transporters on resistance to tyrosine...

TrendsSmall-molecule TKIs are the backboneof cancer therapy. Despite profoundsuccess of imatinib in improving out-come of patients with chronic myeloidleukemia, survival benefits of TKIs inother settings are varying andmoderate.

In addition to genetic variation of thetargeted kinases, and diverse meta-bolic and cellular escape mechanisms,insufficient TKI exposure and intracel-lular accumulation mediated by mem-brane uptake and efflux transportersare important for interindividual varia-bility of TKI response and the occur-

Feature ReviewImpact of Membrane DrugTransporters on Resistance toSmall-Molecule TyrosineKinase InhibitorsClaudia Neul,1 Elke Schaeffeler,1 Alex Sparreboom,2

Stefan Laufer,3 Matthias Schwab,1,4,5,* and Anne T. Nies1

Small-molecule inhibitors of tyrosine kinases (TKIs) are the mainstay of treat-ment for many malignancies and represent novel treatment options for otherdiseases such as idiopathic pulmonary fibrosis. Twenty-five TKIs are currentlyFDA-approved and >130 are being evaluated in clinical trials. Increasing evi-dence suggests that drug exposure of TKIs may significantly contribute to drugresistance, independently from somatic variation of TKI target genes. Mem-brane transport proteins may limit the amount of TKI reaching the target cells.This review highlights current knowledge on the basic and clinical pharmacol-ogy of membrane transporters involved in TKI disposition and their contributionto drug efficacy and adverse drug effects. In addition to non-genetic andepigenetic factors, genetic variants, particularly rare ones, in transporter genesare promising novel factors to explain interindividual variability in the responseto TKI therapy.

rence of drug resistance.

While in vitro studies and studies usingknockout mice clearly demonstrate TKItransport by efflux transporters, thepicture is less clear in pharmacogeneticassociation studies in humans.Furthermore, the role of currently inves-tigated uptake transporters appears tobe limited.

Novel techniques and strategies areneeded to better predict the relevanceof membrane transporters for in vivoTKI disposition.

1Dr. Margarete Fischer-Bosch Instituteof Clinical Pharmacology, Stuttgart,and University of Tübingen, Germany2Division of Pharmaceutics, College ofPharmacy, Ohio State University,Columbus, OH, USA3Department of PharmaceuticalChemistry, University of Tübingen,Tübingen, Germany

TKIs: Response Encounters ResistanceProtein kinases are key players in signal transduction networks mediating fundamentalcellular processes including cell differentiation, proliferation, apoptosis, transcription, metab-olism, and intercellular communication [1–3]. During the past 15 years it has become evidentthat many cancers, as well as metabolic disorders and immunological, neurological, andinflammatory diseases, may originate from dysregulation of these signaling networks [2,4,5].Protein kinases, including tyrosine kinases, have thereby emerged as the most intensivelystudied target structures. Concurrently, drug development has shifted towards smallmolecules that specifically block protein kinases crucial to disease biology. These ‘molecu-larly targeted therapies’ are now routinely used in the therapy of many cancers as first-linetherapy but are also essential in the treatment of drug-resistant malignancies as well asof other diseases such as idiopathic fibrosis and rheumatoid arthritis [5,6]. In 2001 the firstTKI, imatinib, received accelerated approval by the USA FDA for the treatment of Phila-delphia chromosome positive chronic myeloid leukemia (CML) [7]. Since then, 24 furtherTKIs have been approved, mostly for the treatment of various cancers (Figure 1A). A plethoraof >3000 novel agents inhibiting diverse protein kinases are currently beingexplored preclinically, covering not only a broad range of cancers but also ophthalmicand central nervous system disorders, osteoporosis, as well as disease-related complica-tions [2]. At present, more than 130 novel TKIs are being evaluated in clinical trials(Figure 1B).

904 Trends in Pharmacological Sciences, November 2016, Vol. 37, No. 11 http://dx.doi.org/10.1016/j.tips.2016.08.003© 2016 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.tips.2016.08.003http://crossmark.crossref.org/dialog/?doi=10.1016/j.tips.2016.08.003&domain=pdf

4Department of Clinical Pharmacology,Institute of Experimental and ClinicalPharmacology and Toxicology,University Hospital, Tübingen,Germany5Department of Pharmacy andBiochemistry, University of Tübingen,Tübingen, Germany

*Correspondence:[email protected](M. Schwab).

Contrary to the profound improvement of outcomes for CML patients treated with imatinib as first-line therapy [8], the success rate of TKIs in other diseases is varying, and only moderate survivalbenefits are achieved for some disease entities [6,9,10]. Poor initial response (i.e., endogenousresistance) or disease relapse (i.e., acquired resistance) are being discussed as major explan-ations. Of course, the development of mutations in the target receptor tyrosine kinases (RTK) orintracellular non-receptor tyrosine kinases (nonRTK) (Figure 2, Key Figure) is crucial for target-associated drug resistance, thereby preventing kinase inhibition by the TKI in the target cells. Forexample, patients with non-small cell lung cancer (NSCLC) harboring an activating mutation in theRTK epidermal growth factor receptor (EGFR) initially respond very well to erlotinib and gefitinib;however, the acquisition of secondary somatic mutations, such as the gatekeeper mutationThr790Met, leads to drug resistance. The development of second- and third-generation TKIs,such as afatinib and osimertinib, respectively, aimed to overcome this resistance [3,11–13].Another example is provided by CML patients who become refractory to imatinib therapy asa result of point mutations in BCR–ABL, the constitutively-active fusion kinase driving CML.Because imatinib-resistant CML patients frequently acquire the same somatic mutations in BCR–ABL, the second-generation TKIs dasatinib, nilotinib, and bosutinib have been developed andapproved by the USA FDA for treatment of CML patients carrying these mutations. For CMLpatients with the BCR–ABL Thr315Ile gatekeeper mutation, the third-generation TKI ponatinib hasbeen FDA-approved [3,13,14]. In addition to this target-associated resistance, the activation ofnew compensatory intracellular signaling pathways is an alternative explanation (so-called bypassresistance) in which target cells evade the signaling pathway inhibited by TKI as first-line therapy.Several recent reviews excellently summarize these mechanisms of resistance [3,5,6,9–13].

For decades multidrug resistance has been well known as a cause of poor response to cancerchemotherapy [15], and therefore might also contribute to TKI-related molecularly targeted drugresistance [9,10]. This indicates that, in addition to acquired mutations in the target kinases,ADME (absorption, disposition, metabolism, excretion) processes may play an important role inthe pathophysiology of drug non-response. There is an increasing body of evidence thatintracellular concentrations of TKI in target cells (Figure 2) vary substantially between patientseven after identical dosages [16–22], with consequences for drug response. By analogy, drugresistance in HIV-infected patients receiving antiretroviral drug therapy has been significantlylinked to interindividual variability of drug levels of protease inhibitors in peripheral bloodmononuclear cells (PBMC) [23].

Membrane transport proteins, in other words solute carrier transporters (SLC) mediating druguptake and ATP-dependent (ATP-binding cassette, ABC) transporters mediating drug efflux, areinvolved in several ADME processes [24] and thus are promising candidates to explain TKI-related non-response in cancer patients as well as drug toxicity. Membrane transporters arenecessary for TKIs to enter the target cells before they can interact with the intracellular ATP-binding sites of the tyrosine kinases. Various mechanisms, including genetic variation, epigeneticfactors, non-genetic factors such as transporter-mediated drug–drug interaction, as well asregulation processes, are thought to explain interindividual variability in the expression andfunction of transport proteins even in cancer cells, resulting in clinical consequences (e.g., non-response) [25–27].

This review highlights current knowledge on membrane transporters and their potential impacton TKI disposition. Current limitations and challenges in transporter research are summarizedand strategies for future research directions are proposed.

