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3D-QSAR AND CONTOUR MAP ANALYSIS OF TARIQUIDAR ANALOGUES AS MULTIDRUG RESISTANCE PROTEIN-1 (MRP1) INHIBITORS

One of the major obstacles to the successful chemotherapy towards several cancers is multidrug resistance of human cancer cells to anti-cancer drugs. An important contributor to multidrug resistance is the human multidrug resistance protein-1 transporter (MRP1), which is an efflux pump of the ABC (ATP binding cassette) superfamily. Thus, highly efficacious, third generation MRP1 inhibitors, like tariquidar analogues, are promising inhibitors of multidrug resistance and are under clinical trials. To maximize the efficacy of MRP1 inhibitors and to reduce systemic toxicity, it is important to limit the exposure of MRP1 inhibitors and anticancer drugs to normal tissues and to increase their co-localization with tumor cells. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) associated with 3D-Quantitiative structureactivity relationship (3D-QSAR) studies were performed on a series of tariquidar analogues, as selective MDR modulators. Best predictability was obtained with CoMFA model r 2 (non-cross-validated square of correlation coefficient) = 0.968, F value = 151.768 with five components, standard error of estimate = 0.107 while the CoMSIA yielded r 2 = 0.982, F value = 60.628 with six components, and standard error of estimate = 0.154. These results indicate that steric, electrostatic, hydrophobic (lipophilic), and hydrogen bond donor substituents play significant roles in multidrug resistance modulation of tariquidar analogues upon MRP1. The tariquidar analogue and MRP1 binding and stability data generated from CoMFA and CoMSIA based 3D-contour maps may further aid in study and design of tariquidar analogues as novel, potent and selective MDR modulator drug candidates. INTRODUCTION: Multidrug resistance and ABC transporters: Multidrug resistance in cancer can significantly hamper the response to chemotherapy and increase the likelihood of mortality 1, 2 . QUICK RESPONSE CODE DOI: 10.13040/IJPSR.0975-8232.7(2).554-72 Article can be accessed online on: www.ijpsr.com DOI link: http://dx.doi.org/10.13040/IJPSR.0975-8232.7 (2).554-72 Multidrug resistance occurs when cancer cells exposed to one anticancer drug show crossresistance to various anticancer drugs that are structurally and functionally different 1 . Intrinsic and acquired MDR has long been recognized as causes of chemotherapeutic failure 3 . Acquired multidrug resistance is highly problematic in cases where drugs respond to chemotherapy in the beginning but lose sensitivity later 4 . Over expression within the cellular membrane of different efflux pumps from various transporter families can lead to multidrug resistance 5 .

