Methylselenol release as a cytotoxic tool: a study of the mechanism of the activity achieved by two series of methylselenocarbamate derivatives

A molecular modeling study has been carried out on two previously reported series of methylselenocarbamate derivatives that show remarkable antiproliferative and cytotoxic in vitro activity, against a panel of human cancer cell lines. These derivatives can be considered as having been constructed by a selenomethyl fragment located over a carbon atom which is decorated with two carbamate moieties, both aliphatic and aromatic, one of them attached by a single bond to the central carbon atom, while the second is connected by a double bond. According to the data obtained, these derivatives can undergo a water-mediated nucleophilic attack on the carbons with marked electrophilic character, which leads to the rupture of C-Se and carbamate C-O bonds. The aliphatic derivatives, series 1, show an early release of methylselenol and a further release of hydroxyl derivatives (alcohols), whereas the aromatic carbamates, series 2, show an early release of phenols followed by the subsequent release of methylselenol. Thus, the activity of the compounds can be related to the progressive release of active fragments. The data that support this connection are related to the overall molecular topology, volume and surface area as well as to quantum parameters such as the relative electrophilic character of the target carbon atoms (measured in terms of positive charge values) or the bond order values, especially concerning the central C-SeCH3 bond and the carbamate ones. Moreover, the data obtained regarding the chromatographic behavior of some representative compounds confirm this proposal.

Magic Mathematical Relationships for Nanoclusters

Size and surface properties such as catalysis, optical quantum dot photoluminescense, and surface plasmon resonances depend on the coordination and chemistry of metal and semiconducting nanoclusters. Such coordination-dependent properties are quantified herein via "magic formulas" for the number of shells, n, in the cluster. We investigate face-centered cubic, body-centered cubic, simple cubic clusters, hexagonal close-packed clusters, and the diamond cubic structure as a function of the number of cluster shells, n. In addition, we examine the Platonic solids in the form of multi-shell clusters, for a total of 19 cluster types. The number of bonds and atoms and coordination numbers exhibit magic number characteristics versus n, as the size of the clusters increases. Starting with only the spatial coordinates, we create an adjacency and distance matrix that facilitates the calculation of topological indices, including the Wiener, hyper-Wiener, reverse Wiener, and Szeged indices. Some known topological formulas for some Platonic solids when n=1 are computationally verified. These indices have magic formulas for many of the clusters. The simple cubic structure is the least complex of our clusters as measured by the topological complexity derived from the information content of the vertex-degree distribution. The dispersion, or relative percentage of surface atoms, is measured quantitatively with respect to size and shape dependence for some types of clusters with catalytic applications.

Automated methods to test connectedness and quantify indirectness of evidence in network meta-analysis

Network meta‐analysis compares multiple treatments from studies that form a connected network of evidence. However, for complex networks, it is not easy to see if the network is connected. We use simple techniques from graph theory to test the connectedness of evidence networks in network meta‐analysis. The method is to build the adjacency matrix for a network, with rows and columns corresponding to the treatments in the network and entries being one or zero depending on whether the treatments have been compared or not, and with zeros along the diagonal. Manipulation of this matrix gives the indirect connection matrix. The entries of this matrix determine whether two treatments can be compared, directly or indirectly. We also describe the distance matrix, which gives the minimum number of steps in the network required to compare a pair of treatments. This is a useful assessment of an indirect comparison as each additional step requires further assumptions of homogeneity in, for example, design and target populations of included trials. If there are no loops in the network, the distance is a measure of the degree of assumptions needed; it is approximately this with loops. We illustrate our methods using several constructed examples and giving R code for computation. We have also implemented the techniques in the Stata package “network.” The methods provide a fast way to ensure comparisons are only made between connected treatments and to assess the degree of indirectness of a comparison.