Mechanism of Action of TKIsThe family of human tyrosine kinases consists of 90 members: 58 RTKs and 32 intracellularnonRTKs [1]. They share the same catalytic mechanism of using ATP to phosphorylate tyrosine

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residues on target proteins [2,3,13]. This phosphorylation modifies target protein activities andleads to the activation of different downstream signaling pathways. While tyrosine kinases differin their amino acid sequences, the 3D structures of their kinase domains and particularly theATP-binding pockets are highly conserved. The kinase domains consist of an N-terminal lobe, aC-terminal lobe, and a hinge region linking the two lobes and enabling them to move relative toeach other [3,13]. ATP binds in the active site between these two lobes of the ATP-bindingpocket. Until the 1980s the ATP-binding pockets were considered too conserved to permit thedevelopment of kinase-specific small-molecule inhibitors. The synthesis of compounds selec-tively inhibiting EGFR [28] was seminal to the establishment of the new therapeutic class of small-molecule protein kinase inhibitors [5]. Twenty-two of the currently approved TKIs reversibly bindto the ATP-binding pockets of the respective tyrosine kinase, either to their active or their inactiveconformations. Only afatinib, ibrutinib, and osimertinib irreversibly bind to the ATP-bindingpockets. A detailed description of the molecular characteristics of TKIs and their modes ofinhibition can be found in recent reviews [3,5,13].

Factors Affecting Responsiveness to Oral TKIsDrug Exposure as a Determinant of Clinical OutcomeAll currently approved TKIs are administered orally. Most of those have a relatively longelimination half-time (Table 1) resulting in a once- or twice-daily dosing interval with beneficialconsequences for adherence to TKI use. Systemic TKI plasma concentrations are affected byvarious parameters: (i) the fraction of TKI absorbed in the intestine, (ii) TKI binding to serumproteins, (iii) TKI uptake into the hepatocytes, (iv) hepatic metabolism � primarily by cytochromeP450 enzyme CYP3A4/A5, and finally (v) biliary excretion (Figure 2B). All these factors maycontribute to the large interpatient variability of TKI exposure, which is reflected by the area underthe plasma concentration–time curve (AUC) and coefficients of variations of up to 77% (e.g.,axitinib, Table 1). Intestinal absorption may be impaired by concomitant food intake or the use ofantacids (reviewed in [29]). Genetic variation in drug-metabolizing enzymes has been identifiedas a major source of variation in the pharmacokinetics and response of many drugs [30]. It isbeyond the scope of this article to comprehensively review the contribution of genetic variation inmetabolizing enzymes to TKI pharmacokinetics and response. Only three examples for thevarious effects of genetic variation on TKI response and toxicity should be given: (i) imatinibpharmacokinetics appears to be affected only to a minor degree by genetic variation in CYP3A4/5, CYP2C9, CYP2C19, or CYP2D6 [31–33], and had no effect on clinical response in CMLpatients [33]; (ii) early sorafenib-induced severe toxicity has been associated with a geneticvariant in UGT1A9 encoding an enzyme involved in glucuronidation of sorafenib, but not withgenetic variation of CYP3A5 [34]; (iii) sunitinib-induced toxicity and hypertension are associatedwith genetic variants in CYP3A5 and CYP3A4, respectively [35,36]. In addition to metabolizingenzymes, membrane uptake and efflux transporters are important determinants of drug plasmaconcentrations [24].

All TKIs are characterized by large AUC coefficients of variation (Table 1), and it has beensuggested that this wide pharmacokinetic variability of TKI exposure can affect both TKI efficacyand toxicity [37]. Indeed, a large number of clinical studies have been conducted correlatingpharmacokinetic parameters such as the AUC or the minimum TKI steady-state plasmaconcentrations with TKI response and/or adverse reactions. Selected significant correlationsare summarized in Table 2. For example, in patients with gastrointestinal stromal tumors (GIST)the probability of a complete/partial response and toxicity is increased 2.6- and 2.2-fold,respectively, for every doubling of exposure to free imatinib (AUC0–24) [38]. Furthermore, aminimum imatinib steady-state plasma concentration of >1000 ng/ml was consistently associ-ated in five independent studies with an increased probability of complete cytogenetic or majormolecular response in CML patients (summarized in [39]). Therefore, several clinical recom-mendations have been published suggesting therapeutic monitoring of plasma TKI levels to

906 Trends in Pharmacological Sciences, November 2016, Vol. 37, No. 11

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20

40

Phase I Phase II Phase III Pha se IV

0102030

Phase I Phase II Phase III010

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Phase I Phase II

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(A)

(B)

Female reprod uc�vesystem and breas t

Liver and kidney

Other neopl asmsLung

Blood andbone marrow

Non-tumor aldisease s

Solid tumors andsarcom as

Gastrointes �nal tract

Non-small cell lung cancer Medullary thyroid cancer

Breast cancer

Renal cell carcinoma

Polycythemia vera

Mantle cell lymphoma

Rheumatoid arthri�s

Idiopathic pulmonary fibrosis

Hepatocellular carcinoma

Colorectal carcinoma

Chronic myeloid leukemia

Erlo�nib (Tarceva, 2005) Cabozan�nib (Cometriq, 2012)

Lapa�nib (Tykerb/Tyverb, 2008)

Sorafenib (Nexavar, 2005)Suni�nib (Sutent, 2006)Pazopanib (Votrient, 2010)Axi�nib (Inlyta, 2012)

Ruxoli�nib (Jakafi/Jakavi, 2012)

Ibru�nib (lmbruvica, 2013)

Tofaci�nib (Xeljanz, 2012)

Vandetanib (Caprelsa, 2012)Sorafenib (Nexavar, 2013)Lenva�nib (Lenvima, 2015)

Lapa�nib (Tykerb/Tyverb, 2007)Gefi�nib (Iressa, 2009)

Crizo�nib (Xalkori, 2012)Afa�nib (Gilotrif/Giotrif, 2013)

Ceri�nib (Zykadia, 2014)Nintedanib (Vargatef, 2014)

Alec�nib (Alecensa, 2015)Osimer�nib (Tagrisso, 2015)

Nintedanib (Ofev, 2014)

Sorafenib (Nexavar, 2007)

Regorafenib (S�varga, 2012)

Ima�nib (Gleevec/Glivec, 2001)Dasa�nib (Sprycel, 2006)Nilo�nib (Tasigna, 2007)Bosu�nib (Bosulif, 2013)Pona�nib (Iclusig, 2013)

Figure 1.

(Figure legend continued on the bottom of the next page.)

Small-Molecule Tyrosine Kinase inhibitors (TKIs) Approved (A) and in Clinical Trials (B). (A) Small-molecule TKIs approved by the US FDA and theEuropean Medicines agency (EMA) as of May 2016 according to their indications. Following the drug name, tradenames of the FDA/EMA are given. Only one tradename


adjust dosages, subsequently leading to improved efficacy or a reduced rate of adverse drugreactions [20–22,29,39–42]. However, the concept of monitoring serum levels is limited by thefact that plasma and intracellular TKI concentrations in target cells may differ [17], therebysupporting the pharmacological rationale that determination of intracellular TKI concentrations islikely to be a more reliable indicator [40].

Drug Transporters as Major Contributors to Drug ExposureMembrane transporters are well recognized key determinants of therapeutic response contrib-uting to drug resistance and drug toxicity [24,43–45]. The human genome encodes >400membrane transporters that belong to two major groups, the ABC and the SLC superfamilies,which mediate the cellular uptake and efflux of small molecules. About 20 transporters havebeen particularly implicated in drug transport and have been intensively studied with respect totheir function, localization, and regulation. Because these are located in the enterocytes of theintestine, hepatocytes of the liver, proximal tubule cells of the kidney, and endothelial cells oftissue barriers (e.g., blood–brain barrier), they are directly involved in the absorption, distribution,and elimination of drugs, and also indirectly influence metabolism by controlling access to drug-metabolizing enzymes (Figure 2B–F). Transporter-mediated uptake into non-target cells mayalso contribute to drug toxicity, as for example recently demonstrated for OAT6 (SLC22A20),which regulates entry of sorafenib into keratinocytes and contributes to sorafenib-induced skintoxicity [46]. Finally, the presence of membrane transporters in the plasma membrane of cancercells, either of the primary tumor or metastases, is a major determinant of drug resistancebecause decreased transporter-mediated TKI influx or increased TKI efflux may lead to insuffi-cient intracellular TKI concentrations (Figure 2A).

