Environ. Scl. Technol. lQ83, 27, 1394-1402
Modeling OctanoVWater Partition Coefficients by Molecular Topology: Chlorinated Benzenes and Biphenyls Aleksandar SablJ16,'*tf*Hans Giisten,* Joop Hermens,§ and Antoon Opperhulred Institute Ruder Bogkovib, P.O. Box 1016, 41001 Zagreb, Republic of Croatia, Institut fur Meteorologle und Kllmaforschung, KernforschungszentrumKarlsruhe/Unlversit Karlsruhe, D-7500 Karlsruhe 1, Qermany, and Research Institute of Toxlcology, University of Utrecht, P.O. Box 80176, NL-3508 TD Utrecht, The Netherlands
Models based on chemical structure were developed for the accurate predictions of octanoVwater partition coefficients (log KO,)for chlorinated benzenes and biphenyls (PCBs). The molecular connectivity indices and other topological properties, readily available for all chemicals, were used as molecular descriptors. In the modeling process only KO,data measured by the direct slow-stirring method have been used. Derived models account for 99.9% of the variation in the log KO,data, and partition properties are described best by the Oxv index. The developed molecular topology model enables the log KO, data of PCBs to be predicted within 0.10 log unit (a factor of 1.26), the probability being above 95%. Finally, the developed model was used to estimate the log KO,values for 190 PCB congeners whose KO,data have not yet been measured by the slow-stirring method. 1. Introduction
Background Information. During the last 30 years, octanol/water partition coefficients (KO,) have proven to be a valuable tool in many areas of natural sciences: chemistry, biochemistry, biology, pharmacology, environmental sciences, etc. (1, 2). During that period, many hundreds or even thousands of scientificpublications have been made in which the successful application of octanol/ water partition coefficients in correlations with many physical, chemical, or biological properties have been demonstrated for a large variety of organic chemicals (15). Such a broad success of this particular physicochemical property in "mimicking" quantitatively a variety of in vitro and in vivo processes is also responsible for its present status as a correlation tool of a choice. Unfortunately, also many correlations have been published (4-9) in which the processes studied have nothing in common with the process of partition between water and octanol phases (10-13). It must be emphasized that in order to correlate, two properties do not have to be related. Both of them may simply be related to a third property. The shake flask is the classical method for measuring KO,(14), and this procedure will give reproducible and accurate results for chemicals with log KO,below 4-5. Accurate results can also be obtained for more hydrophobic chemicals (15, 16), but this will need a very precise and skilful handling and the use of very pure octanol(16). The problem with this procedure is that the complete phase separation is difficult to achieve, and this may lead to underestimation of KO,,particularly for the more hydrophobic chemicals (17,18). The generator column method (19,20) is a more suitable method for measuring KO,of
* Corresponding author. f
Institute Ruaer Bogkovi6.
t Universitiit Karlsruhe. 8 University of Utrecht. 1394 Environ. Scl. Technol., Vol. 27, No. 7, 1993
more hydrophobic chemicals and more accurate results can be obtained also for extremelyhydrophobicchemicals such as the higher chlorinated biphenyls. Although the generator column method is very suitable for hydrophobic chemicals,the laboriousness of the method is an important disadvantage. Recently, a slow-stirring method was developed by Brooke and co-workers (17)as an alternative for the shakeflask procedure. The advantage of slow stirring instead of shaking is that it will prevent the formation of an emulsion of octanol in the aqueous phase. Experimental values determined by this procedure do show that accurate and reliable results can be obtained also for very hydrophobic chemicals (17, 18,21). At present, accurate KO,values measured by the slowstirring method are available for two sets of hydrophobic chemicals: chlorinated benzenes and chlorinated biphenyls (PCBs) (18). The low standard deviation for individual congeners (0.01-0.03 log unit) allows, for the first time, us to distinguish between partition data within various groups of chlorinated isomers, to identifysecondary structural properties (those not related to molecular size) important for chemical affinity, either for the lipophilic or for the aqueous phase, and finally to learn more about the partition process itself. Isomer-specific information is necessary because of the major environmentaland (eco)toxicological differences between PCB isomers (5). To describe and understand complex relationships between different substitution patterns in isomeric molecules, it is imperative to use advanced structural approaches that can describe relatively simply and fast, also in quantitative terms, the whole wealth of substitution patterns present in substituted benzenes and biphenyls or similar compounds. One attractive possibility that will be tested in this study is to use the topological approach with topological indices and other topological properties as quantitative descriptors. Their calculation is based on a simple information that is readily available for all chemicals, synthesized and hypothetical, either from their structural formulas or from the periodic table of elements. DevelopingStrategy. First, a model will be developed for chlorinated benzenes since their octanoVwater partition coefficients were measured for all 12 derivatives. A successful model will have to fulfill several criteria of fit: (i) The correlation coefficient between observed and calculated partition coefficientsmust be high, around 0.99. (ii) The standard error of calculated partition coefficients must be close to the standard deviation of measured data for chlorinated benzenes. (iii) The sequencesof partition coefficients must be correctly reproduced within each isomeric group. Second, the model developed for chlorinated benzenes will then be applied and, if possible, extended to chlorinated biphenyls; i.e., the knowledge gained by modeling partition data of chlorinated benzenes will be applied to 0013-938X/93/0927-1394$04.00/0
