations with promising results. Results have been reported regarding (i) the extent to which existing chemicals (present in relevant lists of commercial chemicals) group into structurally homogeneous series, (ii) the possibility of selecting training sets based on readily available structural descriptors, and (iii) the performance of QSARs derived thereof (18,20-24). In the following we report the results of the first carried out complete application of the strategy to a rather large series of compounds. The objective was to establish a priority ranking for 100 monosubstituted benzene derivatives (MBs) in respect to their acute toxic effects to target organisms representative of aquatic life. 4. Results and Discussion Step 1: Setting of the Series. As the first step of the
strategy, a large series of MBs was assembled. Compounds were drawn from lists of high-volume industrial chemicals and lists of toxic compounds. The series, supplemented with additional compounds from other sources, included MBs with the most common and well-characterized substituents-a variety of alkyls, electron donors and acceptors, and halogens-for which there are well-defined and widely used descriptors. These MBs are listed in part in Table I and the remaining ones in Table VII. These compounds form a structurally homogeneous series based on the criterion of a common backbone
Table 11. Training Set of MBs Derived by a Complete Factorial Design in the Three Principal Properties (PPI-PP3)of Aromatic Substituents. Sign Combinations and P P Values for the Selected MBs
sign combin (design uoints) 1 2
3 4 6
6
I 8
_ _ _
+ - - + ++- - + + - + -++ +++
setting of the PPs PPI 1.078 3.669 0.000
4.808 1.746 3.499 1.220 4.297
PP, -3.144 -2.653 0.000 -0.633 -1.868 -2.573 -0.213 -0.451
structure (the benzene ring) and variations in the substituent group. This criterion, however, might not be valid for modeling every kind of toxic end point. In fact, a necessary condition for a QSAR to work properly is that the considered chemicals have similar structures and are biologically similar (same mode of action). In general, these conditions are more likely to be met by series of compounds belonging to traditional organic classes (aromatic hydrocarbons, haloalkanes, phenols, nitroaromatics, etc). However, the adopted criterion is expected to be valid in our application, as benzene derivatives are reported to produce acute effects to aquatic organisms through a common largely nonspecific mode of action also named narcosis (25). The existence of strong dissimilarities in the mechanism will however be checked by the data analysis a t later stages in the process. Step 2: Parametrization. Each MB was parametrized by using nine standard substituent descriptors, namely, R (hydrophobic parameter), up and urn (electronic constants), MR (molecular refractimty) (%), and the five steric Verloop parameters (27) (see Table I , variables x1-x9). The parametrization was carried out by taking into account that its ultimate goal is to provide a good and, as far as possible, complete description of the structural properties (so-called X space) of the series (20-24). A rich parametrization with several varied descriptors is important in view of the selection of a training set able to span the X space of the class properly. A multivariate description is also essential in order that those, often a priori unknown, structural factors, whose variations may influence the variations in the biological responses, are included in the QSAR model (see below, step 5). The nine variables are known to be collinear to some extent; Le., they contain overlapping information. This is not a problem for model development. However, for the purpose of statistical design it is preferable to have the compounds described by a few relevant and independent descriptors that may be used as design variables. In order to fulfill these requirements and not to lose useful information contained in the nine variables, the dimensionality of the description was reduced by means of a principal components analysis (PCA) (28, 29) of the 100 X 9 data table. All details of the analysis, results, and interpretation have been reported in ref 30. Here, it is worth mentioning that the PCA contracted the original nine variables to three significant dimensions-latent variables-together accounting for almost 70% of the variance in the data. Thus, these latent variables, also called principal properties (PPs), comply well with the conditions for being used as design variables in the selection of a training set. In fact, in addition to being few and orthogonal (owing to the way they are derived), they also give a good representation of the original X space. Step 3: Selection of a Training Set. The selection was carried out by using the three PPs as the design
pp3 0.513 0.312 0.000
0.227 0.908 2.234 0.816 0.845
MB
substituent group
nitrobenzene ethyl benzoate benzene butyl phenyl ether bromobenzene benzophenone toluene n-butvlbenzene
NO2 COOC2H5
H OW9 Br COCGH, CH, n-CAH,
