Title DRUG DISPOSITION AND DRUG-DRUG INTERACTION DATA...

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Title

DRUG DISPOSITION AND DRUG-DRUG INTERACTION DATA IN 2013 FDA NEW DRUG

APPLICATIONS: A SYSTEMATIC REVIEW

Jingjing Yu, Tasha K. Ritchie, Aditi Mulgaonkar, and Isabelle Ragueneau-Majlessi

Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, USA (J.Y.,

T.K.R., A.M., I.R-M.)

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on September 30, 2014 as DOI: 10.1124/dmd.114.060392

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Running Title Page

a) Running title: A review of drug disposition and DDIs in the 2013 NDAs

b) Corresponding author: Isabelle Ragueneau-Majlessi, Drug Interaction Database Program,

Department of Pharmaceutics, University of Washington, Box 357610, Seattle, WA 98195,

Phone: 206.543.4669, Fax: 206.543.3204, E-mail: [email protected]

c) Number of text pages:

Number of tables: 8

Number of figures: 1

Number of references: 44

Number of words in the Abstract: 168

Number of words in the Introduction: 662

Number of words in the Discussion: 146

d) Abbreviations:

AUC, area under the curve; BCRP, breast cancer resistance protein; BID, twice daily, BLA, biologic

license application; BSEP, bile salt export pump; CYP, cytochrome P450; DDI, drug-drug interaction;

DIDB, Drug Interaction Database®; EM, extensive metabolizer; EMA, European Medicines Agency;

FDA, Food and Drug Administration; FMO, flavin monoooxygenase; HI, hepatic impairment; HLM,

human liver microsomes; IM, intermediate metabolizer; ITC, international transporter consortium;

MATE, multidrug and toxin extrusion; MRP, multidrug resistance-associated protein; NDA, new drug

application; NME, new molecular entity; NTCP, sodium-taurocholate co-transporting polypeptide; OAT,

organic anion transporter; OATP, organic anion transporting polypeptide; OCT, organic cation

transporter; OCTN, organic cation transporter, novel; PBPK, physiologically based pharmacokinetic,

PGx, pharmacogenetics; P-gp, P-glycoprotein; PM, poor metabolizer; PXR, pregnane X receptor; QD,

once daily; RI, renal impairment; SD, single dose; TDI, time-dependent inhibition; TID, three times a

day; UGT, UDP-glucuronosyltransferase; URAT, urate transporter


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Abstract

The aim of the present work was to perform a systematic review of drug metabolism, transport,

pharmacokinetics, and DDI data available in the NDAs approved by the FDA in 2013, using the

University of Washington Drug Interaction Database©, and to highlight significant findings. Among 27

NMEs approved, 22 (81%) were well-characterized with regard to drug metabolism, transport or organ

impairment, in accordance with the FDA drug interaction guidance (2012), and were fully analyzed in

this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least

one drug metabolizing enzyme or transporter. However, in vivo, only half (n = 11) showed clinically

relevant drug interactions, with most related to the NMEs as victim drugs and CYP3A being the most

affected enzyme. As perpetrators, the overall effects for NMEs were much less pronounced, compared to

when they served as victims. In addition, the pharmacokinetic evaluation in patients with hepatic or renal

impairment provided useful information for further understanding of these complex interactions.


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Introduction

Pharmacokinetic drug interactions can lead to severe side effects, and can result in early termination of

development, or withdrawal of drugs from the market. Thus, determining the risk of clinically significant

drug-drug interactions (DDIs) during the development of a new molecular entity (NME) is critical. With

the advancement of pharmaceutical research and novel drug discoveries, it is becoming increasingly

challenging for pharmaceutical companies to design safer and more effective drug molecules, as well as

to devise new approaches circumventing DDIs mediated by various enzymes and transporters (Huang et

al., 2008). Over the past several years, the pharmaceutical regulatory agencies in the US (Food and Drug

Administration, FDA) and Europe (European Medicines Agency, EMA) have issued a series of guidance

documents for in vitro and in vivo drug interaction studies that must be conducted during drug

development (FDA, 1997; FDA, 1999; FDA, 2006; EMA, 2012; FDA, 2012). These guidelines include

assessment of the DDI potential of NMEs, using individual pre-clinical evaluations and clinical

pharmacology studies, with recommended probe substrates and specific inhibitors/inducers of drug

metabolizing enzymes and transporters. Based on the results of these evaluations, one can then predict the

interaction potential of the NME with a series of drugs that are likely to be co-administered (evidence-

based theoretical interactions). The guidance documents reflect a drive by regulatory authorities to

harmonize approaches and study designs to allow for better assessment and comparison of different

NMEs, and to facilitate consistent communication of drug interaction risks to healthcare providers,

through drug labeling. Both the FDA and EMA documents emphasize the use of an integrated and

mechanistic approach to evaluate DDIs and, as such, have dramatically changed the outlook for assessing

the potential incidence of clinically significant interactions, in pre- and post-marketing stages (Huang et

al., 2008; Zhao et al., 2012).

This review encompasses an overall detailed analysis of the pre-clinical and clinical enzyme- and

transporter-mediated DDIs observed for new drug applications (NDAs) approved by the FDA in 2013,

highlighting the main mechanistic findings and discussing their clinical relevance. The analysis was


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performed using the University of Washington Drug Interaction Database® (DIDB) drug interactions,

pharmacogenetics, and organ impairment modules (http://www.druginteractioninfo.org). All of the

parameters were directly extracted from the database, and the changes in mean AUC values are presented

in this review. The DIDB data were curated from a thorough review of the NDA approval packages,

including, but not limited to, the product labels and clinical pharmacology and biopharmaceutics reviews

for each of the NMEs, available at the FDA approved drugs website (Drugs@FDA,

http://www.accessdata.fda.gov/scripts/cder/drugsatfda/). The analysis utilized a mechanistic approach for

evaluating DDIs reported for the individual NMEs, based on the decision criteria recommended by the

most recent FDA drug interaction guidance document (FDA, 2012). In addition to the individual enzyme

and transporter pre-clinical and clinical studies reported in the NDAs, studies looking at mechanisms for

enzyme-transporter interplay, as well as those conducted in diseased populations (i.e., hepatic and renal

impairment) were also systematically analyzed. The metrics used for evaluation of clinical studies is the

area under the curve (AUC) ratio, defined as AUCinhibited or induced/AUCcontrol, with a clinically-significant

interaction resulting in an AUC ratio ≥ 2. In addition, important or significant labeling modifications or

recommendations were also noted. In 2013, a total of 25 NDAs and 2 biologic license applications

(BLAs) were approved by the FDA. A summary of the NDA/BLAs, including DDIs, pharmacogenetics

(PGx), and organ impairment studies, as well as therapeutic classes and approval dates, is presented in

Table 1, with the chemical structures presented in Supplemental Table 1. Eight of these (30%) were

cancer treatments, including 4 kinase inhibitors, making oncology the most represented therapeutic area.

Among the 27 NMEs approved in 2013, 22 (81%) had drug metabolism or transporter data available, and

18 (67%) provided hepatic and/or renal impairment studies, and therefore were fully analyzed in this

review. The NDAs without those studies were not evaluated in this review and comprised radioactive

diagnostic or therapeutic agents, as well as a cytolytic antibody.

Pre-clinical Drug Interaction Data

Metabolism and Enzyme-Mediated DDIs


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The most recent drug interaction guidance released by the FDA has focused on criteria which would

streamline the evaluation procedure for drug metabolizing enzymes, highlighting decision criteria for

evaluation of NMEs as substrates, inhibitors or inducers of clinically important cytochrome P450 (CYP)

enzymes, including: CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A. Furthermore, with the growing

interest in studying DDIs mediated by UGTs, the guidance also highlights the decision criteria for in vitro

and in vivo studies to evaluate the same for UGT1A1, 1A3, 1A4, 1A6, 1A9, 2B7, and 2B15 (FDA, 2012).