@ 1993 American Chemical SOCletY
a set of chlorinated biphenyls. A successful quantitative model for chlorinated biphenyls will have to fulfill all criteria of fit already described for the benzene analogues. Accurate KO, data are available for 19 of 209 possible chlorinated biphenyl derivatives (18). Thus, this is a situation where the whole potential of QSAR modeling can be demonstrated. The model that will be developed for a relatively small set of measured partition data can then be applied to predict those data for a 10 times larger set of derivatives. A number of methods, utilizing (i) aclassicalsubstituent constants approach (18), (ii) the CLOGP program (22, 23), (iii) the correlations with the molecular surface areas (24-26), (iv) the solvatochromic parameters (26,27), (v) the UNIFAC-derived activity coefficients (28),(vi) the molecular connectivity index (29),and (vii) the thermodynamic SOLPAR model (30),have been developed for the estimation of the log KO,dataof chlorinated biphenyls (PCBs). On account of the wide application of calculated log KO,data, it would be valuable to evaluate the reliability of those methods. We will also compare our model for chlorinated biphenyls with those already published and listed above. These evaluations and comparisons will provide important informationabout the performance and range of application of individual models in predicting log KO,data of PCBs as well as about their advantages and limitations. 2. Method of Calculations and Experimental Data
The most successful of all topological indices in quantitative modeling in various areas of physics, chemistry, biology, pharmacology (drug design), and environmental sciences is the molecular connectivity index or a system of molecular connectivity indices (MCIs). The concept of molecular connectivity index, originally called branching index, was introduced by RandiE (31). His original idea was developed further by Kier and Hall (32)into a system of MCIs. The information used in the calculation of MCIs are the number and type of atoms and bonds as well as the numbers of all electrons and valence electrons for nonhydrogen atoms. Those data are readily available for all chemicals from their structural formulas and the periodic table of elements. Several extensive reviews of the theory and method of calculation of molecular connectivity indices have been published (33-38). Thus, only a description of the calculation of the valence zero-order molecular connectivity index used in this study is given here. Molecular connectivity indices are calculated only for the nonhydrogen part of the molecule. In the valence approximation non-hydrogen atoms are described by their atomic valence delta values (av), which are calculated from their electron configuration by eq 1: '6 = (Zh)/(Z- 2" - 1) (1) ZV is the number of valence electrons in an atom, 2 is its atomic number, and h is the number of hydrogen atoms bound to the same atom. The valence zero-order Coxv) molecular connectivity indices are calculated from the electronic input information (atomic 6" values) by eq 2: o x v = C(6y.6 (2) where i corresponds to each individualnon-hydrogen atom and summation is made over all nonhydrogen atoms. The zero-order valence molecular connectivity indices were
%P.PNR
Flgurr 1. Structural formula and numeration pattern for substituted biphenyls. In addition, the structuralformulas for decachlorobiphenyl wlth outlined specific substltutionpatterns, used in this study as local structvaldesalptora,arepresenW. t h e f w ~ k r l n e s u b s t I t w n t 8 (Ch), the four meta-chlorlne substituents ( C ~ A and ) , the four paks of meWparaohlorine substituents (C~WPAIR).
found to be useful in modeling and in estimating the acute toxicity of fish (39, 40) for many classes of commercial chemicals. Other topological properties of chlorinated biphenyls used in this study as molecular descriptors are the following: the number of ortho-chlorinesubstituents (Cb), the number of meta-chlorine substituents ( C ~ ~ T A and ), the number of pairs of mew para-chlorine substituente (C1m.pm). The numbering scheme for chlorine substituents and the principle of counting those structural features are presented for decachlorobiphenyl in Figure 1. Molecular connectivity indices were calculated by the GRAPH I11 computer program on an Apple Macintosh SE/30 personal computer (41,421. There is also aversion of the GRAPH I11program that works on the IBM PC (or PS/2) and compatiblemicrocomputers. It must be pointed out that the Oxv indices and other molecular descriptors described above can be easily calculated by hand. Tables of molecular connectivity indices (up to 6th order), other structural variables, and octanoUwater partition coefficients (measured and predicted) for 12 chlorobenzenes and 209 PCBs may be obtained on diskette (Macintosh or IBM PC format) for a small fee from the corresponding author. A regression analysis was also carried out on an Apple Macintosh SE/30 personal computer using the statistical analysis system (SYSTAT, version 6.0). The following statistical parameters were used to test the quality of generated regression equations: the correlation coefficient (r),the standard error of the estimate (s),a test of the null hypothesis (F-test),and the amount of explained variance (EV). The octanol/water partition coefficients (KOw) used in this study were measured at the Research Institute of Toxicology (RITOX) in Utrecht, and they have been published in a study by Jack de Bruijn and his colleagues (18). The average standard deviations of published log KO,data for individual congeners are 10.015 and 10.027 logarithmic unit for chlorinated benzenes and biphenyls, respectively, while the slow-stirring method standard Envlron. Scl. Technol., Vol. 27,
