Predicting toxicity: a mechanism of action model of chemical mutagenicity

Mutation Research 479 (2001) 141–171

Predicting toxicity: a mechanism of action model of chemical mutagenicity Guido Sello a,∗ , Luca Sala a , Emilio Benfenati b a

Dipartimento di Chimica Organica e Industriale, Universita’ degli Studi di Milano, via Venezian 21, 20133 Milano, Italy b Istituto di Ricerche Farmacologiche ‘Mario Negri’, via Eritrea 62, 20157 Milano, Italy Received 25 January 2001; received in revised form 23 April 2001; accepted 1 May 2001

Abstract The increasing importance of theoretical studies for predicting toxicology has aroused the interest of many computational chemists. A new approach has been developed, based on studying at the molecular level two potential mechanisms of action that are related to compound mutagenicity. This approach is the first example that considers both the toxicant and the biological target molecules involved in the interaction. Using some calculated descriptors and a simulation of the interaction chemical, compounds can be classified. More important, the approach helps in understanding and explaining both the correct and the incorrect results, and gives a deeper understanding of the toxic mechanisms. The model has been applied to many compounds and the results are compared with experimental results reported for the corresponding Salmonella tests. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Theoretical model; Mutagenicity; Mechanism of action; Toxicity prediction

1. Introduction Predicting toxicity is a typical multidisciplinary problem that calls into play several different experts. It is also a point of much discussion because it can affect people’s welfare, with all the ethical and political implications. In recent years, public opinion against the use of animals in toxicological experiments because of their implicit cruelty, and increasing doubts about the validity of extrapolating results from animals to humans [1,2] has boosted efforts to find experimental and theoretical alternatives to this practice. Some — though not many — computer-based methods have contributed new and intriguing insights ∗ Corresponding author. Tel.: +39-02-2663469; fax: +39-02-2664874. E-mail address: [email protected] (G. Sello).

into the problem, giving rise to enthusiastic debate on the real power of theoretical predictions as compared to experimental results [3–6]. The toxicity prediction problem has many parts: at the lowest level, there is the molecular structure of the potential toxicant, and at the highest level, there is the response of the human body, affected by several ingredients. As a consequence, there is an inherent difficulty in the problem, making it nearly impossible to compare predictions from a model with the actual human response. However, this problem is present in all experimental studies. What models can offer is a better understanding and representation of the chemical toxicity at the molecular level, with the final — still distant — goal of a global model that is very difficult to build at the moment [7–11]. A potentially powerful approach is to select a well-defined end-point that can be caused by a

0027-5107/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 7 - 5 1 0 7 ( 0 1 ) 0 0 1 6 1 - 0

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well-defined mechanism of action and that can possibly be validated in an easy and immediate biological test. This will help clarify the toxicity in question, even if extensive use of the model is not directly possible. There is another concern that cannot be ignored: even after selection of a particular mechanism some of the experimental results may not agree with the prediction. This may be caused by inaccuracy of the model, or of the experimental data, or, more probably, to intervening modifications to the structure or bioavailability made by the biological system. However, if the model is defined at all levels, we are in a position to discuss even unexpected results. Computational models for predicting toxicity can be roughly divided into two main groups: statistical or automated algorithms, and rule-based systems. In addition, there are a few approaches that mix the two groups [12–25]. The first subgroup automatically extracts the correlation between structures and activity from the experimental data. This is done by calculating many molecular descriptors which are then combined in such a way as to predict the experimental results as well as possible. Thus, the physical data are both the origin and the control of the models. The procedure is automatic, so it guarantees the absolute impartiality of the analysis, acknowledging neither privileged, nor censured data. This approach can in principle be envisaged as a general system; however, to make it work, each mode of action must be tuned differently. The second subgroup, in contrast, is deeply rooted in the use of rules derived from experts. In this case, knowledge is chosen as the fundamental means of prediction. The general acceptance of the experts’ decisions and the classification of the molecular structures using calculated descriptors, add validity and general applicability to the models, but they still basically rely on the use of a set of rules that reproduce the current experts’ knowledge. We are not going to adopt either of these two views; we would like to restrict our model to a well-defined area using a clear mechanism and, as far as possible, a set of well-behaved experimental data. Our model will use some molecular descriptors calculated applying the methods we have developed in past experience with reactivity modelling [26]. We believe that reactivity at the molecular level is

