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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Mol Cancer Ther. Author manuscript; available in PMC 2012 March 1.
Published in final edited form as:
PMCID: PMC3063525
NIHMSID: NIHMS262373

Interactions of Tyrosine Kinase Inhibitors with Organic Cation Transporters, OCTs, and Multidrug and Toxic Compound Extrusion Proteins, MATEs

Abstract

The drug-drug interaction (DDI) potential of tyrosine kinase inhibitors (TKIs) as interacting drugs via transporter inhibition has not been fully assessed. Here, we estimated the half maximal inhibitory concentration (IC50) values for eight small-molecule TKIs (imatinib, dasatinib, nilotinib, gefitinib, erlotinib, sunitinib, lapatinib, and sorafenib) on [14C]metformin transport by human organic cation transporters (OCT1, OCT2, and OCT3), and multidrug and toxic compound extrusion proteins (MATE1 and MATE2-K), using HEK293 cells stably expressing these transporters. We then compared the estimated IC50 values to the maximum clinical concentrations of unbound TKIs in plasma (unbound Cmax,sys,p). Results showed that imatinib, nilotinib, gefitinib, and erlotinib exerted selectively potent inhibitory effects, with unbound Cmax,sys,p/IC50 values ≥ 0.1, on MATE1, OCT3, MATE2-K and OCT1, respectively. In comparison to the common form of OCT1, the OCT1 polymorphism, M420del was more sensitive to drug inhibition by erlotinib. Major metabolites of several TKIs showed IC50 values similar to those for unchanged TKIs. Taken together, these findings suggest the potential of clinical transporter-mediated DDIs between specific TKIs and OCTs and MATEs, which may affect the disposition, efficacy and toxicity of metformin and other drugs that are substrates of these transporters. The study provides the basis for further clinical DDI studies with TKIs.

Introduction

Accumulating evidence highlights the importance of drug transporters in drug disposition, efficacy, and safety, particularly with respect to drug-drug interactions (DDIs) (1). A recent review has underscored the need for drug development programs to perform relevant clinical studies of transporter-based DDIs as suggested from in vitro studies of drug-transporter interactions (1). In terms of transporter-based DDIs, an interacting drug may be an inhibitor that causes a DDI by inhibition of a transporter's function, or may be a substrate of the transporter. Methodologies and algorithms for predicting transporter-mediated DDIs based on in vitro experiments are under discussion (1). A comparison of the concentration of an inhibitor ([I]) (generally, the maximum unbound plasma concentration) and its half maximal inhibitory concentration (IC50) for a transporter as determined through in vitro studies is performed to determine whether a potential clinical DDI may occur.(1). Obviously, high unbound plasma concentrations relative to the IC50 of the drug is an indicator of a potential clinical DDI. To err on the side of caution, an I/IC50 value ≥0.1 has been suggested as a criterion to use for consideration of conducting a clinical transporter-based DDI (1).

Tyrosine kinase inhibitors (TKIs) (Supplementary Figure S1), a new class of anti-cancer drugs, are rationally designed to target specific tyrosine kinases that are fused, mutated or over-expressed in cancer (2). In spite of their increasing use, only a few studies have examined the interactions of TKIs with influx transporters and to our knowledge there are no known influx transporter-based clinical DDI between TKIs and other drugs on the market. Most studies examining the interaction of TKIs with influx transporters have focused on substrate rather than inhibition interactions (3-10). Moreover, several TKIs have cationic charge and high lipophilicity (Supplementary Table S1), which are requirements of organic cation transporter, OCT inhibition (11), and therefore have potential to inhibit organic cation transporters, including OCT1 (SLC22A1), OCT2 (SLC22A2), OCT3 (SLC22A3), and MATE1 (SLC47A1) and MATE2-K (SLC47A2). However, to our knowledge, there have been no studies assessing the clinical DDI potential of TKIs.