No. 7, 1993 1891
Table I. Measured Octanol/Water Partition Coefficients (log KOw)by Slow-Stirring Method for Complete Set of Chlorobenzenese
compound
log Kow(fSDb)
OXV
ZIPAIR
ClPAlRi4
residualac
benzene chlorobenzene 1,2-dichlorobenzene 1,3-dichlorobenzene 1,4-dichlorobenzene 1,2,3-trichlorobenzene 1,2,4-trichlorobenzene 1,3,5-trichlorobenzene 1,2,3,4-tetrachlorobenzene 1,2,3,5-tetrachlorobenzene
2.186 f 0.009 3.464 0 0 -0.014 2.898 f 0.004 4.519 0 0 0.040 3.433 f 0.023 5.574 1 0 -0.049 3.525 f 0.031 5.574 0 0 0.009 3.444 f 0.030 5.574 0 1 0.007 4.139 f 0.002 6.629 2 0 0.033 4.050 f 0.018 6.629 1 1 -0.011 4.189 f 0.019 6.629 0 0 0.016 4.635 zk 0.004 7.684 3 1 -0.016 4.658 f 0.007 7.684 2 1 -0.026 1,2,4,5-tetrachlorobenzene 4.604 f 0.011 7.684 2 2 -0.001 pentachlorobenzene 5.183 f 0.021 8.739 4 2 -0.013 hexachlorobenzene 5.731 f 0.009 9.794 6 3 0.024 a The values of structural descriptors (Oxv,ClPAIR, ClPAIR14) used in the modeling procedure are also listed. * SD means standard deviation. Equation 3. Table 11. List of Statistical Parameters Showing Progress in Developing the Regression Models for Octanol/Water Partition Coefficients (log KOw)for Chlorinated Benzenes and Chlorinated Biphenyls.
model (descriptors)
r
Chlorinated Benzenes OXV 0.998 OX'; cIPAIRI4 0.999 OXv; ClPAIRi4; c1PAIR 1.OOO
s
F
0.069 0.040 0.029
2277 3368 4393
Chlorinated Biphenyls 0.967 0.287 oxv; ( O X V 0.989 0.175 oxv; ( o x y ; Clo 0.996 0.106 Oxv; NrClo 0.997 0.088 0x V.I (0x v )2 ;NrClo; C~META 0.999 0.055 OXv; NrClo; C~META; Clhlp-pAIR 0.999 0.042 a r = the correlation coefficient; s = the standard error estimate; F = a test of the null hypothesis (F-test). OXV
262 370 686 982 1914 2651 of the
deviation was estimated to be in the range between 0.05 and 0.10 log unit (18). In the modeling process, logarithmically transformed partition coefficients, log KO,, were used. 3. Results
ChlorinatedBenzenes. The list of chlorobenzenes is shown in Table I together with their octanollwater partition coefficients, molecular connectivity indices, and other structural descriptors. Table I contains also the standard deviation for the logKO,value of each compound. Earlier modeling efforts (43, 44) have shown that for hydrophobic chemicalsthe process of distribution between octanol and the aqueous phase depends primarily on their molecular bulk properties. The valence zero-order molecular connectivity index (Ox') was found to correlate best with log KO,data of chlorinated benzenes. However, to estimate the log KO, data of chlorobenzenes within the slow-stirring method standard deviation, two correction factors were needed: the proximity effect (described by the number of pairs of adjacent chlorine atoms, CIPAIR) and the number of chlorine substituents in para positions (ClPAIR]d). All three variables are statistically significant above the 99% level, and the F-test shows (Table 11)that the inclusion of a third variable genuinely improves the correlation. The details of progress in regression results for chlorobenzenes is presented in a stepwise manner in Table 11. 1396 Envlron. Scl. Technol., Vol. 27, No. 7, 1993
log KO,= 0.041(f0.059) + 0.62(f0.01)0~V 0.034(f0.010)C1pAIR - 0.079(f0.016)C1p~1~14 (3)
N = 13
r = 1.000
s = 0.029
Pp9 = 4393
EV = 99.9%
The statistical parameters show that eq 3 is very accurate in calculating log KO,data of chlorinated benzenes. The standard error of the estimate is only 0.029 log unit, which is very similar to the slow-stirring method standard deviation of 0.05-0.10 log unit (18). There was one pair of isomers, 1,2- and 1,4-dichlorobenzenes,whose sequence seems to be reversed by the above model. However, a closer examination of their experimental data shows that they differ only by 0.011 log unit and that the standard deviations of measured data for 1,2- and l,4-dichlorobenzene are higher than this difference, namely, 0.023 and 0.030, respectively. Thus, it is actually not clear which isomer has the smallest log KO, value, and the best conclusion is that their log KO,data are very similar. This is also suggested by our model, eq 3, since calculated log KO,values for 1,2- and 1,4-dichlorobenzene are 3.482 and 3.437. A comparison of the regression coefficients for C~PAIR and ClPAIRl4 variables shows that the number of chlorine substituents in para position (ClPAIR14) is distinctly more significant for the partition properties of chlorinated benzenes than the proximity effect (Clpm). It is also worth noting that the intercept is practically zero for the above model. Furthermore, there is a very strong correlation between the O x V index and the calculated molecular volume (26) of chlorobenzenes and PCBs (correlation coefficient above 0.98). Both these results indicate that the physical meaning of Oxv index may be that of a true molecular volume descriptor. Chlorinated Biphenyls. A list of 19 chlorinated biphenyls, plus parent compound, is shown in Table I11 together with their log KO,data, molecular connectivity indices, and other structural descriptors. Table 111 contains also the standard deviation for the log KO,value of each compound. By modeling log KO,data of chlorinated benzenes, we have learned that the molecular size,crowdingthe chlorine substituents, and a specific substitution pattern play an important role in their partition between water and octanol phases. It was reasonable to assume that the same or analogous structural properties will also play an important
Table 111. Measured Octanol/Water Partition Coefficients (log Kow)by Slow-Stirring Method for Biphenyl and 19 PCB Congenersa no.