modulated by the experimental environment, but is fundamentally the same both in classical chemical reactions and in biological functions; thus, the same method of calculation must be valid in different areas. 2. Methods 2.1. The problem: SAL mutagenicity Mutagenicity and carcinogenicity are two of the most widely studied toxic effects [27–32]. This has resulted in a good supply of experimental data. However, carcinogenicity cannot, at the moment, be described by evident modes of action, while mutagenicity covers a more restricted number of alternative mechanisms, thus, making it a more appealing field of study. In addition, if we limit our experimental data to mutagenicity in Salmonella tests, we can to a first approximation assume that we are analysing only well-defined mechanisms. The Salmonella assay protocol is not a unique test procedure [33]. The results depend closely on the conditions chosen for the assay. The strain used, the pre-treatment with the chemical under study, or the use of special expedients, such as a vapour phase protocol, can give different results; thus, often the data are not homogeneous. Therefore, even if the chosen toxicity is well-defined and the experimental data are probably the most consistent among those currently available, we must be careful when discussing the theoretical results and their validity. Variability of experimental data is common in toxicity experiments, not only in mutagenicity, and is due partly to the protocols and partly to natural variability between organisms. There are many known mechanisms of mutagenicity but they all concern the interaction of chemicals with the nucleic bases that can have a permanent effect without mechanisms of DNA repair. In our case, we are interested in modelling some chemically clear mechanisms in order to check the validity of the theory underlying the approach. We chose two mechanisms: (1) base nitrogen alkylation; (2) base amino group substitution [34]. These two mechanisms both are largely irreversible (dealkylation and ammonia substitution) and have a high level of specificity. The first mechanism can be modelled as a typical SN2 reaction, where the nucleophile is the nitrogen of

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Fig. 1. Mechanism of base alkylation. The example shows the interaction between the nitrogen atom of cytosine and the carbon of a general alkylating compound.

the amino group 1 and the electrophile is any of the atoms bonded to a good leaving group. As an example, in Fig. 1, the interaction between the nitrogen atom of the amino group of cytosine with the carbon of a general alkylating compound is sketched. It is clear that the real mechanism can be an SN1, or any other plausible mechanism but they are well represented by the SN2 mechanism. The second mechanism is more complex; it consists of at least two steps. The example in Fig. 2 shows the interaction between a general amino compound and cytosine. The nitrogen atom of the amino group adds to the C–N double bond of cytosine giving a first intermediate that then eliminates ammonia giving the final modified base that contains the amine residue. However, the first is the rate-determining step. It is an equilibrium and the equilibrium position depends on the stability of the intermediate relative to the starting and final products (base and hydroxylamine, or modified base and ammonia). Obviously, in special cases the final product is further stabilised, giving rise to a special reactivity. Many mechanisms of interaction between toxicant and nucleic bases have been proposed; some of them have been hypothesised on the base of in vitro tests [34]. A limited number of examples is shown in Fig. 3. 1 Oxygen atoms too participate in this class of reaction; however, we are limiting our interest to the amine nitrogens.

Fig. 2. Mechanism of base deamination. Two equilibrium steps. The example shows the interaction between the nitrogen atom of an amino group and the C–N double bond of cytosine. The atom numbering scheme used for the calculation of bond moments of the deamination reaction is also shown.

It is clear that our selection of only two mechanisms is a personal choice that do not want to exclude or criticise other hypotheses; in addition, a complete model should include as many as possible alternatives. For example, a different mechanism of the reaction of aromatic amines with DNA bases is often reported [35]. This passes through the activation of the nitrogen atom by oxidation to the hydroxylamine derivative, which is then deoxygenated through a complex route to the nitrenium ion. This last can react as electrophile with the bases. However, the mechanism we propose can be seen as an alternative, supported by well-known chemical equivalents of the Michael addition type and by mechanism 1 reported in Fig. 3. It must be clear that the present model is a first step towards a more articulated approach and will be the groundstone of future work.

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Fig. 3. Examples of hypothetical mechanisms of action: (1) base deamination; (2) base addition to carbonyls; (3) oxidative base deamination; (4) base alkylation; (5) aromatic activation followed by base alkylation; (6) aromatic amine activation followed by diverse reactions with bases.

2.2. The model The model is ideally based on an analysis of the mechanism of the interaction between the nucleic base and the chemical compound. Both the chosen mechanisms pose the hypothesis of a heteropolar interaction where a positive (electrophile) atom

interacts with a negative (nucleophile) atom. The first action of the model is, therefore, to locate potentially reactive bonds, meaning those with a negative momentum, i.e. those bonds that connect one positively and one negatively charged atoms. In principle, we could select all the correct bonds in both the reaction partners; however, the nucleic bases have a different

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Fig. 4. Nucleic bases. Only highlighted bonds are considered by the model: N–H bonds in alkylation mechanism and C–N bonds in deamination mechanism.

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chemical compounds are broken and the corresponding product bonds formed. The charge calculation is repeated and the new bond moments are calculated; then they are compared to a threshold value. • In the case of deamination, we use a slightly different selection procedure. Here too, the bonds between the atoms of the base and between the atoms of the chemical compounds are broken and the corresponding product bonds made; the charge calculation is repeated and the new bond moments are calculated. But, we use the difference between the final and the starting moments, if it is positive the bond is selected, otherwise it is discarded. µ = µF − µS = (Q1 × Q3 × DFa + Q2 ×Q4 × DFb ) − (Q1 × Q2 × DSa + Q3

role so at the moment we prefer to limit the choice of their reacting atom. We initially limited the choice to all bonds between a nitrogen and a hydrogen atom (N− /H+ ) for the alkylation mechanism, and to all bonds between a carbon atom and an amino group (C+ /NH2 − ) for the deamination mechanism. The interested bonds for all the nucleic bases are shown in Fig. 4. Toxic compounds are, however, always different and the search for reactive bonds comprises all the bonds between non-hydrogen atoms. We, therefore, need a method for calculating bond polarity, more precisely atomic charges. We have such a method available from previous work [36]. The method can calculate atomic charges (Q) in 2D and 3D structure representations; we currently limit the calculation to 2D representations. A brief description of our methodology is given in the Appendix A. The charges are then combined to give bond moments, using Eq. (1): µ = QA × QB × D