Expression of primary active (energy-dependent) ATP-binding cassette (ABC) efflux pumps has been linked to tumor aggressiveness in different tumor types as the drugs are carefully channeled across the biological membrane 1 . ABC transporters are highly expressed in pharmacologically important tissues and translocate a wide variety of solutes across biological membranes 1 . Thus, efflux by ABC transporters serves to detoxify and protect cells from anticancer compounds and have been found to be critical in the absorption, excretion, and distribution of drugs 6 . Elevated expression of ABC transporters and insertion into the bacterial cell membrane decrease the bioavailability of drugs by hindering cellular accumulation below the required standard threshold or by redistributing anticancer drugs away from target organelles, thus conferring MDR ( Fig. 1) 6 . Therefore, ABC transporters are important elements which should be considered when developing anti-cancer drugs; especially when developing specifically targeted MDR-cancer therapy 7 .  Fig. 1 A illustrates the efflux mechanism MRP1 employs to pump anti-cancer drugs out of the cell, thereby imparting drug resistance to cancer cells. In step 1, anti-cancer drug enters the cancer cell through porins. In this case, there is no MRP1 inhibitor in the extracellular space to be taken up by the cancer cell porins. In step 2, because of the absence of MRP1 inhibitor, the efflux mechanism by MRP1 is activated, pumping the anti-cancer drug out of the cancer cell. This leads to decreased concentrations of anti-cancer drug inside the cell, and thus the cancer cell resists the drug and survives. Fig. 1 B illustrates the MRP1 efflux blocking mechanism. In step 1, anti-cancer drug enters the cancer cell through porins. There are tariquidar analogues present in the extracellular space of the cancer cell. These tariquidar analogues will be taken up by the cancer cell through porins. These tariquidar analogues will block the MRP1 efflux pump. In step 2, as the MRP1 efflux pump is blocked, anti-cancer drugs will stay in the cancer cell. Increased concentration of anti-cancer drug inside the cancer cell will aid in the death of the cancer cell. MRP1 blockers can effectively inhibit anti-cancer drug efflux thereby enhancing the efficacy of anti-cancer therapy 8 .
The structure of the MRP1 pump contains 17 transmembrane (TM) helices distributed between three TM membrane spanning domains (MSD) for substrate recognition and transport and two cytosolic nucleotide-binding domains (NBD) for energy generation by ATP hydrolysis (Fig. 2) 3,13,14 . The two NBDs form a common binding site where the energy of ATP is harvested to promote drug efflux through a pore that is delineated by the TM helices [15][16][17] 18,20 .
The MRP1 transporter is expressed in intestine, liver, and kidney cells as well as in the blood brain barrier and regulates the intracellular concentrations of substances by transporting a broad variety of organic anions out of the cell 22,23 . The MRP1 transporter and glutathione conjugates play pivotal roles in mediating drug resistance by modulating pharmacokinetics and altering the bioavailability and toxicity of anticancer compounds, such as anthracyclines, epipodophyllotoxins, vinca alkaloids, camptothecins, vincristine, daunorubicin, taxanes, topoisomerase inhibitors, and antimetabolites [24][25][26] .

Tariquidar analogues to block MRP1 efflux:
Blocking of these MRP1 transporters, which represent significant barriers to chemotherapy, can aid in effective reversal of multidrug resistance in cancer patients 26 . One strategy for the reversal of MRP1transporter-associated chemo-resistance is the combined use of anticancer drugs with efflux modulators or inhibitors that act as chemo sensitizers 26 . Specific binding at the MRP1 active site on cancer cells and related clinical toxicity of currently available MRP1 modulators is uncertain; exploring novel and potent non-toxic modulators with high specificity for cancer cell embedded MRP1 active site is critical 27 . Tariquidar (XR9576) is a MRP1 inhibitor undergoing investigation as an adjuvant against multidrug resistance in cancer ( Fig.3)  Tariquidar non-competitively binds to the MRP1 transporter, thereby inhibiting efflux of anticancer drugs across the membrane showing significant effects on the pharmacokinetics of paclitaxel, doxorubicin, and vincristine ( Fig.1) 8,28 . Previous studies have shown that tariquidar analogues act like MRP1 modulators, thus possibly enhancing the therapeutic potential of anticancer drugs by blocking efflux pumps and overcoming multidrug resistance 8,29 . Inhibition of drug transport across the membrane may result in increased intracellular concentrations of an anticancer drug, thereby augmenting its cytotoxicity 30,31 .
The mechanism of tariquidar analogues acting as MRP1 efflux blockers is delineated in Fig.1 29,[32][33][34] . Because these compounds demonstrate high activity at clinically achievable concentrations, we anticipate that this class of drugs will be a promising and valuable tool for future applications in the fight against cancer and drug resistance 28,[34][35][36][37] .

Molecular modeling, docking, 3D-QSAR and contour map analysis:
Molecular modeling attempts to be the perfect algorithm for fitting flexible molecules into the active sites of corresponding selected proteins 38 . FlexX exploits molecular flexibility of the ligand, and the development of a docking model includes the physicochemical properties of the molecules 39 . Using FlexX to -dock‖ potential drugs gives important insights into their binding mechanisms and makes a focused optimization of the potential drug molecule possible 40 .
3D QSAR analyses generate virtual receptors and determine the quantitative relationships between the biological activity of a set of compounds and their 3D properties via statistical correlation methods 41 . The basic principle behind CoMFA is that changes in binding affinities of ligands are related to changes in shape and strength of noncovalent interaction fields surrounding the molecules, such as steric, electrostatic, hydrophobic, and hydrogen bond accepting or donating fields 42,43 . The CoMFA QSAR equation is summarized graphically as a 3D contour map, showing those fields in which the lattice points are associated with extreme values 44,45 . These contour map values correspond to the molecular fields which are considered crucial for binding affinity 44,46 . CoMSIA is an extension of the CoMFA and involves comparison of molecular similarity, given in terms of similarity indices 47 . In CoMSIA, steric and electrostatic fields along with hydrophobic fields and hydrogen bond donor/acceptor fields are computed 47 .