In accordance with the guidance, the metabolic profile of the NMEs approved in 2013 were well-

characterized from in vitro studies using recombinant enzymes and human liver tissues such as human

liver microsomes (HLMs) or human hepatocytes. Twenty two compounds were shown to be metabolized

by at least one enzyme, though the majority of compounds were primarily metabolized by CYPs. Not

surprisingly, CYP3A4/5 was shown to metabolize the largest number of NMEs in vitro (n = 17, 77% of

NMEs evaluated), although not necessarily as the major enzyme contributing to the drug’s metabolism. In

vivo studies further confirmed that 10 of these compounds (45% of NMEs evaluated) were CYP3A

substrates, with systemic exposure increases of greater than 20% (FDA cut-off: 25%), when co-

administered with potent or moderate CYP3A inhibitors, resulting in the following maximum AUC

ratios: ibrutinib, 23.9; simeprevir, 6.5; riociguat, 2.5; macitentan, 2.3; vilanterol (in combination with

umeclidinium), 1.9; dabrafenib, 1.6; fluticasone (in combination with vilanterol), 1.4; ospemifene, 1.4;

vortioxetine, 1.3; and dolutegravir, 1.2. Inhibition of transporters, especially P-glycoprotein (P-gp), might

also contribute to the increased exposure of some of the drugs which were shown to also be P-gp

substrates (reviewed in the next section). The highest AUC ratio was observed for ibrutinib (over 20) with

concurrent use of ketoconazole (400 mg QD 6 days), indicating the primary role of CYP3A in the

disposition of the drug. Accordingly, contraindications of strong and moderate CYP3A inhibitors and

strong CYP3A inducers were clearly addressed in the product label (FDA, 2013j). The next largest

interaction observed was simeprevir, with an AUC ratio over 5 when co-administered with the CYP3A

inhibitor erythromycin (500 mg TID 7 days), suggesting simeprevir as a sensitive substrate of CYP3A.


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Based on these results, the potential for DDIs with moderate or strong inducers or inhibitors of CYP3A

must be considered prior to and during treatment with this drug, as indicated in the product label (FDA,

2013r). Other CYP isoforms, such as CYP2D6, 2C19, 2C9, and 2C8 were involved in the metabolism of

8, 7, 4, and 3 NMEs, respectively (Figure 1A).

In addition, some NMEs were primarily metabolized by non-CYP enzymes. For example, sofosbuvir, as a

prodrug, is metabolically activated through pathways involving sequential hydrolysis by human cathepsin

A (CatA) or carboxylesterase 1 (CES1) and subsequent phosphorylation to the active triphosphate

compound by kinases. Additionally, the major metabolic pathway of 3 NMEs, canagliflozin, dolutegravir,

and bazedoxifene, is through phase II glucuronidation by UGT2B7/1A9, UGT1A1, and UGT1A1/1A10,

respectively. In vivo, in the case of canagliflozin, its systemic exposure (AUC) was slightly increased by

21% when co-administered with the general UGT inhibitor probenecid. Similarly, for dolutegravir, the

concurrent use of the UGT1A1 inhibitor atazanavir significantly increased the AUC by 91%.

When NMEs were considered as perpetrators, the potential to inhibit drug metabolizing enzymes was

investigated in vitro using HLMs or cDNA-expressed enzymes to determine the inhibitory mechanisms

(e.g., reversible or time-dependent inhibition) and inhibition potency. Seventeen (77%) NMEs inhibited at

least one CYP enzyme (Table 3, Figure 1B), with the most affected enzymes being CYP3A4 (n = 11),

2C9 (n = 10), 2C19 (n = 10), 2C8 (n = 9), and 2D6 (n = 8). Simeprevir was also found to inhibit UGT1A1

weakly in vitro. With regard to the inhibitory mechanism, most inhibitory drug interactions with CYP

enzymes are reversible with the exception of mertansine, the active component of ado-trastuzumab

emtansine, an antibody-drug conjugate for the treatment of cancer, which showed time-dependent

inhibition of CYP3A4 with an IC50 of 0.16 µM after preincubation, while no inhibition was observed

under co-incubation conditions up to 0.678 µM. However, no further in vitro studies were available to

obtain the time-dependent inhibition parameters.

In line with the drug interaction guidance (FDA, 2012), the basic model was first applied by estimating

intrinsic clearance values (R value) in the absence and presence of an inhibitor (or inducer) using both in


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vitro and clinical pharmacokinetic data to determine if an in vivo DDI study was warranted. Based on the

R1 values (for reversible inhibition), the majority of the in vitro inhibitory interactions were not

considered clinically relevant (R1 ≤ 1.1). Among drugs with R1 > 1.1, in vivo studies with sensitive CYP

substrates found only 2 NMEs with positive enzyme inhibition, where simeprevir weakly inhibited

intestinal but not hepatic CYP3A (midazolam, AUC ratio = 1.4), CYP1A2 (caffeine, AUC ratio = 1.3)

and CYP2C19 (omeprazole, AUC ratio = 1.3), while alogliptin weakly inhibited CYP2D6

(dextromethorphan, AUC ratio = 1.3). More complex models, such as mechanistic static models and

PBPK models, were also well incorporated for some drugs in predicting the in vivo DDI risks. For

example, canagliflozin showed positive inhibition of CYP2B6 (IC50 = 16 µM) in vitro and a large R1

value (2.51), however, a physiologically based pharmacokinetic (PBPK) model showed no interaction

with co-administration of the CYP2B6 probe bupropion, hence, no in vivo study was warranted. It should

be noted that although bupropion is considered as the most sensitive CYP2B6 substrate, currently there

are no sensitive CYP2B6 substrates available based on the FDA guidance classification (AUC ratio of at

least 5-fold, or decrease in oral clearance of 80% or more when co-administered with a known inhibitor)

(FDA, 2012).

In terms of enzyme induction potential, 21 NMEs were evaluated using human hepatocytes, and 6 (29%)

were found to induce CYP enzyme expression to some extent (Table 4): alogliptin (CYP3A4), dabrafenib

(CYP2B6/3A4), dolutegravir (PXR activator), macitentan (CYP3A4), ospemifene (CYP1A2/2B6/3A4),

and trametinib (CYP2B6/3A4). In vivo, only dabrafenib (R3 = 0.54) was found to be a moderate CYP3A

inducer, and decreased the systemic exposure of the co-administered CYP3A probe substrate midazolam

by 74% and 61% in AUC and Cmax, respectively. Interestingly, ospemifene, in addition to inducing

CYP2B6 mRNA expression, also showed inhibition of the same enzyme in HLMs, and overall in vivo,

the exposure of the CYP2B6 probe substrate bupropion was not significantly affected (AUC ratio = 0.83,

FDA cut-off: 25%). In contrast, for the prodrug eslicarbazepine acetate, none of its pharmacologically

active metabolites, including eslicarbazepine (main), (R)-licarbazepine, and oxcarbazepine, induced


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CYP3A when tested at concentrations up to 100 µg/mL (eslicarbazepine Cmax = 15 µg/mL) in the in vitro

screenings performed by the sponsor. However, when tested in vivo, co-administration of eslicarbazepine

acetate decreased the exposure of simvastatin by 50% and the oral contraceptive ethinyl estradiol by 31%,

which may be due to CYP3A induction. Warfarin exposure was also decreased by 21%, which may be

reflective of possible CYP2C9 induction. In vitro, eslicarbazepine was also found to not induce CYP1A,

CYP2C19, UGTs, or sulfotransferase by the sponsor. It should be noted that the magnitude of induction

from the positive controls used in these CYP induction screenings were lower than expected. In addition,

oxcarbazepine was previously reported to induce CYP3A mRNA expression as well as enzyme activity in

human hepatocytes (Fahmi et al., 2010).

In summary, regarding drug metabolizing enzymes, CYP3A was involved in the metabolism of the most

NMEs in vitro (17 of 22), and 10 were further confirmed to be substrates of CYP3A, in vivo. In addition,

the largest DDI observed in vivo was caused by CYP3A inhibition, with ibrutinib being the victim drug.

As perpetrators, 17 drugs (77%) showed positive inhibition or induction towards at least one enzyme in

vitro. In contrast, in vivo, only 3 NMEs (simeprevir, alogliptin, and eslicarbazepine acetate) were found to

be enzyme inhibitors and 2 NMEs (dabrafenib and eslicarbazepine acetate) to be enzyme inducers,

highlighting the challenge of translating inhibition and induction data from in vitro to in vivo. The overall

in vivo effect of NMEs as perpetrators was much less pronounced, with the largest AUC ratio less than 2,

compared to when the NMEs served as victim drugs, where the largest AUC ratio observed was greater

than 20.

