Topological Substructural Molecular Design Approach Research Articles

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.

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.

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

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.

Quantitative structure–activity relationship studies for the prediction of antifungal activity of N-arylbenzenesulfonamides against Botrytis cinerea

The Botrytis cinerea is one of the most interesting fungal pathogens. It can infect almost every plant and plant part and cause early latent infections which damage the fruit before ripening. The QSAR is an alternative method for the research of new and better fungicides against B. cinerea. This paper describes the results of applying a topological sub-structural molecular design (TOPS-MODE) approach for predicting the antifungal activity of 28 N-arylbenzenesulfonamides. The model described 86.1% of the experimental variance, with a standard deviation of 0.223. Leave-one-out and leave-group-out cross validation was carried out with the aim of evaluating the predictive power of the model. The values of their respective squared correlations coefficients were 0.754 and 0.741. The TOPS-MODE approach was compared with three other predictive models, but none of these could explain more than 72.8% of the variance with the same number of variables. In addition, this approach enabled the assessment of the contribution of different bonds to antifungal activity, thereby making the relationships between structure and biological activity more transparent. It was found that the fungicidal activity of the chemicals analyzed was increased by the presence of a sulfonamide group bonded to two aromatics rings, making this group the most important of the molecule. The majority of the substituents present in the aromatic rings have an electron withdrawing effect and they contribute to a smaller degree than the sulfonamide group to the property under study. The aromatic moiety plays an important role in this activity; its contribution changes with different substituents. Generally, the nitro group has a positive and great contribution to the biological property but when this group is involved in some compounds in ortho effect with the SO 2 moiety of the sulfonamide group a lower value of contribution is observed for both groups.

Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity

Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods. One possible way of predicting carcinogenicity is to use quantitative structure–activity relationships (QSARs). QSARs have been widely utilized for toxicity testing, thereby contributing to a reduction in the need for experimental animals. This paper describes the results of applying a TOPological substructural molecular design (TOPS-MODE) approach for predicting the rodent carcinogenicity of nitrocompounds. The model described 79.10% of the experimental variance, with a standard deviation of 0.424. The predictive power of the model was validated by leave-one-out validation, with a determination coefficient of 0.666. In addition, this approach enabled the contribution of different fragments to carcinogenic potency to be assessed, thereby making the relationships between structure and carcinogenicity to be transparent. It was found that the carcinogenic activity of the chemicals analysed was increased by the presence of a primary amine group bonded to the aromatic ring, a manner that was proportional to the ring aromaticity. The nitro group bonded to an aromatic carbon atom is a more important determinant of carcinogenicity than the nitro group bonded to an aliphatic carbon. Finally, the TOPS-MODE approach was compared with four other predictive models, but none of these could explain more than 66% of the variance in the carcinogenic potency with the same number of variables.

A topological substructural approach applied to the computational prediction of rodent carcinogenicity

The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of ‘safer’ chemicals were evaluated.

A topological sub-structural approach to the mutagenic activity in dental monomers. 3. Heterogeneous set of compounds

The topological sub-structural molecular design (TOPS-MODE) approach has been applied to the study of mutagenic properties in a heterogeneous set of dental monomers. A model able to describe close to 90% of the experimental variance in the values for mutagenic activity of 41 dental monomers through genetic algorithm was developed with the use of the mentioned approach. Also, a study for the determination of the optimal number of variables in the equation and potential outliers was carried out. Finally, the TOPS-MODE approach was used to derive the contribution of different fragments to the mutagenic activity.

TOPS‐MODE Approach for the Prediction of Blood–Brain Barrier Permeation

The blood–brain barrier permeation has been investigated by using a topological substructural molecular design approach (TOPS‐MODE). A linear regression model was developed to predict the in vivo blood–brain partitioning coefficient on a data set of 119 compounds, treated as the logarithm of the blood–brain concentration ratio. The final model explained the 70% of the variance and it was validated through the use of an external validation set (33 compounds of the 119, MAE = 0.33), a leave‐one‐out crossvalidation (q2 = 0.65, Spress = 0.43), fivefold full crossvalidation (removing 28 compounds in each cycle, MAE = 33, RMSE = 0.43) and the prediction of ± values for an external test set (85.7% of good prediction). This methodology evidenced that the hydrophobicity increase the blood–brain barrier permeation, while the polar surface and its interaction with the atomic mass of compounds decrease it; suggesting the capacity of the TOPS‐MODE descriptors to estimate brain penetration potential of new drug candidates. Finally, by the present approach, positive and negative substructural contributions to the brain permeation were identified, and their possibilities in the lead generation and optimization processes were evaluated. © 2004 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 93:1701–1717, 2004

A topological sub-structural approach to the mutagenic activity in dental monomers. 2. Cycloaliphatic epoxides

The topological sub-structural molecular design (TOPS-MODE) approach has been introduced for the study of mutagenic properties. The mutagenicity of 15 dental monomers was studied with this approach obtaining a good quantitative structure-toxicity model. For purposes of comparison, we employed seven different weights in the diagonal entries of the bond matrix in order to select the best TOPS-MODE model. In addition, our best model was compared with a model previously reported in the literature, and proved to be superior. Finally, the TOPS-MODE approach was used to derive the contribution of different fragments to the mutagenicity of all the compounds studied.

Designing Antibacterial Compounds Through a Topological Substructural Approach.

