SAR of Cu (II) Thiosemicarbazone Complexes as

Short Communication

CANCER BIOTHERAPY AND RADIOPHARMACEUTICALS Volume 25, Number 1, 2010 ª Mary Ann Liebert, Inc. DOI: 10.1089=cbr.2009.0670

SAR of Cu (II) Thiosemicarbazone Complexes as Hypoxic Imaging Agents: MM3 Analysis and Prediction of Biologic Properties Sweta Singh,1,2 Anjani K. Tiwari,1 Himanshu Ojha,1 Nitin Kumar,1 Bachcha Singh,2 and Anil K. Mishra1

Abstract

Copper(II) bis(thiosemicarbazone) are very useful for blood flow and hypoxic imaging. The aim of this study was to identify structure-activity relationships (SARs) within a series of analogues with different substitution patterns in the ligands, in order to design improved hypoxia imaging agents and elucidate hypoxia selectivity mechanisms. Genetic algorithms (GAs) were used to develop specific copper metal-ligand force field parameters for the MM3 force-field calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters. A successful quantitative SAR (QSAR) for predicting the several classes of Cu(II)-chelating ligands was built using a training set of 21 Cu(II) complexes. The QSAR exhibited a correlation between the predicted and experimental test set. The QSAR preformed with great accuracy; r2 ¼ 0.95 and q2 ¼ 0.90 utilizing a leave-one-out cross-validation with multiple linear regression analysis to find correlation between different calculated molecular descriptors of these complexes. The final QSAR mathematical models were found as the following: QSAR model for copper(II) bis(thiosemicarbazone) Log P = f3.01698 ( – 0.0590)g - LUMO f0.1248 ( – 0.068)g + MR f0.3219 ( – 0.086)g n ¼ 21 jrj ¼ 0:972 s ¼ 0:188 F ¼ 98:102

The resulting models could act as an efficient strategy for estimating the hypoxic conditions through imaging and provide some insights into the structural features related to the biological activity of these compounds. Key words: QSAR, linear regression, chelating agent, imaging, genetic algorithms

Introduction

S

everal semicarbazones, as well as their sulphur analogs, have proved the efficiency and efficacy in combating various diseases.1 It is of great interest because of their chemistry and, potentially, beneficial biologic activities, such as antitumor, -bacterial, -viral, and -malarial activities.2–4 One of the important aspect of these compounds are that Cu(II) bis(thiosemicarbazone) complexes have been used in vivo as radiotracers for the evaluation of blood flow in various organs, such as the brain, kidneys, and heart, as well as for the evaluation of hypoxia in tissue.5–7

We have been developing an approach, based on redoxactive copper complexes,8–13 following the demonstration that the copper complex of bis(thiosemicarbazone) is selectively taken up in hypoxic tissue.14 These complexes have a number of interesting properties and uses and have been investigated for their potential in anticancer chemotherapy,15 superoxide dismutase–like activity,16 and in radiolabeled form as nontissue selective blood-perfusion tracers.17 They diffuse into cells because of its low molecular weight, planarity,18,19 and lipophilicity.20 It then becomes trapped by intracellular reduction to a copper (I) complex,21 followed by dissociation and entry of the copper into the normal intracellular copper pool.22

1

Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences, Delhi, India. Division of Chemistry, Benares Hindu University, Varansi, India.

2

Address correspondence to: Anil K. Mishra; Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences; Brig. S.K. Mazumdar Road, Timarpur, Delhi 110054, India E-mail: [email protected]

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A particularly important property to be able to predict for in vivo use is the logarithm of the experimental n-octanol= water partition coefficient (log P). This is an important pharmacologic descriptor of bioavailability for both organic compounds and inorganic complexes. Predicting and altering log P is a key element in rational drug design, since the correct incorporation of lipophilic groups to a chemical compound can result in an increase in biologic activity. Quantitative structure-activity relationships (QSARs) have become a common tool in the field of molecular modeling since their introduction. Indeed, they have found application in both the prediction of biologic activity and, more recently, in the prediction of the gK,23 absorption, distribution, metabolism, and excretion, (ADME) toxicological properties of organic drug-like compounds. QSAR became more attractive for chemists with the development of new software tools, which allowed them to discover and understand how molecular structure influences properties and, very importantly, to predict and prepare the optimum structure. The software is now more amenable for chemical and physical interpretation. In this article, we identify physicochemical properties of these bis(thiosemicarbazone) complexes that control hypoxia selectivity by comparison of a large series of analogs, with various alkyl substitution patterns (Fig. 1), with respect to partition coefficient (log P), which is the most important descriptor for lipophilicity. Some of the results described in this article have been reported in preliminary form.