Computational methods: Molecular modeling:
Homology modeling was adopted in order to construct a three dimensional (3D) structure for human MRP1 protein (NCBI ID: NP_004987). This protein was further employed to perform docking and contour map studies. Swiss-model was employed to thread all the possible templates and to construct the best homology model of MRP1 protein. The crystal structure of the multidrug transporter P-glycoprotein (PDB code: 4F4C; www.pdb.org) was used as a base template to construct the 3D model for MRP1. The quality of the model was analyzed using QMEAN4 (Qualitative Model Energy Analysis 4) and GMQE (Global Model Quality Estimation) values generated by the Swiss-model server.

Geometry optimization:
Twenty six tariquidar analogues reported to be efficient MRP1 blockers were chosen for this study 8 . IC 50 values available in the literature were used to calculate pIC 50 (-log IC 50 ) values for all of the 26 compounds 8 . Among those 26 tariquidar analogues, compound 18 has the least activity (pIC 50 = 5.309804) and compound 20 (pIC 50 = 7.420216) is highly active. Chemical structures of these 26 tariquidar analogues were drawn using ISIS, and were geometrically optimized on SYBYL using default parameters and convergence criterion of 0.001 kcal/mol (Supplementary table 1) 48 . The energy minimization of these 26 compounds was performed viatripos force field and the Gasteiger-Huckel charges using a distance-dependent dielectric and powell conjugate gradient algorithm with a convergence criterion of 0.05 kcal/mol 49,50 . Further geometric optimization of these tariquidar analogues was done using the default set semiempirical program MOPAC 6.0; MOPAC charges were used for entire calculations 51 .

Alignment:
In 3D-QSAR studies, a geometric similarity should exist between the structures, so MOPAC geometry optimized structures were aligned on the most active molecule (determined from the literature) from the set as a template 52 . Doing so adjusts the geometry of the molecules such that the steric and electrostatic fields of the molecules match the fields of the template molecule 52, 53 .

Molecular docking:
Receptor-ligand docking is highly specific and is crucial for many biological functions 54 . In this study FlexX interfaced with SYBYL 6.0 was used to dock tariquidar compounds to the active site of MRP1 efflux pump protein 55 . In SYBYL 6.0 docking model, tariquidar compounds are considered to be the flexible molecules whereas MRP1 protein is predicted to be rigid. In docking automated process, all the new fragments are joined to the base fragment at the active site in every possible angle and conformation 56 . SYBYL 6.0 interfaced with FlexX was used to compute the predictable binding conformations of these 26 inhibitors around an active radius of 6.5Å17 38 . Default FlexX docking parameters were employed to obtain the docking scores. The interactions of 26 tariquidar analogues with MRP1 protein were calculated and the highest scoring compound was considered as the best fit. Energy calculations and structural information was also computed on SYBYL-FlexX.