(1)

where D is D = cov rA + cov rB A first selection is common to both mechanisms, as pointed out above, and concerns the bonds with negative moments. Then it follows as • In the case of alkylation, the bonds between the atoms of the base and between the atoms of the

×Q4 × DSb ) where µ is the moment difference, µF the final bond moment and µS the starting bond moment, Qi the atomic charge of atom i, and DXj is the distance between the atom pair j in compound X. The numbers reported in the formula concern the corresponding atoms of the example mechanism shown in Fig. 2. At this point, we have selected all those bonds that can potentially interact. Now we need another molecular descriptor that can further separate the reactive bonds. The most obvious choice is a descriptor that takes into account the probability of a reaction succeeding, i.e. a measure of the energy requirements favouring the kinetics of the reaction. We have a method to calculate an energy variable that can be viewed as representative of the reaction activation energy; better, we have a method to calculate the electronic energy of both ground and excited states, and their difference can be interpreted as the activation energy [37]. Consequently, we can calculate the ground state energy, build a model of the transition state, and calculate its energy; the difference can be taken as the likelihood of the reaction proceeding. The model of the transition state is simply realised by making a bond between the atoms of the two molecules, the nucleotide and the reactive chemical, partially charging the atoms, and relaxing the intermediate obtained. In other words, we model the transition state by bonding the two molecules, toxicant

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Fig. 5. Transition state model. The alkylation mechanism.

and base, simulating a perturbation of the obtained structure by moving part of an electron from the donating to the receiving atom (e.g. in N-alkylation by dimethylsulphate, we move the charge from the nitrogen to the carbon), and then recalculating the electron distribution, i.e. moving charge until a new stable distribution is reached. The simulation has been performed using different initial charge distribution and the final result is always the same (Fig. 5). We can, thus, calculate the following energy difference: TRANSIT = E(transition state) − E(ground state) The smaller is TRANSIT, the easier the reaction. We must point out that due to the approximation introduced both in the transition state model and in the calculation, the E(transition state) may be more negative than the E(ground state); we accept solutions with negative or slightly positive TRANSIT. The values need not have an absolute meaning, but they must help in separating mutagenic from non-mutagenic substances. 2.3. The mechanisms of action Before going on to present and discuss the results, we would like to make some points about the mechanisms of action we have selected. Some chemicals can directly alkylate nucleic bases (e.g. dimethylsulphate); thus, the first mechanism is a direct model of this class. We expect to see a sepa-

ration between mutagenic and non-mutagenic alkylating agents. It is clear that we cannot expect to classify as mutagenic those chemicals that become mutagenic only after biological activation, such as compounds transformed into epoxides, e.g. aromatic compounds; or compounds containing groups that can be transformed into good leaving groups. Some other considerations are needed for both mechanisms. First, we do not consider any kind of detoxification results, or standard biological transformations, such as hydrolysis, reduction, or oxidation. Second, we do not consider the molecular size; some compounds contain potentially toxic substructures, but are so big that they would have difficulties interacting with the DNA. Third, we do not consider geometry aspects, in the DNA or in the chemicals; it is clear, however, that these aspects can be very important. Nevertheless, all these points can be added later on. Right now, we want to verify whether a model working at the level of the mechanism can add something useful to the current debate on toxicity prediction. 2.4. The program A brief description of the program flow is needed. The program is composed by three main blocks: a C++ block built specifically to deal with the toxicity problem (ONTOX). It controls the overall flow and contains all the operations concerning assessment of toxicity. A second block is a Fortran subprogram (RESCHA) that calculates the atomic charges. The third block is also a Fortran subprogram (REACT) that constructs intermediates and calculates the energies. The flow comprises the following steps. 1. Input of the chemical compound to be analysed, obtained from any commercial drawing program that can save data in MOL format. This format is immediately transformed into an internal format. 2. A first call is made to RESCHA to calculate charges. 3. The main program calculates bond moments and selects bonds with negative moments. 4. The current mechanism of action is selected and the current nucleic base is selected. 5. Bonds that are reactive for the selected mechanism are picked, i.e. those of the current base as shown in Fig. 4.

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6. A file is built containing both the chemical and the base. 7. A call is made to REACT for each reactive bond. The subprogram builds the intermediates and tests all the atom pair combinations. 8. The value of TRANSIT returned by REACT is analysed depending on the mechanism, and the bonds are classified as positive or negative. 9. An output is produced reporting the results with all the variables. The program also contains some thresholds used in selection of the toxicity. These should be fixed in agreement with experimental results and their choice is briefly discussed in Section 3. We used two sets of compounds to fix them, one for each mechanism.

