Topological Substructural Molecular Design Research Articles

Topological sub-structural molecular design (TOPS-MODE): a useful tool to explore key fragments of human A3 adenosine receptor ligands.

Adenosine regulates tissue function by activating four G-protein-coupled adenosine receptors (ARs). Selective agonists and antagonists for A3 ARs have been investigated for the treatment of a variety of immune disorders, cancer, brain, and heart ischemic conditions. We herein present a QSAR study based on a Topological sub-structural molecular design (TOPS-MODE) approach, intended to predict the A3 ARs of a diverse dataset of 124 (94 training set/ 30 prediction set) adenosine derivatives. The final model showed good fit and predictive capability, displaying 85.1 % of the experimental variance. The TOPS-MODE approach afforded a better understanding and interpretation of the developed model based on the useful information extracted from the analysis of the contribution of different molecular fragments to the affinity.

In silico clastogenic activity of dietary phenolic acids

Phenolic acids have been reported to exert multiple biological effects, including their activity as pro-oxidants. Structural alerts to identify the clastogenic activity of phenolic acids with pro-oxidant activity were determined in the current study. The described methodology was based on the application of a quantitative structure–activity relationship (QSAR) study. It was developed a virtual screening method based on a clastogenic model using the topological substructural molecular design (TOPS-MODE) approach. The model has presented a suitable probability of good classification for the external prediction data set. Therefore, it was possible to establish the structural criteria for maximal clastogenicity (chromosomal aberrations) of pro-oxidant reported phenolic acids. The two main criteria were represented by the presence of methoxy and/or hydroxyl substitutions on the benzene ring and polarity of these substituents. In summary, the apolar regions of phenolic acid derivatives contributed negatively to the activity, while the polar groups favored it.

Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure–activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.

Monoamino Oxidase A: An Interesting Pharmacological Target for the Development of Multi-Target QSAR

With the significant increase of life expectancy of populations in societies today, the importance of the discovery of drugs associated with neurodegenerative diseases has emerged. Therefore, neurodegenerative diseases are an important topic in Medicinal Chemistry. Although drug discovery is considered a complex and slow process, new approaches and methods have been developed with the intention of finding new chemical entities in more efficient ways. This work provides a review of virtual methodologies applied in drug discovery and especially a new model for the prediction of MAO-A inhibitors using a multi-target QSAR methodology. This model involves a mixed approach containing simple descriptors based on atom-centered fragments and functional groups (DRAGON) and topological substructural molecular design descriptors (MODESLAB). This unified multi-species QSAR model was validated through a virtual screening of a new series of oxoisoaporphine derivatives, taking into account the information in the calculated fragmental contributions. Therefore, this method represents a useful tool for the in silico screening of MAO-A inhibitors.

Structural Alerts for Predicting Clastogenic Activity of Pro-oxidant Flavonoid Compounds: Quantitative Structure–Activity Relationship Study

Flavonoids have been reported to exert multiple biological effects that include acting as pro-oxidants at very high doses. The authors determined a structural alert to identify the clastogenic activity of a series of flavonoids with pro-oxidant activity. The methodology was based on a quantitative structure–activity relationship (QSAR) study. Specifically, the authors developed a virtual screening method for a clastogenic model using the topological substructural molecular design (TOPS-MODE) approach. It represents a useful platform for the automatic generation of structural alerts, based on the calculation of spectral moments of molecular bond matrices appropriately weighted, taking into account the hydrophobic, electronic, and steric molecular features. Therefore, it was possible to establish the structural criteria for maximal clastogenicity of pro-oxidant flavonoids: the presence of a 3-hydroxyl group and a 4-carbonyl group in ring C, the maximal number of hydroxyl groups in ring B, the presence of methoxyl and phenyl groups, the absence of a 2,3-double bond in ring C, and the presence of 5,7 hydroxyl groups in ring A. The presented clastogenic model may be useful for screening new pro-oxidant compounds. This alert could help in the design of new and efficient flavonoids, which could be used as bioactive compounds in nutraceuticals and functional food.