3D-QSAR-CoMFA and CoMSIA and contour analyses:
Three-dimensional quantitative structure activity relationship (QSAR) studies that include comparative molecular field analysis (CoMFA) and molecular similarity indices in comparative analysis (CoMSIA) methods were conducted on these 26 tariquidar analogues to assess their potential as MRP1 blockers. CoMFA employs tripos force field with a distance-dependent, dielectric constant in all interactions in a regularly spaced (2 × 10 -10 m) grid taking a sp3 carbon atom as steric probe and a +1 charge as electrostatic probe 38,57 . The cut-off was set to 30 Kcal/mol 38,57 . CoMSIA uses a Gaussian-type distance-dependent dielectric constant to minimize changes in atomic positions and charge potentials at the grids 58 .
CoMSIA calculates using a C+ probe atom with a radius of 1 × 10 -10 m placed at a regular grid spacing of 2 × 10 -10 m to enclose all the binding conformations of the inhibitors 59 . Using default parameters, steric, electrostatic, and hydrophobic field parameters were calculated. The steric filed contribution is denoted by the third power of the atomic radii of the atoms and electrostatic properties were given as atomic charges that were obtained from FlexX docking 60 .
Hydrophobicity was calculated as atom dependent parameter and an approximately 4Å lattice grid was used to include all the binding conformations of the inhibitors. In this study, similarity indices were computed using a probe atom (Wprobe,k) with charge +1, radius 1Å, hydrophobicity +1, and attenuation factor, a, of 0.3 for the Gaussian type distance 38 . The statistical analysis for the CoMSIA analyses was similar to CoMFA 61 .
The pIC 50 data will couple 3-log units offering a wide and similar set of data for 3D-QSAR analysis. Compounds were divided into test and training sets in 1:3 ratio to improve the predictability of the 3D-QSAR models. Cross-validation and partial least score (PLS) analyses were used where the crossvalidated coefficient (q 2 ), leaving optimal number of components and lowest standard error of prediction, was considered for the accuracy determination of the predicted models 43,61 .
The reliability of a 3D-QSAR model depends on the activity prediction ability of the model. Pearson's correlation coefficient, r 2 , is the squared correlation coefficient that measures the precision of adjustment for the fitted values to the observed ones 61 . In cross-validation, the outcome of the LOO procedure is a cross-validated correlation coefficient (r 2 , cv or q 2 ) that indicates the robustness and predictive ability of the model 56 .
The cross-validated correlation coefficient, q 2 , is regarded as a measure of internal consistency of the derived model 56 . Fischer statistic (F value) parameter was used as a measure of the level of statistical significance of the regression model; a higher F value implies a more significant correlation 56 .

RESULTS AND DISCUSSION:
Homology modeled protein indicating an effective 3D structural conformation: The 3D structural conformation of the molecular modeled MRP1 protein that was generated using Swissmodel server is shown in Fig. 4. This model was validated based on the statistically significant QMEAN4 value, indicating the current model to be close to the Z-score of the template (PDB ID: 4F4C) from PDB. A higher GMQE value close to 0.5 indicates the greater reliability of the model.

Docking results indicate tariquidar analogues as potential MRP1 modulators because of their interaction with the MRP1 at the active site:
The FlexX dock score and amino acid interactions are considered as the base with which to assess the potential of the tariquidar analogues in blocking the active site of the MRP1 drug efflux pump 11 . This docking study on tariquidar analogues as MRP1 efflux blockers predicted a considerable correlation in the FlexX scores attained. Many polar amino acid residues present in the active site of MRP1 were found to be interacting with the tariquidar analogues in this study ( Table 1).  (Fig. 5) Compound 2 has shown the lowest dock score of -14.8 KJ/ mol with six interactions (Fig. 8).
Compound 20, most active compound, has shown a significant dock score of -23.2 KJ/mol with 11 interactions (Fig.6) and Compound 18, the least active compound, has shown a dock score of -24.6 KJ/ mol with six interactions with the active site of MRP1 protein (Fig. 7). The structure in ball-and-stick model represents compound 9, and the structure in lines represents MRP1 protein. The structure in the ball-and-stick model represents compound 20, and the structure in lines represents the MRP1 protein. The structure in ball-and-stick model represents compound 18, and the structure in lines represents MRP1 protein.

FIG. 8: COMPOUND 2 SHOWING SIX INTERACTIONS (REPRESENTED IN YELLOW LINES) WITH THE ACTIVE SITE OF MRP1 AND HAVING THE LEAST DOCK SCORE OF -14.8 KJ/MOL.
The structure in ball-and-stick model represents compound 2, and the structure in lines represents MRP1 protein.

CoMFA and CoMSIA results agree with the experimental values, signifying tariquidar analogues as MRP1 blockers:
The CoMFA method was used for deriving a 3D-QSAR model for 21 tariquidar compounds, which are reported as multi drug resistance modulators. The molecules were aligned, one over the other, to generate a common-core ring structure. Molecules that do not have a common-core ring were not included in the study. Fig.9 shows the alignment of 26 molecules and the common-core ring. The leave-one-out partial least-squares (PLS) analysis of the obtained model yielded a high, cross-validated q 2 -value of 0.698 (five components) and a non-cross-validated correlation-coefficient, r 2 , of 0.983. This correlation coefficient suggests that our model is reliable and accurate.  The picture shows the aligned structure of all 26 tariquidar analogues and the common core ring present in all those molecules, thus aiding QSAR studies.