Table 3 summarizes currently available functional data on TKIs as substrates for uptake andefflux transporters. While most transporters have only been tested incidentally, almost completefunctional data are available for the efflux transporters ABCB1 (MDR1 P-glycoprotein) andABCG2 (BCRP) as well as for the hepatic uptake transporters SLCO1B1 (OATP1B1), SLCO1B3(OATP1B3), and SLC22A1 (OCT1). ABCB1 and ABCG2 have both been extensively charac-terized over the past decades and have also been implicated in the multidrug resistancephenotype against several cytotoxic chemotherapies [15,47,48]. They are recommended bythe US FDA to be tested during the drug development process [24]. Moreover, the organic aniontransporters OATP1B1 and OATP1B3 and organic cation transporter OCT1 are importanthepatocellular drug uptake transporters [24,49–51]. Because almost all TKIs must enter thehepatocytes for metabolism and biliary elimination (Table 1), several studies have investigatedthe involvement of these transporters in TKI uptake.

In Vitro Studies for the Identification and Assessment of TransportersA common and well-established method to identify whether TKIs are substrates for ABCtransporters is to use polarized cell lines recombinantly expressing the respective ABC trans-porter in the apical membrane and to measure bidirectional TKI transport [52]. The cut-off forsignificant transport is typically a transport ratio of 2 (amount of apically directed transportdivided by the amount of basolaterally directed transport) [52]. Accordingly, all TKIs, except forcabozantinib, ibrutinib, ruxolitinib and vandetanib, are substrates for at least ABCB1 or ABCG2(Table 3). However, methodological issues, notably those associated with cell-based assays,can significantly alter in vitro study results [53], and hence there is still debate in the literature,

indicates that the same tradename is current in the USA and Europe with the following exceptions: (i) EMA refused the marketing authorization of tofacitinib on 25 July2013; (ii) alectinib has not yet been approved by the EMA and has only recently been approved by the FDA (December 2015). The date indicates the FDA registration date,except for nintedanib, which is only approved by the EMA for the treatment of lung cancer. (B) More than 130 novel TKIs are currently being evaluated in different clinicalphases. The pie chart shows their distribution to the neoplasms of the different organs and tissues (percentage of total). In the bar charts, the number of new TKIs in thedifferent clinical study phases is given. All data were taken from http://clinicaltrials.gov using the keyword ‘tyrosine kinase inhibitors’.


http://clinicaltrials.gov/

Key Figure

Tyrosine kinase inhibitor (TKI) resistance mechanisms in cancer cells (A)and the location of membrane transporters involved in absorption, dis-tribution, and excretion of TKIs (B–F)

(A)

(B)

(E)

(C)

(D)

(F)

BloodEntero cyte

CYP

SLC22 A4SLCO2B1Othe r SLCs

ABCB1ABCC2ABCG2

ABCC3

BloodHepatocyte

ABCC4ABCC6

SLCO1B1SLCO1B3SLCO2B1SLC22 A1SLC22 A3SLC22 A7Bile

ABCB1ABCC2ABCG2SLC47 A1

Blood

Renal pro ximal tubule cell

SLC22 A2SLC22 A6SLC22 A7SLC22 A8

ABCB1 SLC47 A1ABCC2 SLC47 A2ABCC4ABCG2

Blood

Brain endo the lial cell

ABCB1ABCC4ABCG2

SLCO1A2SLCO2B1

Decreasedinflux

Increasedefflux

Oral dose

To feces

Enterocyte

Portal vein

Hepatocyte

To systemiccircula�onand target cells

CYP

Bile CYP

TKI metabolites

TKI metabolites

TKI metabolites

TKI

TKI

TKI

TKI

TKI

TKI

Muta�ons in intracellularkinase

Ac�va�on ofcompensatory

signalingpathways

Lysosomalsequestra�on

Muta�onsin RTK

(See figure legend on the bottom of the next page.)


particularly regarding bosutinib, nilotinib, and sorafenib, on whether these TKIs are indeedtransported by ABCB1. Therefore, standardization of cell-based transporter assays is urgentlyrequired to better predict the relevance of ABC transporters for in vivo TKI disposition.

A common method to study function of uptake transporters is to determine substance accu-mulation by transfected versus parental or nontransfected cells. A �twofold-increased accu-mulation by transfected cells is typically considered to demonstrate significant transport [52]. Ingeneral, accumulation ratios for TKIs are

Table 1. Targeted Kinases and Pharmacokinetics of Approved TKIsa

TKI Targeted Kinasesb ProteinBindingc

Dosaged t1/2 (h)e AUCf (ng.h/ml) Metabolizing Enzymeg Elimination (%)h

Major Minor Fecal Renal

Afatinib EGFR, HER2, HER4 95 40 mg single 37 3240–24 (69%) Covalent adducts to proteins, minimal metabolism 85 4

Alectinib ALK, RET >99 600 mg bid 33 74300–12 (46%) CYP3A4 98

Axitinib VEGFR1–3, PDGFR, KIT >99 5 mg bid 3–6 2650–24 (77%) CYP3A4/5 CYP1A2, CYP2C19,UGT1A1

41 23

Bosutinib BCR–ABL, SRC, LYN, HCK >94 500 mg single 22.5 3650 (�425) CYP3A4 91 3Cabozantinib RET, MET, VEGFR1–3, KIT,

TRKB, FLT3, AXL, TIE2�99.7 140 mg qd 55 37 8500–24 (43%) CYP3A4 54 27

Ceritinib EML4–ALK, IGF-1R, INSR,ROS1

�97 750 mg single 41 33900–24 (�121) CYP3A 92 1

Crizotinib EML4–ALK, MET, ROS1,MST1R

91 250 mg single 42 2192–2946 (27–31%) CYP3A4/5 63 22

Dasatinib BCR–ABL, SRC, LCK, YES,FYN, KIT, EPHA2, PDGFRb

96 70 mg bid,after 1 day

3.4 1390–24 (74%) CYP3A4 FMO3, UGT 85 4

Erlotinib EGFR 93 150 mg qd 36.2 24 9000–24 CYP3A4 CYP1A2, CYP1A1 83 8

Gefitinib EGFR 90 525 mg qd 37 14 300–15 7000–24(35–59%)

CYP3A4 CYP2D6 86 4

Ibrutinib BTK 97.3 560 mg qd,after 1 day

6.9 10520–24 (�583) CYP3A CYP2D6 80 10

Imatinib BCR–ABL, KIT, PDGFR �95 400 mg qd 15 36 3410–24 (�16 572) CYP3A4 CYP1A2, CYP2D6,CYP2C9, CYP2C19

68 13

Lapatinib EGFR, ERBB2 >99 1250 mg qd 24 36 2000–t(23 400–56 000)

CYP3A4/5 CYP2C19, CYP2C8 Fecal 99 800 mg qd 30.9 1 037 5000–t (34%) CYP3A4 CYP1A2, CYP2C8 82 3

Ponatinib BCR–ABL, BCR–ABL-T315I,SRC, FLT3, FGFR, VEGFR,PDGFR, KIT, RET, TIE2, EPH

�99 45 mg qd 24 12530–24 (73%) CYP3A4 CYP2C8, CYP2D6,CYP3A5

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Table 1. (continued)