3. Results 3.1. Application The first set is used to fix the thresholds of the alkylation mechanism. It contains 45 compounds selected from the NTP database 2 ; there are 27 compounds positive in the Salmonella assay, 10 negative compounds, and 8 that are negative in the Salmonella assay but are known to be carcinogenic, these last will be considered as negative compounds. The compounds have been selected considering their possibility of reacting as electrophiles; thus, the set contains compounds that have either good leaving groups, or carbonyl groups, or similarly activated groups. The structures are sketched in Figs. 6–8, where the prefix p indicates Salmonella positives and n Salmonella negatives. Table 1 shows the number of bonds that are potentially reactive, the number of those further examined after the first selection that excludes all the bonds whose moments is outside the threshold range, and the final result that classifies the compound as active in alkylation reactions of nucleic bases. The last column reports the experimental results. The first selection (see above) greatly reduces the number of bonds that require the most demanding calculation: only 28% of the potentially reactive bonds pass the test. In addition, on examining the structures, 2

http://ntp-server.neihs.nih.gov/.

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we can affirm that no wrong result would have been avoided, even including more bonds. False negatives (8) and false positives (3) amount to 24% of the results, which calls for some comment. First, many compounds misassigned as negative contains either aromatic amino groups or groups that can be converted into aromatic amino groups. They are, thus, potentially positive in the second mechanism; however, they are not alkylating agents and they will be considered later. Second, compounds p 29, p 146, p 179, p 285, and p 339 also contain functional groups that can give origin to an amino group, so they could also react by the second mechanism, but they are not alkylating compounds. Third, compounds p 15 and p 115 cannot evidently give rise to alkylation without prior activation. The clear result is that carbonyls and similar compounds are not confused with alkylating compounds, as expected. In conclusion, all the wrong predictions are compounds that cannot react by the alkylation mechanism of the nucleic bases. On the false positive side, we have a different situation. Compound n 22 contains an epoxide function, so it is positively an alkylating compound; obviously, its geometric requirements seriously limit its reactivity. Compounds n 135 and n 298 are, in contrast, real wrong predictions; they contain the sequence O–P–O–C that is usually considered reactive, so their different experimental behaviour has no direct explanation. Also, there are several O–C bonds that are considered reactive in spite of their scarce reactivity in common organic chemistry (e.g. ether bonds); this is clearly a point worth consideration. Even if nearly all the compounds that contain such functional groups also contain other well-known alkylating groups (e.g. epoxides), the problem arises when the ether linkage is the only bond present because in this case the compound would be incorrectly classified as toxic. On the other hand, exclusion of the ether linkage would also exclude the epoxide and phosphate groups and at present we prefer to have some false positives rather than false negatives. The thresholds are insensitive to the presence of the non-alkylating compounds because all of them are classified as non-mutagenic. The second set of compounds contains examples of the deamination mechanism. We selected 35 compounds from the NTP database (see footnote 2); there

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Fig. 6. NTP compounds used to fix the thresholds of the alkylation mechanism.

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149

Table 1 NTP compound set used to fix alkylation mechanism thresholds Compound p p p p p p p p p p p p p p p p p p p p p p p p p p p n n n n n n n n n n n n n n n n n n

15 29 72 81 100 112 115 128 142 146 179 207 239 251 257 267 285 329 339 341 342 362 374 376 414 448 452 22 23 24 70 77 82 96 103 135 137 216 217 234 250 253 298 304 360

Total a

Number of reactive bonds

Number of examined bonds

16 8 23 7 9 20 20 24 13 15 12 13 12 17 16 6 24 8 10 12 8 14 4 8 5 11 8 18 11 25 23 25 16 26 18 23 34 26 36 6 12 17 9 12 11

3 2 6 3 6 5 5 11 5 4 2 4 4 6 8 2 11 2 4 4 5 4 3 4 5 3 2 6 5 6 5 3 9 7 3 5 4 0 2 3 1 1 6 4 3

691

196

Incorrect results.

Calculated positive

Salmonella positive

Noa

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No No No No No No No No No No No

Noa Yes Yes Yes Yes Noa Yes Yes Noa Noa Yes Yes Yes Yes Yes Noa Yes Noa Yes Yes Yes Yes Yes Yes Noa Yes Yesa No No Noa No No No No Yes No No No No No No Yesa No No 34/45 (76%)

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Fig. 7. NTP compounds used to fix the thresholds of the alkylation mechanism.

are 27 compounds positive towards Salmonella assay, 5 negative, and 3 negative in the Salmonella assay but recognised as carcinogenic. Here, the selection has been done only including compounds containing amino groups, either primary, or secondary, or

tertiary, because the reaction mechanism is more selective and the objective was to discriminate between similar molecules. The structures are shown in Figs. 9 and 10, where the prefixes have the same meaning as above. Table 2 indicates the number of bonds that

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151

Fig. 8. NTP compounds used to fix the thresholds of the alkylation mechanism.

are potentially reactive, the number examined, and the final result that classifies the compound as active in deamination of nucleic bases. Table 2 clearly shows that here too the first selection that excludes all the bonds whose moments is outside the threshold range, reduces the number of the bonds that require the most demanding calculation. In fact, only 54% of the potentially reactive bonds pass the test, which is less selective in this case. The proportion of false negatives (8) and false positives (4) is higher (34%) than for alkylation. We can further analyse the wrong results in this case too. Compounds p 99, p 143, p 271, p 285, and p 383 are not positive because they contain an electron withdrawing group conjugated with the amino group; they become positive only after reduction was simulated. Compounds p 92 and p 186 cannot give a deamination, so they are positive by a different mechanism. Compound p 233, however, is a real wrong prediction; it is particularly interesting to compare it with compound p 390, which is very similar. The sensitivity of the calculation to the amino group’s higher level of conjugation in the first structure is responsible for the apparently anomalous result. Nevertheless, increasing of the threshold value is enough to correct the classification.