CoMFA analysis and statistical validity predict compound 20 to be the most potent and stable MRP1 modulator:
Eleven and seven out of twenty six MRP1 inhibitors total were used in training and test sets, respectively. The compounds in the test set were chosen manually to ensure that the compounds included possess a broad activity range. The steric and electrostatic field descriptors explain 54.0 % and 46.0 % of the variance, respectively (Table 3). Predicted values support the statistical validity of the developed models and correlate with the experimental values, supporting the reliability of predicted CoMFA model ( Table 3). The q 2 -LOO-cross-validated correlation coefficient, r 2 , non-cross-validated correlation coefficient, n-number of components used in the PLS analysis, SEE-standard error estimation, F-statistic for the analysis values shown demonstrate the accuracy and stability of our model.

CoMSIA analysis demonstrates the accuracy of predicted models:
Four major field descriptors: steric, electrostatic, hydrophobic, and hydrogen bond donor fields were used to run the CoMSIA analysis. The CoMSIA analysis demonstrated a cross-validated q 2 of 0.695, a conventional r 2 of 0.968 with a SEE of 0.154, and F value of 60.628 for training set ( Table 3). The steric, the electrostatic, hydrophobic field, hydrogen bond donor, and hydrogen bond acceptor field descriptors explain 12.8 %, 22.7 %, 26.2 %, 0.07 % and, 31.1 % of the variance, respectively ( Table 3). The above results demonstrate that the predicted CoMSIA model is reliable and accurate. These results demonstrate that the CoMFA and CoMSIA models can be reliably used in the design of novel MRP1 inhibitors.
Contour analysis with all the major field descriptors analyzed predict compound 20 to be the most active and stable MRP1 blocker, whereas compound 18 to be the least active and less stable one: Contour map analysis was performed on SYBYL 6.0 to visualize the generated CoMFA and CoMSIA models. During contour map analysis, contour with contribution values of 80% for favored region and 20% for disfavored region were set as the default level.