Transport and Transporter-Mediated DDIs

In addition to drug metabolizing enzymes, the recent guidance documents, in conjunction with the

International Transporter Consortium (ITC), have advocated the importance of transporters as additional

driving mechanisms for DDIs, along with aiding the enzyme-mediated DDI events (Giacomini et al.,

2010; Huang et al., 2010; Tweedie et al., 2013). The previous FDA guidance (FDA, 2006) only

specifically named P-gp as a transporter that NMEs should be screened against, while the most recent


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document adds six additional transporters to be considered: breast cancer resistance protein (BCRP),

organic anion transporting polypeptides 1B1 and 1B3 (OATP1B1, OATP1B3), organic cation transporter

2 (OCT2), and organic anion transporters 1 and 3 (OAT1 and OAT3). This guidance document

recommends that all NMEs should be screened as inhibitors for these 7 transporters, and also as substrates

for P-gp and BCRP. Additionally, depending on the route of elimination, NMEs should be screened as

substrates for the remaining 5 transporters (> 25% renal excretion, or unknown – OAT1, OAT3 and

OCT2; > 25% biliary excretion, or unknown – OATP1B1, OATP1B3). Finally, other transporters, such as

MRPs, MATEs and/or BSEP should also be considered, when appropriate (FDA, 2012).

Out of the 22 NDAs approved in 2013 which contain DDI studies, nearly all of them (n = 20) include

some type of transporter study, which is reflective of the recent guidance document. Within those 20

NDAs, more than 120 in vitro transporter assays are described, screening compounds against a total of 16

transporters. Not surprisingly, P-gp was the most represented transporter, both in substrate and inhibition

assays performed, as well as positive interactions identified. Though the most recent guidance document

is still in draft form, and only recently released, the remaining transporters recommended therein were

also well represented, along with the following additional transporters: multidrug resistance-associated

protein 2 (MRP2), OCT1, OATP2B1, bile salt export pump (BSEP), multidrug and toxin extrusion

transporter 1 (MATE1), organic cation transporters, novel, 1 and 2 (OCTN1, OCTN2), sodium-

taurocholate co-transporting polypeptide (NTCP), and urate transporter 1 (URAT1).

With the exception of eslicarbazepine acetate and bazedoxifene, all of the NMEs were screened as

substrates of P-gp, and 12 were shown to be substrates, in vitro. In the case of eslicarbazepine acetate, in

vivo drug interaction studies were preemptively performed with two known P-gp inhibitors, cyclosporine

and verapamil, and no interaction was observed in either study. For bazedoxifene, in vitro screening

studies had been published previously (Shen et al., 2010), thus no studies were included in the filing. Out

of the 12 positive P-gp in vitro results, 7 in vivo studies were performed, with the largest interaction

observed in the case of sofosbuvir (AUC ratio = 3.6, when co-administered with cyclosporine). In


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addition to being a P-gp substrate, sofosbuvir was also one of the 4 compounds shown to be a BCRP

substrate in vitro. As cyclosporine is also a BCRP inhibitor, BCRP may have contributed to the effect

seen in the in vivo interaction study. The next largest interaction observed also involved sofosbuvir, with

another NME, simeprevir. Both are antiviral treatments for hepatitis C and could be co-administered in

patients, and both NMEs were shown to be P-gp substrates, however only simeprevir was shown to be a

P-gp inhibitor, in vitro. An AUC ratio of 3.2 was observed for sofosbuvir when co-administered with

simeprevir, however, at the time of NDA submission, the clinical study was still ongoing, and

comparisons were made to historical data.

Of the remaining NMEs tested as the victims in in vivo DDI studies, six were tested as P-gp substrates

(afatinib, alogliptin, canagliflozin, riociguat, umeclidinium, and vilanterol) and two as OATP substrates

(simeprevir and macitentan). With the exception of riociguat, all resulted in < 50% change in AUC with

co-administration of the transporter inhibitor or inducer. Despite this, the product label for afatinib

contains a warning that co-administration of P-gp inhibitors or inducers may alter afatinib exposure and

the dose should be adjusted as necessary, and if tolerated (FDA, 2013i). In the case of riociguat, there was

a 150% increase in AUC when co-administered with ketoconazole. While most of this is likely due to

CYP3A inhibition, the sponsor postulates some of the effect could be due to inhibition of P-gp and/or

BCRP, as riociguat was shown to be a substrate of both transporters, in vitro. Therefore, the product label

advises to consider starting at a lower dose of riociguat when strong CYP3A, P-gp, or BCRP inhibitors

are co-administered (FDA, 2013a).

With regard to inhibitory interactions, P-gp was again the most represented transporter, with 7 NMEs

shown to be inhibitors in vitro (Table 5). Only one NME, trametinib, resulted in both [I]1/IC50 and

[I]2/IC50 values below the guidance cut-off values (0.1 and 10, respectively), therefore no in vivo studies

were warranted (FDA, 2012). Another compound, vilanterol, present in two approved NDAs with

fluticasone or umeclidinium, was shown to inhibit P-gp in vitro, although the IC50 was estimated to be

greater than 100 μM, and given that systemic concentrations of vilanterol are in the sub-nM range, the


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[I]1/IC50 value is far below 0.1. Moreover, as an orally inhaled drug, there is no expected gut interaction

with P-gp. Two NMEs, canagliflozin and simeprevir, resulted in both [I]1/IC50 and [I]2/IC50 values greater

than the cut-off values, warranting in vivo interaction studies. However, in vivo, only limited increases in

digoxin AUC were observed when co-administered with canagliflozin (AUC ratio = 1.2) or simeprevir

(AUC ratio = 1.4). The product label for both compounds reflects this, stating that patients taking digoxin

concomitantly should be monitored appropriately (FDA, 2013r; FDA, 2013k).

In addition, three NMEs resulted in [I]1/IC50 values less than 0.1, but [I]2/IC50 values greater than 10

(vortioxetine, ibrutinib and afatinib). In the case of vortioxetine, the [I]2/IC50 value was neither provided

nor discussed in the NDA Reviews and was calculated by the DIDB Editorial Team based on a 10 mg

dose. For ibrutinib, instead of performing an in vivo interaction study, the sponsor used PBPK modeling

to simulate ibrutinib drug absorption kinetics. The model predicted quick absorption, generally completed

in less than 2.5 hours, therefore by staggering the dose of a P-gp substrate and ibrutinib by at least 2.5

hours, the potential for an interaction could be minimized. The ibrutinib product label, however, does

warn that co-administration of oral narrow therapeutic index P-gp substrates, such as digoxin, may result

in increased blood concentrations of those compounds (FDA, 2013j). Finally, in the case of afatinib, the

sponsor submitted data from three clinical settings demonstrating no clinically relevant effects of afatinib

on orally administered P-gp substrates, including digoxin, thus, no further in vivo studies were carried out.

There were two NMEs where only in vivo (no in vitro) studies were performed with regard to P-gp

inhibition – alogliptin and eslicarbazepine acetate. Both compounds had no effect on digoxin AUC (0.3%

and 5.7% decrease, respectively). Fexofenadine, a substrate of P-gp and OATPs, was also used as a

victim in an alogliptin in vivo DDI study where, in contrast, an effect was observed (though fairly small,

AUC ratio of 1.26), which could be reflective of inhibition of P-gp, as well as OATPs.

Six NMEs were shown to inhibit OATP1B1 and/or 1B3 in vitro. Of those, only 2 had Cmax/IC50 values

above the FDA guidance cut-off of 0.1 (FDA, 2012) – dabrafenib and simeprevir (Table 6). In the case of

dabrafenib, the sponsor evaluated the DDI risk using static mathematical models, as described in the FDA


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guidance, which resulted in R values equal to 1.0 for both transporters, below the cut-off value of 1.25,

suggesting that the DDI risk was minimal and an in vivo study was not warranted. For simeprevir, three in

vivo studies were performed with simeprevir as a perpetrator using statins – atorvastatin, simvastatin

(both also CYP3A and P-gp substrates) and rosuvastatin. The largest change in AUC, and, in fact, the

largest change for any of the transporter-based in vivo inhibition studies, was with rosuvastatin where an

AUC ratio of 2.8 was observed. For atorvastatin and simvastatin, the effect was marginally less, with

AUC ratios equal to 2.2 and 1.7, respectively. Consequently, the product label for simeprevir advises

careful monitoring of patients taking any statins, and particularly with rosuvastatin and atorvastatin not to

exceed a daily dose of 10 or 40 mg statin per day, respectively, when co-administered with simeprevir

(FDA, 2013r).