TKI Targeted Kinasesb ProteinBindingc

Dosaged t1/2 (h)e AUCf (ng.h/ml) Metabolizing Enzymeg Elimination (%)h

Major Minor Fecal Renal

Regorafenib FGFR1/2, PDGFR//b,VEGFR1–3, KIT, RET, RAF1,BRAF, BRAF-V600E, ABL1,TIE2, EPH2A, MAPK11, FRK,NTRK1

99.5 160 mg single 28 70 400 (35%) CYP3A4, UGT1A9 71 19

Ruxolitinib JAK1/2 97 25 mg bid 1.9 1 335 0780–t (� 632 196) CYP3A4 CYP2C9 22 74Sorafenib VEGFR1–3, BRAF, BRAF-

V600E, RAF1, KIT, FLT3, RET,PDGFRb

99.5 400 mg bid 25–48 67 3000–12,steady-state (57%) CYP3A4, UGT1A9 77 19

Sunitinib VEGFR1–3, PDGFR//b, KIT,FLT3, CSF-1R, RET

95 50 mg qd 40–60 1035–17060–24 (2–56%) CYP3A4 61 16

Tofacitinib JAK1/3 40 5 mg bid,after 1 day

3 1610–t (�86) CYP3A4 CYP2C19 70 30

Vandetanib EGFR, RET, VEGFRs, PTK6,TIE2, EPHRs, SRCs

90 300 mg qd 456 17 926–38 6110–24(15–58%)

CYP3A4, FMO1,FMO3

44 25

aAll data were extracted from the Labels and/or the Clinical Pharmacology Biopharmaceutics Reviews of the FDA homepage (www.accessdata.fda.gov/scripts/cder/drugsatfda/). AUC values are from reportedstudies on patients.

bAbbreviations of kinases: ABL1, ABL proto-oncogene 1, non-receptor tyrosine kinase; ALK, anaplastic lymphoma kinase; AXL, AXL receptor tyrosine kinase; BCR–ABL, breakpoint cluster region–Abelson murineleukemia viral oncogene homolog; BRAF, B-Raf proto-oncogene, serine/threonine kinase; BTK, Bruton's tyrosine kinase; CSF1R, colony stimulating factor 1 receptor; DDR1, discoidin domain receptor 1; EGFR,epidermal growth factor receptor; EML4–ALK, echinoderm microtubule-associated protein-like 4–anaplastic lymphoma kinase; EPHA2, ephrin type-A receptor 2; EPH, ephrin receptors; ERBB2, Erb-b2 receptortyrosine kinase 2; FGFR1–4, fibroblast growth factor receptors 1–4; FLT3, Fms-related tyrosine kinase 3; FRK, Fyn-related Src family tyrosine kinase; FYN, proto-oncogene tyrosine-protein kinase Fyn; HCK,hemopoetic cell kinase proto-oncogene, Src family tyrosine kinase; HER1–4, human epidermal growth factor receptors 1–4; IGF-1R, insulin-like growth factor 1 receptor; INSR, insulin receptor kinase; ITK, IL2inducible T-cell kinase; JAK1–3, janus kinase 1–3; KIT, mast/stem cell growth factor receptor; LCK, Lck proto-oncogene, Src family tyrosine kinase; LYN, Lyn proto-oncogene, Src family tyrosine kinase; MAPK11,mitogen-activated protein kinase 11; MET, hepatocyte growth factor receptor; MST1R, macrophage stimulating 1 receptor; NTRK1, neurotrophic receptor tyrosine kinase 1; PDGFR, platelet-derived growth factorreceptor; PTK6, protein tyrosine kinase 6; RAF1, rapidly accelerated fibrosarcoma 1 proto-oncogene, serine/threonine kinase; RET, Ret proto-oncogene receptor tyrosine kinase; ROS1, Ros proto-oncogene 1,receptor tyrosine kinase; SRC, Src proto-oncogene, non-receptor tyrosine kinase; TIE2, TEK receptor tyrosine kinase; TRKB, tropomyosin receptor kinase B; VEGFR1–3, vascular endothelial growth factor receptors1–3; YES, Yes proto-oncogene 1, Src family tyrosine kinase.

cProtein binding in %.dqd, once daily; bid, twice daily.et1/2, elimination half-time.fAUC, area under the plasma concentration-time curve; 0–12, from time 0 to 12 h; 0–24, from time 0 to 24 h; 0–t, from time 0 to next dosing; norm; dose-normalized; variability is given as coefficient of variation (%), as�SD, or as range.gAO, aldehyde oxidase; CYP, cytochrome P450; FMO, flavin-containing monooxygenase; UGT, UDP-glucuronosyl transferase.hElimination measured as % radioactivity recovered in feces or urine after a single oral dose.

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Table 2. Examples of Significant Correlations of Pharmacokinetic Parameters with the Efficacy and/orToxicity of Selected TKIsa

TKI PK Parameterb Cancer Typec Outcome

Efficacyd Toxicity

Imatinib Ctrough CML Hematological,cytogenetic,molecular response

Hematological toxicity,rash, fluid retention,nausea, muscoskeletal pain

Ctrough GIST TTP, PFS Hematological toxicity

Ctrough GIST with KITexon 11 mutation

OOBR

AUC0–24 free GIST CR, PR

AUC0–24 CML, GIST Number of adverse effects

Sorafenib Ctrough Solid tumors SUVmax

AUCmax Melanoma Tumor control,PR + SD, PFS

cumulative AUC Solid tumors Grade �3 toxicitySerum conc. >5.8 mg/ml RCC, HCC Grade �2 hand/foot

skin reaction

Serum conc. >4.8 mg/ml RCC, HCC Grade �2 hypertensionCtrough Solid tumors Tumor response

Sunitinib Ctrough mRCC, GIST,solid tumors

Hypertension

AUC0–24 mRCC, GIST,solid tumors

TTP, OS, ORR, SD Neutropenia

aData extracted from [20,22,39,40] and references therein.bAUC, area under the plasma concentration–time curve; 0–24, from time 0 to 24 h; Ctrough, minimum (trough) concentration.cCML, chronic myeloid leukemia; GIST, gastrointestinal stromal tumor; HCC, hepatocellular carcinoma; KIT, mast/stem cellgrowth factor receptor; mRCC, metastatic renal clear cell carcinoma; RCC, renal clear cell carcinoma.

dCR, complete response; OOBR, overall objective benefit rate; ORR, objective response rate; OS, overall survival; PFS,progression-free survival; PR, partial response; SD, stable disease; SUVmax, maximum standardized uptake value; TTP,time to progression.

Tis sue

?Liver

Kidney

?

Normal Tumor Metastasis Tumor cell lines

OATP1B1 OATP1B1 ↓ OATP1B1OATP1B3 OATP1B3 ↓ OATP1B3OCT1 OCT1 ↓ OCT1

ABCB1 ABCB1 ↓ ABCB1

OCT2 OCT2 OCT2 OCT2

ABCB1 ABCB1 ↓ ABCB1

XXX

X

Figure 3. Protein Expression ofSelected Uptake and Efflux Trans-porters in Normal Human Liver andKidney Compared to RespectiveTumor and Metastases as well asTumor Cell Lines. White boxes indicateexpression in normal tissue or unchangedexpression in comparison to normal tis-sue. Black boxes and down-arrows indi-cate reduced expression compared tonormal tissue. Black boxes with redcrosses indicate lack of expression. Liverdata are from [64,65,91,150] and kidneydata from [62,151,152].

