BJCP
`
`British Journal of Clinical
`Pharmacology
`
`Mechanism-based
`population pharmacokinetic
`modelling in diabetes:
`vildagliptin as a tight
`binding inhibitor and
`substrate of dipeptidyl
`peptidase IV
`
`Cornelia B. Landersdorfer,1,2 Yan-Ling He3 & William J. Jusko1
`
`1Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, USA,
`2Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash
`University, Melbourne, VIC, Australia and 3Translational Science-Translational Medicine, Novartis
`Institutes for BioMedical Research, Cambridge, MA, USA
`
`DOI:10.1111/j.1365-2125.2011.04108.x
`
`Correspondence
`William J. Jusko PhD, Department of
`Pharmaceutical Sciences, State University
`of New York at Buffalo, Buffalo, NY 14260,
`USA.
`Tel.: +716 645 2855
`Fax: +716 645 3693
`E-mail: wjjusko@buffalo.edu
`----------------------------------------------------------------------
`Part of this work has been presented as
`posters at the American Conference of
`Pharmacometrics (ACoP), Tucson, AZ;
`March 9–12, 2008, the NIH Workshop on
`Quantitative and Systems Pharmacology,
`Bethesda, MD; September 25–26, 2008
`and the American Conference of
`Pharmacometrics (ACoP), Mashantucket,
`CT; October 4–7, 2009. The data without
`modelling analysis have been published
`in: He Y-L et al. Clin Pharmacokinet 2007;
`46: 577–588.
`----------------------------------------------------------------------
`Keywords
`DPP-4 inhibitor, mechanism-based
`population modelling, pharmacokinetics,
`type 2 diabetes mellitus, vildagliptin
`----------------------------------------------------------------------
`Received
`1 December 2010
`Accepted
`14 September 2011
`Accepted Article
`Published Online
`10 October 2011
`
`WHAT IS ALREADY KNOWN ABOUT
`THIS SUBJECT
`(cid:129) Vildagliptin is a novel antidiabetic agent that acts
`by inhibiting dipeptidyl peptidase IV (DPP-4).
`(cid:129) DPP-4 inhibition results in higher active
`concentrations of incretin hormone,
`glucagon-like peptide 1 (GLP-1), leading to
`reduced glucose concentrations.
`(cid:129) Mechanism-based modelling of the
`pharmacokinetics (PK) of vildagliptin and its
`DPP-4 inhibition effects in type 2 diabetic
`patients has not been performed.
`
`WHAT THIS STUDY ADDS
`(cid:129) Population pharmacokinetic modelling of the
`vildagliptin concentrations from three different
`doses indicated the presence of a small saturable
`elimination pathway for vildagliptin.
`(cid:129) Simultaneous population modelling of the
`pharmacokinetics and DPP-4 activity in patients
`with type 2 diabetes after treatment with
`vildagliptin revealed:
`1) Saturable binding of vildagliptin to DPP-4 in
`plasma and tissues and partial hydrolysis of
`vildagliptin by DPP-4.
`2) Vildagliptin is both an inhibitor and a substrate
`for DPP-4.
`
`AIMS
`To assess the pharmacokinetics of vildagliptin at different doses and build a
`mechanism-based population model that simultaneously describes vildagliptin
`pharmacokinetics and its effects on DPP-4 activity based on underlying physiology
`and biology.
`METHODS
`Vildagliptin concentrations and DPP-4 activity vs. time from 13 type 2 diabetic
`patients after oral vildagliptin 10, 25 or 100 mg and placebo twice daily for 28 days
`were co-modelled. NONMEM VI and S-ADAPT were utilized for population modelling.
`RESULTS
`A target-mediated drug disposition (TMDD) model accounting for capacity-limited
`high affinity binding of vildagliptin to DPP-4 in plasma and tissues had good
`predictive performance. Modelling the full time course of the vildagliptin-DPP-4
`interaction suggested parallel vildagliptin dissociation from DPP-4 by a slow
`first-order process and hydrolysis by DPP-4 to an inactive metabolite as a disposition
`mechanism. Due to limited amounts of DPP-4, vildagliptin concentrations increased
`slightly more than dose proportionally. This newly proposed model and the
`parameter estimates are supported by published in vitro studies. Mean parameter
`estimates (inter-individual coefficient of variation) were: non-saturable clearance
`36 l h-1 (25%), central volume of distribution 22 l (37%), half-life of dissociation from
`DPP-4 1.1 h (94%) and half-life of hydrolysis 6.3 h (81%).
`CONCLUSIONS
`Vildagliptin is both an inhibitor and substrate for DPP-4. By utilizing the TMDD
`approach, slow dissociation of vildagliptin from DPP-4 was found in patients and the
`half-life of hydrolysis by DPP-4 estimated. This model can be used to predict DPP-4
`inhibition effects of other dosage regimens and be modified for other DPP-4
`inhibitors to differentiate their properties.