In summary, 3 NMEs were shown to be in vivo inhibitors of P-gp – alogliptin, canagliflozin, and

simeprevir, with simeprevir also inhibiting OATP1B1 in vivo. In vitro, in contrast, 85% of NMEs showed

a positive interaction with at least one transporter, either as a substrate or inhibitor. Only three NMEs

showed no interaction with any transporter, and in all three cases, P-gp was the only transporter tested.

These data indicate that when tested in vitro, many NMEs appear to be substrates or inhibitors of at least

one transporter. However, this does not necessarily translate in vivo, and the clinical relevance of the

transporter interaction may be minimal, especially when compared to the effect of drug metabolizing

enzymes. Reasons for this may include extensive protein binding of drugs, resulting in low free

circulating concentrations, as well as interplay between drug metabolizing enzymes and transporters, both

of which have been reviewed recently (Benet, 2010; Giacomini et al., 2010; Chu et al., 2013). For

example, canagliflozin and simeprevir are both greater than 98% protein bound, while simeprevir is also a

substrate and inhibitor of several CYPs, all of which may contribute to the moderate effects observed in

the digoxin interaction studies mentioned earlier. In addition, with the exception of P-gp, for which the

probe substrates or inhibitors used both in vivo and in vitro were fairly consistent among the NMEs, less


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consensus was observed regarding probe substrates and inhibitors for the other transporters, which may

confound translation of in vitro data to the clinical setting.

Pharmacogenetic Studies

As documented in the FDA drug interaction guidance (FDA, 2012), comparative pharmacokinetic data in

subjects with various enzyme genotypes may be used to identify metabolic pathways and estimate the

possible extent of interactions. Two NMEs, dolutegravir and umeclidinium (in combination with

vilanterol), provided pharmacogenetic analyses to evaluate the effect of the genetic status of primary

enzymes on the pharmacokinetics of these drugs. In the case of dolutegravir, which is primarily

metabolized by the polymorphic enzyme UGT1A1, with some contribution from CYP3A, the effect of the

genetic status of UGT1A1 on dolutegravir pharmacokinetics was evaluated through a meta-analysis using

samples (n = 89) collected from subjects with low (poor metabolizers, PMs, *28/*28, *28/*37, *37/*37),

reduced (intermediate metabolizers, IMs, *1/*28, *1/*37, *28/*36, *36/*37), and normal (extensive

metabolizers, EMs, *1/*1, *1/*36, *36/*36) UGT1A1 activity. The analysis showed that, compared to

subjects with normal UGT1A1 activity, the AUC and Cmax increased by 30-50% and 20-30%,

respectively, while clearance decreased by 20-30% in subjects with low and reduced UGT1A1 activity.

According to the sponsor, as the therapeutic index of dolutegravir is wide and adverse effects are mild and

not associated with higher exposures, the effect of UGT1A1 polymorphisms on dolutegravir exposure is

not considered clinically significant, hence no dose adjustment is required for subjects with the UGT1A1

*28/*28 and *28/*37 genotypes (FDA, 2013y). The influence of CYP3A4, CYP3A5, and PXR variants

on dolutegravir pharmacokinetics was also explored, and polymorphisms in CYP3A4/5 were found not to

be associated with any pharmacokinetic changes. Similarly, for umeclidinium, which is mainly

metabolized by the polymorphic enzyme CYP2D6, no clinically significant changes were observed in

systemic exposure in CYP2D6 PMs compared with EMs (specific alleles not available in the NDA

Review). Overall, polymorphisms in primary metabolizing enzymes did not affect the pharmacokinetic

parameters of the metabolized drugs to any clinically significant extent.


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In addition, for simeprevir, while no genotyping data was available and no specific pharmacogenetic

analyses were performed in the NDA reviews, it was discovered that in Phase 3 clinical trials subjects of

East Asian ancestry (n=14) had 3.4-fold higher exposure of simeprevir than the pooled Phase 3

population, and that this higher exposure was associated with an increased frequency of adverse reactions.

The increase in simeprevir exposure is may be clinically relevant and is likely due to some as-yet-

unidentified genetic variation, therefore the product label states, “There are insufficient safety data to

recommend an appropriate dose for patients of East Asian ancestry. The potential risks and benefits

should be carefully considered prior to use in patients of East Asian ancestry” (FDA, 2013r).

Clinically Significant Drug-Drug Interactions

Clinical DDI studies assess the exposure to a potential victim drug (AUC) with and without co-

administration of the perpetrator and the AUC ratio often constitutes the main quantitative DDI outcome

measurement. However, assigning a clinical significance to the pharmacokinetic outcome can be

complex. Additional information is often needed on the drug pharmacokinetic-pharmacodynamic

relationship, the within- and between-individual variability in response, and the clinical context of patient

status and underlying disease. Nevertheless, it is usually acknowledged that a 2-fold change in drug

exposure will often trigger dosing recommendations and thus, an AUC ratio of 2 was considered in this

analysis as a cut-off for further consideration. Overall, it was found that 10 of the 22 drugs analyzed

(45%) had at least one metabolism-based in vivo DDI study with a change in exposure of clinical

significance (AUC increase ≥ 2-fold or AUC decrease ≥ 50% for the affected drugs), with NMEs being

mainly victim drugs. All clinically significant inhibition and induction results observed with NMEs as

victims or perpetrators are presented in Tables 7 (inhibition) and 8 (induction).

For inhibition studies (Table 7), alteration of CYP3A activity was the most common underlying

mechanism, except for ospemifene and vortioxetine. For ospemifene, its exposure was increased by

almost 3-fold when co-administered with the multi-CYP inhibitor fluconazole (200 mg QD for 8 days).

Ospemifene is primarily metabolized by CYP3A, 2C9, and 2C19, each of these enzymes being


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responsible for approximately 40-50%, 25%, and 25% of its clearance, respectively, and fluconazole is

known to inhibit all three of these enzymes. Of note, concurrent administration of the strong CYP3A

inhibitor ketoconazole increased ospemifene AUC by only 1.4-fold, while co-administration of the

CYP2C19 inhibitor omeprazole increased its exposure by 1.2-fold (FDA, 2013t). In addition, ospemifene

is also sensitive to CYP induction, as concomitant dosing with rifampin decreased ospemifene AUC by

58% and Cmax by 51%. Co-administration of both fluconazole and rifampin is contraindicated with

ospemifene (FDA, 2013t). As for vortioxetine, which is primarily metabolized by CYP2D6, co-

administration of the strong CYP2D6 inhibitor bupropion (150 mg BID) increased vortioxetine exposure

by 2.3-fold. A reduction in vortioxetine dose by half is recommended when a strong CYP2D6 inhibitor

(bupropion, fluoxetine, paroxetine, or quinidine) is co-administered (FDA, 2013e). Two NMEs, ibrutinib

and simeprevir, were found to be sensitive substrates of CYP3A, with AUC ratios greater than 5 when co-

administered with known CYP3A inhibitors (ketoconazole and erythromycin, respectively). For ibrutinib,

based on the very large increase in AUC observed (over 20-fold) when co-administered with

ketoconazole (400 mg QD for 6 days), concomitant use of strong CYP3A inhibitors which are taken

chronically is not recommended. Preliminary data also showed that the strong CYP3A inducer rifampin

caused a 14-fold decrease in ibrutinib Cmax and a 12.5-fold decrease in ibrutinib AUC, therefore the

concomitant use of strong CYP3A4 inducers should be avoided, as a dose adjustment cannot be

recommended (there is, however, no specific recommendation when ibrutinib is co-administered with a

weak or moderate inducer (FDA, 2013j)). Simeprevir exposure was increased by 7.2- and 6.5-fold when

co-administered with the strong CYP3A4 inhibitor ritonavir and the moderate CYP3A4 inhibitor

erythromycin, respectively. In addition, co-administration of the CYP3A inducers rifampin and efavirenz

resulted in decreases in simeprevir exposure close to 50%. Based on these results, concomitant use of

simeprevir with strong and moderate CYP3A inhibitors or inducers should be avoided (FDA, 2013r).

