1 Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, 46100 Burjassot, Valencia; 2 Institut de Química Computacional, Universitat de Girona, 17071 Girona; 3 Departamento de Microbiología, Facultad de Medicina y Odontología, Universitat de València, Av. Blasco Ibáñez 15, 46010 Valencia, Spain
Received 27 March 2003; returned 18 July 2003; revised 25 September 2003; accepted 3 October 2003
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Abstract |
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Methods: The activities against MAC of 29 structurally heterogeneous drugs were examined by means of linear discriminant analysis (LDA) and multilinear regression analysis (MLRA) by using topological indices (TI) as structural descriptors. In vitro antimycobacterial activities were determined by a broth microdilution method with 7H9 medium.
Results: The topological model obtained successfully classifies over 80% of compounds as active or inactive; consequently, it was applied in the search for new molecules active against MAC. From among the selected candidates demonstrating in vitro activity, aflatoxin B1, benzalkonium chloride and pentamidine stand out, with MIC50s between 4 and 32 mg/L.
Conclusion: The method described in this work is able to select molecules active against MAC.
Keywords: MAC, QSAR studies, topological indices
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Introduction |
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Alternative antimicrobial treatment has been employed in the therapy of infections caused by these organisms. Agents such as fluoroquinolones, novel macrolides or rifamycin derivates, have been introduced for the control of multidrug-resistant or latent tuberculosis infections,2 but experience shows that the use of high-cost treatment in developing countries is limited, and, on the other hand, an accelerated increase in resistance is observed. Thus, more extensive investigations are needed. Many other new drugs are being evaluated to obtain efficient treatments and entirely novel classes of compounds such as oxazolidinones, nitroimidazoles, riminophenazines, ketolides, nitroimidazopyrans, pyridones, lipophilic analogues of isoniazid,3 thiazine derivates4 and carboxamidrazone analogues,5 showing antimycobacterial activity are being developed. The identification of novel targets requires the characterization of mycobacterium-specific biochemical pathways, but the rational design of new antimycobacterial agents is complex and many metabolic processes are unknown.
Most of the drug design methods currently available demand a previous knowledge of the mechanism of action involved. However, extramechanistic approaches are increasingly being used to design new drugs. Concretely, molecular topology6 has proved a useful formalism to find quantitative structureactivity relationships (QSAR). One of the most interesting advantages of molecular topology is the straightforward calculation of the topological descriptors. In this method, each structure is assimilated to a hydrogen-depleted graph where the atoms are represented by vertices and the bonds by edges; the connectivity between atoms is represented in topological matrices, which can be either distance or adjacency. Mathematical manipulation of such matrices provides different sets of numbers called topological indices (TI),79 which characterize each molecule at different descriptive structural levels.6,10 If well-chosen, these topological descriptors can be used for the selection and design of new analgesics,11 bronchodilators,12 antihistamines,13 antivirals,14 antibacterials,15 antifungals,16 etc., many of which can be considered as lead drugs. In a recent paper, we have developed a study of prediction of quinolone activity against M. avium by molecular topology and virtual computational screening.17
The aim of this study was to develop new QSAR models, based on TI, statistical linear discriminant analysis (LDA) and multilinear regression analysis (MLRA), in order to select new drugs and to predict their in vitro activity, expressed as MIC90 (lowest concentration of an antimicrobial that inhibits 90% of the different strains of bacteria), against M. avium complex.
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Materials and methods |
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The search for new compounds showing antimycobacterial activity was carried out using the following steps:
Structural descriptors. A group of compounds with known anti-MAC activity was selected from several sources.1723 Each compound was characterized by a set of 62 TI24 calculated using the program DesMol (Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Dept. de Química Física, Facultad de Farmacia, Universitat de València, Spain). The TI used were 32 connectivity Randi-Kier-Hall type indices, m
t,6 and differences and quotients, mDt and mCt, between them; 20 topological charge indices, Gm and Jm,25 and 10 other discrete invariants.24
Linear discriminant analysis (LDA). The objective of LDA is to find linear functions able to discriminate between two or more categories. In our case, two groups of compounds were considered for analysis. The first included 22 anti-MAC drugs, and 35 presumably inactive compounds made up the second. These compounds constituted the training set. LDA was carried out with the BMDP package, module 7M (W. J. Dixon, BMDP Statistical Software, University of California, Berkeley, CA, USA). The selection of the descriptors was based on the Fisher-Snedecor F parameter. Stepwise, the variable that adds the most to the separation of the groups is entered into (or the variable that adds the least is removed from) the discriminant function (DF). The classification criterion was the maximal posterior classification probability. It is basically proportional to the Mahalanobis distance from the data points to the centroid. This is the point that represents the means for all variables in the multivariate space defined by the group. The quality of the DF is evaluated by the Wilks parameter () and the percentage of correct classifications into each group (discriminant ability). The independent variables in this study were TI, and the discrimination property was the presence or absence of anti-MAC activity.