Drugs from multiple therapeutic classes that are transported by organic cation transporters may be concomitantly used with TKIs. Metformin is recommended as the initial pharmacologic therapy for type 2 diabetes, which is one of the most common diseases in the world. Diabetes and cancer are common conditions, and their co-diagnosis in the same individual is not infrequent (12). In humans, OCT1 on the basolateral membrane of hepatocytes is involved in the uptake of metformin from the blood into the liver, which is its major site of pharmacologic action (13-14). Similarly, OCT2 on the basolateral membrane of renal tubular cells mediates the uptake of metformin from blood into the kidney, which is the major eliminating organ for the drug (15). Recently, MATE1 and MATE2-K on the apical membrane of renal tubular cells have been proposed to act as the final efflux transporters of metformin from the body, namely from renal cells to the urine (16). Several clinical DDIs between metformin and inhibitors of organic cation transporters, such as cimetidine, have been reported (1). Metformin, a typical substrate of OCTs and MATEs, has been extensively studied. Data obtained for metformin may be applicable to other substrates of OCTs and MATEs. Interestingly, metformin has recently been considered a potential candidate in novel treatment strategies for human pancreatic cancer (17) and in combination with lapatinib-based protocols in the treatment of breast cancer (18). Clinical studies of combination therapies of gemcitabine, erlotinib and metformin in pancreatic cancer and of lapatinib in combination with metformin for various cancers are currently underway (Study ID NCT01210911 and NCT01087983 in http://clinicaltrials.gov/). Recently, a clinical case report regarding lactic acidosis induced by a DDI of erlotinib, a TKI, and metformin has been published, but lacks any mention of transporters (19). Because lactic acidosis associated with metformin treatment is a rare but potentially fatal adverse event, this clinical case report underscores the importance of the safe use of metformin in cancer patients. Given that drug transporters are a determinant of metformin pharmacokinetics and pharmacodynamics, possible risks of concomitant use of metformin and TKIs in terms of drug transporters need to be examined. Oxaliplatin, an anti-cancer platinum analog, is a substrate of organic cation transporters including OCT1, OCT2 and OCT3, which are thought to play a role in its anti-cancer effects by facilitating its entry into tumor cells (20-21). Currently, TKIs are being tested in clinical trials in combination with oxaliplatin for the treatment of various tumors (22-25). Moreover, many trials involving oxaliplatin with TKIs are ongoing or completed (http://clinicaltrial.gov).

Here, we conducted in vitro studies comparing the DDI potential of TKIs on the transport of metformin by OCT1, OCT2, OCT3, MATE1, and MATE2-K to obtain a rationale for clinical DDI studies. We attempted to thoroughly assess the in vitro inhibitory effects of eight small-molecule TKIs (imatinib, dasatinib, nilotinib, gefitinib, erlotinib, sunitinib, lapatinib, and sorafenib; Supplementary Figure S1) on transport of metformin by the transporters. For the TKIs that potentially cause clinical DDIs, time-dependent inhibition, genetic variant-mediated alteration of inhibition potency, and inhibition by major metabolites of the drugs were also investigated. Inter-species differences in the transporter inhibition by TKIs were also examined in vitro to evaluate the utility of mice as possible surrogate in vivo models for assessing clinically relevant transporter-mediated DDI. Relationships between transporter inhibition and physicochemical properties of the TKIs were also determined. In addition, we examined the DDI potential of TKIs with oxaliplatin that may be used in combination with TKIs, and also an important substrate of OCTs and MATEs.

Materials and Methods

Drugs and reagents

Imatinib, dasatinib, nilotinib, gefitinib, erlotinib, sunitinib, lapatinib, sorafenib, N-desmethyl imatinib, O-desmethyl gefitinib, and O-desmethyl erlotinib were purchased from Toronto Research Chemical (Toronto, Canada). [14C]Metformin was purchased from Moravek Biochemicals and Radiochemicals (Brea, CA, USA). Metformin and oxaliplatin were purchased from Sigma-Aldrich (St. Louis, MO, USA). Other reagents and chemicals were purchased from commercial sources.

Cell lines and transfection

Human embryonic kidney (HEK293) Flp-In-293 cells stably transfected with the full-length reference human OCT1 cDNA (reference sequence, NM_003057; HEK-hOCT1), OCT2 cDNA (HEK-hOCT2), OCT3 cDNA (HEK-hOCT3), MATE1 cDNA (HEK-hMATE1), mutant OCT1 M420del cDNA inserts, and the empty vector (HEK-MOCK) were established previously in our laboratory (13, 21, 26-27). Stable cell lines expressing recombinant MATE2-K (HEK-hMATE2-K), and mouse Oct1 (HEK-mOct1) were also established previously in our laboratory (unpublished reports). HEK293 cells stably transfected with the full-length reference mouse Oct2 cDNA (HEK-mOct2), Oct3 cDNA (HEK-mOct3), and Mate1 cDNA (HEK-mMate1) were established by transfection of pcDNA5/FRT vector (Invitrogen, Carlsbad, CA, USA) containing the full-length transporter cDNA into HEK293 Flp-In-293 cells using Lipofectamine 2000 (Invitrogen) as per the manufacturer's instructions. Stable clones were selected using 75 μg/mL hygromycin B. The general characteristics of all transfected HEK293 cell lines were monitored by checking cell morphology microscopically, and measuring the doubling time of each cell line. The expression level of each transporter mRNA in each transfected HEK293 cell line was verified by real-time RT-PCR method. The transport of metformin and oxaliplatin in transporter-transfected HEK293 cells was compared to that in HEK-MOCK in every transport assay. Authentication of HEK293 Flp-In-293 cells was not performed by the authors.

Cell culture

Stably transfected HEK293 cells were maintained in Dulbecco's modified eagle medium (DMEM) H-21 medium supplemented with 10% fetal bovine serum, 100 units/mL penicillin, 100 units/mL streptomycin, and 75 μg/mL hygromycin B. All cell lines were grown at 37 °C in a humidified atmosphere with 5% CO2/95% air.

