QUANTITATIVE STRUCTURE – ACTIVITY RELATIONSHIPSFOR NITROAZOLES WITH ANTITUMOR ACTIVITY

A. I. Khlebnikov,1 I. A. Shchepetkin,2 and R. R. Akhmedzhanov3

Translated from Khimiko-Farmatsevticheskii Zhurnal, Vol. 35, No. 6, pp. 25 – 29, June, 2001.

Original article submitted May 15, 2000.

At present, a large number of methods are available forestablishing the quantitative relationship between chemicalstructure and biological activity of substances. The approac-hes to a description of quantitative structure – activity relati-onships (QSAR) are typically developed for model systemsof ligand – receptor or inhibitor – enzyme types. Less frequ-ently, the QSAR methods are employed for the analysis ofmore complicated systems in which case the biological acti-vity characteristics reflect the behavior of cells (or tissuesand organs) in response to the introduction of a given agent(ligand) into the organism. In the past decade, such QSARmethods were applied to analysis of the relationship betweenantitumor properties and structural features of compoundsbelonging to amino-, anilino-, and pyrazoloacridines [1, 2],N-oxides [3], bufadienolides [4], ellipticines [5], and pyrop-heophorbides (developed as photosensitizers for photodyna-mic antitumor therapy) [6].

The passage from ligand – receptor or inhibitor – enzy-me models to systems in which the integral biological activi-ty in response to a given agent is evaluated involves anever-increasing number of molecular targets (possessing hig-hly diverse spatial structures) for this agent in the organism.In this case, development of the biological effect may be ac-companied either by parallel interactions of the agent withthe corresponding receptors in various tissues, cells, and sub-cell organelles or by cooperative conformational transitionsin the ligand – receptor complexes [7]. Interaction of the li-gand with such a receptor-like structure can be considered asa dynamic process involving the formation of several confor-mational substates of the “superreceptor.”

For solving problems of this (or close) type, we have pre-viously developed the method of frontal polyhedra (FP), inwhich the correspondence between molecule and receptor isbased on the search for optimum superimpositions of the sys-

tem “prints” onto multiplets with allowance for additional in-formation about the characteristics of projections [8, 9]. Thepossible superimpositions may also include those correspon-ding to certain conformational states of the receptor-likestructure, which are formed in the course of the interaction ofthis structure with the ligand (accompanied by changes in theelectron characteristics of the ligand). This approach allowsthe QSAR analysis to be carried out without generating a lar-ge number of special structures. The presence of a good cor-relation between experimental data and theoretical calculati-ons of the activity of a series of compounds may be indicati-ve of the “receptor-mediated” mechanism of their action,involving more than one receptor.

Investigations revealed the ability of compounds belo-nging to the group of nitroazoles to produce antitumor, anti-metastatic, and immunomodulant effects [10 – 13], whichmay be evidence of the specific interaction of these compo-unds with the tumor and�or immunocompetent cells in thetumor-bearing organism. In the search for new nitroazolespossessing antitumor activity, an important task is to deter-mine the weighted contributions of various structural frag-ments in these compounds to the given biological activitymanifestations. Below we describe the application of the FPmethod to the QSAR analysis of nitroazoles (I – XVIII,Fig. 1) with known antitumor, antimetastatic, and colony-in-hibiting properties that were previously established withinthe framework of the experimental model of melanoma B-16[10]. The purpose of this study was to reveal the role of va-rious factors involved in the molecular recognition (electrondensity, hydrophobic characteristics, and volume of the subs-tituents) and the extent of participation of various structuralfragments of molecules in the manifestation of these types ofactivity.