variables in a complete factorial design in two levels, a high (+) and a low (-) level for the variables. This resulted in the selection of eight MBs giving a proper fit to the eight possible combinations of high and low values for the variables. Table I1 shows the sign combinations for the variables, the setting of the PPs giving a proper fit to them, and the corresponding MBs. These are also reported as the first eight entries in Table I. All details regarding the design of the training set, its construction, and the geometrical interpretation can be found in ref 30. We only wish to underline that (i) statistical design permits supervision of the selection of a well-balanced training sets; (ii) the selection may be flexible, as more than one compound may fit the design corners; and (iii) the number of training compounds is independent from the size of the series and reasonably small, which, for a number of practical reasons, is not an irrelevant result. Step 4: Testing of the Training Set. In our study we were interested in the aquatic toxicity of MBs. For a good evaluation of the potential hazards these chemicals represent for the aquatic life, several effects on different organisms should be available. However, for the purpose of implementing the strategy, the availability of consistent biological data for a single toxic effect was sufficient. Unsurprisingly, no consistent, homogeneous set of aquatic toxicity data for the selected MBs was found in the data bases consulted (31, 32). Owing to the importance of having good and homogeneous data for QSAR modeling, all the training set compounds were tested under the same conditions and in the same laboratory. The selected target organism was Daphnia magna, and the toxic effect observed was the immobilization of the daphnids. The toxicity was expressed as 24h-EC(50), i.e., the concentration of toxicant causing immobilization of 50% of the population after 24 h of exposure. The measured 24h-EC(50) values (milligrams per liter) are listed with confidence limits in Table 111. We note that a rather wide range in the toxic response is covered by the training MBs. This shows that a statistical design in appropriate variables, while providing a good mapping of the structural space of a series, may also provide a good mapping of the biological activity spacethe Y space. EC(50) values, transformed into the inverse logarithms (with concentrations expressed in micromolar), were used as the Y data in the model development (see Table I). Step 5: Model Development. In a regression analysis, EC(50)s (here the dependent Y variables) for the eight MBs were related to the corresponding nine aromatic substituent constants (here the independent X-variable block). The method used was partial least squares (PLS) in latent variables (7, 33-35). PLS is a multivariate projection method. The two-block PLS extracts systematic information from the X and Y blocks and relates them in Environ. Sci. Technol., Vol. 25, No. 4, 1991 697
Table 111. Toxicity Data" for the Training and the Validation Set of MBs 24h-EC(50) on Daphnia magnab
subst groups
?
Training Set 11.5 (8.8-15.0) 16.0 (13.0-19.7) 18.0 (14.81-21.90)
NO2
1
2
COOC,H,
3
H
4
OC4H9
(1.7-2.6) 1.6 (1.04-2.46) 0.28 (0.21-0.37) 7.0 (5.23-9.37) 0.52 (0.39-0.69)
Br
6
COPh
7
CH3
8
n-C4H,
9
Validation Set SCHS
10
C2H5
11
c1
12
NH,
13
i-Pr
14
n-Pr
1
2 3 4 5 6 7 8 9 10
2.1
3
1
2 3 (I
27.7 25.3 25.2
70.3 10.6 12.5
27.7 53.0 78.2
70.3 80.9 93.4
p3
XI
1.27
~2
0.10
0.38 0.48
~3
XG
3.59 2.63 0.58 2.41 0.55
~g
1.66 1.01 1.28
0.29 -0.06 -0.45 -0.47 -0.09 -0.27 0.17 -0.31 -0.53
-0.26 -0.01 0.44 0.47 -0.41 0.45
xi
1.01 1.42 0.79 3.41 2.61 4.01 1.64 3.43 2.32 -1.79
xq
~5
xg
~ 1 0
1 4
5 6 7 8
regr coeff b
0.43 0.21 0.25
such a way that Y is predicted from X. The process is carried out under the constraint of maximizing the covariance between the blocks. PLS includes, as an integral part of the data analysis, a cross-validation procedure that represents a safeguard against chance correlations and overfitting (36). Additional information on the method is given in section 6. In our application, after scaling the variables to unit variance, the method extracted three significant dimensions together accounting for 95% of the variance in the toxicity, while 75% of the variance in the X data was used. Table IV shows the separate contributions of each component to the overall explained variance. Relevant variable parameters are reported in Table V; included are the PLS loadings, which describe the contribution of the variables in each model dimension, and the modeling powers that indicate how much the variables contribute to the global model. Loadings and modeling powers are useful parameters for interpretation. The loading values show that lipophilicity and size descriptors are dominating in the Environ. Sci. Technol., Vol. 25, No. 4, 1991
pz
2 3
See statistical methods section.
698
p1
subs group
3.0 (2.2-4.0) 2.2 (1.53-3.17) 4.3 (3.25-5.70) 23.0 (18.0-30.0) 1.4 (1.0-1.97) 2.0 (1.34-2.97)
cumul var expl X block y
mean
-0.00
-0.04 0.39 0.30 0.46 0.34 0.23
MP,," 0.60 0.73 0.47 0.69 0.57 0.57 0.66 0.68 0.66 0.74
0.04
0.37 -0.02
Table VI. PLS Scores for the Training MBs
Table IV. Percentage of Variance Explained, in Each Dimension and Cumulative, by the PLS Model and Regression Coefficient of the Inner Relationn at Each Dimension
a (PLS dim)
scaling
a MP,, = 1 means that all variation in the variable is used in the model and, vice versa, if MP,, = 0, the variable has no contribution at all.