Metformin cellular uptake study

The uptake study was performed as described in our previous report with slight modifications (28). Briefly, HEK293 cells were grown on poly-D-lysine-coated 24-well plates in DMEM H-21 medium supplemented with 10% fetal bovine serum to at least 90% confluence (24 h after seeding). For uptake studies, HEK-MOCK, HEK-hOCT1, HEK-hOCT2, HEK-hOCT3, HEK-mOct1, HEK-mOct2, and HEK-mOct3 were preincubated before the addition of the uptake buffer for 20 min with Hank's balanced salt solution (HBSS; 5.4 mM potassium chloride, 0.44 mM monobasic potassium phosphate, 4.2 mM sodium bicarbonate, 137 mM sodium chloride, 0.34 mM dibasic sodium phosphate, 5.6 mM D-glucose, 1.3 mM calcium chloride, 0.49 mM magnesium chloride, 0.41 mM magnesium sulfate, pH 7.4), and then incubated in the uptake buffer (HBSS containing 10 μM unlabeled and [14C]-labeled metformin). Uptake was performed at 37 °C for the designated period of time for which linear uptake was observed, and then the cells were washed twice with ice-cold HBSS. The cells were lysed with 0.1 N NaOH and 0.1% SDS, and the lysate was used for scintillation counting (Beckman Coulter, Brea, CA, USA) and for the bicinchoninic acid protein assay (Pierce, Rockford, IL, USA). For uptake by MATE, HEK-MOCK, HEK-hMATE1, HEK-hMATE2-K, and HEK-mMate1, cells were preincubated before the uptake study with HBSS (pH 7.4) in the presence of 30 mM ammonium chloride for 20 min (16). The preincubation medium was then removed, and the cells were incubated with uptake medium containing [14C]metformin in the same manner as for OCTs.

Cellular accumulation of platinum

The cellular accumulation of platinum was determined after 2-h exposure to oxaliplatin as previously described (21), with slight modification. Briefly, HBSS (pH 7.4) was used as the uptake buffer, and the platinum content in the cells was determined by inductively coupled plasma mass spectrometry in the Analytical Facility at University of California, Santa Cruz. Cellular platinum accumulation was normalized to the protein content determined via bicinchoninic acid protein assay.

Data analysis

Determination of IC50 values

Half-maximal inhibitory concentration (IC50) values were calculated by simultaneously fitting the data in transporter-expressing cells and mock cells, without a weighting factor, to the following equations (29) using WinNonlin (Mountain View, CA, USA):

equation M1

where CLuptake is the uptake clearance (μL/min/mg protein) in the absence of inhibitor, CLuptake,+inh is that in the presence of inhibitor, [I] is the inhibitor concentration, and Pdif is the component of passive diffusion (μL/min/mg protien). Given that the substrate concentration in the inhibition study was low compared with the Michaelis-Menten constant Km, the IC50 values are virtually equal to the inhibition constant Ki values, assuming competitive or non-competitive inhibition (1). Each set of [I] consists of zero and 6 concentrations, so that each IC50 value was within the concentration range used in the studies.

Clinical concentrations of TKIs

Maximum clinical concentrations of unbound TKIs in systemic plasma (Cmax,sys,p) and unbound fraction in plasma were obtained from the literature (30-38) (Supplementary Table S1). Absorption rate constants (ka) were calculated using the reported time to reach Cmax,sys,p (Tmax) and half life (t1/2) (30), and the following equation (39):

equation M2

where kel is the elimination rate constant, assuming a monoexponential decline of plasma levels of drug with first order absorption and ka>kel. If ka was calculated to be greater than 0.1/min, then it was fixed at 0.1/min (39). Clinical maximum concentration of unbound TKI in portal plasma (unbound Cmax,portal,p) was calculated using the following equation (39):

equation M3

where fu,p is the unbound fraction in plasma, Rb is the blood-to-plasma concentration ratio, Fa and Fg are bioavailability via the first-pass effect in absorption and intestinal metabolism, respectively, and Qh is hepatic blood flow rate (1500 mL/min) (1). Fa, Fg, and Rb were assumed to be 1 in the present study.

Relationship between physicochemical properties of TKIs and transporter inhibition potency

Charge at pH 7.4, and lipophilicity parameters (calculated octanol-water partition coefficient, ClogP, and calculated octanol-water distribution coefficient at pH 7.4, ClogDpH7.4) of TKIs were calculated using the software Marvin View (ChemAxon Kft., Budapest, Hungary) (Supplementary Table S1). Other physicochemical parameters, H-bond donor count, H-bond acceptor count, rotatable bond count, tautomer count, molecular weight, topological polar surface area, heavy atom count, and complexity, were obtained from PubChem (http://pubchem.ncbi.nlm.nih.gov). PubChem is an open repository for experimental data identifying the biological activities of small molecules, which includes computed properties of the compound record (40). The IC50 values of TKIs in inhibiting metformin uptake via human OCTs and MATEs (Supplementary Table S2) were expressed in mol/L, transformed into logarithm, and then multiplied by -1, i.e., negative log10(IC50) values, so that higher values equate to higher inhibitory effects. In cases in which the IC50 value was >30 μM, negative log10(IC50) was assumed to be −log10(3×10-6). Pearson's correlation coefficient (R) and P value (two-tailed) between physicochemical parameters and negative log10(IC50) values were calculated using GraphPad Prism (GraphPad Software, Inc., La Jolla, CA, USA), after confirmation of normality of data distribution by D'Agostino and Pearson omnibus normality test.