`
`© 2011 The Authors
`British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society
`
`Br J Clin Pharmacol
`
`/ 73:3 / 391–401 / 391
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`C. B. Landersdorfer et al.
`
`BJCP
`Introduction
`
`Vildagliptin is a novel antidiabetic agent which belongs to
`the dipeptidyl peptidase IV (DPP-4) inhibitors and acts on
`the incretin system [1]. Glucagon-like peptide 1 (GLP-1)
`which is an incretin hormone is released from the L-cells in
`the gut wall after food ingestion. GLP-1 stimulates insulin
`secretion and inhibits glucagon secretion, but is elimi-
`nated rapidly by DPP-4 [2]. DPP-4 inhibition by vildagliptin
`therefore results in higher active GLP-1 concentrations and
`decreased glucose concentrations [3].The pharmacokinet-
`ics (PK) of vildagliptin in diabetic patients and its effect on
`DPP-4 activity have not been modelled previously.
`In a previous report, vildagliptin PK was assessed in
`diabetic patients using non-compartmental analysis (NCA)
`[4]. Standard NCA is an adequate method for preliminary
`exploration of the PK of a drug. However its value is limited
`in the potential presence of non-linear PK and if the rela-
`tionship between drug concentrations and effects are
`studied based on the mechanism of action of the drug [5].
`In contrast to compartmental modelling, the NCA cannot
`predict the DPP-4 activity vs. time profiles for other than
`the studied dosage regimens.
`Mechanism-based compartmental modelling can
`explicitly account for the full time course of both drug
`concentrations and effects simultaneously for all doses
`and placebo (or baseline). In contrast to empirical models,
`mechanism-based models are more powerful at aiding
`understanding of the underlying kinetic mechanism and
`proposing likely mechanisms of action for a particular
`drug. They can be expanded to include additional physi-
`ological subsystems and support simulation of ‘what if’
`scenarios. A mechanism-based model can be ultimately
`used in pharmacodynamic (PD) simulations to predict the
`probability of successful outcome of anti-diabetic treat-
`ment for different dosage regimens of a drug. In addition
`compartmental modelling by the full population approach
`utilizing state-of-the-art methodology, as reported here,
`takes into account between subject variability in both PK
`and drug effects.
`The aims of our study were to assess the PK of vilda-
`gliptin at different dose levels by population PK modelling
`and to develop a mechanism-based population model
`that simultaneously describes the PK of vildagliptin and its
`effects on DPP-4 activity based on the underlying physiol-
`ogy and biology.
`
`Methods
`
`The study design and bioanalytical methods are briefly
`described below. A detailed report is provided in [4].
`
`Study participants
`The study included 13 patients with type 2 diabetes. The
`subjects had diabetes for at least 3 months prior to screen-
`
`392 / 73:3 / Br J Clin Pharmacol
`
`ing. A washout period from hypoglycaemic drugs for up to
`4 weeks was required. All subjects had to undergo safety
`evaluations before, during and after the study. All adverse
`events were monitored and recorded and regular checks
`of blood and urine chemistry, vital signs and physical
`examination were conducted. All subjects gave their
`written informed consent. The study was approved by the
`local ethics committee and conducted in full compliance
`with the Declaration of Helsinki.
`
`Study design and drug administration
`A randomized, placebo-controlled, double-blind, four-way
`crossover study was conducted. The subjects received
`twice daily oral doses of 10, 25, and 100 mg vildagliptin
`(Galvus™) and placebo as tablets for 28 days. Patients were
`at the study site on day 1 and from the evening of day 26 to
`the morning of day 29 in each study period. During the
`confined periods the patients received a standard diet
`with identical meals for all four treatments. Breakfast and
`dinner were consumed at approximately 30 min after the
`doses. Subjects were requested to abstain from strenuous
`physical exercise and alcohol throughout the study and
`from xanthine-containing foods and beverages during the
`sampling periods.
`
`Sampling schedule and bioanalysis
`Blood samples for measurement of vildagliptin concentra-
`tions were obtained on day 28 of each period pre-dose and
`at 0.25, 0.5, 1, 1.5, 2, 3, 5, 8, 10, 11, 12, 16 and 24 h after the
`morning dose. Blood samples for determination of DPP-4
`activity were collected prior to dosing and at 0.25, 0.75, 1, 2,
`4, 5, 6, 7, 8, 10, 10.5, 11, 12, 14, 16 and 24 h after the morning
`dose. All samples were centrifuged and plasma was frozen
`at -70°C or lower until analysis.