All the false positive compounds are structurally very similar to true positive molecules, so it is difficult to exclude them based on our current understanding and representation of structural similarity. Their inactivity cannot be explained using our mechanistic approach but we believe no other molecular analysis to date can easily select these compounds among the active ones. The threshold have been calculated also excluding the compounds that were misclassified, but they did not substantially change. In addition, when compounds containing nitro groups are transformed into the corresponding amines and the thresholds are recalculated their values did not significantly change. The search for the thresholds has been done automatically by continuous exclusion and insertion of the wrong classified compounds, until no further improvements were obtained. At the end of the training phase, we selected the thresholds for the two mechanisms, which are respectively, µ (toxic moment) = −0.002 and TRANSIT = 0.2, or µ = −0.0055 and TRANSIT = 1, or −0.0055 = µ = −0.002 and TRANSIT = 0.5 (alkylation mechanism); µ (difference between final and starting bond moments) = −0.008 and TRANSIT = −0.1 (deamination mecha-

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Fig. 9. NTP compounds used to fix the thresholds of the deamination mechanism.

nism). Now, we can pass to the use of the thresholds on different compounds. 3.2. The test In order to verify the possibility of applying this approach, we selected further compounds from the NTP (see footnote 2) and GENETOX databases. 3 Here, 3

http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?GENETOX.

the selection has been random, only excluding salt or multimolecular compounds. The first test set comprises 45 compounds (i.e. 36% of the NTP total), also selected from the NTP database (Table 3, Figs. 11–13). They were tested using both mechanisms: 7 compounds react via a diverse mechanism reducing the total to 38 compounds; 20 compounds were correctly classified without elaboration (53%); 10 compounds were correctly classified after modification due to simulated

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153

Fig. 10. NTP compounds used to fix the thresholds of the deamination mechanism.

reduction (26%); 8 compounds were misclassified (21%). The first result is discouraging; meaning that only half of the compounds are directly put into the right set. However, the transformation of the nitro groups into the corresponding amines raises the total correct classifications to 79% that is a good result. In addition, compound n 145 is completely comparable to active compounds. In the second test set, there are 55 positive compounds (gxx p) and 78 negative compounds (gxx n) derived from the GENETOX database (Tables 4 and

5, Figs. 14–20). The total of 133 compounds is three times those derived from the NTP database for the test. The main characteristic of this set is the variability of the compound structures; many classes are represented so the set covers different situations very well. In this case, we expect the results are sufficiently good as regards predictive power but, more important, they should clearly indicate the reasons both for unexpected predictions and for erroneous predictions. First, let us look at the overall performance. Classification is correct for 62% of positive compounds (32 on 52, after exclusion of three compounds that cannot

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Table 2 NTP compound set used to fix deamination mechanism thresholds Compound p p p p p p p p p p p p p p p p p p p p p p p p p p p n n n n n n n n

47 63 84 85 89 92 93 99 107 112 127 142 143 153 160 162 169 186 205 219 233 271 285 339 383 390 401 23 77 82 138 333 196 216 360

Total a

Number of reactive bonds

Number of examined bonds

14 13 14 13 10 12 22 20 12 20 10 13 5 10 16 15 11 24 20 12 12 15 24 10 7 20 14 11 25 16 24 18 19 26 11

8 9 10 9 6 8 12 13 7 11 7 6 5 3 4 8 8 6 12 10 4 8 14 7 7 8 9 10 14 11 14 7 10 5 3

538

293

Calculated positive

Salmonella positive

Yes Yes Yes Yes Yes Noa Yes Noa Yes Yes Yes Yes Noa Yes Yes Yes Yes Noa Yes Yes Noa Noa Noa Yes Noa Yes Yes Yesa Yesa Yesa No No No Yesa No

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No

23/35 (66%)

Incorrect results.

be classified using the two mechanisms), this permits to clearly select the compounds that could act through the two studied mechanisms. In addition, considering possible transformations due to simulated reductions the rate peaks at 83%. Negative compounds show 67% correct classification, excluding also those aromatic halides that the calculation eliminates when examining the reactivity from the side of the carbon atom; this calculation is routinely done. There still remain some points worthy of note. There are some compounds that are classified positive by the alkylation mechanism because they contain

a C–Cl bond, where the C atom can be either aliphatic or aromatic (g2 p, g22 p, g36 p, g45 p, g52 p, g54 p, g67 p, g57 n, g64 n, g79 n). Compound g26 p is, on the contrary, predicted as negative. We can easily reject all the compounds that contain an aromatic C 4 (g36 p, g52 p, g57 n, g64 n, g79 n), but we accept 4 In the case of aromatic C–Cl bonds it is sufficient to calculate the activation energy considering the C as the first interacting atom; this automatically excludes all the compounds. This calculation is in fact routinely performed and does not require further computer time.