"This work; except for MBs 11 and 12, for which toxicity data were drawn from refs 38 and 29, respectively. Confidence limits are in Darentheses. Concentrations are in millierams Der liter.
var exptl in each PLS dim X block y
Table V. Variable Parameters: Scaling Weights ( l / S D ) Used for Autoscaling; Variable Means (Mean-Centering); Loadings, p 1 - p 3 and Modeling Power (1 - SD), MP,,
NO, COOC,H, H OC4H9 Br COC&, CH, n-C4H,
tl
t2
t3
-1.47 0.33 -3.25 1.51 -0.51 2.96 -1.21 1.64
-1.65 -3.18 1.17 0.81 0.43
1.28 -1.44 -0.35 -1.49 1.23 1.74 -0.19 -0.78
-0.00
0.89 1.53
first, the most important, dimension. However, each of the nine descriptors contributes in the second and third dimensions; this point is further confirmed by the modeling powers of the variables, which in no case are negligible. This result hints at the fact that lipophilicity alone cannot properly account for the variations in the biological responses. In particular, it shows the importance, also for interpretation purposes, of having a multivariate description of the compounds. PLS scores, i.e., the projections of the compounds along the three significant model components (tl-t3) are listed in Table VI. A preliminary evaluation of the predictive power of the statistically validated model was made by comparing the calculated with the actual toxicities. The sum of squared errors, Yak - YdJ2, was 0.16 and the standard deviation (SD) 0.14 (see Table I). From the sum of squared errors and the total sum of squares, ss = x(YObS- ymem)' = 4.24, the R2 value, calculated as the difference R2 = 1 - E(Yobs - YdJ2/SS, was 0.96 for the three-dimensional model. We note that the evaluation of the accuracy of the model based on the calculated toxicities for the training compounds may be too optimistic, as it is based on fit and not on predictions. What is really important to know is how good the predicted toxicities are for compounds not included in the QSAR calibration. Thus, R2was recalculated by the leave one out procedure (37), where one compound a t a time is left out of the training set and its biological response is predicted by the resulting model. By this procedure, R2 (now, 1 - E(Yobs - Yp,,d)2)/SS) was 0.74. We note, however, that this measure of the accuracy of the model also is misleading in the present context: it is too conservative within the QSAR approach based on a statistically designed training set. The calculation implies, in fact, the deliberate construction of models that are known to be unbalanced and that, therefore, give poor predictions, especially when the training set is critically small. Within the philosophy of statistically derived training sets, where all the compounds together form a minimal
x(
Observed toxicity
-0.5 -1.0
Table VII. Predicted Toxicities and Ranking for Nontested
t
COPbA' n-Bu
A,;.'
.:'
, ... \ .,. M :* ,.
-1.5
'n-R SMe
-2.0
-2.5 -2.5
-2.0
-1.5
-1.0
MBs
nontested MBs N subsgroup 15 16
t-C,H, CH2C6H5
17
eyclohexyl
18 19 20 21 22 23 24
C&II OC6H, SOICBHS NHC6H, SC3H, CF,CF, SF.
-0.5
Calculated @edict& toxicity 1. Pbt of the observed vs calculatedlprediied toxi3iLms of MBs to Daphnas. Calculated toxicities for the training set compounds are denoted by triangles and predicted toxicities for the validation compounds are denoted by circles. The toxichy is reported as -log EC(50) with C in micromolar.
informationally correct set, the only sound manner to evaluate the predictive capability of a model is to see how well it predicts compounds outside the training set. This implies, as an integral part of the strategy, the need to measure the biological responses in a reasonably large and structurally varied validation set. Step 6 Model Validation. For validating the model we compared predicted with observed data for six validation compounds (see Tables I and 111). Four of them (9, 10, 13, and 14), were tested by us in the same way as the training set was. Whereas, 24h-EC(50)s for chlorobenzene and aniline (obtained from the same test protoeol and, as reported, under practically the same test conditions as we used) were found in the literature (38.39). As may be seen in Table I, experimental and predicted values compare quite well. In Table I, the sum of squared errors, SS, and SD related to the six predictions are reported. R2 for the validation set was 0.93. The plot of observed vs calculated/predicted toxicities is shown in Figure I. At this stage, the model can be considered sufficiently validated. In particular, the absence of outliers in a rather varied ensemble of MBs indicates that a similarity in the mode of action responsible for the observed response is likely for all the compounds in the series. In this situation, the model can be used within the objective of establishing a preliminary priority ranking of MBs for their acute toxicity to daphnids. EC(5O)s for E4 nontested MBs were therefore predicted by the model and their ranking established as shown in Table VII. Supplementary materials for predictions can be requested directly from the authors. 5. Conclusions A conclusive ranking of MBs would necessitate some additional testing in poorly explored areas of the domain of the series. In any case, the information about the toxic effects on daphnids needs to be complemented by additional information regarding the biological responses of other aquatic species prior to making final priorities for further studies (e.g., long-term aquatic toxicity studies). However, the goal of the present study was not solely to rank a large series of MBs for their acute effects on daphnids. The central goal was to show that chemometric tools-namely, statistical design and multivariate analysis-integrated with present knowledge in chemistry and toxicology may permit consideration of a new ap-