METHODS OF INVESTIGATION

The QSAR models were constructed by the FP method[8, 9] taking into account the three-dimensional (3D) simila-

Pharmaceutical Chemistry Journal Vol. 35, No. 6, 2001

3150091-150X/01/3506-0315$25.00 © 2001 Plenum Publishing Corporation

1 Altai State Technical University, Barnaul, Altai District, Russia.2 Research Institute of Oncology, Siberian Division, Russian Academy of

Medical Sciences, Tomsk, Russia.3 Siberian State Medical University, Tomsk, Russia.

rity of molecules, which allowed series of conformationallylabile compounds to be considered. The FP method is basedon the hypothesis of local 3D similarity, according to whichthe presence of like (in a certain sense) regions in the “perip-heral surface” of molecules renders them close with respectto biological activity [14].

In order to solve the problem of conformational lability,we used a modification of the FP method [9] employing sub-division of the molecules into rigid and labile fragments(submolecules). For nitroazoles I – XVIII this is illustrated inFig. 1, where such fragments are indicated by solid and das-hed bonds. Here, rigid submolecules are the heterocycle, nit-ro (–NO2), methyl (–CH3), and peptide (–CONH) groups.The other fragments, possessing internal rotational degreesof freedom, are considered as labile. The prints taken fromrigid fragments using the approach and parameters proposedin [8, 9] include the projections of atoms characterized bytheir distances hX to the inverse image atom X with the ato-mic charge qX. Projections of the boundary atoms (adjacentto other fragments) were additionally characterized by thehydrophobicities HX and the molar refractions RX of the cor-responding substituents at atom X, calculated by the additivescheme [15]. The geometric structures of molecules werecalculated using the HyperChem program package [16] by

the method of molecular mechanics with the MM+ force fi-eld; the qX values were calculated using a semiempiricalAN1 technique [17].

The prints of compounds I – XVIII were subjected to apairwise comparison to establish the degree of local similari-ty of molecules in optimum superimpositions using the follo-wing optimum criterion

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where n0 is the number of assignments in the given optimumsuperimposition [8, 14]; � = 1.5 is the parameter reflectingthe specificity of the superimposition (increasing with n0);rXY is the distance (in Å) between assigned projections in thegiven superimposition; and wr = 0.2, wh = 0.2, wq = 12.8,wH = 0.2, and wR = 0.00032 are the weighting coefficients.Summation in expression (1) is performed over the pairs of

316 A. I. Khlebnikov et al.

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Fig. 1. Structures of nitroazoles I – XVIII (solid and dashed bonds indicate the subdivision of molecules into rigid and labile fragments).

projections (X, Y ) related by assignments. Superimpositionssatisfying the conditions

$F � K0, n0 � N0, (2)

(where K0 and N0 are some parameters) were considered asoptimum.

The QSAR models were constructed using the sets of op-timum superimpositions determined for various sets of K0and N0, that is, for different requirements on the degree ofstructural similarity between molecules. Then the values ofparameters corresponding to the best (base) model were fixedand the weighting coefficients wq, wH, and wR were varied (rela-tive to the values indicated above) in order to assess the roleof electron density, hydrophobicity, and substituent volumein the manifestation of a particular antitumor activity type.

Finally, particular structure – activity relationships wereestablished based on the arrays of optimum superimpositionsin the form of linear equations

$I Zi ii

H

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1

, (3)

where $I is the calculated biological activity characteristic and�i are the regression coefficients. The reduced basis set ofvariables Zi was determined by partial least squares [18] us-ing a procedure considered in [9]. The dimensionality H ofthe basis set was selected as small as possible but still pro-viding for sufficiently high values of the correlation coeffi-cient R and the Rcv

2 parameter. The latter value characterizes

the quality of biological activity prognosis in terms of thesliding control (leave-one-out) procedure

RS

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where S cv2 is the mean-square uncertainty of the prognosis

and S ser2 is the mean-square deviation of activity in the series

of compounds studied. The values of H, R, and Rcv2 for the

compounds studied are given in Table 1.A special feature of the FP method is the possibility of

representing the biological effect of a given compound by thesum of partial effects (weights Wjl ) related to the componentrigid submolecules [9]

$I Wj jll

L

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, (5)

where L is the total number of rigid fragments in the j th mol-ecule. Data on the weights Wjl can be useful for the de nuovodesign of biologically active substances. In this study, thesecharacteristics are used to assess the extent to which the en-vironment of each fragment determines differences inantitumor, antimetastatic, and colony-inhibiting effects.