CoMFA contour maps with steric and electrostatic contours indicate the stability of compound 20 as MRP1 blocker:
Images of CoMFA steric and electrostatic contours with lowest (compound 18) and highest activity (compound 20) compounds are shown in Fig's. 10  to 13. In Fig.10 and 11 of CoMFAsteric interactions in counter maps of tariquidar MDR modulators with lowest (compound 18) and highest activity (compound 20)the green and yellow polyhedrons indicate regions where increased or decreased steric bulk, respectively, are predicted to enhance activity.
In the CoMFA steric contour map of compound 18, a large green contour indicating increased steric bulk was located away from the MRP1 protein whereas in the CoMFA steric contour map of compound 20, this steric bulk was found making interaction with the protein because of the additional functional groups attached ( Fig. 10 and  11). The CoMFA electrostatic counter map of tariquidar MDR modulators with lowest (compound 18) and highest activity (compound 20) are shown in Fig.12 and 13. The red and blue polyhedrons indicate regions of higher electron density with high binding affinity (negative charge) and lower electron density with less affinity of the compounds to bind the protein (partial positive charge), respectively, predicting activity enhancement.
In CoMFA electrostatic contour map, a block in red representing a high electron dense region was located away from the protein whereas in CoMFA electrostatic contour map of compound 20, this highly electron-dense region was found close to the protein MRP1 ( Fig. 12 and 13). This is possibly a reason why compound18 is less potent as a modulator and compound 20, with additional functional groups attached, is more potent. Thus, these CoMFA models demonstrate that the functional groups attached to the modulator increase its activity, thereby making it more potent. The green and yellow polyhedrons indicate regions where increased or decreased steric bulk, respectively, are predicted to enhance activity. The green and yellow polyhedrons indicate regions where increased or decreased steric bulk, respectively, are predicted to enhance activity. The red and blue polyhedrons indicate regions where higher electron density (negative charge) and lower electron density (partial positive charge), respectively, are predicted to enhance activity. The red and blue polyhedrons indicate regions where higher electron density (negative charge) and lower electron density (partial positive charge), respectively, are predicted to enhance activity.
CoMSIA contour maps CoMFA contour maps with steric, electrostatic, hydrophobic, and hydrogen bond donor and acceptor fields indicate compound 20 to be the most active and stable MRP1 blocker: The steric, electrostatic, hydrophobic, and hydrogen bond donor and acceptor fields were used to construct the CoMSIA contours maps (Fig.14-23). The steric and electrostatic contour maps of CoMFA and CoMSIA are almost identical, indicating a similar role. The CoMSIA steric and electrostatic contours maps are shown in Fig.14 (highest activity), 15 (least activity), 16 (highest activity), and 17 (least activity), respectively. In Fig. 15 (showing CoMSIA steric contour map of the least active compound, compound 18) steric bulk was found masking functional groups whereas in Fig. 14(showing CoMSIA steric contour map of modulator with highest activity, compound 20), decreased steric bulk close to the modified functional group is demonstrated to be the reason for increase in the activity.
In Fig.15 (showing CoMSIA electrostatic contour map of least active compound, compound 18) electron density is high, indicating least activity. Electron density is low in Fig.16 (CoMSIA electrostatic contour map of modulator with highest activity, compound 20) demonstrating that the decreased electron density enhances the activity of the molecule. The green and yellow polyhedrons indicate regions where increased or decreased steric bulk, respectively, are predicted to enhance activity. The green and yellow polyhedrons indicate regions where increased or decreased steric bulk, respectively, are predicted to enhance activity. The red and blue polyhedrons indicate regions where higher electron density (negative charge) and lower electron density (partial positive charge), respectively, are predicted to enhance activity. The red and blue polyhedrons indicate regions where higher electron density (negative charge) and lower electron density (partial positive charge), respectively, are predicted to enhance activity. The CoMSIA hydrophobic counter maps of tariquidar MDR modulators having lowest (compound 18) and highest activity (compound 20) are shown in Fig.19 and 18. The favourable hydrophobic region is represented by white contours, and unfavourable regions are represented by yellow contours. In Fig.  19, CoMSIA hydrophobic contour map of compound 18, yellow contours representing unfavourable regions are seen, indicating the least activity of molecule.
In Fig.18, CoMSIA hydrophobic contour map of compound 20, hydrophobicity is found masking the functional groups thereby making the molecule more potent. This indicates that the hydrophobicity will favour the binding of modulator with MRP1 thereby enhancing the activity of compound 20 and hindering the binding of compound 18 to MRP1. The favourable hydrophobic region is represented by white contours and unfavourable regions are represented by yellow contours. The CoMSIA hydrogen bond donor counter maps of tariquidar MDR modulators having lowest (compound 18) and highest activity (compound 20) are shown in Fig. 21 and 20. Cyan polyhedron indicates a hydrogen bond donor group in the ligand that favors biological activity, and purple polyhedrons represent hydrogen bond acceptors in the ligand that are unfavorable for bio-activity ( Fig. 20 and  21). In the CoMSIA hydrogen bond donor counter map of compound 20, the hydrogen bond donor in cyan is present close to the functional group, and the hydrogen bond acceptor in blue that is not favorable for the activity is present away from the functional group (Fig. 20). This indicates that the activity of molecule 20 is enhanced by the presence of a hydrogen bond donor that favors binding of the modulator to MRP1. Cyan polyhedron beyond the ligands where a hydrogen bond donor group in the ligand will be favorable for biological activity, and the purple polyhedron represents hydrogen bond acceptor in the ligands unfavorable for bioactivity. Cyan polyhedron beyond the ligands where a hydrogen bond donor group in the ligand will be favorable for biological activity, and the purple polyhedron represents hydrogen bond acceptor in the ligands unfavorable for bioactivity. The CoMSIA active hydrogen bond acceptor counter maps of tariquidar MDR modulators showing lowest (compound 18) and highest activity (compound 20) are shown in Fig. 23 and 22. Red polyhedrons indicate a hydrogen bond donor group in the ligand that will be favorable for biological activity, and purple polyhedrons represent a hydrogen bond acceptor in the ligand that is unfavorable for bioactivity ( Fig.22 and 23). In Fig.22, the purple polyhedron representing a hydrogen bond acceptor is found interacting with the functional group of compound 20 unlike that in Fig.23 for compound 18. This suggests that the hydrogen bond acceptor and donor presence close to the functional group will determine the activity of the compound. Red polyhedron beyond the ligands where a hydrogen bond donor group in the ligand will be favorable for biological activity, and the purple polyhedron represents hydrogen bond acceptor in the ligands unfavorable for bioactivity. Red polyhedron beyond the ligands where a hydrogen bond donor group in the ligand will be favorable for biological activity, and the purple polyhedron represents hydrogen bond acceptor in the ligands unfavorable for bioactivity.

