Comparison of QSARs and Characterization of Structural Basis of Bioactivity Using Partial Order Theory and Formal Concept Analysis: A Case Study with Mutagenicity

Abstract

Fifteen quantitative structure-activity relationship (QSAR) models developed by various authors for the prediction of mutagenicity of aromatic and heteroaromatic amines were analyzed and thirteen of them, based on 95 amines, were compared using their respective statistics and order theory (Hasse Diagram Technique, HDT) to obtain an ordering of QSAR models. The technique of Formal Concept Analysis (FCA) was applied to the set of 95 amines to extract concepts and, in general, knowledge about the relationship between structural attributes and mutagenicity. HDT may be useful as a general tool for the comparison of different classes of QSAR models. FCA turns out to be a novel mathematical technique for seeking for relationships between molecular structure and activity.

Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations

“…To develop the mechanism-based guidelines to design mutagenicity-free ArNH 2 , we chose a set of 62 diverse ArNH 2 for DFT calculations and statistical analysis ( 1 – 62 ). The set contains 31 Ames-positive and 31 Ames-negative compounds, primarily collected from the AstraZeneca corporate database, and includes well-known carcinogens and challenging structures that existing prediction tools have failed for. ,,,,,,, Specifically, we included three consensus false-negative ( 14 , 33 , and 34 ) and four consensus false-positive ( 10 , 15 , 45 , and 59 ) ArNH 2 from the recent paper by Patel et al that all four in silico mutagenicity prediction tools were unable to classify correctly . The purpose was to find the mechanism-based explanations for the observed structure–mutagenicity relationships exemplified in this set.…”

“…Prediction of mutagenicity of reagent-sized ArNH 2 fragments is vital for the success of drug discovery programs, and special attention has been devoted to this issue in the literature. Currently, the mutagenicity of ArNH 2 can be predicted by three types of computational methods, which are based on either statistics, structural alerts, or mechanistic considerations. − ,− Despite the abundance of experimental data and the availability of computational approaches and relevant databases, the prediction of mutagenicity of ArNH 2 still represents a significant challenge to computational and theoretical chemistry. ,,,,, Quantitative structure–mutagenicity relationships usually suggest a variety of factors that increase mutagenic potency of ArNH 2 : increase of lipophilicity, high energy of the highest occupied molecular orbital (HOMO), low energy of the lowest unoccupied molecular orbital (LUMO), low “chemical hardness” (LUMO–HOMO), high “chemical softness”, stability of ArNH + (reactive electrophilic metabolites of ArNH 2 ), negative charge density at the exocyclic nitrogen of ArNH + , presence of a pyridine-like nitrogen (i.e., aromatic nitrogen with sp 2 lone pair) in the α-position to the NH 2 group, and size of the aromatic system. ,,,− ,,,,− Although these descriptors reflect an array of steps in the mutagenic activation of ArNH 2 and are important for mutagenic activity, ,,,, their ability to discriminate between mutagenic and nonmutagenic compounds , or to describe mutagenic potency in diverse sets of active mutagens ,,,,,, is limited. Thus, high lipophilicity, high energy of HOMO, and even stability of ArNH + turn out to be unimportant to define high mutagenic potency or to discriminate a propensit...…”

“…1,2,4,19,30,32 Quantitative structure−mutagenicity relationships usually suggest a variety of factors that increase mutagenic potency of ArNH 2 : increase of lipophilicity, high energy of the highest occupied molecular orbital (HOMO), low energy of the lowest unoccupied molecular orbital (LUMO), low "chemical hardness" (LUMO−HOMO), high "chemical softness", stability of ArNH + (reactive electrophilic metabolites of ArNH 2 ), negative charge density at the exocyclic nitrogen of ArNH + , presence of a pyridine-like nitrogen (i.e., aromatic nitrogen with sp 2 position to the NH 2 group, and size of the aromatic system. 1,2,4,13−17,19,25,30,32−40 Although these descriptors reflect an array of steps in the mutagenic activation of ArNH 2 and are important for mutagenic activity, 3,17,19,32,35 their ability to discriminate between mutagenic and nonmutagenic compounds 35,41 or to describe mutagenic potency in diverse sets of active mutagens 3,27,28,32,36,42,43 is limited. Thus, high lipophilicity, high energy of HOMO, and even stability of ArNH + turn out to be unimportant to define high mutagenic potency or to discriminate a propensity between mutagens and nonmutagens.…”

QSPR/QSAR Studies of 2-Furylethylenes Using Bond-Level Quadratic Indices and Comparison with Other Computational Approaches

“…Many authors consider high q 2 values (for instance, q 2 > 0.5) as an indicator or even as the ultimate proof of the high predictive power of a QSAR model. The Hasse diagram technique may be used to rank the QSAR models in terms of their respective statistics [37].…”

Structure-Property Relationship for the Pharmacological and Toxicological Activity of Heterocyclic Compounds

“…Previous studies have demonstrated that quantitative structure-activity/ property relationships (QSAR/QSPR) approach is successful in predicting activities, properties, and toxicities including mutagenicity (described as lnR) of aromatic and hetero-aromatic amines. [6][7][8][9][10][11][12] For example, the aryl hydrocarbon receptor binding affinity (described as pEC50) is well documented in the field of toxicology for organics. [13][14][15] Basak et al 16 proposed a hierarchical quantitative structure-activity relationship (HiQSAR) approach for the pEC50 prediction.…”