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Table 3 NTP compound test set Compound NTP 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

p p p p p p p p p p p p p p p p p p p p p p p p n n n n n n n n n n n n n n n n n n n n n

Total a

26 28 39 54 95 117 118 133 140 154 181 184 216 222 248 316 337 340 359 407 409 422 442 476 145 192 214 223 357 379 3clfm 406 8 41 75 83 149 212 213 370 378 423 424 436 467

Calculated positive alkylation

Calculated positive deamination

Salmonella positive

No No (o.m.)b Yesc No Noc No No No Noc No (o.m.)b No (o.m.)b No No No No No (o.m.)b No No (o.m.)b Yesc No No No (o.m.)b No No (o.m.)b No No No Yesc No No No No No No No No No No No No No No No No No

Noa

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No No No No No No No No No No No No No No

20/38 (53%)

30/38 (79%)

No (o.m.)b No Noa No Noa Noa Noa No No (o.m.)b No (o.m.)b Noa Yes Noa Yes No (o.m.)b Noa No (o.m.)b No Noa Yesc No (o.m.)b Noa No (o.m.)b Yesc No No Yesc No No No No No No No No No No No No No No No Yesc No

Incorrect results that can be recovered by group reduction. Indicates compounds that cannot react via tested mechanisms; they are not considered. c Incorrect results. b

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Table 4 GENETOX compound test set (positive compounds)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Compound GENETOX

Calculated positive alkylation

g1 p g2 p g3 p g5 p g6 p g7 p g8 p g10 p g13 p g15 p g16 p g17 p g18 p g19 p g20 p g21 p g22 p g23 p g24 p g25 p g26 p g29 p g30 p g31 p g32 p g33 p g34 p g35 p g36 p g41 p g42 p g43 p g44 p g45 p g46 p g48 p g49 p g50 p g51 p g52 p g53 p g54 p g55 p g56 p g57 p g58 p g59 p g61 p

Yes Yes Noa No No No No (o.m.)c Yes No No (o.m.)c No No Yes No Yes No Yes Yesa No Yes Noa Yes Noa Yesa No No No No Yesa No Yes Yes Noa Yes No (o.m.)c No Yesa Noa No No Yes Yes No Yesa No No No

Calculated positive deamination

Yes Yesb Nob No (o.m.)c Yes No (o.m.)c Nob Noa Nob Yes No Yesa Nob

Yesa Nob Yes Yes Yes Yes Yes

Noa

Nob Yes Nob Nob Yes

Yes Yes Yes Nob Nob Yes

Salmonella positive Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yesd Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

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Table 4 (Continued)

49 50 51 52 53 54 55

Compound GENETOX

Calculated positive alkylation

g62 g63 g64 g65 g67 g68 g70

Yesa

p p p p p p p

No Yes Yes No Yes 32/52 (52%e )

Total

Calculated positive deamination Yes Yes Yes Yes

Salmonella positive Yes Yes Yes Yes Yes Yes Yes

43/52 (83%f )

a

Incorrect results. Incorrect results that can be recovered by simulated reductions. c Indicates compounds that cannot react via tested mechanisms; they are not considered. d This compound is equal to compound n 149 that is defined negative. e Correctly predicted results. f Correctly predicted results after recovery. b

Table 5 GENETOX compound test set (negative compounds)a

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Compound GENETOX

Calculated positive alkylation

g1 n g2 n g3 n g4 n g5 n g6 n g8 n g9 n g10 n g11 n g12 n g13 n g14 n g15 n g16 n g17 n g18 n g19 n g20 n g21 n g22 n g23 n g25 n g26 n g27 n g28 n g29 n g30 n g31 n g33 n g34 n

Yesb No No No Yesb Yesb Yesb No Yesb No No No No No Yesb No No No Yesb No No No No No No No No Yesb Yesb No Yesb

Calculated positive deamination No

Yesb Yesb

Yesb Yesb No No

No

Salmonella positive No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No

158

G. Sello et al. / Mutation Research 479 (2001) 141–171

Table 5 (Continued)

32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 Total a

Compound GENETOX

Calculated positive alkylation

g35 g36 g37 g38 g40 g41 g42 g43 g44 g45 g47 g48 g49 g50 g51 g52 g53 g54 g55 g56 g57 g58 g59 g60 g61 g62 g63 g64 g67 g68 g70 g72 g74 g75 g76 g78 g79 g80 g81 g82 g83 g84 g85 g86 g87 g88 g89

No No No Yesb No No No No No No No No Yesb Yesb Yesb No Noc Noc No No No No Yesb No Yesb No No No No No No No No No Yesb Yesb No No No

n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n n

No Yesb No Yesb No No No 52/78 (67%)

Calculated positive deamination

No

No Yesb

Yesb

No Yesb

Yesb No Yesb

Salmonella positive No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No

4

All test sets: 104/168 (62%); 125/168 (74%). All false negatives: 35/69 (51%); 14/69 (20%). All false positives: 29/99 (30%). Incorrect results. c Correctly predicted results that are lost after metabolic reactions. b