The validation of the selected DF was carried out both by an internal leave-n-out test, in which the program randomly chooses and pulls out a number of compounds and uses them to evaluate the DFs obtained with the remainder, and by an external test set of non-previously used data. A randomness test, in which the classification variable was randomly reassigned and discriminant functions with the same number of variables as DF were obtained, was also carried out in order to compare these equations with DF and detect possible chance effects.
The equation obtained by LDA must be able to predict the existence of the antimycobacterial activity for a given structure and, consequently, must be useful to select new candidate drugs. In order to choose intervals of values derived from this equation in which the active compounds showed their optimal values, a pharmacological activity distribution diagram (PDD), was used.26 This is a histogram-like plot of the calculated DF in which expectancies appear on the ordinate axis. For an arbitrary interval of values of a given function, the expectancy of activity is Ea=a/(i+100), where a is the percentage of active compounds in the interval and i is the corresponding percentage of inactive compounds within the same interval. The expectancy of inactivity is defined likewise as Ei=i/(a+100). This representation provides good visualization of the regions of minimum overlap and helps to select regions in which the probability of finding active compounds is optimal.
Multilinear regression analysis, MLRA. The property, MIC90 against MAC expressed in mg/L and µmol/L, as well as their respective logarithms, were correlated versus TI in order to select the best regression equation.
The MLRA was carried out with the 9R module of the BMDP program, which estimates regression equations for the best subsets of predictor variables and provides detailed residual analysis by using the Furnival-Wilson algorithm.27 Equations with minimal Mallows Cp parameter28 were initially chosen.
The stability of the equation selected was evaluated through a cross-validation by the leave-one-out algorithm.29 To do this, one compound of the set is extracted, and the model is recalculated using the remaining N1 compounds as the training set. The property is then predicted for the removed element. This process is repeated for all the compounds of the set so obtaining a prediction for each one. This procedure also aids in the detection of outlying points.
In order to examine the possible existence of fortuitous regressions, the randomization test was adopted in this paper.30 Thus, the values of the property of each compound are randomly permuted and linearly correlated with the aforementioned descriptors. This process is repeated as many times as compounds are in the set. The usual way to represent the results of a randomization test is by plotting the correlation coefficients versus predicted ones, r2 (squared multiple correlation) and r2cv (squared multiple correlation by cross-validation) respectively.
Virtual screening. The equations obtained and the corresponding intervals constitute a model for the filtering of databases. If a molecule has its output values from the equations within the thresholds, it is selected as potentially active. Otherwise it is discarded. In this work, a homemade database of approx. 20 000 compounds was used, extracted from the Merck Index and the SigmaAldrich databases.
Microbiological study
Thirty-two M. avium complex isolates from respiratory and non-respiratory clinical samples from the Hospital Clínico Universitario de Valencia, Spain, were used in this study. All isolates were identified by DNA hybridization probes (Accuprobe, Gen Probe Inc., San Diego, CA, USA). Susceptibility to conventional antimycobacterial drugs (azithromycin, ciprofloxacin, clarithromycin, clofazimine, ethambutol, moxifloxacin and rifabutin) and the selected drugs was carried out by a microdilution method.31
The organisms were grown in modified Middlebrook 7H9 broth supplemented with 10% OADC (oleic acid/albumin/dextrose/catalase) enrichment (Difco Laboratories) for 7 days at 37°C. The inoculum size was obtained by dilution of a culture suspension in 7H9 broth to yield an absorbance equivalent to that of a 0.5 McFarland Standard for M. avium complex isolates.