RESULTS AND DISCUSSION

We constructed QSAR models for each of the biologicalactivity types studied. The models differed in the values ofN0 (the minimum number of assignments in the optimum su-perimpositions) and K0 (optimum boundary criterion). Themain characteristics of these models were the correlation co-efficient R and the Rcv

2 parameter (Table 1). The best correla-

tion and the maximum accuracy of activity prediction withrespect to antitumor and antimetastatic effects were obtainedfor N0 = 3 and K0 = 0.10, while the best prognosis for the co-lony-inhibiting effect was provided by N0 = 3 and K0 = 0.12.Apparently, an increase in the minimum number of assign-ments to N0 = 4 would pose too strict requirements to the op-timum superimpositions, leading to the partial loss of usefulstructural information still retained with N0 = 3. In the caseof N0 = 4, the optimum superimpositions do not include tho-se involving the nitro group, although this substituent is ob-viously one of the most important structural elements in theseries of nitroazoles studied. Taking this into account, thesubsequent QSAR model construction was performed forN0 = 3 and the K0 values indicated above for the particularactivity types.

The base QSAR equations, obtained for the N0 and K0values selected as described above, were optimized by sequ-ential twofold reduction in the weighing coefficients wq, wH,and wR. The results of this optimization procedure are pre-sented in Table 2. A decrease in the weighting coefficient wqimpairs the QSAR quality in comparison to the base relation-ship for all three types of biological activity, which is eviden-ce of the significant role of the electron density distributionfor the process of molecular recognition of nitroazoles by thereceptors or active centers of an enzyme. With respect to in-hibition of metastasis development and colony formation, an

Quantitative Structure – Activity Relationships for Nitroazoles with Antitumor Activity 317

TABLE 1. QSAR Model Parameters and Sliding Control Charac-teristics for Nitrazoles I – XVIII Inhibiting Tumor Growth, Metasta-sis, and Colony Formation

Activity(for H = 6)

K0

N0 = 3 N0 = 4

R Rcv2 R Rcv

2

Antitumor 0.080 0.777 0.249 0.715 0.079

0.100 0.866 0.555 0.792 0.416

0.120 0.781 0.321 0.781 0.423

0.140 0.815 0.420 0.785 0.215

Antimetastatic

0.080 0.898 0.626 0.881 0.164

0.100 0.933 0.798 0.930 0.770

0.120 0.931 0.672 0.933 0.785

0.140 0.918 0.721 0.926 0.719

Colony-in-hibiting

0.080 0.827 0.475 0.847 0.345

0.100 0.806 0.255 0.791 0.463

0.120 0.871 0.649 0.854 0.427

0.140 0.854 0.540 0.832 0.370

important role belongs to the hydrophobic properties of themolecular groups of nitroazoles, since a decrease in the wH

value also leads to lower R and Rcv2 values as compared to

those determined prior to variation of the weighing coeffici-ents. The decrease in the molar refraction wR (reflecting thesize of the substituents) was significant only for the antitu-mor properties of compounds I – XVIII. After this optimiza-tion, we obtained better regression equations as compared tothe base relationships. Both the correlation coefficient andthe sliding control characteristics were improved by decrea-sing the weights of hydrophobicity wH (significant for tumorgrowth inhibition) or molar refraction wR (for antimetastaticand colony-inhibiting effects), which indicates the less im-portant role of these factors in the given activity manifestati-ons.