Regarding clinical induction data, significant inductions were mainly related to NMEs as victim drugs

and, in most cases, involved induction of CYP3A by known inducers (Table 8). When NMEs were


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considered as perpetrators, two compounds were found to significantly induce CYP3A, dabrafenib and

eslicarbazepine acetate. Dabrafenib significantly reduced the AUC of the sensitive CYP3A substrate

midazolam by over 70%, while eslicarbazepine acetate decreased simvastatin exposure by 50%. Both

drugs have recommendations in their labels regarding their possible inductive effect on co-administered

drugs (FDA, 2013w; FDA, 2013c).

Finally, there were very few purely transporter-based drug interactions with over 2-fold changes in

substrates exposure. Only the interaction between sofosbuvir (400 mg single dose) and cyclosporine

(administered as a high single dose of 600 mg) was related to inhibition of P-gp and BCRP, and yielded

an increase in sofosbuvir AUC of almost 4-fold. However, the exposure of the predominant circulating

inactive metabolite (GS-331007) was unchanged, and considering sofosbuvir safety margins, the effect of

cyclosporine on sofosbuvir pharmacokinetics was not considered clinically significant by the sponsor and

no dose adjustment is required. Also of note, sofosbuvir plasma exposure was also increased by co-

administration of simeprevir (150 mg QD 12 or 24 weeks), an inhibitor of P-gp (AUC ratio of 3.2, as

previously discussed).

Overall, when a cut-off of 2-fold change in drug exposure was considered for clinical relevance, almost

half of the NMEs analyzed had clinically significant DDIs, most of them related to the NMEs as victim

drugs. Not surprisingly, the underlying mechanism for a large number of these interactions was inhibition

or induction of CYP3A.

Hepatic and Renal Impairment studies

Hepatic and renal impairment are important disease conditions to consider while evaluating the potential

plasma exposure that a particular NME would achieve clinically. In addition, such organ impairment may

overlap with different critical disease conditions (e.g., in cancer patients), or may be associated with

patients in certain age groups (e.g., geriatrics). As such, individuals may be receiving a multitude of

medications, and this could lead to more complex DDIs potentially occurring due to multiple mechanisms

involving metabolism and/or transport. Moreover, depending on the severity of impairment of these


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eliminating organs (mild, moderate or severe), the probability and extent of these DDIs may be affected

significantly. Therefore, it has become critical to assess the pharmacokinetics of NMEs in impaired

populations in the pre-marketing drug development stages. For the purpose of this review, the major

outcome measurement of the NMEs was the AUC change or AUC ratio (AUCimpaired/AUCcontrol), studied

using patients with HI or RI and healthy control populations. For this assessment, similar to the in vivo

clinical significance evaluation, an AUC ratio of 2 was considered as a cut-off to systematically evaluate

the NDAs for any dosing and labeling recommendations for the NMEs in question.

Among the 15 NMEs evaluated for HI studies, 4 demonstrated an AUC ratio greater than 2 in the HI

patients versus normal controls. The highest AUC ratio (6.0) was observed for ibrutinib in the moderate

HI population (Child-Pugh B). However, the ibrutinib label states that there was “insufficient data to

recommend a dose in patients with baseline HI,” and recommended that ibrutinib should be avoided in

these patients (FDA, 2013j). The next largest change in AUC was observed for simeprevir, showing 2.4-

and 5.2-fold increases in AUC for moderate and severe HI patients, respectively. Although no dose

recommendations have been provided for simeprevir in these patients, the label states that the potential

risks and benefits of simeprevir should be carefully considered prior to the use in patients with moderate

or severe HI (FDA, 2013r). Moreover, bazedoxifene (with conjugated estrogens) demonstrated AUC

ratios of 3.6, 2.1, and 4.3 in patients with mild, moderate, and severe HI, respectively, hence has been

contraindicated in women with any known HI or disease (FDA, 2013g). The increases in AUC observed

in HI patients may be attributed to the fact that these compounds all undergo extensive hepatic

metabolism, and have also shown high biliary excretion, with > 80% being eliminated in the feces.

Finally, sofosbuvir showed AUC ratios greater than 2 in both moderate and severe HI patients. However,

considering its renal elimination pathway (discussed in the following RI section), no dose adjustment has

been recommended for any HI patients (FDA, 2013v).

With regard to RI studies, 4 out of 17 NMEs demonstrated an AUC ratio greater than 2 in RI patients

versus normal controls. Gadoterate meglumine showed the largest effect in RI patients, with 3.5- and 9.3-


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fold increases in AUC, and 87.3% and 61.3% decreases in clearance, in moderate and severe RI patients,

respectively. These data are reflective of the elimination pathway of gadoterate meglumine, where renal

clearance approximates total clearance. No dose adjustment was suggested for RI populations; however,

the product label contains a black box warning for the risk of the life-threatening adverse event,

nephrogenic systemic fibrosis, in patients with chronic severe kidney disease (FDA, 2013f). The next

largest change in AUC involved alogliptin, where AUC ratios of 2.0, 3.6, and 4.7 were observed in

moderate, severe RI, and the end-stage renal disease (ESRD) populations, respectively, consistent with

fact that almost 80% of alogliptin is eliminated renally. Accordingly, a dose adjustment is recommended

for moderate and severe RI, and ESRD patients (FDA, 2013q). Sofosbuvir, as a prodrug, is eliminated

approximately 80% through renal excretion in the form of metabolites. Mild increases in AUC (AUC

ratios between 1.6 and 2.7) were observed for sofosbuvir in the mild, moderate, severe RI and ESRD

populations. However, the AUC of main (inactive) metabolite, GS-331007, was found to increase by 5.5-

fold in severe RI, and 13.8- and 21.7-fold in ESRD, 1 h before and after dialysis, respectively. Based on

these results, no dose adjustment is needed for patients with mild or moderate renal impairment.

However, as the safety and efficacy of sofosbuvir has not been established in patients with severe RI or

ESRD requiring hemodialysis, dosing recommendations have not been made for these populations (FDA,

2013v). Similarly, the prodrug eslicarbazepine acetate is also primarily eliminated by renal excretion as

eslicarbazepine (the main active metabolite) and its glucuronide conjugate, together accounting for more

than 90% of total metabolites excreted in the urine. In RI patients, 1.6-, 2.1-, 2.5-, and 1.4-fold increases

in eslicarbazepine AUC, as well as 37.9%, 52.6%, 60.6%, and 28.9% decreases in clearance, were

observed in mild, moderate, severe RI and ESRD, respectively. Hence, a dose reduction has been

recommended in the product label for patients with moderate and severe RI (FDA, 2013c).

Overall, 19 NMEs were assessed for the influence of HI or RI on drug pharmacokinetics. One NME,

sofosbuvir, showed significant pharmacokinetic effects in both HI and RI populations (AUC ratio ≥ 2),

however due to the metabolism and excretion properties of the compound, no dose adjustments were


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recommended for either population. Six additional NMEs showed significant effects in the impaired

population (three for HI and three for RI), which resulted in contraindications for respective populations

in five out of six product labels. These data illustrate the importance of studying drug pharmacokinetics in

impaired populations, as the AUC ratios observed in HI or RI patients may be on the same order of

magnitude as those observed in clinical drug interaction studies.

Conclusion

The evaluation of DDIs during the drug development process has profoundly changed over the past two

decades and an integrated and mechanistic approach to these studies is highly recommended. The results

of the detailed analysis of NDA reviews for drugs that have been approved by the FDA in 2013 were

generally consistent with current regulatory recommendations. The drug interaction profiles were well-

characterized using probe markers and known inhibitors and inducers of drug metabolizing enzymes.

Moreover, as significant scientific efforts have focused on elucidating the mechanisms and clinical

significance of drug transporters, many NMEs were also thoroughly evaluated for transporter-based

DDIs. Additionally, a majority of NMEs were also assessed in hepatic or renal impaired populations.

These evaluations shows that, using the knowledge gained from dedicated pre-clinical and clinical

studies, the most significant clinical drug interactions can be identified, allowing effective and targeted

dosing recommendations to be made.

Authorship Contributions:

Participated in research design: Yu, Ritchie, Molgaonkar, Ragueneau-Majlessi

Performed data analysis: Yu, Ritchie, Molgaonkar, Ragueneau-Majlessi

Wrote or contributed to the writing of the manuscript: Yu, Ritchie, Molgaonkar, Ragueneau-Majlessi


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Footnotes

A.M. current affiliation: Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA


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Figure 1. Quantitation of NMEs acting as substrates or inhibitors of drug metabolizing enzymes, in vitro. A – Contribution of phase I and II enzymes to NME metabolism (n = 22); * esterases, nucleases, and flavin monooxygenases (FMOs). B – CYP isoforms inhibited by NMEs (n = 17).