Antimicrobial susceptibility tests were carried out in 96-well microplates using serial two-fold microdilution in 7H9 broth. Initial drug dilutions were prepared in deionized water or, if not soluble, dimethyl sulphoxide. Subsequent two-fold dilutions were carried out in 150 µL of modified 7H9 broth in the microplates to provide a final test range of 128 to 0.125 mg/L. Ten microlitres of a suspension of mycobacteria were added to the wells. Plates were covered with Parafilm M (Laboratory Film, American National Can), and incubated for 5 days at 37°C. Starting at 5 days of incubation, 20 µL of resazurin (Sigma 2127) with a concentration of 250 mg/L was added to the wells, and the microplates were reincubated at 37°C for an additional period of 48 h. MIC50 (lowest concentration of an antimicrobial that inhibits 50% of the different strains of bacteria) and MIC90 values were determined as the lowest concentrations of the compounds yielding no visible changes from blue to pink.32
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Results and discussion |
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The compounds included in the LDA are a structurally heterogeneous set of drugs (ranging from simple structures, such as benzoic acid, up to much more complex structures, such as aminoglycosides). The following equation for DF, obtained by stepwise LDA, classified the compounds as active against MAC if DF > 0 and inactive if DF < 0:
DF = 0.47 1.26 3cv + 0.22 G1v 14.53 J3v 0.22 4Cc Equation 1
N = 57; = 0.42; F = 17.86
The topological descriptors selected in this equation were: the valence Randi-Kier-Hall index 3
cv, related to the presence of three-cluster sub-fragments, which decrease the possibility of being active; the topological charge-transfer indices G1v and J3v, which are measures of the contribution of molecular topological structure to the charge transfer at topological distance 1 and 3, respectively; the quotient index 4Cc = 4
c/4
cv, where 4
c and 4
cv are, respectively, single and valence Randi
-Kier-Hall indices of order 4 and cluster-type. It is related to the four-cluster fragment electronic densities and its magnitude diminishes the activity profile. The dominant term in Equation 1 is the contribution of G1v, which is the only one that it is positive and can be considered as the total molecular electronic density. Its maximum value corresponds to the viomycins, neomycin B and rifampicin that also exhibit the greater DF value. Conversely isoniazid and tuberin, having the lowest values of G1v also present low DF, even negative for isoniazid. Charge transfers through heteroatoms separated by three bonds are limiting in this equation. So, small molecules such as azaserine and carnitine are inactive.
Table 1 shows the results of the classification for each one of the compounds included in the LDA. As can be seen in Table 1, the linear equation gave good results since most compounds are classified with a posterior probability of over 80%. Under this framework, the error percentage in the active set was about 14%, whereas in the inactive it was about 6%.
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The LDA results on the external validation test are shown in Table 3. For the active set, we found a misclassified compound, namely imipenem. The same occurs in the opposite group, where chloropal and benorylate are misclassified.
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After carrying out correlations with the MIC90 values (in mg/L and µmol/L) and with their logarithms versus TI (Table 5), the best linear regression equation obtained corresponded to MIC90 expressed in mg/L (Table 5). The activity expressed in micromolar concentration was not so well correlated. This can be explained because some of the descriptors used (connectivity indices, G charge indices and discrete invariants) are obtained by summations of fragment contributions. Thus, they are not absolutely independent from the molecular mass. The use of molar magnitudes introduces a redundant factor. In relation to the logarithmic correlations, they usually give better results with variables that span several magnitude orders, which is not the case.
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P = 0.59 + 43.94 4vc2.91G4v + 7.50G5 + 9.29 2C 2.43 4Cc 0.93 PR3 Equation 2
N = 29; r2 = 0.78; r2cv=0.69; SEE = 2.18; Cp = 3.76; F = 14.5; p < 0.00001
where P is correlated activity (MIC90); N, number of cases; SEE, standard error of estimates and p is significance. Six topological descriptors are used in this function: the valence Randi-Kier-Hall index 4
vc related to the presence of four-cluster sub-fragments; the charge indices G4v and G5, related to the total contribution of the molecular topological structure to the charge transfers at topological distances 4 and 5; the quotient indices 2C = 2
/2
v and 4Cc = 4
c/4
cv and the discrete invariant PR3 (pairs of branches separated by three bonds). Two terms are dominant in Equation 2, those corresponding to G5 and 2C. The first is related to electronic density and is partially compensated by the G4v term that is of the same type. Fluoroquinolones give great values for the balance between these terms. Likewise, the term with 2C, related to electronic transfers through two bonds, is partially compensated by 4Cc in the case of aminoglycosides, rifabutin and macrolides because these are the only members of the training set that display carbons bonded to four different non-hydrogen atoms.