Figure 2 shows plots illustrating the optimized structu-re – activity relationships in experiment – model coordina-tes. A good coincidence between the experimental data andmodel predictions was obtained for compounds XIII, XV,and XVII possessing high antitumor activity, for compoundsIV – VI with high antimetastatic properties, and for compo-unds X – XIII and XV exhibiting strong colony-inhibitingaction. It should be noted that the maximum difference bet-ween the calculated values from experimental data for thesetypes of activity takes place for compound XIII (representingthe drug sanazole), where the deviations reach +16.0% forthe antitumor effect, –24.8% for the antimetastatic effect, and–24.7% for the colony growth inhibition. There is some evi-dence that the main biological effects (including antitumoraction) are produced by one-electron reduction products rat-her than by the initial nitroazoles [19, 20]. In this context, wemay suggest that the electron and spatial characteristics ofsanazole change to a greater extent in the course of suchone-electron reduction process.

In order to establish the extent to which the main structu-ral fragments of nitroazoles I – XVIII affect activity differen-tiation, we determined the additive activity contributions Wjlfor each of the four rigid fragments: azole heterocycle andthe –NO2, –CH3, and –CONH groups. For this purpose, thecompounds were divided into two classes: (i) active substan-ces with a quantitative measure of activity exceeding half ofthe maximum value in the series studied (class A) and (ii) theremaining substances of the series possessing lower activityof the given type (class B). These classes are presented inTable 3 (the investigation revealed that the weights of pepti-

318 A. I. Khlebnikov et al.

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Fig. 2. Correlation between experimental and calculated activity values for (a) antitumor, (b ) antimetastatic, and (c) colony-inhibiting effectsfor QSAR models with optimized weighting coefficients of charge, refraction, and hydrophobicity of atomic groups in compounds I – XVIII(R is the coefficient of correlation between experiment and theory). Experimental data on the antitumor, antimetastatic, and colony-inhibitingactivity of nitroazoles were taken from [13]. Nitrazoles were injected intraperitoneally in a dose of 1.0 mg�kg for 10 days before and 10 days af-ter implantation of melanoma B-16 in BDF1 mice; the tumor growth, metastasis frequency, and number of colonies in lungs of test mice was de-termined (relative to untreated control) 28 days after tumor inoculation.

TABLE 2. QSAR Model Parameters upon Optimization of theWeighting Coefficients for Charge (wq ), Refraction (wR ), andhydrophobicity (wH ) for Nitrazoles I – XVIII Inhibiting TumorGrowth, Metastasis, and Colony Formation in Comparison with theBase Model Characteristics

Weighingcoeffi-cients

Activity (for K0 = 0.100, N0 = 3)

Antitumor Antimetastatic Colony-inhibiting

R Rcv2 R Rcv

2 R Rcv2

wq 0.820 0.351 0.913 0.696 0.865 0.474

wR 0.806 0.372 0.952 0.854 0.895 0.655

wH 0.920 0.699 0.911 0.753 0.867 0.467

Base 0.866 0.555 0.933 0.798 0.871 0.649

de fragment are 0% for all activity types and these data wereomitted).

An analysis of the antitumor activity showed that theaverage Wjl values of the heterocycle in compounds of clas-ses A and B differ by 13.3% and the corresponding valuesfor the nitro and methyl groups differ by 8.7 and 0.5%, res-pectively, in comparison to the total difference between ave-raged activities in the classes reaching 22%. As for the anti-metastatic activity, the average Wjl values of the heterocyclein compounds of classes A and B differ by 29.4% and thecorresponding values for the nitro and methyl groups differby 11.0 and 4.9%, respectively (for the total difference bet-ween averaged activities in the classes reaching 54.2%). Fi-nally, for the colony-inhibiting activity, the average Wjl valu-es of the heterocycle in compounds of classes A and B differby 29.8% and the corresponding differences for the nitro andmethyl groups are 5.5 and –0.1%, respectively, at a total dif-ference between averaged activities in the classes amountingto 35.3%.Thus, classification of the compounds into highlyactive and weakly active ones is related for the most part totheir structural distinctions or differences in the environmentof the azole heterocycle fragment and the nitro group. In ad-dition, a significant role in the antimetastatic activity is play-ed by the methyl group and its surroundings.