Knockout Mouse Studies To Assess the In Vivo Relevance of ABCTransportersA valuable tool to assess the in vivo relevance of ABC transporters are studies with micegenetically deficient for either Abcb1a/1b or Abcg2, or both Abcb1 and Abcg2 (Table 3). Abcb1and Abcg2 are not only localized in enterocytes, hepatocytes, and renal proximal tubule cells,


Table 3. TKIs as Substrates of Membrane Uptake and Efflux Transporters

TKI Charge at 7.4a ABC Transporterb,c

ABCB1In Vitro

Abcb1a/bKO Mice

ABCG2in vitro

Abcg2KO mice

Abcb1/g2KO Mice

ABCB11in vitro

ABCC1in vitro

ABCC2in vitro

ABCC4in vitro

Afatinib +1 1.4 Y

Alectinib +0.3


TKI Charge at 7.4a ABC Transporterb,c

ABCB1In Vitro

Abcb1a/bKO Mice

ABCG2in vitro

Abcg2KO mice

Abcb1/g2KO Mice

ABCB11in vitro

ABCC1in vitro

ABCC2in vitro

ABCC4in vitro

Sunitinib +1 �2 2.9 (4 h oral)2.3 (6 h oral)

�2 1.3 (6 h oral) 23.4 (6 h oral) N* Y*

Tofacitinib +0.3 Y N

Vandetanib +1 1

TKI SLC Transporter (In Vitro)b,d Refse

SLCO1A2 SLCO1B1 SLCO1B3 SLCO2B1 SLC22A1 SLC22A2 SLC22A3 SLC22A4 SLC22A5 SLC22A6 SLC22A7 SLC22A8 SLC22A20 SLC47A1

Afatinib FDA

Alectinib FDA [114]

Axatinib Y Y FDA [115]

Bosutinib 1.0 [116,117]

Cabozantinib N N N N N N [118]

Ceritinib 1.3 1 1 1.4 FDA [119]

Crizotinib 1.2 1.3 [77,120]

Dasatinib 1.1 1.3 2.5 N [66,77,101,116,121–125]

Erlotinib 1 1 1 1.5 0.6 1 0.9 0.8 1 1.6 [126–129]

Gefitinib 1.3 1.6 1.0–1.2 1 [77,129–132]

Ibrutinib FDA

Imatinib 1.5 1.2 1.6 1.0–1.7 1 0.9 1.1–1.6 1.0–1.4 0.9 0.8 0.9 1.4 [63,67,68,70,123,133–139]

Lapatinib [140,141]

Lenvatinib N N N N N N FDA

Nilotinib 1.2–1.3 1.6–2.0 1 N [77,101,116,128,142,143]

Nintedanib N N N Y N FDA

Osimertinib N N FDA

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TKI SLC Transporter (In Vitro)b,d Refse

SLCO1A2 SLCO1B1 SLCO1B3 SLCO2B1 SLC22A1 SLC22A2 SLC22A3 SLC22A4 SLC22A5 SLC22A6 SLC22A7 SLC22A8 SLC22A20 SLC47A1

Pazopanib 1.0–1.3 1.0–1.3 N FDA [77,101,128]

Ponatinib N N N FDA

Regorafenib 2.5 1.2 [144,145]

Ruxolitinib FDA

Sorafenib 0.9 1.0–1.5 1.0–1.5 1.1–1.3 1 1.3 1 1.2 Y* [46,59,66,74,76,77,146–148]

Sunitinib 1.0 1.0–1.4 0.9–1.0 1 1.1 1.1 1.2 1.2 FDA [76,77,148,149]

Tofacitinib FDA

Vandetanib 1.1–1.3 1.4 N FDA [77,101,128]

aCharge at pH 7.4 was calculated with MarvinSketch 15.9.14 using structures downloaded as SMILES from the PubChem compound library (http://www.ncbi.nlm.nih.gov/pccompound/).bAlternative/alias names of transporters: ABCB1, MDR1 P-glycoprotein; ABCG2, breast cancer resistance protein BCRP; ABCB11, bile salt export pump BSEP; ABCC1, multidrug resistance protein MRP1; ABCC2,multidrug resistance protein MRP2; ABCC4, multidrug resistance protein MRP4; SLCO1A2, organic anion transporting polypeptide OATP1A2; SLCO1B1, organic anion transporting polypeptide OATP1B1;SLCO1B3, organic anion transporting polypeptide OATP1B3; SLCO2B1, organic anion transporting polypeptide OATP2B1; SLC22A1, organic cation transporter OCT1; SLC22A2, organic cation transporter OCT2;SLC22A3, organic cation transporter OCT3; SLC22A4, organic cation/zwitterion transporter OCTN1; SLC22A5, organic cation/zwitterion transporter OCTN2; SLC22A6, organic anion transporter OAT1; SLC22A7,organic anion transporter OAT2; SLC22A8, organic anion transporter OAT3; SLC22A20, organic anion transporter OAT6; SLC47A1, multidrug and toxin extrusion MATE1.

cValues of in vitro studies are transport ratios (amount of apically directed transport divided by the amount of basolaterally directed transport) from bidirectional transport assays in polarized cell monolayers withtransporter-transfected cells or Caco2 cells with transport inhibitors. Values of �2 are considered to reflect significant transport [52]. Y, yes but no transport ratios given; N, no but no transport ratios given; Y*, yesfrom accumulation studies with transporter-transfected cells; N*, no from accumulation studies with transporter-transfected cells. Values of knockout (ko) mice studies are fold TKI increase in brain accumulation orbrain penetration in comparison to wild-typemice at indicated time points after oral administration (oral), intravenous injection (iv), subcutaneous application (sc), continuous infusion (infus) or microdialysis (microd). Allbrain accumulation data are for the respective parent TKI compound, except for the data of imatinib accumulation 2 h iv, which result from measurement of total radioactivity of imatinib and its metabolites [134].

dData are fold stimulation in uptake caused by the transporter. Values of�2 are considered to reflect significant transport [52]. Studies on knockout mice are only available for Slco1b and Slc22a1 and are discussed inthe main text in the section ‘In Vitro and Knockout Mouse Studies To Assess the Role of OCT1 as a Transporter of Imatinib and Sorafenib’.

eFDA: data from Clinical Pharmacology Biopharmaceutics Reviews downloaded from the FDA homepage (www.accessdata.fda.gov/scripts/cder/drugsatfda/).

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http://www.ncbi.nlm.nih.gov/pccompound/http://www.accessdata.fda.gov/scripts/cder/drugsatfda/

but also in the luminal membrane of brain endothelial cells, where they efflux TKIs back into theblood, thereby limiting TKI entry into the brain (Figure 2B–F). Therefore, in Abc transporterknockout mice, the increase in brain accumulation of the investigated TKI in comparison to wild-type mice reflects the contribution of the respective Abc transporters to TKI disposition.Notably, these in vivo studies mainly support the results obtained by cell-based assays(Table 3). Abcb1 appears to be more important than ABCG2/Abcg2 for brain disposition ofmost TKIs, except for erlotinib, regorafenib, and sorafenib. Of interest, the combined knockoutof Abcb1 and Abcg2 often leads to a much higher TKI brain accumulation than anticipated fromthe single knockouts of Abcb1 and Abcg2. This is particularly observed for axitinib, ceritinib,dasatinib, gefitinib, imatinib, lapatinib, sorafenib, and sunitinib, indicating that deficiency of onetransporter may be compensated by the other. Notably, while complete knockout of Abcb1and Abcg2 leads to 58-, 28- and 20-fold higher dasatinib, sorafenib and sunitinib brainconcentrations, respectively, brain accumulation is only 2.9-, 1.5-, and 2.4-fold, respectively,in heterozygous mice that have 50% of the levels of Abcb1 and Abcg2 relative to wild-type mice[66]. This indicates that the transport activity of each Abc transporter is apparently large enoughto sufficiently efflux either drug.

