Jaroslaw Polanski

Quinoline-Based Antifungals

Although the assortment of antifungal drugs is broad, the most commonly used agents have major drawbacks. Toxicity, serious side effects or the emergence of drug resistance are amongst them. New drugs and drug candidates under clinical trials do not guarantee better pharmacological parameters. These new medicines may appear effective; however; they may cause serious side effects. This current review is focused on the recent findings in the design of quinoline based antifungal agents. This field seems to be especially interesting as 8-hydroxyquinoline and its metal complexes have been well known as antifungals for years. Structural similarities between quinoline based antifungals and allylamines or homoallylamines, e.g. terbinafine is another interesting fact. Quinoline can be identified in a number of synthetic and natural antifungals, which indicates natures preference for this fragment and identifying it as one of the so-called privileged structures. We have discussed new trends in the design of quinolines with antifungal properties, their possible targets and the structure activity relationships within the antifungal series developed.

The comparative molecular surface analysis (COMSA): a novel tool for molecular design☆

A new method allowing for 3-D QSAR analysis and the prediction of biological activity is presented. Unlike comparative molecular field analysis (CoMFA)-like techniques, it is based not on a comparison of the properties characterizing a discrete set of points but on the mean electrostatic potential (MEP) calculated and labeling specific areas defined on the molecular surface. A Kohonen self-organizing neural network and partial least square (PLS) analysis have been used for performing such an operation. The series of steroids complexing the corticosteroid (CBG) and testosterone (TBG) globulins, which forms a benchmark measuring the performance of the methods in molecular design, and a series of benzoic acids described by the Hammett sigma constants is used for testing the method. It is demonstrated that a method can be used efficiently to evaluate the responses determined both by the combination of electrostatic and steric effects or by electrostatic effects alone, therefore, two different schemes were developed. The first one, which involves PLS analysis of the full comparative networks, covers both steric and electrostatic effects. This scheme works well for both the CBG and TBG data. The second scheme takes into account only the properties (MEP) of these regions within molecules that can be superimposed with the template molecule. This scheme provides the best predictive power for the benzoic acids series. Comparison of the results from a CoMFA analysis proves that method is at least as effective for the responses limited by electrostatic effects, although it significantly outperforms CoMFA for CBG affinity which is dominated by steric effects.

The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids

SummaryIt is shown how a self-organizing neural network such as the one introduced by Kohonen can be used to analyze features of molecular surfaces, such as shape and the molecular electrostatic potential. On the one hand, two-dimensional maps of molecular surface properties can be generated and used for the comparison of a set of molecules. On the other hand, the surface geometry of one molecule can be stored in a network and this network can be used as a template for the analysis of the shape of various other molecules. The application of these techniques to a series of steroids exhibiting a range of binding activities to the corticosteroid-binding globulin receptor allows one to pinpoint the essential features necessary for biological activity.

Modeling robust QSAR

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

Investigating biological activity spectrum for novel quinoline analogues 2. Hydroxyquinolinecarboxamides with photosynthesis-inhibiting activity

Two series of amides based on quinoline scaffold were designed and synthesized in search of photosynthesis inhibitors. The compounds were tested for their photosynthesis-inhibiting activity against Spinacia oleracea L. and Chlorella vulgaris Beij. The compounds lipophilicity was determined by the RP-HPLC method. Several compounds showed biological activity similar or even higher than that of the standard (DCMU). The structure-activity relationships are discussed.

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

Privileged Structures - Dream or Reality: Preferential Organization of Azanaphthalene Scaffold

The concept of privileged structures/substructures (PS) is the idea that certain structural features produce biological effects more often than others. The PS method can be seen as an offspring of fragonomics, which is based on recent experimental measurements of protein-ligand interactions. If PS prove to be true, then chemical motives that enrich biological activity can be used when designing new drugs. However, PS remain controversial because we cannot be sure whether the excess of active structures does not result from an abundance in chemical libraries. In this review, we will focus, in particular, on the preferential organization of azanaphthalene scaffolds (AN) in drugs and natural products (NP), which are preferred by Nature in evolution. We will show that knowledge discovery in molecular databases can reveal interesting time-trends profiles for important classes of potentially privileged scaffolds. The chemical library of AN is dominated by monoaza-compounds, among which quinoline appears to be the most frequently investigated scaffold; however; more sophisticated database mining seems to indicate different PS patterns within the AN scaffold family.

The Receptor-like Neural Network for Modeling Corticosteroid and Testosterone Binding Globulins

A neural-net method for simulation of corticosteroid and testosterone binding globulin (CBG, TBG)-ligand interactions is presented. Molecular modeling provides the geometry and partial atomic charges of 31 steroid molecules. The atomic coordinates within the molecule of the compound of the highest affinity are then used to train a self-organizing map (SOM) that forms a template for the comparison to other molecules. Comparison is done using a series of normalized patterns produced by the SOM. The template SOM, after overlaying on the set of random vectors, mimics the topology of the receptor site and is used to train unsupervisedly a neuron capable of recognizing the degree of similarity between the reference and tested patterns. A good correlation is observed for signals generated by the neuron plotted against the experimental CBG affinities. For TBG affinity modeling a modified procedure is designed which is capable of separating electrostatic and shape effects. The high predictive power of the model is achieved by keeping close analogy to the processes taking place at the real receptor sites.

The Comparative Molecular Surface Analysis (CoMSA) with Modified Uniformative Variable Elimination-PLS (UVE-PLS) Method: Application to the Steroids Binding the Aromatase Enzyme

The application of the CoMSA method to analyze 3D QSAR of 50 steroid aromatase inhibitors is described. The 3D QSAR model obtained, reaching a value of cross-validated q(2) = 0.96 (s = 0.31), significantly outperforms those reported in the literature for the CoMFA or CoSA (CoSASA). It is shown that the Uniformative Variable Elimination UVE-PLS or modified iterative UVE procedure (IVE-PLS) can be used for indicating the regions contributing to the binding activity. Thus, after separating the series into two groups of the training and test molecules quite correct external predictions result from the processing of the training set. We proved that the procedure of the data elimination provides stable results, if tested in 50 random runs of the IVE-PLS-CoMSA with different training/test sets. Depending upon the procedure used the quality of the predictions for 25 test molecules is given by SDEP = sum(y(pred)-y(obs))(2)/n)(1/2) = 0.321 - 0.782.

Ring-substituted 4-Hydroxy-1H-quinolin-2-ones: Preparation and Biological Activity

In the study, a series of twelve ring-substituted 4-hydroxy-1H-quinolin-2-one derivatives were prepared. The procedures for synthesis of the compounds are presented. The compounds were analyzed using RP-HPLC to determine lipophilicity and tested for their photosynthesis-inhibiting activity using spinach (Spinacia oleracea L.) chloroplasts. All the synthesized compounds were also evaluated for antifungal activity using in vitro screening with eight fungal strains. For all the compounds, the relationships between the lipophilicity and the chemical structure of the studied compounds are discussed, as well as their structure-activity relationships (SAR).