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
cyclopropyl 2-thienyl i-C,H, NCS SCOCH, NHCJH, SO,F OC8H, SH OCF, N, CH,CI S02C2HS OS02C6H, NHC2H,
y,, -0.19 -0.28 -0.39 -0.45 -0.48 -0.53 -0.63 -0.66 -0.70 -0.71 -0.80 -0.83 -0.90 -0.94 -0.96 -0.96 -0.96 -0.98 -1.01 -1.05 -1.12 -1.15 -1.17 -1.21 -1.24 -1.27 -1.28 -1.29 -1.31 -1.32 -1.34 -1.34 -1.35 -1.38 -1.38 -1.51 -1.51 -1.52 -1.58 -1.61 -1.65 -1.69
N 57 58 59
60 61 62 63 64 65 66
67 68 69
70 71 72 73 74 75 76
77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
96 97 98
nontested MBs subsgroup NMe2 CH,CN OCHMe, C-CH NHCN OCH,CH, SO,CH, N=NC,H, OCH, NHCH, CH=CH, SO,NH, OCOCH, C02CH3 NHCOCH, COCHJ F CH,CH,COOH NHCSNH, COOC8H, NHNH, CH,0CH3 NO CN COOH CH=CHCOCH, N=CHC6H, CH,OH CH=CHNO, OH CHO NHCOOC,H, OSO&H, N=CCI, CH=NOH CH=NC,H, NHSO,CH, SOCH, CONF~CH, CONH, NHCONH, NHCHO
UP4
-1.75 -1.76 -1.77 -1.78 -1.79 -1.81 -1.90 -1.91 -1.92 -1.92 -1.95 -1.97 -1.99 -1.99 -2.01 -2.02 -2.04 -2.04 -2.05 -2.05 -2.10 -2.10 -2.11 -2.12 -2.18 -2.20 -2.20 -2.22 -2.22 -2.25 -2.26 -2.35 -2.43 -2.46 -2.47 -2.47 -2.50 -2.51 -2.51 -2.71 -2.74 -2.88
proach for dealing with the toxicological screening of large number of compounds in a transparent effective way. We have shown that it may be possible, with limited testing effort, to construct models that are good vehicles t o transfer data from very few to very many compounds. In the present study, the testing of a factorial design selected set of eight MBs and some additional tests allowed formulation of preliminary predictions for 84 nontested MBs solely based on physicochemical descriptors, which are usually cheap to develop (or already tabulated as in our application). Additional MBs can be predicted by the model, provided that the relevant descriptors are developed and the new compounds are proved to belong to the domain of the investigated series. Before closing, some additional comments seem appropriate. The discussed strategy, while useful to identify, through a quantitative ranking of toxic effects, the potentially most hazardous chemicals within a series may, a t the same time, permit identification of promising substitutes for hazardous chemicals (40). In fact, seemingly minor structural features may correspond to substantial differences in toxicity. The strategy can be expanded to deal also with the problem, very critical in risk assessment, of the validation of screening tests in respect to their capability to anticiEnviron. Sci. Technol.. Vol. 25. No. 4,
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600
pate, for example, irreversible toxic effects that are detected in animal testing. This would entail (i) the biological characterization of statistically designed class training sets by means of test data for the candidate screening tests and the corresponding animal test data and (ii) a multivariate data analysis to check in a quantitative transparent manner whether the screening data can model the toxic effect of interest. This class-by-class approach would most probably lead to some conclusive results about the validity of screening test batteries and provide some understanding about intersystem or interspecies correlations. Other approaches to validation that include all kind of structures cannot be recommended. Despite considerable efforts, poorly conclusive results may be obtained (41). Finally, high quality and homogeneously developed biological data for critically informative compounds are essential in QSAR studies. It is risky to use data from any source, as their quality and homogeneity are often difficult to assess. In order to improve and speed up the assessment of toxic substances, and to build confidence in the usewhere and when possible-of QSARs, it would be useful for the statistical design concept, not even mentioned in recent reviews (3-5), to be included as an additional element among those already considered when establishing toxicity testing programs. 6 . Materials and Methods Chemicals. Twelve monosubstituted benzenes were tested. Ethyl benzoate and butylbenzene were from EGA-Chemie (99-100% pure); butyl phenyl ether, bromobenzene, benzophenone, and thioanisole were from Aldrich (99% pure); benzene, toluene, ethylbenzene, isopropylbenzene, and propylbenzene were Carlo-Erba GC standards (>99.7%). Toxicity Testing Method. The toxicity test was based on the OECD Guideline Daphnia Acute Immobilization Test (49), equivalent to the EEC test protocol Acute Toxicity to Daphnia (50). Testing conditions were as follows. Dilution water was prepared by mixing Milli-Q and tap water in a basically 1:l ratio, which was adjusted to attain a hardness of 150 mg/L (CaCO,); total hardness 130 mg/L (expressed as CaCO,), p H 8.2 f 0.5, conductivity 400 f 25 pS/cm. Stock solutions were prepared by adding the chemicals to the dilution water under mechanical stirring. Test solutions were obtained by diluting the stock solutions. The concentration of the test solutions was determined at the beginning and at the end of the