Results

TKIs, at clinically relevant concentrations, inhibit the transport of metformin by OCTs and MATEs

The IC50 values of the seven TKIs against, OCT1, OCT2, OCT3, MATE1 and MATE2-K are shown in Supplementary Table S2. Of note, most of the TKIs including imatinib, dasatinib, nilotinib, gefitinib and sunitinib were potent inhibitors of the transporters, with IC50 values generally in the low micromolar range. Lapatinib and sorafenib, in contrast, were poor inhibitors of [14C]metformin uptake by the transporters. Comparison of the unbound Cmax,sys,p (Supplementary Table S1) and IC50 values of TKIs against human OCT1, OCT2, OCT3, MATE1, and MATE2-K (Supplementary Table S2) showed that imatinib (for OCT1, hMATE1, and MATE2K), nilotinib (for OCT3), gefitinib (for MATE2K) and erlotinib (for OCT1, and MATE2K) exerted potent inhibitory effects at clinically relevant concentrations, with [I]/IC50 ≥ 0.1 using unbound Cmax,sys,p as [I] (Figure 1). If unbound maximum concentrations in the portal vein, Cmax,portal,p (Supplementary Table S1) were used as [I] for the liver transporter (hOCT1), several of the TKIs (imatinib, dasatinib, gefitinib, and sunitinib) were predicted to inhibit metformin uptake into the liver at concentrations in the hepatic sinuosoids that may be observed after oral doses of the drugs.

Figure 1
Unbound [I]/IC50 values for tyrosine kinase inhibitors as inhibitors of human OCT1, OCT2, OCT3, MATE1, and MATE2-K

Other factors such as genetic polymorphisms and pre-incubation modulate inhibition potencies of TKIs

Because erlotinib was a potent inhibitor of OCT1 at clinically relevant concentrations, we studied its potency as an inhibitor of a common polymorphism of OCT1, M420del, which has reduced function with respect to metformin uptake. Our findings showed that erlotinib exerted a more potent inhibitory effect on the polymorphism than it did on the reference OCT1 (Figure 2). Further, erlotinib showed an even more potent inhibitory effect on metformin transport by the reference OCT1 after pre-incubation with cells before initiating the uptake reaction (Figure 3).

Figure 2
Inhibitory effects of erlotinib on the uptake of [14C]metformin (10 μM for 5 min) by human OCT1 reference and variant (M420del)
Figure 3
The effect of preincubation on the inhibitory effects of erlotinib on the uptake of [14C]metformin (10 μM for 5 min) by human OCT1

Major metabolites of TKIs inhibit the transport of metformin by OCTs and MATEs

N-desmethyl imatinib and O-desmethyl gefitinib, which are major metabolites of imatinib and gefitinib, respectively, showed inhibition properties similar to those of imatinib (potent and selective inhibition of MATE1) and gefitinib (potent and selective inhibition of MATE2-K) (Supplementary Table S3). In contrast, O-desmethyl erlotinib, a major metabolite of erlotinib, was not a potent inhibitor of OCT1 (Supplementary Table S3) as was the parent compound (Supplementary Table S2).

Great inter-species differences in inhibition potencies of imatinib, nilotinib, and erlotinib for human and mouse OCTs and MATEs are observed

We noted large differences in IC50 values and selectivity of inhibition of TKIs between human and mouse transporter orthologs (Figure 4). In particular, imatinib, nilotinib, and erlotinib exerted less potent inhibition of mouse transporters mMate1, mOct3 and mOct1, respectively, in comparison to the human orthologs.

Figure 4
Interspecies differences in the inhibition potencies (IC50 values) for imatinib, nilotinib, and erlotinib between human and mouse orthologs of OCT1-3, and MATE1

Cellular uptake of oxaliplatin in the presence of erlotinib and nilotinib

To determine whether erlotinib and nilotinib would inhibit oxaliplatin uptake at concentrations in the range of clinically achieved unbound concentrations in plasma, we tested 2 μM as the highest concentration. Cellular platinum uptake after incubation of HEK-hOCT1, -hOCT2, and -hOCT3 with oxaliplatin (2 μM for 2 h) was decreased in the presence of erlotinib and nilotinib in a concentration-dependent manner (Figure 5). Erlotinib and nilotinib showed potent uptake inhibition of HEK-hOCT1 and HEK-hOCT3, respectively, between transporter-expressing HEK cells.