log KO,(fSDb)
compound
OXV
NrCloc
C ~ ~ T AClm.pm
residualsd -0.014 0.070 -0.024 -0.007 -0.014 0.027 -0.049 -0.002 -0.045 0.012 -0.030 0.023 -0.007 -0.014 -0.014 0.100 0.016 0.009 -0.030 -0.005
0 0 O.OO0 4.008 i 0.022 6.774 1 biphenyl 0 0 0.530 4.531 i 0.029 7.828 2 2-chlorobiphenyl 0 0 8.883 0.858 4.965 i 0.047 3 2,2’-dichlorobiphenyl 4 2,6-dichlorobiphenyl 4.982i 0.013 8.883 0.858 0 0 1 1 0.388 5.860 i 0.017 9.938 5 2,3&trichlorobiphenyl 1 1 0.388 5.901 f 0.007 9.938 6 2,4,5-trichlorobiphenyl 0 0 0.777 5.711 i 0.014 9.938 7 2,4,64richlorobiphenyl 8 2’,3,4-trichlorobiphenyl 5.872 i 0.036 9.938 0.388 1 1 2 0 0.685 10.993 9 2,2’,3,3’-tetrachlorobiphenyl 6.178i 0.011 1 0 0.685 6.361 i 0.046 10.993 10 2,2‘,4,5’-tetrachlorobiphenyl 0 0 1.370 5.936 i 0.031 10.993 11 2,2’,6,6’-tetrachlorobiphenyl 2 2 10.993 0.342 12 2,3,4,5-tetrachlorobiphenyl 6.406f 0.069 2 2 O.OO0 6.630i 0.018 10.993 13 3,3’,4,4’-tetrachlorobiphenyl 2 2 0.613 6.754 i 0.015 12.048 14 2,3,4,5,6-pentachlorobiphenyl 2 2 0.554 7.321 f 0.027 13.103 15 2,2’,3,3’,4,4’-hexachlorobiphenyl 2 0 13.103 1.108 16 2,2’,3,3’,6,6‘-hexachlorobiphenyl 7.118i 0.034 0 0 1.108 7.287 f 0.065 13.103 17 2,2’,4,4‘,6,6’-hexachlorobiphenyl O.OO0 4 4 13.103 7.408 i0.005 18 3,3’,4,4’,5,5’-hexachlorobiphenyl 4 0 15.213 0.931 7.729 i0.031 19 2,2’,3,3’,5,5’,6,6’-octachlorobiphenyl 4 4 17.323 0.802 8.274i 0.001 20 decachlorobiphenyl a The values of structural descriptors (Oxv, NrClo,C I ~ T AClm-pm) , used in the modeling procedure are also listed. SD = standard deviation. NrClo = [no.of ortho-Cl/(molecularweight/100)]. Equation 4.