experiment. Quantitative analyses were carried out on Varian 5000 HPLC equipment under the following conditions: C-18 reverse-phase column (Micro Pack MCH-5, length 15 cm); eluting mixture methanol-water (3:l); flow rate 1mL/min; volume injected, as adequate (mainly 10 pL). Chromatographic peaks were recorded on a Varian 4270 integrator and quantified by the external standard method. Experimental animals were daphnids less than 24 h old from our laboratory culture of Daphnia magna maintained in the same water as the dilution water and fed yeast and Selenastrum capricornutum. Ten animals, subdivided in two replicates, were used for each of the concentrations tested (a minimum of five) and the control. The tests were performed in closed bottles (150 mL) filled to the top in order to avoid loss of chemical due to volatilization. The temperature was 20 f 1 “C and the photoperiod was 16 h light, 8 h dark. The 24h-EC(50) value was estimated by the Litchfield and Wilcoxon method (42). Statistical Method. Partial least squares in latent variables, also called projection to latent structures, is a 700
Environ. Sci. Technol., Vol. 25, No. 4, 1991
method of multivariate data analysis. The method is described in detail by Wold et al. (43). Statistical properties of PLS are given in refs 35 and 44-46 while ref 34 provides a good introduction to the method. Here, we briefly summarize how the method works. In QSAR applications, PLS works on two blocks of data, the X block and the Y block, which usually contain the structural parameters and the biological activity data, respectively. Prior to PLS analysis the data are usually scaled and mean-centered. If no precise knowledge exists about the relative importance of the variables, all of them are scaled to unit variance. With X and Y properly scaled and mean-centered, PLS decomposes X and Y simultaneously into PC-like (principal component like) bilinear models according to eqs 1and 2, respectively, where T and
X = 1%+ T-P’ + E
(1)
Y = 13 + U-Q’ + F
(2)
U are latent variables matrices and P and Q are loading matrices that extract the systematic part of the information in the data. E and F are the residual matrices containing the nonsystematic part of the information in X and Y, respectively. 1 represents a column vector with the element 1 in all positions; x and 3 are the mean values of the variables 3c and y , respectively. The X block is related to the Y block by a PLS model that predicts Y from X. Maximal covariance between the X- and Y-block models is obtained by using a PLS weight matrix W’. The relation between U and T, the “inner relation”, can therefore be modeled by eq 3, where B is a U = T-B + H
(3)
diagonal matrix [B = diag(b), with b regression coefficients of the models in each PLS dimension] and H is a residual matrix. The statistical significance of each model dimension is determined by cross-validation (36, 47). Equation 3, also called the inner relation, may be inserted into eq 2, which becomes Y = T.B*Q’+ F’
(4)
Predictions of biological activities ( Y values) for new compounds are obtained by inserting the x data of these compounds into the PLS model. The sequence is x t -u-y. In the present application, where there is only one dependent y variable, eq 4 reads y = 9 + T-B F’
-
+
--
and the predictions for new compounds (validation set) y. Here, x’ is a are obtained in the sequence: x’ t row vector that contains the structural data for a new compound for which y can be predicted. PLS can handle some missing data in the X and Y blocks, and types of discontinuous data (e.g., 0/1 data) that are often used for expressing results of toxicological tests. Software. All calculations were made with the software package SIMCA-BB, for IBM and IBM-compatible PCS, developed by Wold and co-workers (48). Noted Added in Proof. Since the paper was submitted, further studies have shown that the predictions for MBs with N-donor substituents should be considered with caution: it is possible that these compounds form a subset of MBs that it may be worth analyzing and modeling separately (51). Registry No. 1, 98-95-3; 2, 93-89-0; 3, 71-43-2; 4, 1126-79-0; 5, 108-86-1; 6, 119-61-9; 7, 108-88-3; 8, 104-51-8; 9, 100-68-5; 10, 100-41-4; 11, 108-90-7; 12, 62-53-3; 13, 98-82-8; 14, 103-65-1; 15,
98-06-6; 16, 101-81-5; 17, 827-52-1; 18,538-68-1; 19, 101-84-8; 20, 945-51-7; 21,122-39-4; 22,874-79-3; 23, 309-11-5; 24, 2551-62-4; 25, 495-40-9; 26, 93-99-2; 27, 620-05-3; 28, 135-98-8; 29, 591-50-4; 30,426-58-4; 31, 501 -65-5; 32,456-56-4; 33,622-38-8; 34,1126-78-9; 35, 5285-87-0; 36, 92-52-4; 37,98-08-8; 38, 946-80-5; 39, 93-98-1; 40, 100-39-0; 41,672-66-2; 42,873-49-4; 43,825-55-8; 44,538-93-2; 45, 103-72-0; 46,934-87-2; 47,622-80-0; 48,368-43-4; 49,622-85-5; 50,108-98-5; 51, 456-55-3; 52,622-37-7; 53,100-44-7; 54,599-70-2; 55,4358-63-8;56,103-69-5;57,121-69-7; 58,140-29-4; 59,2741-16-4; 60,536-74-3; 61,622-34-4; 62, 103-73-1;63,3112-85-4; 64,103-33-3; 65, 100-66-3; 66, 100-61-8; 67, 100-42-5; 68,98-10-2; 69, 1864-94-4; 70, 93-58-3; 71, 103-84-4; 72, 98-86-2; 73, 462-06-6; 74, 501-52-0; 75,103-85-5; 76,2315-68-6;77,100-63-0; 78,538-86-3; 79,586-96-9; 80, 100-47-0;81, 65-85-0; 82, 122-57-6; 83,538-51-2; 84, 100-51-6; 85, 102-96-5; 86, 108-95-2;87, 100-52-7;88, 101-99-5;89, 16156-59-5; 90,622-44-6; 91,932-90-1; 93,1197-22-4; 94,1193-82-4; 95,613-93-4; 96, 55-21-0; 97, 64-10-8; 98, 103-70-8.