`Vildagliptin concentrations in plasma were determined
`by liquid chromatography-tandem mass spectrometry.The
`lower limit of quantification (LLQ) was 2 ng ml-1, inter-day
`precision was 1.8-3.9% and accuracy was 99.1-104.5%.
`DPP-4 activity was measured by use of H-Gly-Pro-7-amino-
`4-methylcoumarin, which is cleaved by DPP-4 to yield the
`fluorescent product 7-amino-4-methylcoumarin. The LLQ
`for DPP-4 activity was 0.24 mU ml-1 min-1, inter-day preci-
`sion was 3.6-7.9% and accuracy was 96.4-107.2%.
`
`Data from absolute bioavailability study
`In the process of PK model development, data from
`healthy volunteers after a single intravenous dose of
`25 mg vildagliptin [6] were modelled simultaneously with
`the vildagliptin concentrations after oral administration
`from the type 2 diabetic patients. The intravenous data
`were included during model building to distinguish
`between elimination and absorption rate constants, and
`between distribution and absorption rate constants. The
`clearances and volumes were similar for models with and
`without the intravenous data. For the combined PK/PD
`model the bioavailability was fixed to the estimate from
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`co-modelling intravenous and oral data.There was no indi-
`cation for a change of bioavailability with dose and there-
`fore bioavailability was assumed to be constant for the
`range of doses investigated.
`
`Data analysis
`The model for PK and DPP-4 activity was first developed
`utilizing the first order conditional estimation method
`(FOCE) with interaction in NONMEM VI version 1.1 [7]. For
`the combined PK/PD model
`including vildagliptin PK,
`DPP-4 activity, active GLP-1, glucose and insulin, S-ADAPT
`version 1.56 [8] was utilized due to the high level of com-
`plexity and long computation times. The PD part of the
`model,
`including active GLP-1, glucose and insulin,
`is
`described in the companion manuscript [9].The parameter
`estimates and visual predictive checks of the PK and DPP-4
`portions from simultaneous modelling of the full PK/PD
`model in S-ADAPT are shown in this report.
`The PK and DPP-4 profiles from the three different
`dosing regimens and placebo from all patients were mod-
`elled simultaneously. Model discrimination was based on
`the following criteria: 1) visual inspection of the observed
`and predicted profiles, 2) visual comparison of the patterns
`of systematic and random residuals, 3) the objective func-
`tion in NONMEM or S-ADAPT, 4) visual predictive checks
`and 5) precision of parameter estimates.
`For the visual predictive checks, plasma concentration
`and DPP-4 activity time profiles were simulated for 5000
`subjects in NONMEM or S-ADAPT for each competing
`model. From these data we calculated the median and the
`nonparametric 80% prediction interval (10% to 90% per-
`centile) for the predicted vildagliptin concentrations and
`DPP-4 activity. These prediction interval lines were then
`overlaid on the observed data. If the model described the
`data adequately, then 10% of the observed data points
`should be below the 10th percentile and 10% of the
`observed data points should be above the 90th percentile
`over all time points. The median predicted concentrations
`and the 80% prediction interval were visually compared
`with the observed data. Competing models were exam-
`ined to assess whether the median and the 80% prediction
`interval adequately mirrored the central tendency and the
`variability of the observed data.
`Standard errors as a measure for precision of parameter
`estimates were obtained from the full PK/PD model by
`utilizing the type 1 bootstrap method as implemented in
`S-ADAPT [8]. By this method sets of patients are randomly
`selected from the dataset, and while refits of population
`parameters are not performed, the new population param-
`eters and their variances are obtained from averaging the
`individual parameters and their intra-individual covariance
`matrices. The errors were obtained from 200 bootstrap
`runs. Performing a full bootstrap including refitting of
`population parameters was not feasible due to long run
`times of the full PK/PD model.
`
`G1
`
`= −
`
`×
`
`k
`
`
`
`a1
`
`A
`
`
`G1
`
`dA
`
`dt
`where ka1 is the first-order absorption rate constant (h-1).
`In order to describe a time lag in absorption, we
`included a second (sequential) absorption compartment
`(AG2, nmol). This was numerically more stable than includ-
`ing a lag-time.The ka1 was estimated as the sum of ka2 + dka
`
`Modelling of vildagliptin and inhibition of DPP-4 activity
`
`BJCP
`
`Structural models
`One-, two- and three-compartment disposition models
`were tested for modelling the vildagliptin concentrations.
`The drug input was modelled as first order absorption with
`a lag time or an additional
`lag compartment for the
`absorption.The presence of target-mediated drug disposi-
`tion (TMDD) was not suspected a priori for a small mol-
`ecule drug such as vildagliptin. Therefore, initial attempts
`were made to explain nonlinearity in PK and explore
`potential reasons for the nonlinearity. Models with differ-
`ent bioavailabilities and absorption rate constants for the
`three different doses could not adequately describe the
`data. In addition models with three different volumes of
`distribution or three different clearances and models with
`linear, saturable and parallel linear and saturable elimina-
`tion were tested.