Experimental Section Molecular modeling and QSAR study QSAR studies are mathematic tools of prediction endpoints of interest on organic compounds acting as drugs, which have not been experimentally determined. Many physiologic activities can be related to the molecular structure. The biologic activities are being modeled from using a set of molecular descriptors that often include chemical, electronic, constitutional, and topologic indices. The derived relationship between descriptors and activity from mathematic equations are used to estimate the property of other molecules and or finding the parameters affecting the biologic activity. In QSAR studies, a linear regression model of the form Y ¼ X b þ e may be used to describe a set of predictor variables. Genetic algorithms (GAs) were used to develop specific copper metal-ligand force-field parameters for the MM3 force field, from a combination of ab initio calculations. The MM3* force field is one of the most accurate force fields available for the modeling of organic ligands. It is relatively convenient to successfully developed specific metal-ligand parameters compatible with this accurate force field for a variety of metalamide complexes. In this study, first, the optimized three-dimensional (3D) structures of the molecules were obtained by MM1 calculations. To insure not locating in local minima, geometry optimization was many times with different initial conformations.

FIG. 1. Compounds for quantitative structure–activity relationship studies.

QSAR OF HYPOXIC IMAGING AGENT

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Table 1. Values of Descriptors Calculated for Copper(II) Bis(thiosemicarbazone) Analogs Name

Log P

MR

CMR

DD

CLogP

C.T

LUMO

VDW

t PSA

ETS KTS n-prTS i-Pr TS n-BuTS PhTS PTSM n-PrTS i-PrTSM Bu-TSM PhTSM PTSE] PTSM2 ETSM KTSM ATSM ATS CTS DTSM GTSM PTS


0.33
0.17 0.58 0.56 0.99 1.74 0.71 1.62 1.6 2.03 2.78 1.39 1.47 1.2 1.63 0.34 1.2
0.22 1.31 1.2 4.04

60.27 75.88 69.47 69.65 74.07 80.02 70.27 79.47 79.65 84.07 90.02 79.87 80.35 74.87 95.96 76.06 74.87 70.62 85.22 74.87 109.4

6.3401 7.8846 7.2677 7.2677 7.7315 8.4635 7.2677 8.1953 8.1953 8.6591 9.3911 8.1953 6.1953 7.7315 9.7398 7.7315 7.7315 7.2677 8.6591 7.7315 11.3625

792.64 883.26 838.4 837.96 861.28 933.72 793.68 839.44 839 862.32 934.76 839.44 763.98 816.56 854.6 816.44 816.56 838.28 862.2 816.56 1075.84


1.00906
0.74486
0.04894
0.08106 0.57794 0.50084 0.72044 1.77844 1.64844 2.30744 2.26284 1.77844 1.13294 1.24944 1.39714 1.02944 1.24944
0.17106 2.08744 1.24944 3.96688

865.83 882.48 874.85 869.59 880.42 937.55 850.68 863.06 857.23 870.25 924.79 863.06 852.26 856.54 875.06 855.28 856.54 873.91 868.55 856.54 999.95

43.74 30.56 34.44 34.77 30.86 36.25 31.14 26.4 26.61 23.11 26.54 25.4 24.65 28.05 18.72 28.2 28.05 34.64 23.23 28.05 22.89

371.6 172.18 330.32 325.04 309.68 504.93 369.68 328.4 323.12 307.76 503.01 928.4 356.52 349.04 157.1 339.25 349.04 320.53 297.97 349.04 636.34

88.46 97.69 88.46 88.46 86.46 88.46 60.84 60.48 60.48 60.48 60.48 60.48 42.9 60.48 52.13 60.48 60.48 88.46 60.48 60.48 60.48

Some geometric properties of molecules, including dihedral angles and atomic distances, were calculated. The QSAR investigations were carried out by the linear free-energy relationship (LFER) model proposed by Hansch and Leo.23 Selection of parameters is the first step in any QSAR study. In the present study, parameters that were considered relevant to the bis(thiosemicarbazone) series (electronic, hydrophobic, and steric) were selected and considered as consistent, which included molar refractivity (MR), Vander Waals volume (VDW), Connolly accessible area (CAA), Connolly molecular area (CMA), Connolly solventexcluded area (CSEV), dipole-dipole energy (DD), partition coefficient (log P), HOMO, and so forth. The above-mentioned parameters were calculated by MM2 studies, using Chem 3D 6.0 software. Geometries of all compounds were completely optimized by the same software package. A classical Hansch multivariate regression analysis, using the least-square method, was chosen to derive QSPR equations for the dataset. The level of significance of each coefficient was judged by statistical procedures, such as F-tests. By employing the method of least square with the stepwise selection and elimination procedure, a statistical analysis was carried out. For each equation, several indices of best fit were considered: the regression coefficient ‘‘r,’’ the standard deviation ‘‘s,’’ and the measure of level of statistical significance ‘‘F.’’ The structures of substituted analogs containing com-