role in the partitioning of PCBs. It was also logical to expect that more structural descriptors will be needed to describe correctly the partition behavior of PCBs due to the enormous wealth of substitutional patterns present in chlorinated biphenyls. It was gratifying to learn that our reasoning has been correct. The valence zero-order molecular connectivity index (Ox’? was found again to correlate best with log KO,data of chlorinated biphenyls. However, from the analysis of residuals and the correlation diagram (plot of log KO,data vs Oxvindices), it was obvious that a nonlinear relation exists between the Oxvindex and log KO, data of PCBs. Several nonlinear models were tested with the OXvindex,and a quadratic function (model of parabola) was found to be the most appropriate relationship (correlation coefficient 0.989). However, to estimate the log KO,data of chlorinated biphenyls within the slow-stirring method standard deviation, three additional local structural descriptors were needed (i) the number of ortho-chlorine substituents normalized to the molecular weight of the compound (NrCh), (ii)the number of meta-chlorine Substituents (C~META), and (iii) the number of meta/para pairs of chlorine substituents (ClMp. PAIR). Their relative weight in the proposed model, eq 4, is in the same sequence as they are listed above. The progress in regression for chlorinated biphenyls is presented in Table 11. log KO,= -2.81(f0.17) + 1.20(*0.03)0xv0.028(f0.001)(0xv)2- 0.74(&0.06)NrCb0.i3(*O.O~)C~MET, - 0.048(*0.014) C l ~ p - p (4) ~r~
N = 20
r = 0.999
s = 0.042 I;b914 =
2651
EV = 99.9%
The statistical parameters show that eq 4 is very accurate in calculating log KO,data of chlorinated biphenyls. The standard error of the estimate is only 0.042 log unit, which is very similar to the slow-stirring method standard deviation of 0.05-0.1 log unit (18).This topological model was also effective in predicting correct sequences of log KO,values within isomeric groups. Still, the proposed
Table IV. Comparison of Octanol/Water Partition Coefficients (log Kow)Measured by Slow-Stirring Method ( 8 5 ) or by Generator Column method (GC)with Those Calculated by eq 4 for Biphenyl and Several Mono- and Dichlorinated PCB Congeners. log Kow
compound biphenyl 2-chlorobiphenyl 3-chlorobiphenyl 4-chlorobiphenyl 2,2’-dichlorobiphenyl 2,6-dichlorobiphenyl 2,5-dichlorobiphenyl 4,4’-dichlorobiphenyl 3,4-dichlorobiphenyl 2,4’-dichlorobiphenyl
4.008 4.531 4.965 4.982
3.76-4.00 4.30-4.55 4.58-4.83 4.49-4.61 4.90-6.00 4.93-5.00 6.10-5.16 5.43-5.53 6.29 6.14
(eq 4)
literature
4.022 4.461 4.729 4.855 4.989 4.989 5.180 5.627 5.453 5.308
19,20,22,25,28 19,20,22,25,28 20,22,25 20,22,25 22,% 19,20,25,28 19, 20,25,28 20,22,25 20,25 20,25,28
The literature sources of measured data are also listed.
model was not able to discriminate between one pair, 2,2/and 2,6-dichlorobiphenyl,and one triplet, 2,3,4-, and 2,4,5-, and 2/,3,4-trichlorobiphenyl, of PCB isomers. However, the examination of experimental data for the pair and triplet of isomers shows that their log KO,data differ only by 0.017 and 0.041 log unit, respectively, and these differences are in the range of the standard deviations of measured data (see Table 111). Because of ita high statistical significance and ability to explain all the variations in the measured KO,data, eq 4 has been used to estimate the log KO,values for the 190 PCB congeners, with 1-9 chlorine substituents, whose octanol/water partition coefficients have not yet been measured by the slow-stirring method. The predicted partition coefficient for mono- and dichlorinated biphenyls are presented in Table IV (Table A-I in the supplementary material has a complete list of calculated log KO,values for 190 PCBs). The quality of predicted log KO,values for mono- and dichlorinated biphenyls will be evaluated by comparison with an independent set of measured K,,, data, i.e., the available shake-flask and generator column methods KO,data presented in Table IV as a range of reported log KO,data. The results of the test are presented in Table IV. It can be seen that our model is very accurate Envlron. Scl. Technol., Vol. 27, No. 7. 1993 1397
in predicting log KO,data for mono- and dichlorinated biphenyls. For half of those PCBs, the calculated log KO, values are within the experimental range, while for the others the calculated values are very close with an average deviation of 0.12 log unit. Unfortunately, for PCB congeners with three or more chlorine substituents, the difference between reported slow-stirring and generator column KO,data is above 0.4 log unit (18). Furthermore, it seems that the generator column method systematically underestimates the KO, data of those PCB congeners. Thus, those data seem to be inadequate to evaluate the quality of calculated log KO,data for PCB congeners with three or more chlorine substituents. 4. Discussion
Structural Considerations of Octanol/ Water Partitioning. More than 99% of the variances in the octanov water partition coefficients of chlorinated benzenes and chlorinated biphenyls can be accounted for by relatively simple models which are entirely based on the chemical structure. The present QSAR analysis with topological features shows that their octanol/water partition coefficients are influenced primarily by their bulk properties, i.e., the size of these molecules. In the models developed, this property is described best by the O x v index. The same index was also found to be the most important structural descriptor for the log KO, data of polycyclic aromatic hydrocarbons (PAHs) and their derivatives (43,441. For chlorinated benzenes, the O x v index is linearly related to the log KO,data. On the other hand, a nonlinear relation evidently existed between the Oxvindex and log KO,data of PCBs. Such a nonlinear behavior of PCBs was expected since PAHs modeling results (43) have indicated that the nonlinear behavior starts at around 6 log units, and the large majority of KO,data of PCBs are above 5.5 log units. A quadratic function was found to be the most appropriate nonlinear relationship. Recently, a possible physical explanation was proposed (26) for the nonlinear behavior of log KO,data for highly hydrophobic chemicals. It was suggested (26)that the most probably cause is a solubilizing effect of the octanol present in the aqueous phase. A recent study on effect of octanol and other alcohols on aqueous solubility of several PCB congeners (45)supports proposed solubilizing effect. Finally, not all atomic fragments of