Automated drawing of network plots in network meta-analysis

In systematic reviews based on network meta-analysis, the network structure should be visualized. Network plots often have been drawn by hand using generic graphical software. A typical way of drawing networks, also implemented in statistical software for network meta-analysis, is a circular representation, often with many crossing lines. We use methods from graph theory in order to generate network plots in an automated way. We give a number of requirements for graph drawing and present an algorithm that fits prespecified ideal distances between the nodes representing the treatments. The method was implemented in the function netgraph of the R package netmeta and applied to a number of networks from the literature. We show that graph representations with a small number of crossing lines are often preferable to circular representations.

Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH)

Quantitative structure-activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict toxic endpoints for three bioassays specific to several stages of carcinogenesis. The ethoxyresorufin O-deethylase assay (EROD), the Salmonella/microsome assay, and a gap junction intercellular communication (GJIC) assay were chosen for their ability to measure toxic endpoints specific to activation-, induction-, and promotion-related effects of polycyclic aromatic hydrocarbons (PAH). Shape-electronic, spatial, information content, and topological descriptors proved to be important descriptors in predicting the toxicity of PAH in these bioassays. Bioassay-based toxic equivalency factors (TEF(B)) were developed for several PAH using the quantitative structure-toxicity relationships (QSTR) developed. Predicting toxicity for a specific PAH compound, such as a bioassay-based potential potency (PP(B)) or a TEF(B), is possible by combining the predicted behavior from the QSTR models. These toxicity estimates may then be incorporated into a risk assessment for compounds that lack toxicity data. Accurate toxicity predictions are made by examining each type of endpoint important to the process of carcinogenicity, and a clearer understanding between composition and toxicity can be obtained.

Topological Distance Based 3D Descriptors for Use in QSAR and Diversity Analysis

In topological autocorrelation approaches molecular descriptors are calculated by summing up properties located at given topological distances. Since the relationship between topological and Euclidean distance contains 3D structural information, in the present paper a modified version of an autocorrelation approach is proposed to include this type of information. Steric, electronic, and indicator-variable-type descriptors are calculated and used in QSAR studies with three different data sets. The results demonstrate that the descriptors can be efficiently used in cluster- and QSAR analysis. The models obtained are highly predictive and comparable to those obtained by other commonly used 3D-QSAR methods.

Development of binary classification of structural chromosome aberrations for a diverse set of organic compounds from molecular structure

Classification models are generated to predict in vitro cytogenetic results for a diverse set of 383 organic compounds. Both k-nearest neighbor and support vector machine models are developed. They are based on calculated molecular structure descriptors. Endpoints used are the labels clastogenic or nonclastogenic according to an in vitro chromosomal aberration assay with Chinese hamster lung cells. Compounds that were tested with both a 24 and 48 h exposure are included. Each compound is represented by calculated molecular structure descriptors encoding the topological, electronic, geometrical, or polar surface area aspects of the structure. Subsets of informative descriptors are identified with genetic algorithm feature selection coupled to the appropriate classification algorithm. The overall classification success rate for a k-nearest neighbor classifier built with just six topological descriptors is 81.2% for the training set and 86.5% for an external prediction set. The overall classification success rate for a three-descriptor support vector machine model is 99.7% for the training set, 92.1% for the cross-validation set, and 83.8% for an external prediction set.

Polynomial expressions for the hyper-Wiener index of extended hydrocarbon networks

General expressions are derived for the hyper-Wiener index (WW) for several series of hydrocarbons, both benzenoid and non-benzenoid, including some two-dimensional networks. Indeed, such expressions were found for all but one series studied. Methods are also described for eliminating the register overflow problem that besets computer-based approaches to calculating WW for large structures by means of the distance matrix.