DISCUSSION AND CONCLUSIONS:
In silico analysis and 3D-QSAR studies on efflux blockers are important tools in the fight against drug resistance in anti-cancer treatment 21 . Contour map analysis and docking studies on MRP1 inhibitors are critical as the MRP1 efflux pump is a major factor behind the failure of anti-cancer drug therapies 8 . The tariquidar analogues tested as MRP1 efflux inhibitors in the current study demonstrate the mechanism of inhibitor interacting with the active site of MRP1, binding it tightly; thus making the cell unable to pump anti-cancer drugs out using the blocked MRP1 efflux pumps. A receptor-independent 3D-QSAR has been established for tariquidar analogues employing the most widely used techniques CoMFA and CoMSIA. This work highlights the importance of ligand orientation and selection of the training set molecules in the development of statistically significant QSAR models.
Interestingly, the CoMSIA models provided better statistical models than CoMFA, which points to the significance of hydrogen bond donor and hydrophobic fields in the selectivity and activity of these ligands in addition to steric and electrostatic fields. The statistical significance and robustness of the generated 3D-QSAR models were confirmed using an external set of molecules. The structural requirements identified in the present study can be utilized strategically in the design of novel, potent, and unique tariquidar analogue compounds with multidrug resistance modulation activities 62 .
Even though the dock score of compound 20 is not the highest (the highest dock score was compound 9 with -31.4 KJ/mol) among all the 26 compounds tested in the study, from the FlexX docking interactions and contour map analysis it can be predicted that the amino acid interactions and force fields of compound 20 with MRP1 are critical for rendering this compound potent 16 . Based on the interactions of compound 20 with the active site of MRP1 and force-field interactions we predict that compound 20 can be effectively used in biological systems as a MRP1 inhibitor as it will be potent and stable. Compound 18, being the least active compound, has shown a FlexX dock score of -24.6 KJ/mol with six interactions. Even though the dock score of compound 18 is considerably high, it can be predicted that this compound is not stable (based on contour map analysis) and that the interactions with the active site of MRP1 (six interactions) are not strong enough to block the pump. Therefore, from molecular simulated docking studies and from contour map analysis together, we predict that compound 20 can be demonstrated as an effective MRP1 efflux pump blocker because of its stability in the biological environment and its potency to interact with and block MRP1 pump. Quantitative structure activity relationship study (3D-QSAR) results indicate a better fit between MRP1 and the tariquidar efflux modulator analogues. The CoMFA and CoMSIA studies, PLS statistical analysis, and contour map analysis support the accuracy of predicted and docked models. From our docking simulation and contour map analysis, we predicted which tariquidar compounds out of 26 different analogues might be most effective as MRP1 efflux inhibitors. We believe that by using the methodology of this study and the predictive models therein as a base, other potential efflux pump inhibitors might be discovered. Additionally, we expect that future investigations into the tariquidar analogues identified in this study will yield innovate and effective MRP1 blockers, thereby enhancing the efficacy of anti-cancer therapy.

ACKNOWLEDGEMENTS:
This work was made possible by the NM-INBRE program grant P20GM103451 from the National Center for Research Resources. This publication was also supported by an internal research grant awarded by the Department of Biology, Eastern New Mexico University, Portales, New Mexico, USA.