SAR of Cu (II) Thiosemicarbazone Complexes as Hypoxic Imaging Agents: MM3 Analysis and Prediction of Biologic Properties

Introduction

Several 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 redox-active 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

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.

OPEN IN VIEWER

FIG. 1.

Compounds for quantitative structure–activity relationship studies.

Experimental Section

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 r ² (r ² > 0.9). The calculated parameters and correlation matrix needed for MRA (multiple regression analysis) are shown in Figure 2. \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6} \begin{document} \begin{align*}&\hbox{QSAR model for copper(II) bis(thiosemicarbazone)}\\&\hbox{Log P}=\{ 3.01698 (\pm 0.0590)\} - {\rm LUMO} \{ 0.1248(\pm 0.068)\} \\&\qquad\qquad + {\rm MR}\{ 0.3219 (\pm 0.086)\} \\&\qquad n=21\ \ |r|= 0.972\ \ s= 0.188\ \ F= 98.102 \tag{1} \end{align*} \end{document}

OPEN IN VIEWER

FIG. 2.

Relationship between experimental versus calculated log P.

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 -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.