Cellular quantitative structure-activity relationship (Cell-QSAR): conceptual dissection of receptor binding and intracellular disposition in antifilarial activities of Selwood antimycins

Abstract

We present the cellular quantitative structure-activity relationship (cell-QSAR) concept that adapts ligand-based and receptor-based 3D-QSAR methods for use with cell-level activities. The unknown intracellular drug disposition is accounted for by the disposition function (DF), a model-based, nonlinear function of a drug's lipophilicity, acidity, and other properties. We conceptually combined the DF with our multispecies, multimode version of the frequently used ligand-based comparative molecular field analysis (CoMFA) method, forming a single correlation function for fitting the cell-level activities. The resulting cell-QSAR model was applied to the Selwood data on filaricidal activities of antimycin analogues. Their molecules are flexible, ionize under physiologic conditions, form different intramolecular H-bonds for neutral and ionized species, and cross several membranes to reach unknown receptors. The calibrated cell-QSAR model is significantly more predictive than other models lacking the disposition part and provides valuable structure optimization clues by factorizing the cell-level activity of each compound into the contributions of the receptor binding and disposition.

Figure 1

Conformational switching of an intramolecular H-bond in the salicylamide ring of antimycin derivatives upon ionization.

Figure 2

Four torsions of compound 7 (Table 1) characterizing the conformations of three-ring analogues displayed in red color. They are Φ₁ (C3–C4–C11–O12), Φ₂ (C11–N13–C15–C20), Φ₃ (C17–C18–O22–C23), and Φ₄ (C18–O22–C23–C28).

Figure 3

Superposition procedure: (a) four basic templates of compound 7, generated by conformational analysis and numbered as in Table 2; (b) superposition of the four basic templates; (c) superpositions of all compounds (Table 1) to the four basic templates in respective species and conformations, numbered as in row a; (d) final superpositions, enriched by flipping the benzene rings by 180° for the MSMM situation (I; 200 molecules = 31 compounds × 2 species × 2 skeleton conformations × 1, 2, or 4 ring-flipping conformations), the MM situation (II; 100 molecules created as in situation I but with 1 species), and the standard one-mode situation (III; 31 molecules).

Figure 4

Calculated and predicted activities plotted against experimental data for the training set (a) and test set (b), generated with the DF-MSMM (black), MSMM (blue), SSMM (red), and standard (green) CoMFA models (models 1–3 and 11 in Table 4, respectively). In plot b, the two experimental values of log(1/EC₅₀) = 5 are actually <5 (Table 1, compounds 17 and 30), as indicated by the arrows.

Figure 5

Contour maps of the DF-MSMM CoMFA model with embedded superposition of all 200 molecules to indicate the applicability space. Green and yellow contours indicate regions that are sterically favorable and unfavorable, respectively. Blue regions favor electropositive groups, and red regions favor electronegative groups. The following ligands are presented in ball and stick models: (a) compound 2 (Table 1) binding in ionized and open conformation, (b) compound 2 binding in neutral and closed conformation, showing the clash of the 3-Cl in the second ring with one of the sterically unfavorable regions, (c) compound 18 with a shorter alkyl chain, one of the strongest binders, (d) compound 19 with the 3-acetamide group in the salicylamide ring occupying a sterically favorable region, showing clashes of the long chain with one of the sterically unfavorable regions.

Figure 6

Speciation of free and bound molecules according to the DF-MSMM CoMFA model. Neutral and ionized species are shown in blue and red, respectively, as the compound number (Table 1).

Figure 7

Dependence of the pseudoequilibrium, membrane-based DF on lipophilicity and acidity, as given by the disposition part of eq 1 with the optimized coefficient values listed in the text. The data for best binders with poor disposition (compounds 17 and 18, Table 1) are shown as open points.

Figure 8

Dissection of affinity (log K; red symbols) and disposition (log DF; blue symbols) contributions to overall bioactivity by the calibrated DF-MSMM CoMFA model. Stars mark the best binders 17 and 18 with poor disposition. All data are given in Table 1.