G. Sello et al. / Mutation Research 479 (2001) 141–171

159

Fig. 11. NTP compounds used to test the model.

the remaining compounds even if there could be some doubts about the alkylating power of alkyl chlorides. This limitation is not captured during the training phase. In the same incorrect prediction area, there are all the alkyl–aryl ethers. They are classified positive by the alkylation mechanism, but their scarce reactivity in classical chemistry suggests that the prediction is wrong; thus, we will consider them as misclassifications. Then, we have the cases of positive compounds by the alkylation mechanism that owe their reactivity to the presence of an alcoholic group. Alcohols are not alkylating compounds unless activated by an acid; the

simulation of the mechanism partially transforms these groups to charged alcohols (remember that the oxygen atom, the donor, will be positively charged when simulating the transition state) that can be alkylating, a reaction that could be well present in the biological reaction. At the present level, we prefer to leave the calculation as it is in order to preserve the general principle, but it is clear that the problem must be dealt with in the future. Some compounds are misclassified as negative, but can become positive by the deamination mechanism after simulated reduction (g6 p, g7 p, g16 p, g19 p, g24 p, g31 p, g48 p, g50 p, g58 p, g59 p); however, by the same activation mechanism some compounds

160

G. Sello et al. / Mutation Research 479 (2001) 141–171

Fig. 12. NTP compounds used to test the model.

are predicted as negative but become predicted positive (g53 n, g54 n). Finally, compound g44 p, that is incorrectly classified, is very similar to compound n 149, that is correctly classified as inactive; this case shows the disagreement between two experimental data evidenced by the analysis. Considering all the test sets together, the result is 104/168 (62%) correct prediction; 125/168 (74%)

after the simulated reductions. The corresponding false negative values are 35/69 (51%); 14/69 (20%). The corresponding false positive values are 29/99 (30%). In conclusion, if we accept the possibility of reducing some groups (nitro and azo groups, mainly) and introduce this additional transformation into the model, the prediction is correct for three compounds on four with a prevalence of false positive (30–20%) in the misclassifications.

G. Sello et al. / Mutation Research 479 (2001) 141–171

161

Fig. 13. NTP compounds used to test the model.

4. Discussion We present an approach to toxicity prediction that attempts to calculate the mutagenicity of chemical compounds on the basis of mechanisms of action. The model analyses the problem at the molecular level, thus, giving not only a number indicative of the mutagenicity of the compound but also the reason and the groups responsible for the activity. This permits a discussion of the results from a logical viewpoint and indicates both the deficiencies of the model and potential solutions. In addition, it assesses the validity of the experimental measures. These results would have been impossible using models that do not describe the reactions at the molecular level. On the other hand, the present approach cannot be used directly for predicting toxicity unless it is extended to include more representative mechanisms and metabolic activities. Alternatively, it could be applied in a more restricted sense if rules were applied

to determining which chemicals could be adequately predicted. Classically, two approaches have been used for toxicity prediction [5,38]. In the first case, no previous knowledge of the toxicity mechanism is used, and the program is supposed to extract the useful information itself. Since in several cases some information is already available, other approaches have codified the knowledge into rules. Our method draws on the previous knowledge about mechanisms which may lead to the mutagenic effect and uses thresholds and adjustments in predictions in the course of training, so it could be considered a “supervised” training approach. However, our approach is completely different from the others, in which the knowledge used is related to the presence of residues which should be responsible for the toxic effect. In these cases, the program (such as Oncologic, or HazardExpert) detects the residue and the toxicity

162

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Fig. 14. GENETOX compounds used to test the model.

G. Sello et al. / Mutation Research 479 (2001) 141–171

163

Fig. 15. GENETOX compounds used to test the model.

is indicated. The toxicity value for a given residue is fixed, but the final value may be reduced or eliminated in some cases, due to the presence of other residues. In any case, only the toxic molecule is considered, and not the reactive endogenous molecules involved in the

toxic effect. Indeed, in most cases the eventual receptor or the affected molecule is unknown, and there are actually several mechanisms. In the case of mutagenicity, however, some mechanisms are known or have been suggested. Thus, it is

164

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Fig. 16. GENETOX compounds used to test the model.

possible to predict a mutagenic effect on the basis of the postulated mechanisms. Our investigation of the feasibility of this approach gave promising results. Of course, more mechanisms can be added, improving the general applicability of the method. Using direct knowledge of the mechanism gives several advantages. It is immediately possible to understand the reactive pathways involved, so the result is clearly translated into a biochemical process, while with automatic systems (for instance using molecular descriptors) this is not always obvious. Furthermore, our approach introduces some modulation of the activity within defined mechanisms, related to the chemical reactivity of the bonds involved, which is calculated individually for each bond of each molecule within a defined mechanism. This is again a major difference from other programs for toxicity prediction, in which the activity is simply linked to the presence or absence of a given rule. The program can, thus, generate new knowledge, finding reactivity involving bonds which have not been considered in the literature. Thus, our

program does not limit itself simply to finding previously defined residues. Lewis adopts a different approach to reflect knowledge of the role of cytochrome P450 in a toxic mechanism [39]. He defines the dimension that the planar aromatic molecule should have in order to show toxicity and, thus, takes the receptor characteristics into consideration. Then, the activity is modulated by quantomechanical variables (Elumo and atomic Ehomo density). Lewis’ approach was original and has been further developed in a series of papers dedicated to the modelling of mutagenicity and carcinogenicity through the interaction with cytochrome P450 [40–42]. However, in this case too, the system considers only the exogenous molecule, and not a reaction occurring with a second molecule. Ford and co-workers [43,44] also considered the formation of an intermediate: the nitrenium cation for mutagenicity. However, this ion was used to calculate the reaction energy, which was simply used like other molecular descriptors, to generate a regression