Topological sub-structural molecular design approach: Radical scavenging activity

In the last decades phenolic compounds have gained enormous interest because of their beneficial health effects such as anti-inflammatory, anticancer, or antiviral activities. The pharmacological effects of phenolic compounds are mainly due to their antioxidant activity and their inhibition of certain enzymes. This antoxidant activity is related to the structure and has been extensively reported throught SAR or QSAR models. These studies confirmed that the number and position of hydroxyl groups, the related glycosylation and other substitutions in the phenolic ring largely determined radical scavenging activity. Most of these models are based on certain families of chemicals (flavonoids, cinnamic acids, etc…) and the model by itself is not useful for other substances of a different family. In this study we developed a QSAR model for a heterogeneous group of substances with TOPS-MODE descriptors for an interpretation of the antioxidant activity of these compounds in the form of bond contributions. The model developed, able to describe more than 90% of the variance in the experimental activity, also has a good predictive ability and stability. The information extracted from the QSAR model revealed that the major driving forces for radical scavenging activity are hydrogen bond donation and polarity. With this work we have managed to unify the different families of antioxidants in a single model with sufficient capacity to make predictions of radical scavenging activity for unknown substances.

Structural Contributions of Substrates to their Binding to P-Glycoprotein. A TOPSMODE Approach

A topological substructural molecular design approach (TOPS-MODE) has been used to formulate structural rules for binding of substrates of P-glycoprotein (P-gp). We first review some of the models developed in the recent literature for predicting binding to P-gp. Then, we develop a model using TOPS-MODE, which is able to identify 88.4% of substrates and 84.2% of non-substrates. When the model is presented to an external prediction set of 100 substrates and 77 nonsubstrates it identifies correctly 81.8% of all cases. Using TOPS-MODE strategy we found structural contributions for binding to P-gp, which identifies 24 structural fragments responsible for such binding. We then carried out a chemico-biological analysis of some of the structural fragments found as contributing to P-gp binding of substrates. We show that in general the model developed so far can be used as a virtual screening method for identifying substrates of P-gp from large libraries of compounds.

TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains

Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)μ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)μ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

A topological substructural molecular design approach for predicting mutagenesis end-points of [formula omitted], [formula omitted]-unsaturated carbonyl compounds

Chemically reactive, α , β -unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of α , β -unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of α , β -unsaturated carbonyl compounds over two endpoints – the Ames and mammalian cell gene mutation tests – based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.

QSPR modelling with the topological substructural molecular design approach: β-cyclodextrin complexation

This study aims at developing a quantitative structure–property relationship (QSPR) model for predicting complexation with β-cyclodextrins (β-CD) based on a large variety of organic compounds. Molecular descriptors were computed following the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach and correlated with β-CD complex stability constants by linear multivariate data analysis. This strategy afforded a final QSPR model that was able to explain around 86% of the variance in the experimental activity, along with showing good internal cross-validation statistics, and also good predictivity on external data. Topological substructural information influencing the complexation with β-CD was extracted from the QSPR model. This revealed that the major driving forces for complexation are hydrophobicity and van der Waals interactions. Therefore, the presence of hydrophobic groups (hydrocarbon chains, aryl groups, etc.) and voluminous species (Cl, Br, I, etc.) in the molecules renders easy their complexity with β-CDs. To our knowledge, this is the first time a correlation between TOPS-MODE descriptors and complexing abilities of β-CDs has been reported. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4557–4576, 2009

Designing novel antitrypanosomal agents from a mixed graph‐theoretical substructural approach

Chagas disease is nowadays the most serious parasitic health problem. This disease is caused by Trypanosoma cruzi. The great number of deaths and the insufficient effectiveness of drugs against this parasite have alarmed the scientific community worldwide. In an attempt to overcome this problem, a model for the design and prediction of new antitrypanosomal agents was obtained. This used a mixed approach, containing simple descriptors based on fragments and topological substructural molecular design descriptors. A data set was made up of 188 compounds, 99 of them characterized an antitrypanosomal activity and 88 compounds that belong to other pharmaceutical categories. The model showed sensitivity, specificity and accuracy values above 85%. Quantitative fragmental contributions were also calculated. Then, and to confirm the quality of the model, 15 structures of molecules tested as antitrypanosomal compounds (that we did not include in this study) were predicted, taking into account the information on the abovementioned calculated fragmental contributions. The model showed an accuracy of 100% which means that the "in silico" methodology developed by our team is promising for the rational design of new antitrypanosomal drugs.

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

Synthesis and Quantitative Structure−Antifungal Activity Relationships of Clovane Derivatives against Botrytis cinerea

Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).