The obtained Wjl values can be used in the de novo de-sign of substances possessing antitumor properties. It shouldbe noted that the design of biologically active compoundsfrom fragments by no means reduces to simply joining thesubmolecules possessing maximum Wjl values. In addition,optimization of the environment of each fragment taking intoaccount the molar refractions and hydrophobicities of thesubstituents has to be optimized as well [9, 21]. The differen-ce in the contributions of substituents, primarily of the azoleheterocycle and nitro group, to the biological activity mani-festations for nitroazoles I – XVIII probably indicates theexistence of different molecular targets (receptors) involvedin the antitumor, antimetastatic, and colony-inhibiting inte-ractions. The role of such targets can be played by enzymespossessing nitroreductase activity, by various surface and int-racell receptors (see, e.g., [22]), and by Ca-ATPase [23].

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Quantitative Structure – Activity Relationships for Nitroazoles with Antitumor Activity 319

TABLE 3. Contributions (Weights) of the Main Rigid Fragments tothe Calculated Values of Antitumor, Antimetastatic, and Colony-In-hibiting Activity of Nitroazoles I – XVIII

Compound Activity Heterocycle –NO2 –CH3

Antitumor activityA. X 62.9 37.6 25.3 –

XVII 55.8 56.2 –0.4 –XV 50.5 24.9 25.6 –XIII 50.0 20.4 29.6 –XIV 44.4 20.2 24.2 3.2VIII 42.5 42.9 –0.4 2.6XII 37.9 32.7 5.2 –VI 33.8 5.6 28.2 3.7III 32.4 25.2 7.2 –VII 32.0 32.4 –0.4 –

B. V 31.1 7.1 24.0 3.1 (3.1)XI 30.6 3.8 26.8 –II 28.6 30.7 –2.1 –IV 27.1 28.8 –1.2 2.3XVI 20.0 20.5 –0.5 –I 17.5 17.9 –0.4 –XVIII 14.3 14.7 –0.4 2.7IX 8.4 8.8 –0.4 –

Antimetastatic activityA. V 90.4 6.4 38.6 22.7 (22.7)

VI 83.5 9.5 47.3 26.7IV 76.4 18.8 39.6 18.0XI 56.6 13.6 43.0 –XIII 47.7 –6.5 54.2 –

B. X 35.3 –6.4 41.7 –XII 30.8 –3.9 33.9 –XVIII 27.5 –25.7 37.1 16.1VIII 25.3 –25.8 35.6 15.5II 19.8 21.1 –1.3 –XVII 19.2 –22.9 42.1 –VII 16.5 –24.2 40.7 –XV 14.4 –27.4 41.8 –XIV 9.3 –48.2 36.2 21.3IX 6.4 –32.9 39.3 –XVI 6.3 –37.0 43.3 –I 6.2 –17.8 24 –III –0.3 –21.3 21.0 –

Colony-inhibiting activityA. XI 98.0 107.7 –9.7 –

X 92.4 106.7 –14.3 –XIII 93.7 83.9 9.8 –XV 83.8 74.7 9.1 –XII 78.7 86.0 –8.9 –V 70.0 35.0 7.2 13.9 (13.9)XVI 69.8 60.4 9.4 –XVII 65.5 80.1 –14.6 –II 65.5 68.9 –3.4 –VI 60.9 35.9 8.6 16.4VIII 58.3 62.0 –14.6 10.9XVIII 58.2 61.5 –13.4 10.1IX 49.9 64.4 –14.5 –

B. III 47.9 43.8 4.1 –XIV 45.5 50.6 –20.2 15.1I 36.7 44.9 –8.2 –VII 29.2 45.6 –16.4 –IV 27.5 22.4 –6.0 11.1

Note: Compound V is characterized by the weights of two methylfragments (see Fig. 1).

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