In Vitro and Knockout Mouse Studies To Assess the Role of OCT1 as aTransporter of Imatinib and SorafenibOCT1 has been discussed most intensely and controversially as a transporter of imatinib [63,67–71], and is given here as an example to demonstrate the difficulty of reconciling conflicting dataand the urgent need for standardized cell-based assays. Several in vitro studies using OCT1-transfected HEK cells, which express functional OCT1 at high levels, detected only a 1.0- to 1.2-fold increase of cellular imatinib uptake [63,67,69,70], indicating that OCT1 is not involved inimatinib uptake. This is supported by in vivo studies in knockout mice in which the geneticdeficiency of Oct1 did not affect imatinib disposition [70]. Even so, another study detected a�1.7-fold increase of imatinib accumulation when OCT1 was overexpressed in the CML cell lineKCL22, which was interpreted as OCT1-mediated uptake [68]. However, according to recentrecommendations [52] this should not be considered as significant transport. In addition,imatinib uptake assays using ‘specific’ inhibitors to inhibit OCT1 function [72,73] should beinterpreted cautiously because those inhibitors are not specific for OCT1 and may also interferewith non-OCT1-dependent imatinib uptake mechanisms [69,70].

OCT1 has also been suggested as uptake transporter for sorafenib in some studies [74,75], butnot in another study [76]. As for imatinib, accumulation ratios in OCT1-transfected HEK cells orOCT1-expressing oocytes did not exceed 1.6, demonstrating that OCT1 does not transportsorafenib. The accumulation ratio of sorafenib by OATP1B1- and OATP1B3-transfected cells isalso

Transporter func�on and expression Dr

ug u

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e

Drug concentra�on Transportergenotype

Tran

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ter

expr

essi

on

Sources of interindividual variability in drug responseGene�c varia�on

Rare

Very rare

Common

Pharmacokine�cs

Post-transla�onal

Regula�on

Trafficking

Lipid environment

Transcrip�onal

Post-transcrip�onal

Non-gene�c factors

DietAgeSexCircadianrhythmDiseaseRenal func�onCardiov.func�on G.I.

func�on Hepa�cfunc�on

Smoking

Exercise

Weight

Drugs

Starva�onPregnancy

Epigene�csHistone

modifica�on

DNA methyla�on

miRNA

Occupa�onal

Drug efficacy Drug resistanceDrug response Drug toxicity

CC CT TT

Immuno-localiza�on

Tissuemicroarray

Conc

entr

a�on

Time

Figure 4. Factors Affecting the Interindividual Variability of Transporter Function and Expression. Variability of transporter function and expression can beexplained by several different factors and processes, such as (i) non-genetic factors, including age, sex, organ function, underlying disease, and concomitant medicationspotentially leading to drug–drug interactions; (ii) genetic variation, whereby very rare variants with minor-allele frequencies T (rs1128503, p.G412G), 2677G > T/A (rs2032582, p.A893S/T), and 3435C > T(rs1045642, p.I1145I), which occur at high allele frequencies in different ethnicities and createa common haplotype [47,84]; for ABCC2, it is the promoter variant �24C > T (rs717620)


Table 4. Effect of Genetic Variants on Pharmacokinetics and Outcome for Selected TKIs

Transporter Genetic VariantsTested (CommonName)

Number ofPatients

Ethnicity Effects on Drug PKa SignificantAssociationwith PK

Effects on DrugTreatment Outcomea

SignificantAssociationwith Outcome

Refs

Imatinib

ABCB1 1236C > T 87 Caucasian $ MMR No [153]1236C > T 33 Caucasian $ response No [154]2677G > T/A 96 Egyptian 2677TT:

" CCR, MRYes [155]

3435C > T 82 Caucasian $ Oral Cl No [31]3435C > T 70 Iranian 3435CC:

# CRYes [156]

1236C > T3435C > T

82 Korean $ Cmin No $ MMR No [33]

2677G > T/A3435C > T

84 Caucasian 3435TT:" time to MMR

Yes [90]

2677G > T/A3435C > T

111 Indian $ Cmin No $ Thrombocytopenia No [157]

1236C > T2677G > T/A3435C > T

22 Australian Haplotype TTT:" Cl

Yes [158]

1236C > T2677G > T/A3435C > T

90 Caucasian 1236TT:" Cmin

Yes 1236TT: " MMR2677G: # responseHaplotype CGC:# MMR

Yes [159]

1236C > T2677G > T/A3435C > T

229 Mainly Caucasian 3435TT: # OS Yes [160]

1236C > T2677G > T/A3435C > T

52 Chinese 1236TT:" resistance2677GT:" resistance3435CC:# resistance

Yes [161]

1236C > T2677G > T/A3435C > T

67 Japanese $ Cmin No $ MMR No [32]

1236C > T2677G > T/A3435C > T

46 Caucasian 1236TT, 1236CT:" resistance2677TT:" resistance3435TT:" resistance

Yes [162]

65 Caucasian Yes [163]

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Number ofPatients




Refs

1236C > T2677G > T/A3435C > T

Haplotype TGC:" resistance

1236C > T2677G > T/A3435C > T

34 Japanese 3435CC: " Cl Yes [164]

1236C > T2677G > T/A3435C > T

118 Brazilian Haplotype 1236CT/2677GT/3435CT:" MMR

Yes [165]

1236C > T2677G > T/A3435C > T

189 Caucasian 3435CC: " CMR Yes [166]

1236C > T2677G > T/A3435C > T

215 Malaysian Haplotype CGC:" resistance

Yes [167]

1236C > T2677G > T/A3435C > T

60 Caucasian $ Cl No $ EFS No [89]

1236C > T2677G > T/A3435C > T

284 Korean $ Cmin No $ PFS No [168]

1236C > T2677G > T/A3435C > T

48 Chinese 1236TT:# CR

Yes [169]

1236C > T2677G > T/A3435C > THaplotype withOCT1 variants

38 Asian Effect on Cl Yes $ Response No [170]

ABCC2 �24C > T 67 Japanese $ Cmin No $ MMR No [32]�24C > T1249G > A3972C > T

215 Malaysian Haplotype TGT:# CR, MR

Yes [171]

ABCG2 421C > A 82 Caucasian $ Oral Cl No [31]421C > A 67 Japanese 421CA/AA: " Cmin Yes $ MMR No [32]421C > A 229 Mainly Caucasian " MMR Yes [172]421C > A 15 Japanese $ Conc. in leukocytes No [17]421C > A 34 Japanese $ Cl No [164]421C > A 111 Indian $ Cmin No $ Thrombocytopenia No [157]

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Number ofPatients




Refs

421C > A 100 Korean $ Cmin No $ CR No [33]34G > A421C > A

229 Mainly Caucasian 34GG: # CCR421CA/CC:# MMR, CMR

Yes [160]

34G > A421C > A

189 Caucasian $ MCR, CCR,MMR, CMR

No [166]

34G > A421C > A

118 Brazilian $ Response No [173]

34G > A421C > A

215 Malaysian Haplotype AA:" response

Yes [167]

34G > A421C > A

284 Korean $ Cmin No 421 CC/CA:# PFS

Yes [168]

rs12505410 T > Grs2725252 T > G(intron variants)

105 Caucasian Haplotype GG:" rate of MMR

Yes [174]

OCT1/SLC22A1 181C > T 32 Caucasian No $ MMR No [175]480G > C 229 Caucasian 480GG:

" rate of LORYes [160]

480G > C 33 Caucasian $ Response No [154]1022C > T 15 Japanese $ Conc. in

leukocytes, Css

No [17]

1022C > T 34 Japanese $ Cl No [164]1022C > T 82 Asian $ Cmin No $ CCR, MMR No [33]1201G > A 132 Caucasian 1201GA: " MMR Yes [176]480G > C1222A > G

118 Brazilian $ Response No [173]

480G > C1222A > G

84 Caucasian $ Cmin No $ Response No [90]

156T > C480G > C1022C > T1222A > G

67 Japanese $ Cmin No 1222GG: " MMR Yes [32]

181C > T1393G > A

74 Caucasian $ Cl No [67]

181C > T480G > C

167 Caucasian 1222AA/AG:# median EFS, OS

Yes [177]