A novel application of TOPological Substructural MOlecular DEsign (TOPS-MODE) was carried out in antibacterial drugs using computer-aided molecular design. Two series of compounds, one containing antibacterial and the other containing non-antibacterial compounds, were processed by a k-means cluster analysis in order to design training and predicting series. All clusters had a p-level < 0.005. Afterward, a linear classification function has been derived toward discrimination between antibacterial and non-antibacterial compounds. The model correctly classifies 94% of active and 86% of inactive compounds in the training series. More specifically, the model showed a global good classification of 91%, i.e., 263 cases out of 289. In predicting series, the model has shown overall predictabilities of 91 and 83% for active and inactive compounds, respectively. Thereby, the model has a global percentage of good classification of 89%. The TOPS-MODE approach, also, similarly compares with respect to one of the most u...

Designing antibacterial compounds through a topological substructural approach.

A novel application of TOPological Substructural MOlecular DEsign (TOPS-MODE) was carried out in antibacterial drugs using computer-aided molecular design. Two series of compounds, one containing antibacterial and the other containing non-antibacterial compounds, were processed by a k-means cluster analysis in order to design training and predicting series. All clusters had a p-level < 0.005. Afterward, a linear classification function has been derived toward discrimination between antibacterial and non-antibacterial compounds. The model correctly classifies 94% of active and 86% of inactive compounds in the training series. More specifically, the model showed a global good classification of 91%, i.e., 263 cases out of 289. In predicting series, the model has shown overall predictabilities of 91 and 83% for active and inactive compounds, respectively. Thereby, the model has a global percentage of good classification of 89%. The TOPS-MODE approach, also, similarly compares with respect to one of the most useful models for antimicrobials selection reported to date.

A topological-substructural molecular design (TOPS-MODE) approach to determining pharmacokinetics and pharmacological properties of 6-fluoroquinolone derivatives

The topological-substructural molecular design approach was used to estimate the human bioavailability (F) and the minimum inhibitory concentration (MIC 90) against Streptococcus pneumoniae from a data set of 17 and 19 fluoroquinolone derivatives, respectively. Both pharmacokinetics and pharmacological properties were well described by the present approach. The total spectral moments and local spectral moments that include the different fluoroquinolone rings, polar and non-polar areas and their interactions were calculated and weighted with the standard dipole moments and the electronegative difference between the atoms that form a bond. In order to obtain a qualitative model that permits the classification of drugs with high and moderate bioavailability, a linear discriminant analysis was carried out. The percentage of correct classification was 100% for compounds of the training set. The leave-one-out cross validation procedure showed an 88.23% of correct classification. Also, a quantitative model, by the piecewise linear regression was developed. The theoretically predicted values for human bioavailability was assessed by a correlation with in vivo rat bioavailability and the regression equation was used to predict this biopharmaceutical property for two new pre-clinical 6-fluoroquinolone derivatives. On the other hand, a linear regression model that explained the 84% of variance was developed to predict the MIC 90 values. Finally, the role of a pharmacokinetic and pharmacological relationship in the design of new fluoroquinolones was evaluated in the Sitafloxacin framework, where 13 substituents were analyzed; halogens and methoxy groups had the best contributions to both properties. The present approach proved to be a good method for studying the pharmacokinetics and the pharmacological properties of new 6-fluoroquinolone candidates in drug development studies.

What are the limits of applicability for graph theoretic descriptors in QSPR/QSAR? Modeling dipole moments of aromatic compounds with TOPS-MODE descriptors.

The numerous possibilities of using graph theoretic descriptors in QSPR/QSAR are analyzed, and some misunderstandings on the role of this theoretical approach in chemistry are clarified. Principal component analysis is used to obtain a property space for several physicochemical properties of aromatic compounds. It is proved that most of the QSPR applications of the graph-theoretic structure descriptors are concentrated to the description of properties in a very limited region of this property space. Here, we show that graph-theoretic approaches are also applicable to the modeling of physicochemical properties that are far away from this region traditionally studied. The molecular dipole moments of benzene derivatives, mono-, ortho-, meta-, and para-susbtituted, are modeled by using the Topological Sub-Structural Molecular Design (TOPS-MODE) approach. The TOPS-MODE approach used permits to calculate group dipole moments that are given for several substituents. The differences between these group dipoles and those obtained by simple difference between experimental values are analyzed. Some difficulties arising from this traditional way of deriving substituent constants are identified and analyzed.

Modeling diamagnetic and magnetooptic properties of organic compounds with the TOSS-MODE approach.

The topological substructural molecular design (TOSS-MODE) approach is used to describe the diamagnetic susceptibility of organic compounds. Two data sets composed of 233 aliphatic and 85 aromatic compounds were studied for which good linear correlations were found. The contributions of many different structural fragments and atomic groups were computed by the current approach. The predictive ability of the models developed was tested by using external prediction sets of compounds of different classes than those used in training. A quantitative model based on the current approach was developed to compute the diamagnetic susceptibility exaltation of aromatic compounds, which is exemplified by the study of polycyclic aromatic hydrocarbons. The rotatory power of organic compounds in a magnetic field was also described by the TOSS-MODE approach. Good linear correlations were obtained for this property in aliphatic and aromatic compounds. The predictive abilities of the models found were tested by external prediction sets for which good correlations between calculated and experimental values are found.