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TABLE 1. NDA/BLAs approved by the FDA in 2013 (ordered by approval date)

Compound Name DDI PGx HI/RI Therapeutic Class Approval Date Reference

Alogliptin Y N Y Endocrinology 01/25 (FDA, 2013q)

Mipomersen Y N Y (RI) Endocrinology 01/29 (FDA, 2013m)

Pomalidomide Y (in vitro) N N Hematology/Oncology 02/08 (FDA, 2013u)

Ado-trastuzumab emtansine Y (in vitro) N Y (RI) Hematology/Oncology 02/22 (FDA, 2013l)

Ospemifene Y N Y Obstetrics/Gynecology 02/26 (FDA, 2013t)

Technetium Tc-99M tilmanocepta N N N Diagnostics 03/13 (FDA, 2013o)

Gadoterate meglumine N N Y (RI) Diagnostics 03/20 (FDA, 2013f)

Dimethyl fumarate Y N N Neurology/Neurosurgery 03/27 (FDA, 2013x)

Canagliflozin Y N Y Endocrinology 03/29 (FDA, 2013k)

Fluticasone and vilanterol Y N Y Pulmonary/Critical Care 05/10 (FDA, 2013d)

Radium Ra 223 dichloridea N N Yb Hematology/Oncology 05/15 (FDA, 2013{)

Dabrafenib Y N N Hematology/Oncology 05/29 (FDA, 2013w)

Trametinib Y N Yb Hematology/Oncology 05/29 (FDA, 2013p)

Afatinib Y N Y (HI) Hematology/Oncology 07/12 (FDA, 2013i)

Dolutegravir Y Y Y Infectious Disease 08/12 (FDA, 2013y)

Vortioxetine Y N Y Psychiatry 09/30 (FDA, 2013e)

Conjugated estrogens and bazedoxifene Y N Y Obstetrics/Gynecology 10/03 (FDA, 2013g)

Riociguat Y N Y Cardiology 10/08 (FDA, 2013a)

Macitentan Y N Y Cardiology 10/18 (FDA, 2013s)

Flutemetamol F-18a N N N Diagnostics 10/25 (FDA, 2013z)

Obinutuzumaba N N Yb Hematology/Oncology 11/01 (FDA, 2013h)

Eslicarbazepine acetate Y N Y Neurology/Neurosurgery 11/08 (FDA, 2013c)

Ibrutinib Y N Y Hematology/Oncology 11/13 (FDA, 2013j)

Luliconazole Y N N Infectious Disease 11/14 (FDA, 2013n)

Simeprevir Y N Y Gastroenterology 11/22 (FDA, 2013r)

Sofosbuvir Y N Y Gastroenterology 12/06 (FDA, 2013v)

Umeclidinium and vilanterol Y Y Y Pulmonary/Critical Care 12/08 (FDA, 2013b)

Y – Studies included in the NDA Reviews, N – Studies not included in the NDA Reviews

a Not evaluated in this review

b Population PK data presented, not included in this review


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TABLE 2. Enzymes and transporters involved in the NDA/BLA elimination pathways

Drug Name Main Elimination Route Enzymes Involved a Transporters Involved a Reference

Ado-trastuzumab emtansine (T-DM1)

DM1: metabolism; no mass balance study for T-DM1

T: proteolytic enzymes; DM1: CYP3A4, CYP3A5

P-gp b (FDA, 2013l)

Afatinib minimal metabolism, fecal (85% mostly

as parent) FMO, CYPs P-gp, BCRP c (FDA, 2013i)

Alogliptin minimal metabolism, renal (76% mainly

as parent), fecal (13%) CYP2D6, CYP3A4 none b (FDA, 2013q)

Canagliflozin

metabolism, fecal (60.4% mainly as parent), renal (32.5 mainly as

metabolites)

UGT2B4, UGT1A9, CYP3A4, CYP2D6

P-gp, MRP2 (FDA, 2013k)

Conjugated estrogens and bazedoxifene

E: metabolism, renal (as parents and metabolites); B: metabolism, fecal (85%

mainly as parent)

E: CYP3A4; B: UGT1A1, UGT1A10, UGT1A8

not tested d (FDA, 2013g)

Dabrafenib metabolism, fecal (71%), renal (23%)

CYP2C8, CYP3A4, CYP2C9, CYP2C19

P-gp b (FDA, 2013w)

Dimethyl fumarate metabolism, exhalation of CO2 (60%),

renal (15.5%) esterases, tricarboxylic acid cycle

(non-CYP) none b (FDA, 2013x)

Dolutegravir metabolism, fecal (53% as parent), renal

(31% mainly as metabolites) UGT1A1, UGT1A3, UGT1A9 P-gp, BCRP c (FDA, 2013y)

Eslicarbazepine acetate metabolism, renal (90% as parent and

metabolites) non-CYP hydrolytic enzymes, UGT1A9, UGT2B4, UGT2B17

not tested (FDA, 2013c)

Fluemetamol F-18 fecal (52%), renal (37%) not available not tested (FDA, 2013z)

Fluticasone and vilanterol F: metabolism, fecal (90%); V:

metabolism, renal (70%), fecal (30%) CYP3A4 P-gp b (FDA, 2013d)

Gadoterate meglumine renal (86.6% as parent) none not tested (FDA, 2013f)

Ibrutinib metabolism, fecal (80.6% mostly as

metabolites) CYP3A4, CYP2D6 none b (FDA, 2013j)

Luliconazole metabolism (topical use) CYP2D6, CYP3A4 not tested (FDA, 2013n)

Macitentan metabolism, renal (50% as inactive

metabolites), fecal (24%) CYP3A4 (99%), CYP2C19 none (FDA, 2013s)

Mipomersen metabolism in tissues, renal (<2%)

endonucleases and exonucleases (non-CYP)

none b (FDA, 2013m)

Obinutuzumab minimal hepatic or renal elimination proteolytic enzymes not tested (FDA, 2013h)

Ospemifene metabolism, fecal (75% mainly as

metabolites) CYP3A4 (40-55%), CYP2C9 (25%),

CYP2C19 (25%) none b (FDA, 2013t)

Pomalidomide

metabolism, renal (73% mainly as metabolites), fecal (15% mainly as

metabolites)

CYP1A2 (54%), CYP3A4 (30%), CYP2C19, CYP2D6, non-CYP

hydrolytic enzymes P-gp b (FDA, 2013u)


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Radium Ra 223 dichloride fecal none not tested (FDA, 2013{)

Riociguat

metabolism, fecal (53% mainly as metabolites), renal (40% mainly as

metabolites) CYP1A1, CYP3A, CYP2C8, CYP2J2 P-gp, BCRP c (FDA, 2013a)

Simeprevir

metabolism, fecal (91% mainly in metabolites)

CYP3A4, CYP2C8, CYP2C19 OATP1B1, OATP1B3, P-gp, MRP2, OATP2B1, mBcrp e

(FDA, 2013r)

Sofosbuvir

metabolism, renal (80% mainly as metabolites), fecal (14% mainly as

parent)

non-CYP hydrolytic enzymes (Cathepsin A, carboxylesterase 1, etc.)