`For the effect of vildagliptin on DPP-4 activity, a simple
`Imax model and various models for TMDD [10, 11] were
`tested. The TMDD model had two disposition compart-
`ments for vildagliptin. Different structures of the TMDD
`model were tested: 1) binding of vildagliptin to DPP-4 only
`in the central compartment, 2) binding of vildagliptin in
`the central compartment to both DPP-4 in the central com-
`partment and DPP-4 in the peripheral compartment and 3)
`binding of vildagliptin to DPP-4 in the central compart-
`ment and binding of vildagliptin to DPP-4 in the peripheral
`compartment. The structure of our final TMDD model is
`shown in Figure 1.
`Changes in amounts of vildagliptin in the gut compart-
`ment (AG1, nmol) and initial conditions are:
`
`( ) =
`1 0G
`
`A
`
`
`×
`Dose F
`
`metabolite
`
`kdeg
`Vilda-DPP-4
`complex central
`DRC
`
`Vilda-DPP-4
`complex periph.
`DRP
`kdeg
`
`metabolite
`
`ka
`
`CL
`
`Vildagliptin
`central
`AC VC
`
`CLic
`Vildagliptin
`peripheral
`AP VP
`
`+
`
`+
`
`Free DPP-4
`central
`(RmaxC - DRC)
`
`Free DPP-4
`peripheral
`(RmaxP - DRP)
`
`VmaxC
`Kd
`
`koff
`
`VmaxP
`Kd
`
`koff
`
`VmaxC = (RmaxC - DRC) x k2
`VmaxP = (RmaxP - DRP) x k2
`DPP-4 activity in plasma = (RmaxC - DRC) x cf1
`
`Figure 1
`Model diagram. Symbols are defined in Table 1
`
`Br J Clin Pharmacol
`
`/ 73:3 / 393
`
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`BJCP
`
`C. B. Landersdorfer et al.
`
`with dka constrained to positive values to retain identifi-
`ability of the two absorption rate constants.
`
`The amount of vildagliptin – DPP-4 complex in VC (DRC,
`nmol) is
`
`2
`
`dA
`G
`dt
`
`=
`
`×
`
`k
`
`
`
`a1
`
`A
`
`
`G1
`
`−
`
`k
`
`a
`
`2
`
`×
`
`A
`G
`
`2
`
`( ) =
`2 0
`
`A
`G
`
`0
`
`The amount of vildagliptin in the central (or plasma) com-
`partment (AC, nmol) is
`
`−
`
`(
`
`k
`
`off
`
`+
`
`k
`
`deg
`
`)×
`
`DR
`C
`
`C C
`A V
`
`(
`
`R
`max
`
`C
`
`−
`
`DR
`C
`
`)× ×
`k
`
`2
`
`0
`
`C C
`A V
`
`( ) =
`0
`
`+
`
`K
`
`d
`
`DR
`C
`
`dDR
`C
`dt
`
`=
`
`where kdeg is the first order rate constant for hydrolysis of
`vildagliptin by DPP-4 (h-1).
`The amount of vildagliptin – DPP-4 complex in VP (DRP,
`nmol) is
`
`(
`
`−
`
`ic
`
`CL
`V
`P
`
`×
`
`A
`P
`
`−
`
`+
`
`k
`
`off
`
`×
`
`DR
`C
`
`C C
`A V
`
`+
`CL CL
`V
`C
`)× ×
`k
`
`(
`
`R
`max
`
`C
`
`−
`
`DR
`C
`
`2
`
`C C
`A V
`
`d
`
`dA
`C
`dt
`
`=
`
`k
`
`a
`
`2
`
`×
`
`A
`G
`
`2
`
`−
`
`ic
`
`×
`
`A
`C
`
`+
`
`++
`K
`( ) =
`AC 0
`
`
`
`0
`
`where CL is the non-saturable linear clearance (l h–1), CLic is
`the inter-compartmental clearance (l h–1), VC the central
`volume of distribution (l), VP the volume of the peripheral
`(tissue) compartment (l), RmaxC the total amount of DPP-4 in
`VC (nmol), DRC the amount of vildagliptin – DPP-4 complex
`in VC (nmol), k2 the first order rate constant for conversion
`of the low affinity complex to the high affinity complex
`(h-1), Kd the equilibrium dissociation constant (nmol l–1),
`and koff is the first order rate constant for dissociation of
`intact vildagliptin from DPP-4 (h-1).