Figure 5
Cellular platinum uptake into HEK-hOCT1, -hOCT2, and -hOCT3 after 2-h incubation with oxaliplatin (2 μM) in the absence and presence of erlotinib and nilotinib

Relationship between physicochemical properties of TKIs and transporter inhibition potency

Lipophilicity parameters, ClogP and ClogDpH7.4, correlated negatively with negative log10(IC50) of TKIs for human OCT1 and OCT2 (Figure 6). With regard to human MATE1, the charge at pH7.4 correlated positively with negative log10(IC50) values (Figure 6). For OCT3 and MATE2-K, lipophilicity or charge did not significantly correlate with negative log10(IC50) (data not shown). In addition, for all tested transporters, there was no significant correlation between negative log10(IC50) values and the parametes of H-bond donor count, H-bond acceptor count, rotatable bond count, tautomer count, molecular weight, topological polar surface area, heavy atom count, and complexity (data not shown).

Figure 6
Correlation between physicochemical properties of TKIs and negative log10(IC50) for human OCT1, OCT2 and MATE1

Discussion

Here, we investigated the possibility of clinical DDIs via inhibitory effects of eight small-molecule TKIs on the transport activity of human OCT1, OCT2, OCT3, MATE1, and MATE2-K. Our results showed that imatinib, nilotinib, gefitinib and erlotinib exerted selectively potent inhibitory effects with unbound Cmax,sys,p/IC50 ≥ 0.1 on MATE1, OCT3, MATE2-K and OCT1, respectively. In particular, unbound Cmax,sys,p/IC50 was greater than 1 for the inhibition of MATE1 by imatinib and of OCT1 by erlotinib, suggesting a high probability of a DDI if the compounds are administered with metformin.

Because drug concentrations at the inlet of the liver after oral administration are generally expected to be higher than in the systemic circulation (39), actual risks of DDI may be underestimated by unbound Cmax,sys,p/IC50. Therefore, unbound Cmax,portal,p values (Supplementary Table S1) (39) may be used as [I] to predict potential DDIs with transporters in the liver (hOCT1) to avoid false-negative predictions. Prevalence of type 2 diabetes ranges from 2%-10% in industrialized countries, with rates on the rise (41). Because cancer and type 2 diabetes are two common diseases that may share risk factors (41), many present and future patients will likely undergo concomitant treatment with TKIs and metformin. Importantly, because metformin appears to have some efficacy in cancer management, clinical studies with TKIs and metformin are ongoing. Though relatively safe, at high concentrations, metformin can cause lactic acidosis, a life-threatening adverse reaction. DDIs with TKIs may result in increased levels of metformin and therefore, increase patient susceptibility to lactic acidosis. Such DDIs may be particularly problematic in patients with widespread metastases who are at increased risk for lactic acidosis. Our study strongly supports the need for clinical DDI studies between metformin and TKIs.

Methods used in the in vitro assessment of drug transporter interactions are important to obtain accurate predictions of potential clinical DDIs. The current standard in vitro method of evaluating a drug as an inhibitor of an uptake transporter involves simultaneous addition of the test drug inhibitor and model substrate and determination of uptake of the substrate. In real clinical situations, however, a drug may be metabolized into several distinct chemical species, and the transporter may be exposed to the inhibitor drug for a long period of time before a substrate is administered (28). We found that preincubation of erlotinib with the cells before initiation of metformin uptake enhanced the inhibitory effects of erlotinib on OCT1 (Figure 3). This suggest that the interaction of erlotinib with the lipophilic and negatively charged cell membrane, which is supposed to be crucial step in achieving OCT1 inhibition (11), or other steps required for erlotinib to reach its binding domain on OCT1 may be slow. We also observed that the inhibition potency of erlotonib was increased for the M420del (Figure 2), a common polymorphism of OCT1 found at allele frequencies of about 20% in white Americans (13). Our data are consistent with a previous study showing that M420del is more sensitive to drug inhibition by glibenclamine, simvastatin, and verapamil, with IC50 values up to eight times lower for the variant than those observed for the reference OCT1 (28).

Major metabolites of TKIs showed IC50 values similar to those for unchanged TKIs. Plasma concentrations of O-desmethyl gefitinib in CYP2D6 extensive metabolizers are higher than for unchanged gefitinib (42). Therefore, O-desmethyl gefitinib may contribute to potential clinical DDIs that may occur following concomitant metformin and gefitinib administration. Given that plasma concentrations of N-desmethyl imatinib are approximately 10-fold lower than those of the unchanged imatinib (43), and the unbound fractions of N-desmethyl imatinib and imatinib are nearly equal (44), the inhibitory effects of N-desmethyl imatinib on the tested transporters may be negligible at therapeutic doses of imatinib. O-desmethyl erlotinib is a 10-fold weaker inhibitor of OCT1 than erlotinib (Supplementary Table S3), and the metabolite concentration in plasma is more than 10-fold lower than that of the unchanged erlotinib (45), suggesting little inhibitory effects at normal doses.