chlorinated benzenes and chlorinated biphenyls contribute equally to their affinity for the lipophilic phase. The contributions from the chlorine substituents are approximately twice as high as those from the carbon fragments in phenyl rings (C or CH). This is valid for both the Oxv index and the log KO,values. Other structural features that control the magnitude of log KO, data of chlorinated benzenes and chlorinated biphenyls and also play an important role in their partition between water and octanol phases are the crowding of chlorine substituents as well as specific substitution patterns. The specific structural features of chlorinated benzenes are the pairs of chlorine substituents in the 1,4position (c1PAIR11) and the pairs of adjacent chlorine substituents (Clpm). For chlorinated biphenyls, the specific structural features are the number of orthochlorine substituents (NrCh), the number of meta-chlorine substituents (C~META), and the number of meta/para pairs of chlorine substituents (C~W-PAIR). The negative regression coefficients associated with all those structural 1388 Envlron. Scl. Technol., Vol. 27, No. 7. 1003
descriptors show that their presence will reduce the magnitude of partition coefficients. The relative importance, in quantitative terms, of those structural features in the proposed models (eqs 3 and 4)is in the same sequence as listed above. All those specific structural features can be viewed only as fine-tuning elements for the affinity of organic chemicals for the lipophilic phase since they influence only 1-2% of the variations in log KO,data, However, to calculate octanol/water partition coefficients within isomeric groups, all those specific structural features must be considered. Thus, it can be concluded that the magnitude of log KO,data for chlorinated benzenes and chlorinated biphenyls will be the balance between the size of these molecules and several chlorine substitution patterns that have a negative influence on the magnitude of log KO,data. Now, we will speculate on possibilities to expand the approach used in this study to cover other classes of hydrophobic chemicals. The most promising candidates seem to be two large classes of hydrophobic chemicals, chlorinated dibenzodioxins (PCDDs), and chlorinated dibenzofurans (PCDFs),due to their structural similarity with PCBs. A simple mechanical implementation of models developed in this study on new classes of chemicala will most probably fail. However, the results of this study indicate that the general features of quantitative models for PCDDs and PCDFs should be the following. Their models should have one descriptor, most probably the OxV index, and a nonlinear relation is expected between the size descriptor and log KO,data of PCDDs and PCDFs since a large majority of their KO,data will be above 5.5 log units. Other structural features that will control the magnitude of log KO,data of PCDDs and PCDFs will be the structuralcount of (i)the specific substitution positions and (ii) the specific substitution patterns of two or more chloro substituents. It is logical to expect that at least three such structural descriptors will be needed to describe accurately the partition behavior of PCDDs and PCDFs due to the enormous wealth of substitutional patterns, i.e., 75 and 135 congeners, respectively. A recent study (46) on modeling gas chromatographic retention indices of chlorinated dibenzodioxins supports our reasoning on general features for the log KO,models of PCDDs and PCDFs. Finally an important prerequisite in developing reliable log KO,models for PCDDs and PCDFs is a set of accurately measured KO, data for each class of these chemicals. Evaluating Models for Calculating PCBs log KO, Data. Great research efforts have been devoted to measure and model octanol/water partition coefficients of polychlorinated biphenyls (17-20,23-25,27,29,47-52). We will first evaluate the performance of 12 models for calculating PCBs partition coefficients that have been published since 1988. The log KO,data calculated for each model will be compared to the set of partition coefficients measured by the slow-stirring method. The results are expressed by means of residuals, i.e., the difference between calculated and observed values, and they are presented in Figure 2 as a set of bar-chart diagrams (Table A-I1 in the supplementary material has a complete list of calculated residuals). The average residuals, also presented in Figure 2, will be used to evaluate whether the individual models perform successfully. Even a brief examination of average residuals clearly shows that the results for half of the tested models do not
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1.2 1.3
MODEL
I
!I --:; -45
I
m
I
AVERAGE RESIDUAL 0.213
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2 L 1 -
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I
17 16 15 14 13 12 11 10 9 -
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8 -
6 5 4 3 2 1
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I I I
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AVERAGE RESIDUAL 0.382 I
--
0.352
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I
I
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MODEL CLOGP2
I
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AVERAGE RESIDUAL
0.336
- MODEL -- -CHIPAT
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AVERAGE RESIDUAL I
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-MODEL SURF1 18
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AVERAGE RESIDUAL 0.389
-
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AVERAGE RESIDUAL 0.763 I
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1
RESIDUALS (in logarithmic units) Flgure 2. Bar charts with the calculated residuals for biphenyl and 19 PCB congeners listed in Table 111. Each chart Is labeled with the name of the model used to calculate log KO, data and corresponding average residual. PCBs numbering corresponds to their numbering In Table 111. The posltiie residuals indicate overestimated values, and the negative residuals Indicate underestimatedvalues. The numbers on the right-hand side of this graph Indicated residuals larger than 1.0 log unit.
comply with the requirements on the accuracy of OECD guidelines for testing chemicals for octanol/water partition coefficients (53). The average residuals for six models (CLOGPB, SURF1, SURFS, SOLVCHl, UNIFAC, CHIPAT) are larger than 0.30 log unit, and these models are not suitable for reliably calculating PCBs log KO,data.
