CASE Plots for the Chemotype‐Based Activity and Selectivity Analysis: A CASE Study of Cyclooxygenase Inhibitors

Abstract

Structure-activity characterization of molecular databases plays a central role in drug discovery. However, the characterization of large databases containing structurally diverse molecules with several end-points represents a major challenge. For this purpose, the use of chemoinformatic methods plays an important role to elucidate structure-activity relationships. Herein, a general methodology, namely Chemotype Activity and Selectivity Enrichment plots, is presented. Chemotype Activity and Selectivity Enrichment plots provide graphical information concerning the activity and selectivity patterns of particular chemotypes contained in structurally diverse databases. As a case study, we analyzed a set of 658 compounds screened against cyclooxygenase-1 and cyclooxygenase-2. Chemotype Activity and Selectivity Enrichment plots analysis highlighted chemotypes enriched with active and selective molecules against cyclooxygenase-2; all this in a simple 2D graphical representation. Additionally, the most active and selective chemotypes detected in Chemotype Activity and Selectivity Enrichment plots were analyzed separately using the previously reported dual activity-difference maps. These findings indicate that Chemotype Activity and Selectivity Enrichment plots and dual activity-difference maps are complementary chemoinformatic tools to explore the structure-activity relationships of structurally diverse databases screened against two biological end-points.Key words: chemotypes, cyclooxygenases, dual activity-difference maps, selectivity, structure-activity relationships, visualization Abbreviations: CASE, chemotype activity and selectivity enrichment; COX-1, cyclooxygenase-1; COX-2, cyclooxygenase-2; DAD, dual activity-difference; SAR, structure-activity relationships. Structure-activity relationships (SAR) play a central role in drug discovery (1). To this end, a number of predictive and descriptive methods can be employed. Among them, some of the commonly used methodologies include quantitative SAR, rule-based methods, neural networks and pharmacophore modeling (2-5). However, the application of these methodologies is highly dependent of the structural nature of the database and the experimental information.The chemotype-based classification of structurally diverse databases, associated with one or multiple targets, requires a robust and flexible strategy (6). An approach developed by Xu and Johnson (7,8) shows a considerable promise in this regard. Their method decomposes molecules in terms of characteristic structural patterns of variable resolution and complexity called chemotypes and provides tools for a hierarchical classification based on these chemotypes (6-9).Chemotype-enrichment plots were previously designed to identify chemotype classes of active molecules with activity against one biological end-point in compound databases. The main goal of that approach is to characterize the relationship of occupancy to activity enrichment for a set of chemotypes at a given level of structural resolution (9). Hence, thi...

Rapid Scanning Structure–Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches

“…16,17,24,25 Given a set of N compounds tested with targets I and II , the DAD map depicts N ( N −1)/2 pairwise potency differences for each possible pair in the data set against both targets. The potency differences for target T for each molecule pair are calculated with the expression: $$\mathrm{normal\Delta }{\text{pK}}_{\text{i}}{false(\text{T}false)}_{ab}={\text{pK}}_{\text{i}}{false(\text{T}false)}_a-{\text{pK}}_{\text{i}}{false(\text{T}false)}_b$$ where pK i (T) a and pK i (T) b are the activities of molecules a and b ( b > a ) against the two targets and T = FPR1, FPR2.…”

“…As previously noted, the classification of data points in an activity-difference map is independent of the structure similarity. 16,17,24,25 …”

Cyclic Systems Distribution Along Similarity Measures: Insights for an Application to Activity Landscape Modeling

“…However, based on the fact that similarity measures take into account the entire molecules, information concerning to molecular scaffolds or skeletons can be missed or can be biased if the size of the molecules being compared is very different. On the other hand, chemotype characterization of molecular databases provides useful information concerning the activity profile among molecular scaffolds 13. However, this last approach does not consider the influence of side chains (side groups) 13.…”

“…The chemical structures and biological activities ( IC 50 values) of 658 previously reported cyclooxygenase inhibitors were obtained from the Binding Database 14. The same dataset was previously used by our group in a recently published work 13. SMILES representations of the compounds and their biological activities expressed as p IC 50 (p IC 50 =log IC 50 , where IC 50 is expressed in mol/L) can be found in the supplementary data, Table S1.…”

“…At least two major criteria have been employed thus far to establish a threshold in structure similarity for activity landscape modeling using SAS and SAS‐like maps i.e., median similarity of the actives and median similarity of the entire database 9,12. However, since similarity measures consider the entire molecule, information related to molecular scaffolds is missed 13…”