To determine whether mice could be used as animal models to study transporter based human DDIs between TKIs and OCTs and MATEs, we investigated the inhibition potency of TKIs for mouse orthologs of OCTs and MATEs. Our data showed striking species differences in the interaction of erlotinib, nilotinib, and imatinib, with mouse orthologs of OCT1, OCT3 and MATE1, respectively in comparison to the human orthologs (Figure 4). All three compounds had weaker interactions with the mouse transporters in comparison to the human orthologs. This finding suggests that mice are unlikely to be suitable animal models for in vivo DDI studies of TKIs and OCTs/MATEs, The structural basis for the reduced potency of interaction of the TKIs with the mouse orthologs of the transporters is not known and needs investigation. Because high resolution crystal structures of OCTs and MATEs are not available for construction of molecular models, it is difficult to explain the structural basis for the striking interspecies differences that were observed in the interaction of TKIs with OCTs and MATEs.

In this study, we observed that the cellular uptake of platinum was reduced after oxaliplatin treatment in the presence of clinically relevant concentrations of erlotinib and nilotinib (Figure 5). These experiments suggest that combination therapy involving oxaliplatin and these TKIs may not be optimal, particularly for tumors in which OCTs are involved in the uptake of oxaliplatin. Co-administration of erlotonib or nilotinib may reduce the anti-cancer effects of oxaliplatin by inhibiting the drug's uptake into such tumors. Nilotinib appears to be a more potent inhibitor of oxalplatin transport by hOCT1, hOCT2 and hOCT3 than of metformin transport. This may suggest that the inhibition potency of TKIs on OCTs may be different depending on the substrate and/or that experimental condition such as incubation time may affect the results of in vitro assessment.

Negative correlations between lipophilicity (ClogP and ClogDpH7.4) and negative log10(IC50) of the TKIs for human OCT1 and OCT2 (Figure 6) suggest that excessive lipophilicity weakens the inhibitory effects of TKIs on metformin uptake by OCT1 and OCT2. These data are not in agreement with studies demonstrating that high lipophilicity is one of the key physicochemical properties for OCT1 inhibition (11). Excessive lipophilicity may make it difficult for a drug to access the substrate binding region of OCT1 that is in contact with the aqueous phase (46). ClogP values of the tested TKIs (Supplementary Table S1) met the minimum requirement of lipophilicity needed for OCT1 inhibition (11). Our study also suggests that positive charge may be one of key factors for MATE1 inhibition (Figure 6). Because the number of tested compounds is small, further studies are needed for accurate drug-structure-based prediction of transporter inhibitory effects.

In the present study, we investigated the possibility of clinical DDIs in response to the inhibitory effects of eight small-molecule TKIs on the transport activity of human OCT1, OCT2, OCT3, MATE1, and MATE2-K. Results showed the potential for clinical DDIs when certain TKIs were used concomitantly with drugs that are transported by organic cation transporters, such as metformin or oxaliplatin. The results of this study provide the basis for further clinical studies investigating the transporter-based DDI potential of TKIs.

Supplementary Material

Acknowledgments

Financial support: This work was supported by National Institutes of Health (NIH) Grants GM36780 and GM074929

Abbreviations list

ClogDpH7.4
calculated octanol-water distribution coefficient at pH 7.4
ClogP
calculated octanol-water partition coefficient
Cmax portal,p
maximum clinical concentration of TKI in portal plasma
Cmax sys,p
maximum clinical concentrations of TKIs in plasma
DDI
drug-drug interaction
[I]
concentration of inhibitor
IC50
half maximal inhibitory concentration
HBSS
Hank's balanced salt solution
MATE
multidrug and toxic compound extrusion proteins
OCT
organic cation transporter
TKI
tyrosine kinase inhibitor