S. A Strategy for Systematic Analysis and Priority Ranking
(19)
(20) (21)
(22) (23)
Literature Cited
U.S.National Academy of Science, National Research Council Toxicology testing. Strategies to determine needs and priorities; National Academy Press: Washington, DC, 1984; p p 3-12. OECD, Organization for Economic Co-operation and Development. Chemicals on which data are currently inadequate: selection criteria for health and environmental purposes. Final Report of the ad hoc Expert Group, 1984; p p 143-145. Nirmalakhandam, D.; Speece, R. E. Enuiron. Sci. Technol. 1988, 22, 606-615. Calamari, D.; Vighi, M. Quantitative Structure-Activity Relationships in Ecotoxicology. Value and Limitations. Final Report, EEC Contract 86-B6602-11-00l-N; 1987. Turner, L.; Choplin, F.; Dugard, P.; Hermens, J.; Jaeckh, R.; Marsmann, M.; Roberts, D. Toxicol. in Vitro 1987, I , 143-171. Wold, S.; Albano, C.; Dunn, W. J.; Edlund, U.; Esbensen, K.; Geladi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. In Chemometricsand Statistics in Chemistry; Kowalski, B. R., Ed.; NATO AS1 Series C 138; D. Reidel: Dordrecht, Holland, 1984; p p 17-95. Wold, S.; Dunn, W. J. J. Chem. Inf. Comput. Sci. 1983,23, 6-15. Wold, S.; Dunn, W. J.; Sjostrom, M.; Hellberg, S. In Proceedings, International Seminar on Chemical Testing and Animal Welfare; Stockholm 1986; Swedish National Chemicals Inspectorate: Stockholm, 1986; p p 81-90. Clementi, S.; Cruciani, G.; Cesareo, D.; Tosato, M. L. Chim. Oggi. 1989, 3, 57-61. Hellberg, S. A Multivariate Approach to QSAR. Thesis, U m e i University, 1986; pp 1-50. Box, G. E. P.; Draper, N. R. Empirical Model Building and Response Surfaces;John Wiley and Sons: New York, 1987. Box, G. E. P.; Hunter, W. J.; Hunter, J. S. Statistics for Experimenters; Wiley: New York, 1978. Wold, S.; Sjostrom, M.; Carlson, R.; Lundstedt, T.; Hellberg, S.; Skagerberg, B.; Wikstrom, C.; Ohman, J. Anal. Chim. Acta 1986, 191, 17-32. Hellberg, S.; Sjostrom, M.; Skagerberg, B.; Wikstrom, C.; Wold, S. Acta Pharm. Jugosl. 1987, 37, 53-65. Tosato, M. L.; Geladi, P. In Practical Application of Structure- Activity Relationships (QSAR) in Enuironmental Chemistry and Toxicology;Karcher, W., Devillers, J., Eds.; Kluwer: Dordrecht, Holland, 1990; p p 326-551. Tosato, M. L. Statistical designs and multivariate QSAR models for priority setting of chemicals. International Conference. Structure-Activity Relationships, SAR, in the Toxicological Evaluation of Chemicals. Pisa-Volterra, May 1987; Report of the Conference. Chim. Oggi 1987,12,76-78. Tosato, M. L.; Clementi, S. In Photocatalysis and Enuironment. Trends and Applications; Schiavello, M., Ed.; NATO AS1 237; Kluwer: Dordrecht, Holland, 1988; p p 583-597. Tosato, M. L.; Cesareo, D.; Clementi, S.; Eriksson, L.; Jonsson, J.; Marchini, S.;Passerini, L.; Skagerberg, B.; Wold,
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of Chemicals. Report of a Preliminary Feasibility Study. Background Paper at the OECD Workshop. Co-operation on existing chemicals: charting the course. Ottawa, NOvember 1987. Tosato, M. L.; Marchini, S.;Passerini, L.; Pino, A.; Jonsson, J.; Eriksson, L.; Hellberg, S.; Skagerberg, B.; Sjostrom, M.; Wold, S. In Proceedings of the 1st European Conference on Ecotoxicology. Copenhagen, October 1988; Lokke, H., Tyle, H., Bro-Rasmussen, F., Eds.; Technical University of Danmark: Lyngby, Denmark, 1989; p p 492-495. Tosato, M. L.; Cesareo, D.; Galassi, S.; Vigano, L.; Cruciani, G.; Clementi, S.; Skagerberg, B. Chirn. Oggi 1988,3,41-45. Jonsson, J.; Eriksson, L.; Sjostrom, M.; Wold, S.; Tosato, M. L. Chernom. Intell. Lab. Syst. 