For the second batch of evaluated models, the average residuals are very similar, and for five models (SUBST, CLOGP1, SURF4, SOLVCHS,SOLPAR),they range from 0.14 to 0.19 log unit. It seems that these five models are sufficiently accurate to be recommended for a reliable calculation to be made of PCBs log KO,data. However, Envlron. Scl. Technol., Vol. 27, No. 7, 1993 1399
there are some limitations of individual models that must be observed in order to make estimations of the log KO,data. The substituent constants models (SUBST, CLOGP1) are only reliable for biphenyls with up to five chlorine substituents since they overestimate by up to 1 order of magnitude the log KO, data for highly chlorinated biphenyls. For SURF4 and SOLPAR models, the calculated logKO,data are not sufficiently accurate only for biphenyl and decachlorobiphenyl. However, this limitation is not serious since reliably measured KO, data are available for both compounds (28). It will be also valuable to describe the advantages and limitations of models presented in Figure 2 on a categoryby-category basis; i.e., for models based on (i) asubstituent constants approach, (ii) topological and geometric properties, and (iii)thermodynamically oriented theories. The SUBST, CLOGPl, SURF3, SURF4, SOLVCH2, and SOLPAR models are based on the slow-stirring method KO, data while all other models are based on KO, data obtained by other available methods. Thus, this evaluation process can be viewed as an internal validation for models based on the slow-stirring method, while for the other models it may be better described as an external validation. (i) The most recent version of the CLOGP program (MedChem Software version 3.53 or higher) (23) and substituent constants developed by de Bruijn et al. (18) are sufficiently reliable for PCBs with up to five chlorine substituents. Older versions of the CLOGP program should not be used to calculate KO, data of PCBs and other polyhalogenated hydrocarbons. (ii) The model based on a single topological property (CHIPAT) has low accuracy, and it cannot differentiate between PCBs within isomeric groups. Two models based on calculated molecular surface areas (SURF1, SURF2) were parameterized on a set of measured KO, data which differ by 0.4log unit from reported slow-stirring KO, data. Thus, it is not surprising that these models cannot reproduce KO, data of PCBs measured by the slow-stirring method. The model based on molecular surface areas calculated by a shorthand method (SURF4) as suggested by Opperhuizen (20) seems to be the most reliable one. Surprisingly, this model performs somewhat better than an analogous model (SURF3) based on molecular surface areas which are calculated for optimized geometries obtained by a sophisticated semiempirical quantum mechanical procedure AM1 (54). However, the regularity in the distribution of calculated residuals for SURF4 (Figure 2) implies that a nonlinear relationship is more appropriate between calculated molecular surface areas and log KO, data of PCBs. (iii) The model based on the UNIFAC-derived activity coefficients (UNIFAC) has low accuracy, and it cannot differentiate between PCBs within isomericgroups. Thus, it seems that the UNIFAC-derived activity coefficients (28) must be reevaluated for PCB congeners and other polyhalogenated hydrocarbons. The model based on general solvatochromicparameters for hydrophobic chemicals (SOLVCHl), developed by Kamlet et al. (273, has also low accuracy and should not be used to calculate octanol/water partition coefficients of PCBs. However, the model based on recalculated solvatochromic parameters (SOLVCHS), specifically for KO, data of PCBs measured by the slow-stirring method, is accurate enough to be recommended for a reliable calculation of PCBs log KO,data. This result raises the following question. Is it 1400 Envlron. Scl. Technol., Vol. 27, No. 7, 1993
possible to derive general solvatochromic parameters for hydrophobic chemicals? The next step will be to compare the results of our model, eq 4, in predicting log KO, data of PCBs with results obtained by five models (SUBST, CLOGPl, SURF4, SOLVCH2, SOLPAR) that are found to be sufficiently accurate according to the requirements of OECD guidelines for testing chemicals. The average residual of log KO, data calculated by our model is 0.026 log unit for chlorinated biphenyls. This result is by 5-7 times better than those obtained by the models listed above. Furthermore, there are no limitations in application of our model for this extensive class of chemicals, which is not the case for some other models (SUBST, CLOGP1). Thus, even a simple analysis shows that the molecular topologymodel developed in this study (eq 4) is superior in accuracy to all models described in the previous section. Equation 4 enables the log KO,data of chlorinated biphenyls to be predicted within 0.10 log unit (a factor of 1-26), the probability being above 95%. This result is comparable with the accuracy obtained for KO, data measured by the slow-stirring method (18). A particular advantage of our model is the ease of use since the Oxv indices and other molecular descriptors used in this model can be easily calculated by hand. New Ideas for the Substituent Constants Approach. Our models based on the molecular topology approach have provided useful ideas on how to improve the results for the substituent constants approach. First, the chlorine substituent constant must describe the presence of a pure and unperturbed chlorine substituent, and it should be determined directly as a difference between log KO, values for chlorobenzene and benzene. Second, the nearly equal log KO, value for 1,2- and 1,4dichlorobenzene have clearly indicated that, in