`The equation for slow tight binding of vildagliptin was
`used to account for the fact that the rate constant for
`binding (kon) changes with vildagliptin concentration. The
`kon can be calculated as
`
`=
`
`k
`
`on
`
`+
`
`K
`
`d
`
`2
`
`k
`C
`
`vildagliptin
`
`The parameter k2 is included in the model due to the slow
`tight binding of vildagliptin to DPP-4.
`The maximum rate of binding of vildagliptin to DPP-4
`in the central compartment (VmaxC, nmol h–1) depends on
`the amount of free DPP-4 and can be calculated as
`=
`−
`)× 2
`(
`k
`The amount of vildagliptin in the peripheral (or tissue)
`compartment (AP, nmol) is
`
`R
`max
`
`C
`
`DR
`C
`
`V
`max
`
`C
`
`+
`
`k
`
`off
`
`×
`
`DR
`P
`
`P P
`A V
`
`P P
`A V
`
`d
`
`−
`)× ×
`(
`k
`DR
`R
`2
`max
`P
`P
`−
`- - - - -
`+
`K
`
`) −
`
`P P
`A V
`
`C C
`A V
`
`(
`
`dA
`P
`dt
`
`CL=
`
`ic
`
`×
`
`( ) =
`AAP 0
`
`0
`
`−
`
`(
`
`k
`
`off
`
`+
`
`k
`
`deg
`
`)×
`
`DR
`P
`
`P P
`A V
`
`R
`max
`
`P
`
`DR
`P
`
`)× ×
`k
`
`2
`
`0
`
`P P
`A V
`
`( ) =
`0
`
`+
`
`K
`
`d
`
`DR
`P
`
`dDR
`P
`dt
`
`=
`
`The DPP-4 activity (mU ml–1 min-1) in the central (or
`plasma) compartment is calculated as
`−
`=
`−
`(
`
`DPP
`
`4
`
`activity
`
`R
`max C
`
`DR
`C
`
`)×
`
`cf1
`
`where cf1 is the conversion factor between the free DPP-4
`enzyme and DPP-4 activity. This output equation links the
`DPP-4 activity measured in plasma to the estimated time
`course of free DPP-4 in the central (plasma) compartment.
`
`Between-subject variability model
`The between-subject variability (BSV) was estimated for all
`parameters. A log-normal distribution was assumed for the
`PK parameters and a full variance-covariance matrix for the
`PK parameters was included in S-ADAPT. For convenient
`interpretation, the square root of the variance is reported
`for BSV, as this is an approximation to the apparent coeffi-
`cient of variation of a normal distribution on log-scale. It
`was not feasible to include between-occasion variability
`(BOV) in the model due to high model complexity and
`long run times.The PK and PD (reported in the companion
`manuscript [9]) parts of the model were estimated simul-
`taneously and including BOV would considerably increase
`the already long run times. In addition BOV should be used
`with caution in models including nonlinear PK, in order to
`prevent masking of potential systematic differences
`between the dose levels.
`
`Residual error model
`The residual unidentified variability was described by a
`combined additive and proportional error model for both
`vildagliptin concentrations and DPP-4 activity.
`
`Results
`
`where RmaxP is the total amount of DPP-4 in VP (nmol), and
`DRP the amount of vildagliptin – DPP-4 complex in VP
`(nmol). The maximum rate of binding of vildagliptin to
`DPP-4 in the tissue compartment (VmaxP, nmol h–1) can be
`calculated as
`
`V
`max
`
`P
`
`=
`
`(
`
`R
`max
`
`P
`
`−
`
`DR
`P
`
`)× 2
`k
`
`Twelve subjects completed all four periods of the study
`and one patient only completed the 10 and 25 mg treat-
`
`394 / 73:3 / Br J Clin Pharmacol
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`Modelling of vildagliptin and inhibition of DPP-4 activity
`
`BJCP
`
`A 10 mg
`
`B 25 mg
`
`•••
`• •
`
`' \
`
`'\
`
`1320
`
`1324
`
`1328
`
`1332
`
`1336
`
`1340
`
`1344
`
`Time (h)
`
`D Median concentrations
`normalized to the 10 mg dose
`
`512
`
`256
`
`128
`
`64
`
`32
`
`16
`
`8 4 2 1
`
`1
`316
`
`5
`10
`15
`20
`Time after morning dose (h)
`
`25
`
`64
`
`32
`
`16
`
`8 4 2
`
`1
`0
`
`Median dose-normalized conc.