The observed and calculated P for each compound and the P obtained in the cross-validation study are shown in Table 6. The prediction success of P was very satisfactory if we consider that we are working with a non-logarithmic property, and it is also significant that it can distinguish between low and high values of P.
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The results obtained from the stability and randomness analysis for P (Equation 2) are shown in Figure 2. Figure 2(a) shows the stability of the regression equation for cross-validated-residuals versus residuals, whilst Figure 2(b) illustrates the randomness analysis results. Regressions showing r2 < 0.5 were obtained by random reassigning P values.
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Topological mathematical model and virtual screening
The model to filter potential active molecules for database searches against MAC is constituted by the equations DF (Equation 1) and P (Equation 2) with their corresponding intervals: 0 < DF < 4 and 0 < P < 20.
After building a home-made database, a virtual screening with the MAC model to select potentially active molecules was carried out. Table 7 shows the molecules found, their values for DF (Equation 1) and P (Equation 2) and the classification results.
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Microbiological study
The results of the susceptibility test against MAC for conventional drugs are shown in Table 8, and the experimental results for the new compounds are illustrated in Table 9. The two tables show the MIC range (variation between sensitive and non-sensitive strains) in column 2, the MIC50 in column 3 and the MIC90 in column 4.
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As can be seen in Table 9, all of the selected compounds classified as +/ by the model were inactive against MAC except ribavirin. Those classified as + exhibited antimycobacterial activity against the tested strains, with MIC50 ranging from 4 to 64 mg/L (Table 9). These results confirm the validity of the model.
Particularly active were benzalkonium chloride, paromomycin, pentamidine and trifluoperazine with MIC50 values 16 mg/L. Benzalkonium chloride is a quaternary ammonium compound (QAC) with activity over bacterial membranes and activity against Gram-positive bacteria such as Staphylococcus aureus isolates. In an experimental disinfection assay, QACs are mycobacteriostatic agents even at low concentrations.34 Our results indicate the possibility of increasing the susceptibility of mycobacteria to other agents for the treatment of cutaneous processes. Paromomycin is an oligosaccharide-type antibiotic with demonstrated antibacterial35 and antiamoebic activity, but it is not used as a primary antimycobacterial drug. Pentamidine, a dibenzamidine derivate, is an antimicrobial agent with activity against protozoa and Pneumocystis carinii; it inhibits aerobic glycolysis. Trifluoperazine is a phenotiazine employed in the treatment of psychosis, which has demonstrated inhibition of in vitro growth of multidrug-resistant M. tuberculosis36 by a calmodulin antagonist mechanism.
Less active but still showing significant inhibition results, were aflatoxin B1 (MIC50 = 32 mg/L), reserpine and ribavirin (MIC50 = 64 mg/L for both). Reserpine, an antihypertensive drug, demonstrated an inhibitory effect in Gram-positives by an efflux inhibition mechanism. A probable drug efflux protein has been characterized in M. tuberculosis and other mycobacteria.37 Aflatoxin B1, a secondary fungal metabolite, and ribavirin, the first synthetic non-interferon-inducing broad-spectrum antiviral nucleoside, are two molecules with limited applications as antibacterial drugs as a result of important toxicological problems.
The structures of the selected compounds are presented in Figure 3. The wide structural diversity of the selected compounds is noteworthy, since they could eventually be considered as new leads in this field.
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Concluding remarks |
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The great structural heterogeneity of the selected compounds must be noted although some of them, paromomycin, reserpine and trifluoperazine, show described antimycobacterial activity. For these compounds, we have checked that our theoretical calculations of activity are comparable to those obtained in vitro and to those reported by other groups. On the other hand, trifluoperazine had been described as an inhibitor of the in vitro growth of multidrug-resistant M. tuberculosis. However, no reference of activity against MAC appears in the literature.
The fact that the method described in this work is able to select molecules that have been identified by other means enhances the validity of our approach. These results confirm other previously published work, so indicating the usefulness of molecular topology as a potent tool to identify new drugs, especially new leads. It also overcomes the need for previous knowledge on the drug mechanism of action.
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Acknowledgements |
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Footnotes |
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References |
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