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Number ofPatients




Refs

1222A > G1503G > A

181C > T262T > C659G > T1022C > T1201G > A1222A > G1258delATG1393G > A

136 Caucasian $ Response No [178]

181C > T1222A > G1258delATG�1795G > A

65 Caucasian �1795AA: " overallinadequate response

Yes [163]

181C > T480G > C848C > T859C > G1022C > T1258delATG

189 Caucasian Combination ofvariants: " MMR

Yes [166]

181C > T480G > C848C > T859C > G1258delATG

60 Caucasian 480CG/GG: # Cl, " Cmin Yes 480CG/GG: # EFS Yes [89]

23 variants including 181C > T480G > C1222A > G1258delATG

336 Caucasian 1258delATG: " failure Yes [179]

1022C > T1222A > G1386C > A

111 Indian $ Cmin No $ Thrombocytopenia No [157]

1201A > G1222A > G1239G > A1239delATG1275_1276 + 6T

153 Caucasian 1275_1276 + 6T:" time to MMR

Yes [180]

Haplotype including 1222A > Gand ABCB1 variants

38 Asian effect on Cl Yes $ Response No [170]

OCTN1/SLC22A4 1507C > T 189 Caucasian 1507TT: # MMR, $ CMR Yes [166]1507C > T 54 Caucasian 1507CC/CT: " TTP Yes [181]

54 Caucasian �207GC/GG: " TTP Yes [181]

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Number ofPatients




Refs

�207C > G�2087G > C

OCTN2/SLC22A5 38T > C516A > C

94 Caucasian $ Css No [137]

OATP1A2/SLCO1A2 �361G > A 34 Japanese -361GG: # reduced Cl Yes [182]516A > C 189 Caucasian $ MMR No [166]�361G > A516A > C

118 Brazilian $ CMR, MMR No [183]

OATP1B3/SLCO1B3 334T > G 67 Japanese $ Cmin No $ MMR No [32]334T > G 15 Japanese 334TT: " conc. in leukocytes Yes [17]334T > G 86 Egyptian $ HR, CR, MR No [184]334T > G 118 Brazilian 334TT: # CMR, $ MMR Yes [183]

Sorafenib

ABCB1 3435C > T 33 Japanese " Risk for grade �3 skin rash Yes [185]2677G > T/A3435C > T

54 Caucasian $ AUC No " Risk for grade �3 toxicity Yes [34]

ABCC2 �24C > T 33 Japanese "Risk for grade �3 skin rash Yes [185]ABCG2 34G > A

421C > A1143C > T

54 Caucasian $ AUC No " Risk for grade �3 toxicity Yes [34]

SLC15A2 rs2257212 C > T 174 Koreans " PFS for CT and TT vs CC Yes [186]Sunitinib

ABCB1 3435C > T 31 Asian 3435CC: " Cmin Yes 3435CC: " risk for rash,mucositis, diseaseprogression

Yes [187]

2677G > T/A3435C > T

92 Caucasian $ AUC No [188]

1236C > T2677G > T/A3435C > T

333 Mainly Caucasian Haplotype CGT:" risk for HFS, " PFS

Yes [35]

1236C > T2677G > T/A3435C > T

136 Caucasian Haplotype TCG: " PFS Yes [189]

1236C > T2677G > T/A3435C > T

19 Japanese Haplotype TTT:$ AUC

No [190]

101 Caucasian $ PFS, OS, toxicity No [191]

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Number ofPatients




Refs

1236C > T2677G > T/A3435C > T

1236C > T2677G > T/A3435C > T

88 Mainly Caucasian 1236TT: # PFS, OS Yes [192]

1236C > T2677G > T/A3435C > T

96 Mainly Caucasian 1236TT: " time todose reduction

Yes [193]

1236C > T2677G > T/A3435C > T

65 Korean $ Risk for grade �3 toxicity No [194]

1236C > T2677G > T/A3435C > T

114 Mainly Caucasian 3435TT: " Cl Yes [195]

1236C > T2677G > T/A3435C > T

97 Asian 1236T, 2677T, 3435T, haplotypeTTT: # risk for neutropeniahaplotype TTT: # PFS, OS

Yes [196]

ABCG2 34G > A 136 Caucasian $ PFS No [189]421C > A 19 Japanese 421CA, 421AA:

" higher AUC, oral ClYes [190,197]

421C > A 101 Caucasian $ PFS, OS, toxicity No [191]421C > A 65 Korean 421AA: " risk for grade �3 toxicity Yes [194]421C > A 97 Asian 421AA: # risk for neutropenia Yes [196]421C > A 219 Japanese 421A: " risk for thrombocytopenia Yes [198]

�15622C > T 1143C > T 219 Caucasian Haplotype TT: " risk forgrade �2 toxicity

Yes [199]

�15622C > T421C > A

333 Mainly Caucasian 421AA: " risk for hypertension Yes [35]

�15622C > T421 C > A1143 C > T

114 Mainly Caucasian $ Cl No [195]

34G > A421C > A1143C > T

92 Caucasian 421AA: " higher AUC Yes [188]

aSymbols and abbreviations:$, no effect; ", increased; #, decreased; C, concentration; Cmin, minimum concentration; Css, concentration at steady-state; CCR, complete cytogenetic response; Cl, clearance; CMR,complete molecular response; CR, cytogenetic response; EFS, event-free survival; HR, hematological response; LOR loss of response; MCR, major cytogenetic response; MMR, major molecular response; MR,molecular response; OS, overall survival; PK, pharmacokinetics; TTP: time to progression.

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[84,85]; for ABCG2, the intron variants �15622C > T (rs559306529) and 1143C > T(rs2622604), and the two coding sequence variants 34G > A (rs2231137, p.V12 M),421C > A (rs2231142, p.Q141K), have been intensely studied [84,86].

Table 4 summarizes studies investigating the effects of transporter genetic variants on thepharmacokinetics and outcome of the selected TKIs imatinib, sorafenib, and sunitinib. Withrespect to the investigated ABC transporters, the clinical relevance of common genetic variantsin humans for TKI pharmacokinetics and TKI resistance appears to be limited despite theconvincing functional evidence of ABC transporters in TKI transport in vitro and in knockoutmouse experiments. Based on recent studies [27,87] it is likely that rare rather than commontransporter genetic variants will have much larger effects on TKI disposition, and their analysesshould therefore be the focus of future research. The reasons for the difficulty in translating thepreclinical in vitro and knockout mouse studies on ABC transporters into the clinical context aremanifold. Interpretation of pharmacogenetic association studies can be difficult owing to thecomplex variables involved, such as the small sample sizes in most studies, different ethnicities,different genetic variants tested from one study to another, studying only common geneticvariants, and further confounders such as underlying disease and the presence of transporterswith redundant function. In addition, as detailed in the next paragraph, differential regulation ofABC transporter expression may be an important reason for variable TKI responses.

Notably, despite the limited role of uptake transporters in the in vitro uptake of imatinib, inaddition to the other TKIs (Table 3), several pharmacogenetic association studies have beenperformed to investigate the impact of common and rare OCT1/SLC22A1 genetic variants onimatinib pharmacokinetics and response, as well as few studies on OCTN1/SLC22A4, OCTN2/SLC22A5, OATP1A2/SLCO1A2, and OATP1B3/SLCO1B3 (Table 4). Here again, most studieshave focused on variants that were previously shown to have consequences for the transport ofprototypic substrates as well as for transporter expression and drug disposition [50,71,88](Table S1). Similarly to findings for ABC transporters, the results of these clinical studies areambiguous and sometimes contradictory. For example, one study detected a significantassociation between the genetic variant c.480G > C (Leu160Phe) in OCT1/SLC22A1 andminimum imatinib plasma concentrations [89], while others did not [32,90]. No further variantswere associated with imatinib pharmacokinetic parameters. Based on the notion that cellularimatinib uptake is independent from OCT1 (Table 3), correlations between OCT1/SLC22A1genetics and clinical outcome of imatinib therapy are surprising, and suggest that the testedgenetic variants are not the primary cause underlying imatinib failure. Instead, it can bepostulated that those OCT1/SLC22A1 variants may be genetically linked to variants in othergenes relevant for imatinib uptake and subsequent action [70].