1989,5, 169-186. Eriksson, L.; Jonsson, J.; Sjostrom, M.; Wold, S. Chemorn. Intell. Lab. Syst. 1989, 7, 131-141. Tosato, M. L.; Marchini, S.; Passerini, L.; Pino, A.; Eriksson, L.; Hellberg, S.; Jonsson, J.; Skagerberg, B.; Sjostrom, M.; Wold, S. Enuiron. Toxicol. Chem. 1990, 9, 265-277. Eriksson, L.; Jonsson, J.; Hellberg, S.; Lindgren, F.; Skagerberg, B.; Sjostrom, M.; Wold, S.; Berglind, R. Enuiron. Toxicol. Chem. 1990,9, 1341-1353. Veith, G. D.; Call, D. J.; Brooke, L. T. Can. J. Fish Aquat. Sci. 1983, 40, 743-748. Hansch, C.; Leo, A. J. Substituent Constants f o r Correlation Analysis in Chemistry and Biology; J. Wiley and Sons: New York, 1979. Verloop, A.; Hoogenstraaten, W.; Tipker, J. Drug Des. 1976, 7, 165. Jolliffe, I. T. Principal Component Analysis; Springer Verlag: New York, 1986. Wold, S.; Esbensen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 37-52. Skagerberg, B.; Bonelli, D.; Clementi, S.; Cruciani, G.; Ebert, C. Quant. Struct.-Act. Relat. 1989, 8 , 32-38. HSDB, Hazardous Substances Data Bank, Bethesda, MD, National Librarv of Medicine. AQUIRE, Aqua& Information Retrieval Toxicity Database, U.S. EPA. Wold, H,; Joreskog, K. G. Systems under Indirect Observation; North-Holland: Amsterdam, 1982. Geladi, P.; Kowalski, B. R. Anal. Chem. Acta 1986, 185, 1-7. Hoskuldsson, A. J. Chemom. 1988, 2, 211-228. Cramer, R. D., 111; Bunce, J. D.; Patterson, D. E.; Frank, I. E. Quant. Struct.-Act. Relat. 1988, 7, 18-25. Cruciani, G.; Clementi, S.; Baroni, M.; Ebert, C.; Skagerberg, B. Quant. Struct.-Act. Relat. 1990, 9, 101-107. Calamari, D.; Galassi, S.; Setti, F.; Vighi, M. Chemosphere 1983,12, 253-262. Calamari, D.; d a Gasso, R.; Galassi, S.; Provini, A.; Vighi, M. Chemosphere 1980,9, 753-762. Clementi, S.;Bonelli, D.; Cruciani, G.; Skagerberg, B.; Ebert, C.; Linda, P.; Cesareo, D.; Tosato, M. L. Chirn. Oggi 1986, 6, 19-22. Tosato, M. L.; Cesareo, D.; Passerini, L.; Clementi, S. J . Chemom. 1988,2, 171-187. Litchfield, J. T.; Wilcoxon, F. J . Pharmacol, Exp. Ther. 1949, 96, 99-113. Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J., 111SIAM J . Sci. Stat. Comput. 1984,5, 735-743. Manne, R. Chemom. Intell. Lab. S3ist. 1987, 2, 187-197. Naes, T.; Martens, H. Commun. Stat., Simul. Comput. 1985, 14, 545-576. Lorber, A.; Wangen, L. E.; Kowalski, B. R. J . Chemom. 1987, I , 19-31. Wold, S. Technometrics 1978, 20, 397-405. Wold, S.; e t al. SIMCA 3B Manual, 3rd ed.; U m e i University: Umei, Sweden, 1985. Daphnia Sp. Acute Immobilization Test and Reproduction Test. P a r t I-the 24h EC50 Acute Immobilization Test. In OECD Guidelines f o r Testing of Chemicals, Guideline No. 202 (Adopted April 1984). OECD Publications: 2, rue Andr6-Pascal, 75775 Paris, 1981; and subsequent editions. Environ. Sci. Technol., Vol. 25, No. 4, 1991 701
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(50) Acute Toxicity to Daphnia. In EEC Commission Directive 841449, Test No. (2-2, Official Journal of the European Communities 1984, No. L251, p p 155-159. (51) Tosato, M. L.; Marchini, S.; Paolangeli, G.; Passerini, L.; Pino, A.; Skagerberg, B. In Proceedings of the 8th European Symposium on QSARs, Sorrento, Italy, September 9-13, 1990; Silipo, C., Vittoria, A., Eds., Elsevier: Amsterdam,
T h e Netherlands, in press. Received for review September 23, 1988. Revised manuscript received July 2, 1990. Accepted October 20, 1990. Financial grants from the European Economic Communities (Contract B6641-7-32-87) and the Italian Minister of Education are gratefully acknowledged.