`
`(ng ml-1/10mg vildagliptin)
`
`~
`ti
`
`\
`\
`
`\ ' ' \
`\ ' ' ' ' '
`
`652
`
`657
`
`662
`
`667
`
`672
`
`C 100 mg
`
`L
`
`128
`
`64
`
`32
`
`16
`
`8
`
`4 2 1
`
`0.5
`
`0.25
`647
`
`1024
`
`512
`
`256
`
`128
`
`64
`
`32
`
`16
`
`Vildagliptin concentration (ng ml-1)
`
`8
`1990
`
`1995
`
`2000
`
`2005
`
`2010
`
`2015
`
`2020
`
`Time (h)
`
`Figure 2
`(A–C) Visual predictive checks for plasma concentrations of vildagliptin. The plots show the observed data (filled diamonds), the median predicted
`concentrations (solid line) and the 80% prediction interval (10–90% percentile, broken lines). In order to show all data from each dose level in the same plot
`it was assumed in the graphs that all virtual subjects received the doses in the same sequence. The doses on the observation days as shown in the figure
`were at 648 and 658 h for the 10 mg dose, 1320 and 1330 h for 25 mg and 1992 and 2002 h for 100 mg. The actual sequence of dosing in the randomized
`clinical trial for each individual patient was observed for all estimation model runs. (D) Median vildagliptin concentrations normalized to the 10 mg dose.
`Only four of 13 patients had a quantifiable concentration at 8 h after the 10 mg dose and only five of 13 patients had a quantifiable concentration at 24 h
`); 25 mg (
`);
`after the 25 mg dose. Those two time points were left out of the figure as otherwise the slopes of the curves would be biased. 10 mg (
`)
`100 mg (
`
`ments. The average (range) weight was 91 (65–116) kg,
`height was 166 (148–183) cm and age was 53.5 (37–
`64) years. Six patients were male and seven were female.
`The individual observed vildagliptin concentrations on
`day 28 of twice daily oral dosing are shown in Figure 2. For
`the 10 mg dose all concentrations at 10 h after each dose
`
`were below the limit of quantification (LOQ) of 2 ng ml-1.
`For the 25 mg dose only part of the concentrations were
`below the LOQ. One subject had extremely high concen-
`trations after the morning dose of 25 mg vildagliptin for
`unknown reasons and those results were included in the
`population analysis, although this resulted in an increased
`
`Br J Clin Pharmacol
`
`/ 73:3 / 395
`
`
`
`MPI EXHIBIT 1054 PAGE 5MPI EXHIBIT 1054 PAGE 5
`
`Apotex v. Novo - IPR2024-00631
`Petitioner Apotex Exhibit 1054-0005
`
`

`

`BJCP
`
`C. B. Landersdorfer et al.
`
`•
`~-
`'•
`I•
`
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`
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`
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`
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`
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`
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`
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`
`2 0
`
`1318
`
`652
`
`657
`
`662
`
`667
`
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`
`100 mg
`
`Placebo
`
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`
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`
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`2672
`
`•
`
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`
`2678
`
`2684
`
`2690
`
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`
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`
`14
`
`12
`
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`
`8
`
`46
`
`1.5
`
`1
`
`0.5
`
`0
`1990
`
`1996
`
`2002
`
`2008
`
`2014
`
`2020
`
`2666
`
`2 0
`
`2660
`
`1996
`
`2002
`
`2008
`
`2014
`
`2020
`
`Time (h)
`
`18
`
`16
`
`14
`
`12
`
`10
`
`8
`
`46
`
`2 0
`
`647
`
`18
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`16
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`14
`
`12
`
`10
`
`8
`
`46
`
`2 0
`
`1990
`
`DPP-4 activity (mU ml-1 min-1)
`
`Figure 3
`Visual predictive checks for DPP-4 activity. Symbols and x-axes are explained in Figure 2
`
`residual error for PK. Plotting the dose-normalized vilda-
`gliptin concentration-time profiles after all three doses
`for each individual subject revealed a faster decline with
`the two lower doses compared with the 100 mg dose
`(Figure 2D).
`Observed DPP-4 activity after placebo and the three
`different vildagliptin doses is shown in Figure 3. For the
`placebo treatment the DPP-4 activity was relatively con-
`stant throughout the observation period, both overall and
`when considering the individual patients separately. For
`the population analysis three data points from three
`
`396 / 73:3 / Br J Clin Pharmacol
`
`different patients during the placebo period were consid-
`ered outliers and excluded from the analysis (DDP-4 activ-
`ity 0.59, 1.4 and 5.3 mU ml-1 min-1) as no rational
`explanation was available for these low values.
`After
`vildagliptin treatment
`the DPP-4 activity
`decreased with increasing vildagliptin concentrations and
`recovered when the vildagliptin concentrations were
`declining for all three doses. After the 100 mg vildagliptin
`dose the DPP-4 activity was inhibited almost completely
`over the entire 24 h observation period, which showed
`prolonged inhibition of DPP-4 by vildagliptin.