G. Sello et al. / Mutation Research 479 (2001) 141–171

Fig. 17. GENETOX compounds used to test the model.

165

166

G. Sello et al. / Mutation Research 479 (2001) 141–171

Fig. 18. GENETOX compounds used to test the model.

G. Sello et al. / Mutation Research 479 (2001) 141–171

Fig. 19. GENETOX compounds used to test the model.

167

168

G. Sello et al. / Mutation Research 479 (2001) 141–171

Fig. 20. GENETOX compounds used to test the model.

curve. That group did not use the reactivity with the nucleotide. In summary, our approach is new in its nature and principle. It starts from existing knowledge on biochemisms and reactivity towards molecules such as nucleotides. It is closer to the actual toxicological phenomenon than other approaches, using black-box systems and autonomous extraction of the knowledge, without any explicit assumptions (such as systems based on molecular descriptors and regression). As we explained, starting from this knowledge on mechanisms, our system is able to explore reactivity and generate autonomous knowledge. Further research is needed to improve the experimental basis: more mechanisms can be easily added, improving the general applicability. Furthermore, this approach can be used to investigate toxicity, even without previous knowledge, but as a tool to generate predictions on the basis of reactivity, evaluating different hypothetical pathways until satisfactory results are obtained. This would be a new way to exploit this approach, which we have not evaluated so far.

nologica, and from the European Commission (ENV4-CT97-0508) is gratefully acknowledged. The authors would like to thank one of the referee whose comments greatly improved the paper.

Appendix A We developed an empirical method for calculating atomic charges. It is based on the Gordy’s approach to calculation of atomic electronegativities (χ ), i.e. on the formula: χ=

0.31(n + 1) r + 0.50

where n is equal to the number of valence electrons; as modified by Pritchard: χ=

a × Zeff r +b

where a and b are constants depending on the element row and Zeff is calculated from Slater. We extended this formula to non-constant Zs and rs obtaining: a × Zeff (i) r(i) + b

Acknowledgements

χ (i) =

Partial financial support from the Consiglio Nazionale delle Ricerche, from the Ministero dell’Universita’ e della Ricerca Scientifica e Tec-

where Z and r refer to a hypothetical perturbed status, Zeff is naturally related to the effective (also fractional) electron number and r is connected to the same factor

G. Sello et al. / Mutation Research 479 (2001) 141–171

by the Pauling’s equation: r(i) =

e.e. =

 (i)0 r(i)0 Zeff  (i) Zeff

where Z is equal to Z for complete electron shielding. Another Pauling’s equation correlates bond ionic percent to χ difference: % = 100 × (1 − exp(−0.25χ ))

169





dn = dN3 µ  = k3 [N − (aN1 + bN2 + c(N3 − 1))] µ

×[N − (aN1 + bN2 + cN3 )] − k2 = k3 (A + B + C) − k2 N3 + K where

2

A = (N 2 + aN − 2NN1 − 2bNN2 + N12 + 2bN1 N2

and, as a consequence,

−aN1 + b2 N22 − abN2 )N3 ,

Q = q(1 − exp(−0.25χ )) 2

+2aN1 + 2abN2 − a

The equation can be extended to multiple interactions giving QT = Q(i) At χ equalisation, we reach a stable state and we get the atomic charges. Given the correlations between electronic energy (e.e.), χ (or chemical potential µ), and hardness (η)

2  ∂(µ) ∂ (e.e.) η= = ∂n Z ∂n2 Z and considering the equation for χ calculation as a continuous function (at least in the valence shell), we can derive the corresponding formulas for hardness and e.e. by the following mathematical manipulations. Zeff = N − (aN1 + bN2 + c(N3 − 1))  Zeff = N − (aN1 + bN2 + cN3 )

Zeff + k2 r N − (aN1 + bN2 + c(N3 − 1)) = −k1 + k2 r = −k1 [N − (aN1 + bN2 + c(N3 − 1))] N − (aN1 + bN2 + cN3 ) + k2 × Z  0eff × r 0 = k3 [N − (aN1 + bN2 + c(N3 − 1))]

µ = −χ = −k1

×[N − (aN1 + bN2 + cN3 )] − k2 and




∂(µ) η= ∂n






Z

k3 (N3 (N3 − 1)) = N3 − k 2

 Z

= k3 (2N3 −1)

2

)N32 ,

B = 0.5(−2aN C = a 2/3 N33

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