P-gp, BCRP (FDA, 2013v)

Technetium Tc-99M tilmanocept

not available none not tested (FDA, 2013o)

Trametinib

minimal metabolism, fecal (50-75% as parent), renal (<20%)

CYP1A2, CYP2C9, CYP2D6, CYP2C19, CYP3A4, non-CYP

hydrolytic enzymes none c (FDA, 2013p)

Umeclidinium and vilanterol

U: metabolism, fecal (92% as parent and metabolites); V: metabolism, renal (70%

as metabolites), fecal (30% as metabolites)

U: CYP2D6, CYP3A4, UGTs; V: CYP3A4, CYP2D6

U: P-gp, OCT1, OCT2, V: P-gp

(FDA, 2013b)

Vortioxetine

metabolism, fecal (59% as metabolites), renal (26% as metabolites)

CYP2D6, CYP3A4/5, CYP2C19, CYP2C9, CYP2A6, CYP2C8,

CYP2B6, UGTs none b (FDA, 2013e)

Enzymes described as major contributors in the NDA Reviews are presented in bold

a - determined in vitro

b - only P-gp tested

c - only P-gp and BCRP tested

d - previously published study showed bazedoxifene is a substrate of P-gp

e - murine Bcrp was tested as a stable cell-line expressing human BCRP was not available


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TABLE 3. Enzyme inhibition interactions, in vitro to in vivo translation

Perpetrator IC50 (µM) R1 valuea AUC ratio Reference

Alogliptin 27% at 100 µM (CYP2D6) N/Ab 1.30 (Dextromethorphan, CYP2D6) (FDA, 2013q)

Ado-trastuzumab emtansinec 0.16d (CYP3A4) N/Ab (FDA, 2013l)

Canagliflozin 16 (CYP2B6) 2.31 PBPK modeling negative (bupropion) (FDA, 2013k)

75 (CYP2C8) 1.28

55 (CYP2C9) 1.40 1.06 (S-warfarin, CYP2C9)

1.02 (Glyburide, CYP2C9)

39 (CYP2C19) 1.54 N/T

65 (CYP2D6) 1.32 N/T

18 (CYP2E1) 2.11 N/T

27e (CYP3A4) 1.78 1.07 (Ethinyl estradiol, CYP3A)

1.06 (Levonorgestrel, CYP3A4)

1.12 (Simvastatin, CYP3A)

Dabrafenib 8.2 (CYP2C8) 1.69 N/T (FDA, 2013w)

7.2 (CYP2C9) 1.79 N/T

22 (CYP2C19) 1.26 N/T

16e (CYP3A4) 1.36 N/T

Dimethyl fumarate 27.6 (CYP2D6) <1.1 (FDA, 2013x)

Dolutegravir 12.5f (CYP1A2) N/Ab (FDA, 2013y)

33c,e (CYP3A4) N/Ab

Eslicarbazepine acetateg 38% at 393 µM (Eslicarbazepine,

CYP2C9)

N/Ab (FDA, 2013c)

912 (Eslicarbazepine, CYP2C19) 1.2 1.35 (Phenytoin, CYP2C19)

27%e at 393 µM (Eslicarbazepine,

CYP3A4)

N/Ab

38.8%h at 393 µM

(Eslicarbazepine, UGT1A1)

-

49% at 1180 µM ((R)-

licarbazepine, CYP2C19)

N/Ab

666 (Oxcarbazepine, CYP2C19) <1.1j

Fluticasone (F) and vilanterol (V) 4.0 (F, CYP2B6) <1.1 (FDA, 2013d)

0.58 (F, CYP2C8) <1.1

2.4 (F, CYP2C9) <1.1


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5.5 (F, CYP2C19) <1.1

3.2 (F), 12 (V) (CYP2D6) <1.1

0.74e (F), 3.5 (V), (CYP3A4) <1.1

Ibrutinib 4.8i (CYP2B6) 1.08 (FDA, 2013j)

12i (CYP2C8) 1.03

5.9i (CYP2C9) 1.06

6.6i (CYP2C19) 1.06

12.5i (CYP2D6) 1.03

10.0e,i (CYP3A4) 1.04

Luliconazole 0.029i (CYP2C19) 1.55 Post-marketing requirement (FDA, 2013n)

0.13i (CYP3A4) 1.22 Post-marketing requirement

Macitentan 21 (CYP2C8) 1.03j (FDA, 2013s)

5.0 e,i (CYP2C9) 1.12j

24e (CYP3A4) 1.02j

Ospemifene 7.8 (CYP2B6) 1.81j 0.83 (Bupropion, CYP2B6) (FDA, 2013t)

36.4 (CYP2C8) 1.17j

10 (CYP2C9) 1.63j 0.96 (S-warfarin, CYP2C9)

22.5e (CYP2C19) 1.28j 0.83 (Omeprazole, CYP2C19)

48.7 (CYP2D6) 1.13j

37.9k (CYP3A4) 1.17j

Riociguat 0.8 (CYP1A1) 1.70j N/T (FDA, 2013a)

44 (CYP2C19) 1.01j

Simeprevir 59.7 (CYP2A6) 1.49j N/T (FDA, 2013r)

49.1 (CYP2C8) 1.59j N/T

86.1 (CYP2C19) 1.34j 1.32 (Omeprazole, CYP2C19)

42.9 (CYP2D6) 1.68j 0.98 (Dextromethorphan, CYP2D6)

84.5e (CYP3A4) 1.34j 2.19 (Atorvastatinl, CYP3A)

1.90 (Erythromycinm, CYP3A)

1.71 (Simvastatinl, CYP3A)

1.43 (Midazolam, CYP3A)

1.32 (Ritonavir, CYP3A)

1.20 (Cyclosporine, CYP3A)

119i (UGT1A1) N/Ab

28% at 300 µM (CYP1A2) N/Ab 1.26 (Caffeine, CYP1A2)

46.5% at 300 µM (CYP2C9) N/Ab 1.03 (S-warfarin, CYP2C9)


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Trametinib 0.34 (CYP2C8) 1.24 N/T (FDA, 2013p)

4.1 (CYP2C9) 1.02

5 (CYP2C19) 1.02

Umeclidinium (U) and vilanterol (V) 0.1 (U), 11.5 (V), (CYP2D6) <1.1 (FDA, 2013b)

8.0 (U), 3.5e (V), (CYP3A4) <1.1

Vortioxetine 9.34 (CYP2C8) <1.1 (FDA, 2013e)

15e (CYP2C9) <1.1

N/A – not applicable; N/T – not tested in NDA Reviews; bolded values exceed FDA cut-off to warrant in vivo study

a R1 cut-off value: 1.1

b R values not calculated due to the lack of availability of the appropriate parameter (e.g. Ki, EC50, or Emax) used for calculation

c Antibody-drug conjugate, the active drug, mertansine, was evaluated

d IC50 values obtained via pre-incubation, no inhibition observed via co-incubation, however no further experiments addressing the inhibition

mechanism

e Multiple IC50 or percent inhibition values provided with different substrates or systems, the most potent is presented

f IC50 value obtained in recombinant enzyme, IC50 estimated to be >33 µM in HLMs

g Prodrug, pharmacologic active drugs eslicarbazepine (main), (R)-licarbazepine, and oxcarbazepine were evaluated in vitro h Activation

i Ki valuesj R value computed by the DIDB Editorial Teamk IC50 obtained with omeprazole as the substrate, IC50 estimated to be >100 µM with

midazolam or testosteronel Inhibition of OATP1B1 may also contribute

m Inhibition of P-gp may also contribute


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TABLE 4. Enzyme induction interactions, in vitro to in vivo translation

Perpetrator Induction effect R3 valuea AUC ratio Reference

Alogliptin 2- to 6-fold, 28% of rifampin at 100 µM (CYP3A4) - 1.08 (Midazolam, CYP3A) (FDA, 2013q)

Dabrafenib 32-fold, 320% of phenytoin at 30 µM (CYP2B6) - (FDA, 2013w)

30-fold, 150% of rifampin at 30 µM (CYP3A4) - 0.26 (Midazolam, CYP3A)

Dolutegravir 58% of rifampin up to 10 µM (PXRb) - 0.95 (Midazolam, CYP3A) (FDA, 2013y)

Eslicarbazepine acetate c no induction up to 393 µM (Eslicarbazepine,

CYP1A, CYP2C19, CYP3A4, UGTs,

sulfotransferases)

no induction up to 40 µM ((R)-licarbazepine,

CYP3A4)

no induction up to 40 µM (Oxcarbazepine,

CYP3A4)

- 0.77 (S-warfarin, CYP2C9) (FDA, 2013c)

- 0.51 (Simvastatin, CYP3A)

- 0.58 (Ethinyl estradiol, CYP3A)

- 0.63 (Levonorgestrel, CYP3A4)

Macitentan 11-fold (5-fold activity) at 10 µM (CYP3A4) - 1.15 (Sildenafil, CYP3A) (FDA, 2013s)

5.8-fold, 121% of rifampin, EC50=1.1-1.2 µM

(PXR)

-

Ospemifene 52.4-fold at 20 µM (1/4 donors, CYP1A2) - (FDA, 2013t)

2.0-fold at 20 µM (2/4 donors, CYP2B6) -

2.4-fold at 20 µM (1/3 donors, CYP3A4) -

Trametinib 76% of phenytoin up to 10 µM (CYP2B6) - (FDA, 2013p)