Footnotes

Potential conflicts of interest: No potential conflicts of interest

References

1. Giacomini KM, Huang SM, Tweedie DJ, et al. Membrane transporters in drug development. Nat Rev Drug Discov. 2010;9:215–36. [PMC free article] [PubMed]
2. Hartmann JT, Haap M, Kopp HG, Lipp HP. Tyrosine kinase inhibitors - a review on pharmacology, metabolism and side effects. Curr Drug Metab. 2009;10:470–81. [PubMed]
3. Hu S, Chen Z, Franke R, et al. Interaction of the multikinase inhibitors sorafenib and sunitinib with solute carriers and ATP-binding cassette transporters. Clin Cancer Res. 2009;15:6062–9. [PMC free article] [PubMed]
4. Thomas J, Wang L, Clark RE, Pirmohamed M. Active transport of imatinib into and out of cells: implications for drug resistance. Blood. 2004;104:3739–45. [PubMed]
5. Davies A, Jordanides NE, Giannoudis A, et al. Nilotinib concentration in cell lines and primary CD34(+) chronic myeloid leukemia cells is not mediated by active uptake or efflux by major drug transporters. Leukemia. 2009;23:1999–2006. [PubMed]
6. Hu S, Franke RM, Filipski KK, et al. Interaction of imatinib with human organic ion carriers. Clin Cancer Res. 2008;14:3141–8. [PubMed]
7. Hiwase DK, Saunders V, Hewett D, et al. Dasatinib cellular uptake and efflux in chronic myeloid leukemia cells: therapeutic implications. Clin Cancer Res. 2008;14:3881–8. [PubMed]
8. Giannoudis A, Davies A, Lucas CM, Harris RJ, Pirmohamed M, Clark RE. Effective dasatinib uptake may occur without human organic cation transporter 1 (hOCT1): implications for the treatment of imatinib-resistant chronic myeloid leukemia. Blood. 2008;112:3348–54. [PubMed]
9. Polli JW, Humphreys JE, Harmon KA, et al. The role of efflux and uptake transporters in [N-{3-chloro-4-[(3-fluorobenzyl)oxy]phenyl}-6-[5-({[2-(methylsulfonyl)ethy l]amino}methyl)-2-furyl]-4-quinazolinamine (GW572016, lapatinib) disposition and drug interactions. Drug Metab Dispos. 2008;36:695–701. [PubMed]
10. Tanihara Y, Masuda S, Katsura T, Inui K. Protective effect of concomitant administration of imatinib on cisplatin-induced nephrotoxicity focusing on renal organic cation transporter OCT2. Biochem Pharmacol. 2009;78:1263–71. [PubMed]
11. Ahlin G, Karlsson J, Pedersen JM, et al. Structural requirements for drug inhibition of the liver specific human organic cation transport protein 1. J Med Chem. 2008;51:5932–42. [PubMed]
12. Chowdhury TA. Diabetes and Cancer. QJM. 2010
13. Shu Y, Sheardown SA, Brown C, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest. 2007;117:1422–31. [PMC free article] [PubMed]
14. Shu Y, Brown C, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics. Clin Pharmacol Ther. 2008;83:273–80. [PMC free article] [PubMed]
15. Chen Y, Li S, Brown C, et al. Effect of genetic variation in the organic cation transporter 2 on the renal elimination of metformin. Pharmacogenet Genomics. 2009;19:497–504. [PMC free article] [PubMed]
16. Tanihara Y, Masuda S, Sato T, Katsura T, Ogawa O, Inui K. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H(+)-organic cation antiporters. Biochem Pharmacol. 2007;74:359–71. [PubMed]
17. Kisfalvi K, Eibl G, Sinnett-Smith J, Rozengurt E. Metformin disrupts crosstalk between G protein-coupled receptor and insulin receptor signaling systems and inhibits pancreatic cancer growth. Cancer Res. 2009;69:6539–45. [PMC free article] [PubMed]
18. Vazquez-Martin A, Oliveras-Ferraros C, del Barco S, Martin-Castillo B, Menendez JA. mTOR inhibitors and the anti-diabetic biguanide metformin: new insights into the molecular management of breast cancer resistance to the HER2 tyrosine kinase inhibitor lapatinib (Tykerb) Clin Transl Oncol. 2009;11:455–9. [PubMed]
19. Pellegrinotti M, Fimognari FL, Franco A, Repetto L, Pastorelli R. Erlotinib-induced hepatitis complicated by fatal lactic acidosis in an elderly man with lung cancer. Ann Pharmacother. 2009;43:542–5. [PubMed]
20. Yokoo S, Masuda S, Yonezawa A, Terada T, Katsura T, Inui K. Significance of organic cation transporter 3 (SLC22A3) expression for the cytotoxic effect of oxaliplatin in colorectal cancer. Drug Metab Dispos. 2008;36:2299–306. [PubMed]
21. Zhang S, Lovejoy KS, Shima JE, et al. Organic cation transporters are determinants of oxaliplatin cytotoxicity. Cancer Res. 2006;66:8847–57. [PMC free article] [PubMed]
22. Pectasides D, Nikolaou M, Pectasides E, Koumarianou A, Valavanis C, Economopoulos T. Complete response after imatinib mesylate administration in a patient with chemoresistant stage IV seminoma. Anticancer Res. 2008;28:2317–20. [PubMed]
23. Van Cutsem E, Verslype C, Beale P, et al. A phase Ib dose-escalation study of erlotinib, capecitabine and oxaliplatin in metastatic colorectal cancer patients. Ann Oncol. 2008;19:332–9. [PubMed]
24. Fisher GA, Kuo T, Ramsey M, et al. A phase II study of gefitinib, 5-fluorouracil, leucovorin, and oxaliplatin in previously untreated patients with metastatic colorectal cancer. Clin Cancer Res. 2008;14:7074–9. [PMC free article] [PubMed]