`
`
`
`MPI EXHIBIT 1054 PAGE 6MPI EXHIBIT 1054 PAGE 6
`
`Apotex v. Novo - IPR2024-00631
`Petitioner Apotex Exhibit 1054-0006
`
`

`

`Modelling of vildagliptin and inhibition of DPP-4 activity
`
`BJCP
`
`Non-compartmental analysis
`The results from the NCA are reported by He et al. [4].
`Average terminal half-life increased with dose from 1.32 h
`at the 10 mg dose to 2.43 h at the 100 mg dose, while
`clearance calculated from dose/AUC decreased from
`84.0 l h-1 at 10 mg to 53.5 l h-1 at the 100 mg dose, indicat-
`ing the presence of saturable elimination.
`
`Compartmental modelling
`The newly developed model which includes capacity-
`limited high-affinity binding of vildagliptin to DPP-4, par-
`allel vildagliptin dissociation from DPP-4 by a slow first-
`order process and hydrolysis by DPP-4 to an inactive
`metabolite adequately describes the observed PK and
`DPP-4 activity. The visual predictive checks showed excel-
`lent predictive performance for both PK and DPP-4 activ-
`ity for the three different doses and placebo (Figures 2
`and 3).
`The parameter estimates, their BSV and standard errors
`(SE) as a measure for precision are reported in Table 1. In
`the final model vildagliptin absorption could be described
`by dose-independent F and ka parameters. A slight absorp-
`tion delay was described by a lag time for absorption.
`The estimate for kdeg was 0.110 h-1, which corresponds
`to a half-life for metabolism of vildagliptin by DPP-4 of
`6.3 h. The estimate for koff was about 5.6 times as high as
`kdeg, predicting that only a small part of the dose was
`metabolized by DPP-4 and the majority of the vildagliptin
`molecules dissociated unchanged from the vildagliptin-
`DPP-4 complex. Based on simulations, vildagliptin clear-
`
`Table 1
`Population parameter estimates for vildagliptin pharmacokinetics
`
`ance was approximately 74 l h-1 at an extremely low dose
`of 1 mg, suggesting that as vildagliptin concentrations
`approach zero the saturable clearance accounts for
`approximately half of total clearance. After a single dose of
`100 mg, total clearance was 42.4 l h-1 and the saturable
`clearance by DPP-4 accounted for approximately 14% of
`total. When concentrations approach infinity, total clear-
`ance approaches 36.4 l h-1, the estimate for the non-
`saturable clearance.
`The apparent amount of DPP-4 in the tissue compart-
`ment is estimated to be much higher (perhaps > 2000-fold)
`than in the central compartment, based on the population
`parameter estimates for RmaxC (5 nmol, BSV 12%, SE 4%) and
`RmaxP (13 mmol, BSV 64%, SE 23%). This suggests that the
`capacity of DPP-4 inhibition at the tissue level may be
`much higher than observed in plasma, and the tissue com-
`partment is responsible for most of the non-linearity seen
`in vildagliptin PK. However the estimates for RmaxC and RmaxP
`as apparent amounts of available DPP-4 in the absence of
`vildagliptin should be interpreted carefully and might not
`represent the actual amounts of enzyme in plasma and
`tissue, as amounts of DPP-4 in plasma or tissue and DPP-4
`activity in tissues were not available, and the factor cf1 was
`estimated to relate measured DPP4 activity to RmaxP. In
`addition, estimates of Rmax do not take into account, for
`example, spare receptors or effects of endogenous ligands
`[11]. Sensitivity analyses where RmaxC was fixed to several
`different higher values than the current estimate resulted
`in inadequate description of the observed DPP-4 activity
`profiles over time.
`
`Parameter (units)
`
`Definition
`
`Estimate
`
`BSV (%)
`
`SE† (%)
`
`CL (l h–1)
`VC (l)
`VP (l)
`CLic (l h–1)
`ka1 (h-1)
`ka2 (h-1)
`F (%)
`Kd (nmol l–1)
`k2 (h-1)
`koff (h-1)
`kdeg (h-1)
`RmaxC (nmol)
`RmaxP (mmol)
`cf1 mU ml-1 min-1 nmol-1
`
`CVVilda (%)
`SDVilda (ng ml–1)
`CVDPP-4 (%)
`SDDPP-4 (mU ml–1 min-1)
`
`Non-saturable vildagliptin clearance
`Volume of central compartment
`Volume of tissue compartment
`Intercompartmental clearance
`Absorption rate constant
`Absorption rate constant
`Bioavailability
`Equilibrium dissociation constant
`Rate constant for conversion of weak complex to high-affinity complex
`Dissociation rate constant of intact vildagliptin from DPP-4
`Rate constant for hydrolysis of vildagliptin by DPP-4
`DPP-4 in VC
`DPP-4 in VP
`Conversion factor between free DPP-4 enzyme and observed
`DPP-4 activity
`Proportional error for vildagliptin
`Additive error for vildagliptin
`Proportional error for DPP-4 activity
`Additive error for DPP-4 activity
`
`36.4
`22.2
`97.3
`40.1
`1.26
`1.05
`77.2*
`71.9
`23.4
`0.612
`0.110
`5.0
`13.0
`2.80
`
`48.7
`0.99
`19.6
`0.061
`
`25
`37
`37
`34
`46
`14
`–
`54
`70
`94
`81
`12
`64
`17
`
`9
`11
`13
`11
`15
`4
`
`–
`16
`22
`27
`26
`4
`23
`5
`
`*Fixed to the estimate from the model including i.v. vildagliptin data. †Standard errors (SE) were obtained by bootstrap method 1 as implemented in S-ADAPT and are reported as
`coefficients of variation (%). Standard errors for BSV parameter estimates were between 25 and 57%.