A TOPological Sub-structural Molecular Design (TOPS-MODE)-QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210)

Lately, Quantitative Structure–Activity Relationship (QSAR) studies have been afar used to predict anticancer activity taking into account different molecular descriptors, statistical techniques, cell lines and data set of congeneric and non-congeneric compounds. Herein we report a QSAR study based on a TOPological Sub-structural Molecular Design (TOPS-MODE) approach, aiming at predicting the anticancer leukemia activity of a diverse data set of indolocarbazoles derivatives. Finally, several aspects of the structural activity relationships are discussed in terms of the contribution of different bonds to the anticancer activity, thereby making the relationship between structure and biological activity more transparent.

Quantum-Chemical Foundations of the Topological Substructural Molecular Design

The topological substructural molecular design (TOPS-MODE) approach is formulated as a tight-binding quantum-chemical method. The approach is based on certain postulates that permit to express any molecular property as a function of the spectral moments of certain types of molecular and environment-dependent energies. We use several empirical potentials to account for these intrinsic and external molecular energies. We prove that any molecular property expressed in terms of a quantitative structure-property and structure-activity relationships (QSPR/QSAR) model developed by using the TOPS-MODE method can be expressed as a bond additivity function. In addition, such a property can also be expressed as a substructural cluster expansion function. The conditions for such bond contributions being transferable are also analyzed here. Several new statistical-mechanical electronic functions are introduced as well as a bond-bond thermal Green's function or a propagator accounting for the electronic hopping between pairs of bonds. All these new concepts are applied to the development and application of a new QSAR model for describing the toxicity of polyhalogenated-dibenzo-1,4-dioxins. The QSAR model obtained displays a significant robustness and predictability. It permits an easy structural interpretation of the structure-activity relationship in terms of bond additivity functions, which display some resemblances with other theoretical parameters obtained from first principle quantum-chemical methods.

Quantitative Structure−Carcinogenicity Relationship for Detecting Structural Alerts in Nitroso Compounds: Species, Rat; Sex, Female; Route of Administration, Gavage

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

Cytotoxicity of selected imidazolium-derived ionic liquids in the human Caco-2 cell line. Sub-structural toxicological interpretation through a QSAR study

Room-temperature ionic liquids (ILs) are considered green chemicals that may replace volatile organic solvents currently used by industry. However, toxicological effects of ILs are not well known. In this study, we describe the cytotoxicity of selected imidazolium-derived ILs in Caco-2 cells, prototypical human epithelial cells. The most toxic IL was 1-decyl-3-methylimidazolium chloride ([C10mim][Cl]), whereas the least toxic was 1,3-dimethylimidazolium methyl sulfate ([C1mim][MSO4]). Using the toxicological experimental data obtained we developed a Quantitative Structure–Activity Relationship (QSAR) study using the Topological Sub-Structural Molecular Design (TOPS-MODE) approach. The model found showed excellent statistical parameters and from their interpretation, we arrived to some important conclusions such as: For 1-alkyl-3-methylimidazolium chloride derivatives, a correlation between the R2 alkyl chain length and toxicity was observable. A positive contribution of p-chloro substituent in benzyl ring is detected among 1-methylarylimidazolium chloride derivatives. Regarding the contribution of the anion, anion chloride presents a positive contribution to toxicity whereas for compounds [C1mim][MSO4] and [C1eim][ESO4], a negative fragment contribution can be detected in anion methyl or ethyl sulfate. The EC50 values determined for the ILs analysed in this study in Caco-2 cells are lower than the data reported in the literature on the toxicity of classical solvents assessed with cell lines. In conclusion, our results indicate that the possible cytotoxic effect of ILs should be considered in the design and overall evaluation of these compounds. Nevertheless, any toxicity evaluation of ILs should take their bioavailability into account, which will be affected by their low volatility, compared to conventional solvents.

Quantitative Structure−Antifungal Activity Relationships of Some Benzohydrazides against Botrytis cinerea

Fourteen benzohydrazides have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The best antifungal activity was observed for the N',N'-dibenzylbenzohydrazides 3b-d and for the N-aminoisoindoline-derived benzohydrazide 5. A quantitative structure-activity relationship (QSAR) study has been developed using a topological substructural molecular design (TOPS-MODE) approach to interpret the antifungal activity of these synthetic compounds. The model described 98.3% of the experimental variance, with a standard deviation of 4.02. The influence of an ortho substituent on the conformation of the benzohydrazides was investigated by X-ray crystallography and supported by QSAR study. Several aspects of the structure-activity relationships are discussed in terms of the contribution of different bonds to the antifungal activity, thereby making the relationships between structure and biological activity more transparent.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds

Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here – Quantitative Structure–Activity Relationship (QSAR) – are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions – expressed as fragment contributions to the carcinogenic activity – that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.