pounds taken for the study are depicted in Figure 1 and their descriptors and log P values are shown in Table 1. Results and Discussion The squared correlation matrix of the physicochemical properties used in the stepwise regression analysis is shown in Table 2. From Table 2, one can see that LUMO, VDW, and MR are highly interdependent, and there is no covariance. Table 2 also describes the corelation of other parameters with log P. All the parameters showed significant correlation with biologic activity (r < 0.8) (Table 2), but the LUMO and MR exhibited the best correlation (r > 0.9) of high statistical significance >99.9%. The statistical quality of the resulting models depicted in Equation 1 is determined by r2 (r2 > 0.9). The calculated parameters and correlation matrix needed for MRA (multiple regression analysis) are shown in Figure 2. QSAR model for copper(II) bis(thiosemicarbazone) Log P ¼ f3:01698ð – 0:0590Þg
LUMOf0:1248ð – 0:068Þg þ MRf0:3219ð – 0:086Þg n ¼ 21 jrj ¼ 0:972 s ¼ 0:188 F ¼ 98:102

ð1Þ

The graphs between observed -log P and calculated -log P are shown in Figure 2. The calculated -log P were calculated by putting the MR values in the equations, and the predicted

Table 2. Stepwise Regression Equation No. 1 2 3 4 5 6

Equation Log Log Log Log Log Log

P ¼ {0.01592 ( 0.0199)} – VDW {0.1040 (0.062)} þ MR{0.1048 (0.023)} P ¼ {1.01504 ( 0.0648)} – LUMO {0.1026 (0.142)} þ (VDW)2{0.1641 (.058)} P ¼ {1.2104 ( 0.1102)} – DD {0.0028 (0.019)} þ MR{0.1058 (0.102)} P ¼ {1.02708 ( 0.0812)} – DD {0.0102 (0.092)} þ (VDW)2{0.1004 (.014)} P ¼ {2.41352 ( 0.0184)} – DD {0.0136 (0.021)} þ MR{0.8023 (0.042)} P ¼ {3.01698 ( 0.0590)} – LUMO {0.1248 (0.068)}þ MR{0.3219 (0.086)}

n

R2

Q2

s

Overall F-test

21 21 21 20 19 21

0.24 0.40 0.58 0.65 0.86 0.95

0.15 0.25 0.39 0.91 0.92 0.90

0.68 0.62 0.52 0.19 0.35 0.18

38.452 69.195 46.876 92.180 90.532 98.102

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SINGH ET AL.

FIG. 2.

Relationship between experimental versus calculated log P.

-log P were calculated by the NCSS software, taking all the values between the upper limits and lower limits. Since MR is a ‘‘corrected’’ form of the molar volume, it reflects the effect of size and polarizability, as indicated by Equation 1, suggesting that MR plays a significant role toward the expressed biologic activities, which is probably due to steric interactions occurring in the polar spaces. It has generally been assumed that a positive coefficient with an MR term in a correlation equation suggests a binding action via dispersion forces. Such binding could produce a concomitant conformational change in a macromolecular binding site; however, if the conformational change is detrimental, a negative coefficient could result for the MR term. Negative coefficients with MR have also been assumed to reflect steric hindrance of one kind or another. The log P values have been very useful in understanding the distribution of a drug between different mediums. There are different methods of calculations of log P. All the compounds are with low log P values. It must be noted that molecules having log P values below 5 are found to have good drug absorption and permeability of compounds. Conclusions The results illustrate the effectiveness of the application of GA to the method of developing metal ligand parameters for the MM3* force field. These new parameters were utilized in the development of the QSPR model to predict, first, the lipophilicity of a series of common copper(II)-chelating agents, which are specific for hypoxic imaging. A multivariate regression analysis of the application of the QSPR methodology to the prediction of biodistribution would be facilitated by a larger, more diverse dataset, an issue which we hope to address in future work. Disclosure Statement There is no conflict of interest among the authors and no competing financial interests exist. References 1. Fahmi N, Singh RV. Spectroscopic, antifungal, and antibacterial studies of some manganese heterochelates. J Ind Chem Soc 1996;73:257.

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