Role of Transporter Regulation for TKI ResistanceTransporter expression in non-tumor tissue has been investigated in great detail; however, dataon transporter expression, particularly at the protein level, in primary tumor or metastatic tissueare sparse. Transporter expression may change during progression from normal tissue to tumortissue (Figure 3). For example, in histologically normal human liver, genetic variants in SLC22A1/OCT1 cause interindividual differences in OCT1 expression [49]. However, in hepatocellularcarcinoma, epigenetic regulation of OCT1 is the predominant mechanism, and DNA methylationof the SLC22A1/OCT1 gene leads to significant downregulation of OCT1 mRNA and protein inthe tumor tissue [91]. Furthermore, epigenetic regulation by miRNAs has been identified as animportant regulatory mechanism of ABC transporter expression in hepatocellular carcinoma andother cancer types [92–94].

In addition to genetic variation and epigenetics, numerous studies have identified variousregulatory mechanisms that affect transporter expression at different levels (Figure 4).


Outstanding QuestionsWhich membrane transporters medi-ate the uptake of the currentlyapproved TKIs?

Which membrane transporters areinvolved in the uptake and efflux ofthe >130 novel TKIs currently beingevaluated in clinical trials for cancerand other disease entities?

What innovative techniques in preclini-cal and early clinical drug testing arenecessary to enhance the study ofdrug transport processes and TKIresistance?

How can systems-medicineapproaches and omics technologiesbe used to identify the factors whichaffect drug transporter expression andfunction and which predict tumorresponse or resistance?

How can an improved understandingof the molecular basis of drug resis-tance help to devise rational drug com-binations for selected patientpopulations or individual patients?

For example, at the transcriptional level, many drugs and xenobiotics, such as rifampin,phenobarbital, and carbamazepine, induce hepatic expression of ABCB1 and ABCG2, as wellas of OATP1B1 and OATP1B3, by binding as ligands to the nuclear receptors pregnane Xreceptor (PXR) and/or constitutive androstane receptor (CAR) [95–97]. Moreover, severalpathophysiological conditions such as inflammation or hypoxia have been shown to affectABCB1 gene expression by activating stress-induced transcription factors [97,98]. At the post-transcriptional level, miRNAs have been recognized as important regulators of ABC transporterexpression [93,94]. It may be speculated that treatment with a drug leads to a distinct cellularmiRNA expression pattern and a drug-specific ABC transporter expression pattern [94]. Forexample, earlier studies showed that chronic TKI exposure may lead to induction of drug effluxtransporters ABCB1 and ABCG2, and thereby reduced intracellular accumulation, as shown forimatinib in the target CML cells [99]. Recent studies indeed identified an association between thedownregulation of particular miRNAs and concomitant upregulation of ABCG2 expression byimatinib treatment of leukemic K562 cells in vitro [100].

In addition to the above-described mechanisms affecting transporter expression, rapid regula-tion of transporter activity may occur by post-translational modifications within minutes. Forexample, a recent study showed that phosphorylation of OCT2 by the Src family kinase Yes1 isessential for OCT2 transport function [101]. Inhibition of Yes1 by TKIs including dasatinibresulted in reduced OCT2 function in vitro and in vivo [101]. Other post-translational modifica-tions include glycosylation, which is apparently required for proper trafficking of ABCB1 to theplasma membrane or for protein stability, without affecting transport function [102]. Moreover,interacting proteins may be required for targeting of transporters to the plasma membrane [102].Finally, the lipid environment of the membrane, in which the transporters are embedded, andtransporter–lipid interactions have been identified as important determinants of ABCB1 andABCG2 expression and function [103].

These numerous studies demonstrate the variety of regulatory processes by which transporterexpression and function can be modulated. However, much more work will be necessary to fullyexplore all these different aspects of regulation, not only in non-tumor but also in tumor tissues,and their role in TKI resistance.

Concluding Remarks and Future DirectionsSmall-molecule TKIs have emerged as the backbone of cancer therapy and the treatment ofother diseases. However, in contrast to the remarkable success of imatinib in the improvementof outcome of patients with CML, the degree of success of other TKIs is variable, and mostachieve only moderate survival benefits. Similar to the situation when using conventionalcytotoxic chemotherapy, the occurrence of drug resistance often counteracts successfultreatment. In addition to somatic variation of the targeted tyrosine kinases, insufficient drugexposure may significantly contribute to drug resistance. Drug uptake and efflux transporters inenterocytes, hepatocytes, proximal tubule kidney epithelial cells, and not least in the cancer cellsthemselves, may preclude sufficient intracellular TKI accumulation.

While the role of ABC efflux transporters in in vitro transport and in vivo disposition in knockoutmice has been established for most TKIs, their role in TKI disposition and occurrence of drugresistance in humans – as assessed by pharmacogenetic association studies – is less clear. As aconsequence, future research is warranted to include not only rare variants but also a compre-hensive definition of clinical phenotypes based on representative and valid sample sizes [104].Moreover, the currently identified and investigated uptake transporters play apparently only aminor role in TKI uptake (see Outstanding Questions). Therefore, novel techniques such asthermal proteomics profiling [105,106] will be necessary to identify novel transporters engaged inTKI uptake. Once these are identified, the contributions of non-genetic, epigenetic, and common


and rare genetic variants in these transporter genes on the function and expression of thetransporters need to be assessed. In this context, it will be important to study transporterexpression not only in the primary tumor but also in metastases because therapeutic responsesto TKI may differ. As recently demonstrated for clear cell renal cell carcinoma, drug transporterexpression was shared between primary tumors and metastases, indicating comparable drugtransport processes and drug effects in tumors and metastases [62]. However, this may bedifferent for other tumor entities and needs to be investigated.

Because cell lines often do not correspond to the conditions in primary tumors or metastases invivo [62], other innovative techniques such as patient-derived tumor grafts (PDx) may be used tostudy drug transport processes [107]. Furthermore, novel imaging techniques such as hyper-spectral stimulated Raman scattering [108] and PET imaging with TKIs [109] will help to trackand quantify intracellular TKI concentrations in vivo to improve TKI efficacy.

Moreover, owing to the large number of factors (non-genetic, genetic, epigenetic, regulatory)that may affect transporter expression and function, systems-medicine approaches and omicstechnologies [79] will be required for identifying novel mechanisms of drug resistance, includingdrug transporters, as well as molecular signatures and genotypes that may predict tumorresponse or resistance. This improved understanding of the molecular basis of drug resistancewill foster the design of rational drug combinations for selected patient populations. This is ofparticular importance in view of the fact that currently >130 novel TKIs are being evaluated inclinical trials (Figure 1B), not only for the treatment of cancer but also for other disease entities.

It can be expected that these strategies of identifying drug transporters and resistance mecha-nisms will also be important when inhibitors for kinases other than tyrosine kinases are clinicallydeveloped [110], as well as for novel classes of anticancer agents such as small-moleculecheckpoint inhibitors [111].

AcknowledgmentsThis work was supported in part by the Robert-Bosch Foundation, Stuttgart, Germany, the Interfaculty Centre for

Pharmacogenomics and Pharma Research (ICEPHA) Grant Tübingen–Stuttgart, Germany, the Bundesministerium für

Bildung und Forschung, Germany (LiSyM 031L0037) and European Commission Horizon 2020 UPGx grant (668353). The

authors thank Bernd Borstel for drafting the figures.

Supplemental InformationSupplemental information associated with this article can be found online at http://dx.doi.org/10.1016/j.tips.2016.08.003.

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