Emax=37.3%, 67% of rifampin up to 10 µM 0.54 1.16 (Everolimus, CYP3A)

EC50=2.7 µM (CYP3A4)

50% of rifampin up to 10 µM (PXR) -

N/T – not tested in NDA Reviews; bolded values are below the FDA cut-off to warrant in vivo study

a R3 cut-off value: 0.9

b CYP3A4 mRNA levels were not increased (1.1-fold of vehicle control) with rifampin treatment

c Prodrug, pharmacologic active metabolites eslicarbazepine (main), (R)-licarbazepine, and oxcarbazepine were evaluated in vitro


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TABLE 5. P-gp inhibition interactions, in vitro to in vivo translation

Perpetrator In vitro

substrate

IC50

(μM)

Dose

(mg) [I]1

a (μM) [I]1/IC50 [I]2b (μM) [I]2/IC50

In vivo

victim

AUC

ratio Reference

Trametinib Digoxin 5.5 2 0.04 0.007 11.5 2.09 (FDA, 2013p)

Vilanterol Digoxin >100 0.025 4.4 x 10-4 4.4 x 10-6 c - - (FDA, 2013d;

FDA, 2013b)

Canagliflozin Digoxin 19.3 300 10.5 0.54 2645 137c Digoxin 1.2 (FDA, 2013k)

Simeprevir Paclitaxel 85.9 200 14.5 0.169c 1066 12.4c Digoxin 1.4 (FDA, 2013r)

Sofosbuvi

r

3.2d

Vortioxetine N/S 4.4 10 0.047 <0.1 (0.01c) 105 23.9c N/T - (FDA, 2013e)

Ibrutinib N/S 4.9 560 0.28 <0.1 (0.06c) 5085 >10

(1038c)

N/T - (FDA, 2013j)

Afatinib Digoxin 3.4e 40 0.078 0.023f 329.3 97c N/T - (FDA, 2013i)

N/T- not tested in NDA Reviews, N/S - not specified, bolded values exceed FDA cut-off to warrant in vivo study, 0.1 for [I]1/IC50, 10 for [I]2/IC50

a Mean steady-state total (free and bound) Cmax following administration of the highest proposed clinical dose

b Intestinal exposure, dose (in mol) / 250 mL

c Value computed by the DIDB Editorial Team

d Clinical study ongoing at time of NDA submission therefore final result not available

e Ki value

f [I]/Ki


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TABLE 6. Hepatic OATP inhibition interactions, in vitro to in vivo translation

Perpetrator OATP In vitro substrate IC50

(μM)

Dose

(mg) Cmax (μM) Cmax/IC50 In vivo victim

AUC

ratio Reference

Fluticasone 1B1 N/S 0.2 0.8 1.95 x 10-4 <0.1

(0.001a)

(FDA, 2013d)

Macitentan 1B1 Atorvastatin 6.9 10 0.6 0.087a (FDA, 2013s)

1B3 Estrone-3-sulfate 14 0.043a

Sofosbuvir 1B3 Fluo-3 203.5 400 2.0 <0.1 (0.01a) (FDA, 2013v)

Trametinib 1B1 Estradiol 17-β-

glucuronide

1.3 2 0.04 0.031 (FDA, 2013p)

1B3 Estradiol 17-β-

glucuronide

0.94 0.043

Dabrafenib 1B1 N/S 1.4 150 2.84 2.03 N/T, Rb = 1.0 - (FDA, 2013w)

1B3 N/S 4.7 0.60 N/T, Rb = 1.0 -

Simeprevir 1B1 Estradiol 17-β-

glucuronide

0.26 200 14.5 55.9a Rosuvastatin 2.8 (FDA, 2013r)

Atorvastatin 2.2

Simvastatin 1.7

N/T- not tested in NDA Reviews, N/S - not specified, bolded values exceed FDA cut-off (0.1) to warrant further investigation

a Value computed by the DIDB Editorial Team

b R = 1 + (fu x Iin,max/IC50); fu – fraction unbound, Iin,max –maximum [inhibitor] at liver inlet, if R ≥ 1.25, in vivo study is warranted


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TABLE 7. Metabolism-based clinically significant inhibitions, NMEs as substrates or perpetrators

Victim Drug (Dose) Inhibitor (Dose) Enzyme(s)

possibly involved

Max AUC

ratio Study Design / Population Reference

Ibrutinib (120 mg alone,

40 mg with ketoconazole)

Ketoconazole (400 mg QD 6

days)

CYP3A 23.9 (dose-

normalized)

One-sequence / 18 healthy

subjects

(FDA, 2013j)

Simeprevira (150 mg QD

7 days)

Erythromycin (500 mg TID 6.5

days)

CYP3A 6.5 Random Crossover / 24

healthy subjects

(FDA, 2013r)

Ospemifene (60 mg SD) Fluconazole (400 mg on Day 1;

200 mg QD 7 days)

CYP3A, CYP2C9,

CYP2C19

2.8 Random Crossover / 14

post-menopausal healthy

women

(FDA, 2013t)

Riociguat (dose not

available, SD)

Ketoconazole (400 mg QD

repeated doses)

CYP3Aa 2.5 Not provided / healthy

subjects

(FDA, 2013a)

Vortioxetine (10 mg QD

28 days)

Bupropion (75 mg BID Day 1-

3; 150 mg BID 11 days)

CYP2D6 2.3 One-sequence / 24 healthy

subjects

(Chen et al., 2013;

FDA, 2013e)

Macitentan (10 mg SD) Ketoconazole (400 mg QD 24

days)

CYP3A 2.3 Random Crossover / 10

healthy subjects

(FDA, 2013s)

Atorvastatin (40 mg SD) Simeprevir (150 mg QD 12

days)

CYP3Aa,b 2.2 One-sequence / 36 healthy

subjects

(FDA, 2013r)

Maximum changes in the victim AUC are presented. SD: single dose; QD: once daily; BID: twice daily; 2013 NMEs are presented in bold

a Inhibition of P-gp and/or BCRP may also contribute

b Inhibition of OATP1B1 may also contribute


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TABLE 8. Metabolism-based clinically significant inductions, NMEs as substrates or perpetrators

Victim Drug (Dose) Inducer (Dose) Enzyme(s)

possibly involved

Max AUC

decrease (%) Study Design / Population

Reference

(NDA#)

Ibrutinib

(dose not available)

Rifampin

(dose not available)

CYP3A 92.0a Not available (FDA, 2013j)

Macitentan (30 mg on

Day 1; 10 mg QD 11

days)

Rifampin (600 mg QD 7

days)

CYP3A 79.0 One-sequence / 10 healthy

subjects

(Bruderer et al.,

2012; FDA, 2013s)

Midazolam (3 mg SD) Dabrafenib (150 mg

BID repeated dosing)

CYP3A 74.1 Not provided / 12 patients (FDA, 2013w)

Simeprevir (150 mg

QD 14 days)

Efavirenz (600 mg QD

14 days)

CYP3A 70.6 Random Crossover / 23 healthy

subjects

(FDA, 2013r)

Dolutegravir (50 mg

QD 19 days)

Etravirine (200 mg BID

14 days)

UGT1A1, CYP3A 70.5 One-sequence / 15 healthy

subjects

(FDA, 2013y)

Dabrafenib (150 mg

BID 21 days)

Phenytoin (300 mg

BID)

CYP3A, CYP2C8 62.0 1 patient receiving dabrafenib

with phenytoin (control data

from 8 patients)

(FDA, 2013w)

Ospemifene (60 mg

SD)


days)

CYP3A, CYP2C9,

CYP2C19

59.5 Random Crossover / 12

postmenopausal healthy women

(FDA, 2013t)

Vortioxetine (20 mg

SD)

Rifampin (600 mg QD

11 days)

CYP3A, Other

CYPs

54.6 One sequence / 14 healthy

subjects

(Chen et al., 2013;

FDA, 2013e)

Canagliflozin (300 mg

SD)


days)

UGT (2B4/1A9) 51.0 Not provided / healthy subjects (FDA, 2013k)

Simvastatin (80 mg

SD)

Eslicarbazepine

(acetate) (800 mg QD

14 days)

CYP3A 49.4 Random Crossover / 24 healthy

subjects

(Falcão et al., 2013;

FDA, 2013c)

Maximum changes in the victim AUC are presented. SD: single dose; QD: once daily; BID: twice daily; 2013 NMEs are presented in bold

a Preliminary data


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