On finding nonisomorphic connected subgraphs and distinct molecular substructures

The problem of finding all nonisomorphic subgraphs of a given graph (all distinct substructures of a given molecular structure) is discussed. A computer program is introduced that first generates all connected subgraphs and then uses a combination of well-discriminating graph invariants to eliminate duplicates. The program is broadly applicable, in particular for molecular graphs which may or may not contain unsaturation or heteroatoms. The number of distinct substructures (Ns), proposed earlier as a measure of a compound's complexity which takes into account its symmetry, is thus automatically obtained. As was to be expected, due to the nature of the problem the computational effort increases exponentially with problem size, whence in most cases complexity measures other than Ns are to be preferred.

Distance indices and their hyper-counterparts: intercorrelation and use in the structure-property modeling

Intercorrelation between the Wiener index, hyper-Wiener index, Harary index, hyper-Harary index, detour index and hyper-detour index is studied on three sets of branched and unbranched alkanes and cycloalkanes with up to eight carbon atoms. First set (S-39) contains all alkanes from ethane to octane (39 molecules), the second set (S-139) 139 cyclic hydrocarbons from cyclopropane to branched and unbranched polycyclic octanes and the third set (S-178) is a combination of the first two sets (178 molecules). It is found that the pairs of distance indices and the corresponding hyper-counterparts are highly intercorrelated for all three sets. The use of the distance indices of both kinds in structure-boiling point modeling was analyzed. Distance indices and hyper-distance indices do not lead to particularly good models for any of the three sets. When used as composite indices they give much-improved models. However, they are most useful when combined with such indices as the number of carbon atoms in a hydrocarbon, Hosoya Z index and/or total walk count index. The following standard errors of estimate are obtained for the best models based on the combination of descriptors: 2.1 degrees C (S-39), 4.4 degrees C (S-139) and 4.1 degrees C (S-178). They compare favorably with the related models in the literature.

Topological organic chemistry. 13. Transformation of graph adjacency matrixes to distance matrixes

A sequence of simple arithmetic operations is described that generates distance matrixes from the adjacency matrixes of graphs.

Combinatorial library design for diversity, cost efficiency, and drug-like character

Most computational techniques for the design of combinatorial libraries have concentrated solely on maximizing the diversity of the selected subset or its similarity to a known target. However, such libraries can produce high-throughput screening hits with properties that make them unsuitable to take forward into medicinal chemistry. This article describes software that allows the design of library subsets to simultaneously optimize a library's diversity or similarity to a target, properties (such as drug likeness) of the library members, properties (such as cost) of the reagents required to make them, and efficiency of synthesis in arrays or mixtures. Example are given showing that libraries can be designed to contain drug-like molecules with only a small trade-off in terms of the maximum possible diversity, and that the cost of the library, in terms of the reagents required to make it, can be contained. Other examples show that libraries can be designed to minimize the deconvolution problem or to maximize the number of molecules predicted to be active while also being designed for efficiency of synthesis.

Maximum topological distances based indices as molecular descriptors for QSPR: 2--application to aromatic hydrocarbons

Several standard topological indices based upon two different distance matrix definitions are employed to analyse their use to predict normal boiling points of a representative set of aromatic hydrocarbons. Results are quite satisfactory and they reveal the suitability of resorting to the maximum distance concept to compute the molecular indices, being in complete agreement with our previous findings on this issue.

On the relation between W'/W index, hyper-Wiener index, and Wiener numbers

It is shown analytically that the W'/W index, the hyper-Wiener index, and the Wiener number are closely related graph-theoretical invariants for acyclic structures. A general analytical expression for the hyper-Wiener index of a tree is derived too.

Estimation of octanol-water partition coefficients from a molecular similarity parameter

For 1966 compounds for which experimental values of the octanol-water partition coefficient (as logKow) were known, the value of an intermolecular similarity parameter, S, was computed for all 3,863,190 possible pairs. The S parameter differs from those most often used to predict molecular properties in that S depends on the difference between whole structures rather than on the difference between two numbers derived from the structures individually. For each compound, the largest S values were used to estimate logKow, and the statistical quality of the results was compared with results for the same 1966 compounds obtained by fragment-constant methods.