25. Kopetz S, Lesslie DP, Dallas NA, et al. Synergistic activity of the SRC family kinase inhibitor dasatinib and oxaliplatin in colon carcinoma cells is mediated by oxidative stress. Cancer Res. 2009;69:3842–9. [PMC free article] [PubMed]
26. More SS, Li S, Yee SW, et al. Organic cation transporters modulate the uptake and cytotoxicity of picoplatin, a third-generation platinum analogue. Mol Cancer Ther. 2010;9:1058–69. [PMC free article] [PubMed]
27. Chen Y, Zhang S, Sorani M, Giacomini KM. Transport of paraquat by human organic cation transporters and multidrug and toxic compound extrusion family. J Pharmacol Exp Ther. 2007;322:695–700. [PubMed]
28. Ahlin G, Chen L, Lazorova L, et al. Genotype-dependent effects of inhibitors of the organic cation transporter, OCT1: predictions of metformin interactions. Pharmacogenomics J. 2010 [PubMed]
29. Minematsu T, Hashimoto T, Aoki T, Usui T, Kamimura H. Role of organic anion transporters in the pharmacokinetics of zonampanel, an alpha-amino-3-hydroxy-5-methylisoxazole-4-propionate receptor antagonist, in rats. Drug Metab Dispos. 2008;36:1496–504. [PubMed]
30. van Erp NP, Gelderblom H, Guchelaar HJ. Clinical pharmacokinetics of tyrosine kinase inhibitors. Cancer Treat Rev. 2009;35:692–706. [PubMed]
31. le Coutre P, Kreuzer KA, Pursche S, et al. Pharmacokinetics and cellular uptake of imatinib and its main metabolite CGP74588. Cancer Chemother Pharmacol. 2004;53:313–23. [PubMed]
32. Demetri GD, Lo Russo P, MacPherson IR, et al. Phase I dose-escalation and pharmacokinetic study of dasatinib in patients with advanced solid tumors. Clin Cancer Res. 2009;15:6232–40. [PubMed]
33. Tanaka C, Yin OQ, Sethuraman V, et al. Clinical pharmacokinetics of the BCR-ABL tyrosine kinase inhibitor nilotinib. Clin Pharmacol Ther. 2010;87:197–203. [PubMed]
34. Nakagawa K, Tamura T, Negoro S, et al. Phase I pharmacokinetic trial of the selective oral epidermal growth factor receptor tyrosine kinase inhibitor gefitinib (‘Iressa’, ZD1839) in Japanese patients with solid malignant tumors. Ann Oncol. 2003;14:922–30. [PubMed]
35. Yamamoto N, Horiike A, Fujisaka Y, et al. Phase I dose-finding and pharmacokinetic study of the oral epidermal growth factor receptor tyrosine kinase inhibitor Ro50-8231 (erlotinib) in Japanese patients with solid tumors. Cancer Chemother Pharmacol. 2008;61:489–96. [PubMed]
36. Shirao K, Nishida T, Doi T, et al. Phase I/II study of sunitinib malate in Japanese patients with gastrointestinal stromal tumor after failure of prior treatment with imatinib mesylate. Invest New Drugs. 2009 [PubMed]
37. Nakagawa K, Minami H, Kanezaki M, et al. Phase I dose-escalation and pharmacokinetic trial of lapatinib (GW572016), a selective oral dual inhibitor of ErbB-1 and -2 tyrosine kinases, in Japanese patients with solid tumors. Jpn J Clin Oncol. 2009;39:116–23. [PubMed]
38. Okamoto I, Miyazaki M, Morinaga R, et al. Phase I clinical and pharmacokinetic study of sorafenib in combination with carboplatin and paclitaxel in patients with advanced non-small cell lung cancer. Invest New Drugs. 2009 [PMC free article] [PubMed]
39. Ito K, Iwatsubo T, Kanamitsu S, Ueda K, Suzuki H, Sugiyama Y. Prediction of pharmacokinetic alterations caused by drug-drug interactions: metabolic interaction in the liver. Pharmacol Rev. 1998;50:387–412. [PubMed]
40. Bolton E, Wang Y, Thiessen P, Bryant S. PubChem: Integrated Platform of Small Molecules and Biological Activities. Annual Reports in Computational Chemistry. 2008;4 Chapter 12:217–241.
41. Hemminki K, Li X, Sundquist J, Sundquist K. Risk of cancer following hospitalization for type 2 diabetes. Oncologist. 2010;15:548–55. [PMC free article] [PubMed]
42. Swaisland HC, Cantarini MV, Fuhr R, Holt A. Exploring the relationship between expression of cytochrome P450 enzymes and gefitinib pharmacokinetics. Clin Pharmacokinet. 2006;45:633–44. [PubMed]
43. Gschwind HP, Pfaar U, Waldmeier F, et al. Metabolism and disposition of imatinib mesylate in healthy volunteers. Drug Metab Dispos. 2005;33:1503–12. [PubMed]
44. Kretz O, Weiss HM, Schumacher MM, Gross G. In vitro blood distribution and plasma protein binding of the tyrosine kinase inhibitor imatinib and its active metabolite, CGP74588, in rat, mouse, dog, monkey, healthy humans and patients with acute lymphatic leukaemia. Br J Clin Pharmacol. 2004;58:212–6. [PMC free article] [PubMed]
45. Frohna P, Lu J, Eppler S, et al. Evaluation of the absolute oral bioavailability and bioequivalence of erlotinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in a randomized, crossover study in healthy subjects. J Clin Pharmacol. 2006;46:282–90. [PubMed]
46. Popp C, Gorboulev V, Muller TD, Gorbunov D, Shatskaya N, Koepsell H. Amino acids critical for substrate affinity of rat organic cation transporter 1 line the substrate binding region in a model derived from the tertiary structure of lactose permease. Mol Pharmacol. 2005;67:1600–11. [PubMed]