TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds
The TOPological Sub-Structural Molecular Design (TOPS-MODE) approach has been applied to the study of the anti-inflammatory compounds. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441).
Introduction
Anti-inflammatory drugs are widely used for the treatment of pain, inflammation, rheumatoid arthritis and osteoarthritis. The common dose limiting toxicity of anti-inflammatory compounds is the increased risk of gastrointestinal ulceration, perforation and hemorrhage.1 The enzyme cyclooxygenase (COX) catalyses the biooxygenation of arachidonic acid to prostaglandin G2, which serves as a precursor for the synthesis of prostaglandins, prostacyclins and thromboxanes, which are collectively termed as prostanoids.2 The cyclooxygenase activity of the enzyme is the site of action of NSAIDs.[3], [4] However, inhibition of prostanoid biosynthesis is associated with side effects such as ulceration and impairment of renal functions.5 It has been well established that the cells express two isoforms of cyclooxygenases, namely cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2).6
COX-1 is expressed in many normal tissues and is the major form present in platelets, kidneys, gastrointestinal tract and plays a key role in physiological processes,7 whereas COX-2 is an inducible form by many pro-inflammatory cytokines and mitogens. The second isoform is generally not detectable in normal tissues, but is elevated in inflammatory conditions8 and is also implicated in colon cancers,9 and Alzheimer's disease.10 COX-2 is also constitutively expressed in kidneys,11 brain12 spinal cord13 and in mucosa of stomach.14
For this reason, novel anti-inflammatory compounds with more selectivity and less toxicity are needed for the future.[15], [16] Many researchers worldwide have been worked in the synthesis and evaluation of novel compounds.[17], [18], [19], [20], [21], [22]
On the other hand, Graph–Theoretical methods have shown to be very useful in QSAR problems in order to perform a rational analysis of different pharmacological, toxicological and other activities.[23], [24] In the context of the Graph–Theoretical and Topological methods for modelling physicochemical and biological properties of chemical there has been introduced the TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach. The TOPS-MODE has been applied to the description of physicochemical properties of organic compounds. Several applications for the design of biologically active compounds have been described.[25], [26], [27], [28], [29], [30], [31], [32] Thereby, the aim of this work is to find rationality in the search of novel anti-inflammatory compounds using TOPS-MODE approach. Secondly, to continue the validation of the methods for describing biological activity of heterogeneous series of compounds.
Section snippets
Linear discriminant analysis and TOPS-MODE approach
Here, we use the TOPS-MODE approach to obtain molecular descriptors through which we developed the QSAR function. The mathematical details of the method have been largely reported,[24], [25], [26], [27], [28], [29], [30] thus we will outline only the fundamental remarks.
Briefly, this method codifies the molecular structure by means of the edge adjacency matrix E (likewise called bond adjacency matrix B). The E or B matrix is a square table of order m (the number of chemical bonds in the
Computation of fragment contributions
Each of the μk spectral moments given in Eq. 1 contains structural information on the molecules that can be directly obtained by the following computational approach.31 In this approach we calculated the spectral moment for all the fragments contained in a given substructure and by difference of these moments obtained the contribution of the substructure.
The general algorithm followed in this computational approach is as follows. First, we select the substructures whose contribution to the
k-Means cluster analysis
The k-MCA may be used in training and predicting series design.[43], [44] The idea consists of carrying out a partition of either active or nonactive series of compound in several statistically representative classes of chemicals. Thence, one may select from the member of all these classes of training and predicting series. This procedure ensures that any chemical classes (as determined by the clusters derived from k-MCA) will be represented in both compounds series (training and predicting).
Development of the discriminant function
Once we perform a representative selection of training series it could be used to fit the discriminant function. The model selection was subjected to the principle of parsimony. Then we chose a function with high statistical significance but having few parameters (bk) as possible.
In order to derive a discriminant function that permit the classification of chemicals as active (anti-inflammatory) or inactive (nonanti-inflammatory) we use the linear discriminant analysis in which spectral moments
Fragment contributions
As we previously explain, the TOPS-MODE approach is able to compute the contribution of any structural fragment (real or hypothetical) to the biological property or activity studied.[24], [25], [26] In the present case, we can find the positive and negative contributions of such fragments to the development of the anti-inflammatory activity. These fragments will be named here as active and inactive, respectively. The presence of active fragments does not presuppose the development of the
Concluding remarks
In spite of some criticism, there is an increase necessity of topological-indices-based QSAR models in order to rationalize the drug discovery process. In this sense, the TOPS-MODE approach has been extended not only to the discovery of novel leads but also to the study of the physicochemical and absorption properties of drugs.[49], [50] On the other hand, QSAR make use of reduced or homologous series of compounds. Consequently, decays the model capability to predict the activity of different
Acknowledgements
The authors would like to express their gratitude to FAPESP (Brazil) and CNPq (Brazil) for financial support and kindness. Maykel Pérez thanks the owner of the software Modeslab 1.0 for the donation of this valuable tool for the realization of this work.
Last but yet importantly, the authors would like to express they more sincerely gratitude to unknown referees and the editor Prof. Dr. Chi-Huey Wong for useful comments and kind attention.
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