In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions

Abstract

P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp-drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r (2)=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Kiexp or pKiexp; experimental Ki or negative decimal logarithm of Kiexp) converted from experimental IC50 (half maximal inhibitory concentration) values with POLSCORE-predicted constants (KiPOLSCORE or pKiPOLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.

Keywords: ATP-binding cassette transporter; P-gp inhibitors; POLSCORE; molecular docking; multidrug resistance.

Figure 1

QZ59-RRR-associated binding pocket in the crystalline structure of murine P-glycoprotein. The shown molecular surface is color-coded as follows: H-bonding areas are white; and hydrophobic and mild polar areas are colored in green and red, respectively. Hydrogen atoms are omitted for clarity. Abbreviation: QZ59-RRR, cyclic-tris-(R)-valineselenazole.

Figure 2

P-gp inhibitor/non-inhibitor clustering of chemical compounds based on the minimal and maximal ΔG values to detect false-positive (A) and false-negative (B) results. Note: Mean value is shown as a bold line. Abbreviations: ΔG, Gibbs free energy of binding; P-gp, P-glycoprotein.

Figure 3

ROC curve of the AutoDock Vina (Scripps Research Institute, San Diego, CA, USA) screening test for the bottom-ranked 150 compounds. Abbreviations: ADVina, AutoDock Vina; ROC, receiver operating characteristic.

Figure 4

Relationship between experimental (pKi_exp) and predicted pKi values using linear regression analysis to measure correlation strength for examined chemical compounds. The predicted values were calculated from the iDOCK runs (Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong) (pKi_pred) and POLSCORE (pKi_POLSCORE) (A and B). The Althotas virtual laboratory webserver (Virtua Drug Ltd, Budapest, Hungary) was used to estimate the inhibition constants (pKi₁–pKi₃) applying the human homology and mouse P-gp models, respectively (C–E). Abbreviations: pKi, negative decimal logarithm of inhibition constant; pred, predicted; POLSCORE, polynomial empirical scoring; P-gp, P-glycoprotein.

Figure 5

Receptor–ligand interaction diagrams for six binding sites of the same protein (murine 3G60 P-glycoprotein) each with a different ligand molecule bond. The ligands are: (A) GF120918, (B) itraconazole, (C) ketoconazole, (D) astemizole, (E) quinidine, and (F) verapamil. The spoked arcs represent protein residues making nonbonded (hydrophobic) contacts with the ligand. The contacted ligand atoms are shown with spokes radiating back. The red ellipses in each plot indicate protein residues that are in common to those associated with the QZ59-RRR ligand. Abbreviation: QZ59-RRR, cyclic-tris-(R)-valineselenazole.