addition to the proximity effect, there is at least one more effect which is important to the partition properties of chlorobenzenes. Our modeling efforts have shown that the additional correction factor is the number of chlorine substituentsin para positions. Its magnitude is two times larger than the proximity effect. Thus, the appropriate substituent constant and correction factors for chlorobenzenes are 0.67 i 0.01 for the chlorine substituent constant ( m ~ -0.08 ), 3t 0.02 for the correction factor indicating para substitution (7r*14), and -0.04 f 0.01 for the correction factor for the proximity effect (n*). Those substituent constants enable the log KO,data of chlorobenzenes to be calculated with an average error of 0.025 log unit. In those calculations the constant term, the log KO, value of benzene, was 2.19 f 0.01, At present, it is not possible to develop a substituent constants model for PCBs that will predict their log KO, data within experimental errors. The molecular topology model developed for PCBs indicates that it would be essential to have KO, data for four additional PCB congeners: 3-chlorobiphenyl, 2,3-dichlorobiphenyl, 3,4dichlorobiphenyl, and 2,5-dichlorobiphenyl. Naturally, it will be far more advantageous to have measured data for all mono- and dichlorinated PCB congeners. Unfortunately, the essential KO,data are either not available or their uncertainty is too large to calculate the necessary correction factors. The average uncertainty for biphenyl and monochlorobiphenylKo,data obtained by shake-flask and generator column methods (19,20,22,25,28)is 0.2 log unit which makes them unfit for calculating correction factors which will be around 0.1 log unit or smaller.
5. Conclusions and Perspectives In this investigation we have demonstrated that simple models, based on topological properties of molecules, can be used to predict the octanol/water partition coefficients (log KO,)within an average experimental error for chlorinated benzenes and chlorinated biphenyls whose partition coefficients were measured by the slow-stirring method. The molecular topology model developed enables the log KO,data of PCBs to be predicted within 0.10 log unit, the probability being above 95%. This result is considerably better than the deviation range of 0.30 log unit that is acceptable according to the OECD guidelines for testing chemicals (53). It seems that the slow-stirring method and the molecular topology approach are an ideal match for future experimental and modeling work on KO,data of other classes of hydrophobic compounds. A comparison of our PCBs model with 12 different models, based on a substituent constants approach, topological or geometric properties, and thermodynamically oriented theories, showed that the molecular topology model developed in this study is superior in accuracy to all other models. The average residual of log KO, data calculated by our model is 5-7 times smaller than those obtained by any other model. A particular advantage of our model is the ease of use since the O x v indices and other molecular descriptors used in this model can be easily calculated by hand. Another valuable outcome of this study is the identification of a priority area for future experimental investigations. One area where more experimental data are needed concerns mono- and dichlorinated PCB congeners. Such data will enable us to calculate all necessary substituent constants as well as correction factors in order to develop an accurate substituent constants model for polychlorinated biphenyls. Molecular connectivity indices and other topological properties have been shown here and in previous studies (43, 44) to correlate highly with log KO,data of several classes of important hydrophobic chemicals. This result demonstrates that topological quantities derived from the molecular structure may confidently replace log KO,data in many QSAR models for various physical, chemical, biological, and environmental properties of hydrophobic chemicals. Several general and practical advantages of the molecular topology approach, such as the speed at which topological quantities can be calculated, their nonempirical nature, and their ability to describe global as well as local and specific structural properties, will make the adjusted QSAR models even more useful and widely applicable. Acknowledgments This work was supported in part by Grant 1-07-159 awarded by the Ministry of Science, Technology, and Information of the Republic of Croatia. A.S. gratefully acknowledgesthe financial support by the Internationales Biiro, Forschungszentrum Julich (KFA). Supplementary Material Available One table giving the octanoVwater partition coefficients estimated for a set of 190 polychlorinated biphenyls with 1-9 chlorine substituents by the molecular topology model and one
table giving the log KO,measured by the slow-stirring method and calculated residuals for biphenyl and 19 PCB congeners (8 pages) will appear following these pages in the microfilm edition of this volume of the journal. Photocopies of the supplementary material from this paper or microfiche (105 X 148 mm, 24X reduction, negatives) may be obtained from Microforms Office, American Chemical Society, 1155 16th St. N.W., Washington, DC 20036. Full bibliographic citation (journal, title of article, names of authors, inclusive pagination,volume number, and issue number) and prepayment, check or money order for $17.50 for photocopy ($19.50 foreign) or $10.00 for microfiche ($11.00 foreign), are required. Canadian residents should add 7% GST.
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Received for review October 28, 1992.Revised manuscript received March 9,1993.Accepted March 17, 1993.