Bonding of Chlorophenols on Iron and Aluminum Oxides King-Hsl S. Kung" and Murray 6. McBrlde
Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York 14853
The adsorption of 10 chlorophenols on synthetic, naturally occurring iron and aluminum oxides was studied to elucidate the mechanism of binding and relative bond strength of the chlorine-substituted phenols on oxide surfaces. Surface-enhanced deprotonation of chlorophenols was identified by spectroscopic methods. Chlorophenolates were found to be chemisorbed on oxide surfaces via an inner-sphere coordination. Chlorophenols also bonded on oxides by weak physical forces (H bonding and condensation), but these types of weak bonding were identified only when adsorption occurred from the vapor phase onto dry surfaces. Physisorbed chlorophenols, unlike chemisorbed molecules, were readily removed from oxide surfaces by washing with water. Poorly crystallinzed iron and aluminum oxides showed similar mechanisms of chlorophenol binding, although the bond for chlorophenolate chemisorbed on iron oxide was stronger than that on aluminum oxide. Only physically adsorbed chlorophenols were detected on crystalline gibbsite, suggesting that the dominant (001) crystal face, with surface hydroxyl groups doubly coordinated to Al, was not specifically reactive with the chlorophenols. Chemisorption, however, was identified on the crystalline iron oxide, goethite. From the extent of perturbation of aromatic ring electrons, the surface bond strength for chlorophenolates on aluminum oxide was found to correlate with the Lewis basicity of the phenolate anions (the higher the pK, of the chlorophenols, the stronger the surface bond). Nevertheless, the amount of chlorophenol adsorbed on noncrystalline iron oxide a t controlled pH of 5.4 was limited by the extent of deprotonation (the lower the pK,, the more adsorption).
Introduction Chlorophenols are widely distributed in soils and aquatic environments, arising mostly from their use as biocides and preservatives. The U.S. EPA has listed all of these xenobiotic chemicals as toxic pollutants, some of them having been designated as priority pollutants (1). In the past decade, much effort has been directed toward the study of their sorption and degradation in the environment (2-4). In a recent study, Urich and Stone (5) proposed a mechanism of degradation involving specific adsorption of chlorophenols onto manganese oxide followed by surface oxidation. However, bonding of chlorophenols on metal oxides has not been directly confirmed experimentally. Therefore, a study involving spectroscopic methods was considered necessary to explore the interaction between chlorophenols and metal oxides. In particular, the use of self-supporting oxide films was considered essential to avoid matrix effects on the organic-oxide interaction and
* Current address: Environmental and Water Resources Engineering, The University of Michigan, Ann Arbor, MI 48109-2125. 702
Environ. Sci. Technol., Vol. 25, NO. 4, 1991
to allow direct observation of the chemical environment of the adsorbed organics and of water competition for adsorption sites. The purpose of this work was to elucidate the mechanism of surface bonding of chlorophenols on metal oxides and determine the relative strength of the oxide-organic bond. Iron and aluminum oxides were chosen as adsorbents since they are the most common metal oxides in soil and aquifer materials. Adsorption of 10 chlorinated phenols including mono-, di-, and polychlorophenols was studied from the vapor and/or solution phase. Ultraviolet (UV) spectrometry was utilized to determine the degree to which adsorption perturbed the aromatic ring electrons of chlorophenols. Fourier transform infrared spectroscopy (FTIR) of the phenol-oxide complexes allowed the type of binding mechanism to be investigated. Adsorption of chlorophenols from dilute solution was quantified by isotherms to estimate the surface reactivity.
Experimental Section Unless otherwise noted, chlorophenols and all the other chemicals used in this work were analytical grade, commercially available reagents (from Aldrich or Eastman) and were used without further purification. 2,3,4,6-Tetrachlorophenol was technical grade (Eastman) and was purified twice by sublimation. Water was distilled, deionized, and filtered through 0.2-km Millipore filters. All experiments were conducted a t 22 f 1 "C. Oxide Preparation and Characterization. a. Aluminum Oxides. Pseudoboehmite was prepared by fast hydrolysis of aqueous AlC1, with NaOH followed by mixing and aging a t raised temperature for several days. The gellike product was then dialyzed against fresh water to remove excess salts. The final suspension, with a concentration of -40 mg/mL and pH 5.5, was used to prepare transparent self-supporting films. The X-ray diffraction (XRD) pattern of freeze-dried material confirmed this aluminum oxide to be pseudoboehmite, with peaks indicating d spacings near 6.2 (very broad peak), 3.2, 2.3, 1.8, and 1.5 A (weak peaks). This poorly crystallized material was an incompletely dehydrated boehmite (Le., pseudoboehmite), containing more sorbed water than crystalline boehmite (6). The surface area of the freeze-dried material calculated from N2 adsorption by the three-point BET method was 324 m*/g. Crystalline gibbsite used in this work was obtained from the Macaulay Institute and has been characterized before (7). This gibbsite is composed of sheets of edge-sharing A1 octahedra forming hexagonal platelets with well-developed (001) faces. The BET surface area of this gibbsite was 32.5 m2/g (8). Poorly crystallized gibbsite was prepared by titrating aqueous A1C13 solution with dilute NaOH. The milky
00 13-936X/91/0925-0702$02.50/0
0 1991 American Chemical Society