`
`Br J Clin Pharmacol
`
`/ 73:3 / 397
`
`MPI EXHIBIT 1054 PAGE 7
`
`MPI EXHIBIT 1054 PAGE 7
`
`Apotex v. Novo - IPR2024-00631
`Petitioner Apotex Exhibit 1054-0007
`
`

`

`BJCP
`
`C. B. Landersdorfer et al.
`
`A 10 mg
`
`B 25 mg
`
`, •• ~ • • - • •~ ............ ""'■ •-I•~ . . . . . . . . . . . . , . . . . . . .
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`
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`
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`
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`
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`
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`
`1e-1
`
`650
`
`655
`
`660
`
`665
`
`670
`
`650
`
`655
`
`660
`
`665
`
`670
`
`C 100 mg
`
`Time (h)
`
`1e+5
`
`1e+4
`
`1e+3
`
`1e+2
`
`1e+1
`
`1e+0
`
`1e-1
`
`1e+5
`
`1e+4
`
`1e+3
`
`1e+2
`
`1e+1
`
`1e+0
`
`1e-1
`
`Amount (nmol), concentration (ng ml-1),
`
`DPP-4 activity (mU ml-1 min-1)
`
`650
`
`655
`
`660
`
`665
`
`670
`
`Time (h)
`
`Figure 4
`Simulations showing indicated model components vs. time for
`activity central (mU ml-1min) (
`); Vildagliptin peripheral (nmol) (
`)
`(
`
`(ng ml-1)
`); DPP4
`(
`the three doses of vildagliptin. Vildagliptin central
`); Vilda-DPP4 complex central (nmol) (
`); Vilda-DPP4 complex peripheral (nmol)
`
`Signature profiles for vildagliptin and the vildagliptin-
`DPP-4 complex in the central and peripheral compartment
`based on model simulations are shown in Figure 4. Simu-
`lations suggest that the apparent amount of vildagliptin-
`DPP-4 complex is almost constant over the observation
`period in both compartments for the 100 mg dose and,
`therefore, DPP-4 activity is almost completely inhibited.
`Based on the simulations, DPP-4 enzyme in plasma is satu-
`rated at lower vildagliptin doses. However, the capacity of
`DPP-4 inhibition is larger in tissues due to the larger
`amount of enzyme available and therefore most of the
`non-linearity in PK is explained by DPP-4 inhibition in
`tissues in the model.
`
`398 / 73:3 / Br J Clin Pharmacol
`
`Discussion
`
`Overall diabetes has prevalence worldwide and approxi-
`mately 50% of the population is likely to suffer from dia-
`betes in 2050. Vildagliptin belongs to the DPP-4 inhibitors,
`one of the more recently introduced classes of antidiabetic
`agents. In this report the PK and effect on DPP-4 activity of
`vildagliptin is described and it is shown that insights into
`the mechanism of action of a drug in patients with type 2
`diabetes can be gained by mechanism-based mathemati-
`cal modelling. Population modelling of vildagliptin PK
`revealed slightly non-linear PK and co-modelling with data
`from the i.v. study suggested the existence of a deep
`
`
`
`MPI EXHIBIT 1054 PAGE 8MPI EXHIBIT 1054 PAGE 8
`
`Apotex v. Novo - IPR2024-00631
`Petitioner Apotex Exhibit 1054-0008
`
`

`

`Modelling of vildagliptin and inhibition of DPP-4 activity
`
`BJCP
`
`peripheral compartment, such as may be observed due to
`binding in tissues.Vildagliptin, as a small molecule, was not
`expected a priori to exhibit TMDD, including metabolism
`by its own PD target. However, such a model could
`describe the data and results from a rat study which has
`meanwhile been published [12] confirmed DPP-4 as likely
`being responsible for part of vildagliptin elimination. In
`addition the slow tight binding found in in vitro studies
`could be confirmed also in humans and was necessary to
`be included into the model in order to describe the DPP-4
`activity profiles.The