Justification of Drug Product Dissolution Rate and Drug Substance

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Justification of drug product dissolution rate and drug substance particle size specifications based on absorption PBPK modelling for lesinurad immediate release tablets Xavier J.H. Pepin, Talia R. Flanagan, David J. Holt, Anna Eidelman, Don Treacy, and Colin E. Rowlings Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.6b00497 • Publication Date (Web): 20 Jul 2016 Downloaded from http://pubs.acs.org on July 24, 2016

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Molecular Pharmaceutics

Justification of drug product dissolution rate and drug substance particle size specifications based on absorption PBPK modelling for lesinurad immediate release tablets Xavier J. H. Pepin*1, Talia R. Flanagan1, David J. Holt1, Anna Eidelman2, Don Treacy2, Colin E. Rowlings2 1 AstraZeneca, Global Medicines Development, Pharmaceutical Development, Silk Road Business Park, Charter Way, Hurdsfield Industrial Estate, Macclesfield, SK10 2NA, UK. 2 Ardea Biosciences, Pharmaceutical Sciences, 9390 Towne Centre Drive, San Diego, CA 92121, USA. * AstraZeneca, Pharmaceutical Technology and Development. UG22 Redesmere. Silk Road Business Park, Charter Way, Hurdsfield Industrial Estate, Macclesfield, SK10 2NA, United Kingdom. +44 (0)7469 408 258, [email protected]

1 ACS Paragon Plus Environment


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ABSTRACT

In silico absorption modelling has been performed, to assess the impact of in vitro dissolution on in vivo performance for ZURAMPICTM (lesinurad) tablets. The dissolution profiles of lesinurad tablets generated using the quality control method were used as an input to a GastroPlusTM model to estimate in vivo dissolution in the various parts of the GI tract and predict human exposure. A model was set up which accounts for differences of dosage form transit, dissolution, local pH in the GI tract and fluid volumes available for dissolution. The predictive ability of the model was demonstrated by confirming that it can reproduce the Cmax observed for independent clinical trial. The model also indicated that drug product batches that passes the proposed dissolution specification of Q=80% in 30 minutes, are anticipated to be bioequivalent to the clinical reference batch.

To further explore the dissolution space, additional simulations were performed using a theoretical dissolution profile below the proposed specification. The GastroPlusTM modelling indicates that such a batch will also be bioequivalent to standard clinical batches despite having a dissolution profile which would fail the proposed dissolution specification of Q=80% in 30 minutes. This demonstrates that the proposed dissolution specification sits comfortably within a region of dissolution performance where bioequivalence is anticipated, and is not near an edge of failure for dissolution, providing additional confidence to the proposed specifications.

Finally, simulations were performed using a virtual drug substance batch with a particle size distribution at the limit of the proposed specification for particle size. Based on these


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simulations, such a batch is also anticipated to be bioequivalent to clinical reference, demonstrating that the proposed specification limits for particle size distribution would give products bioequivalent to the pivotal clinical batches.

This work was accepted in discussions with FDA to establish lesinurad immediate release tablet control strategy.



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KEYWORDS Biowaiver, PBPK, modelling, dissolution, specifications, control strategy.


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FOR TABLE OF CONTENTS USE ONLY

Justification of drug product dissolution rate and drug substance particle size specifications based on absorption PBPK modelling for lesinurad immediate release tablets Xavier J. H. Pepin, Talia R. Flanagan, David J. Holt, Anna Eidelman, Don Treacy, Colin E. Rowlings



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1. Introduction Physiologically based pharmacokinetic (PBPK) modelling is widely used within the pharmaceutical industry to predict oral drug absorption. Its usefulness as a biopharmaceutics tool stems from its ability to predict the net impact of the many factors that can influence oral drug absorption, including properties of the API and dosage form, and the dynamic environment of the GI tract, on the overall pharmacokinetic profile. In this way, it can integrate discreet in vitro measurements of properties such as solubility, permeability, and dissolution performance to build an overall picture of biopharmaceutics risk. A number of different in silico PBPK modelling tools are currently available, such as GastroPlusTM, SimCYP and PK Sim®; the most commonly used oral absorption modelling packages were recently reviewed by Kostewicz et al.1.

The potential utility of PBPK absorption modelling in the regulatory setting has been highlighted by both industry2 and regulators3. However, a recent survey of the pharmaceutical industry highlighted that, while in silico PBPK absorption modelling is widely used during development to address a variety of biopharmaceutics issues, it is rarely submitted to regulatory authorities2. As of 2013, only 5% (n=4) of NDA submissions to US FDA contained absorption related PBPK modelling3. Correspondingly, published examples of its use in regulatory defense are scarce. Okumu et al.4 used GastroPlusTM modelling to develop a scientific justification that the BCS Class 2 drug etorocoxib should be eligible for biowaivers. However, the authors do not report whether this justification was ever presented to regulatory


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authorities, and if so whether the approach they applied was successful in obtaining a biowaiver.

Lesinurad (ZURAMPIC®) is a selective uric acid reabsorption inhibitor5, administered orally as an immediate release tablet. Lesinurad was recently licensed for treatment of hyperuricemia associated with gout in combination with a xanthine oxidase inhibitor6. During review of the marketing applications, a package of in silico PBPK modelling work was submitted to US FDA in support of the proposed control strategy, to provide evidence that the proposed specifications for dissolution and particle size would assure suitable clinical performance of batches. This manuscript describes the work performed and the modelling strategy applied, which was used to successfully defend the specifications proposed.

2. Materials and methods 2.1 Materials SGF pH 1.6 consists of 0.03 M HCl and 0.079 M NaCl. FaSSIF pH 6.5 and FeSSIF pH 5.0 were prepared from Phares SIF Powder (Phares Drug Delivery AG), The pH 3, 4 and 5 buffers were prepared using 50mM Citrate Buffers (Sodium Citrate tribasic dehydrate, ACS reagent, Citric Acid, Anhydrous Powder, USP grade). The QC dissolution medium (pH 4.5 plus 1% SLS) was prepared from 50 mM acetate buffer (Sodium acetate anhydrous, Sodium lauryl sulfate (SLS), Glacial Acetic acid, ACS grade, Sodium hydroxide 1 N, HPLC reagent grade).



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Lesinurad 200mg and 400mg tablets have identical composition and are manufactured using the same standard wet granulation process, the only difference being compression weight to produce the different tablet strengths. Inactive ingredients of the tablet core comprise lactose monohydrate, crospovidone, microcrystalline cellulose, hypromellose 2910 and magnesium stearate. The tablet cores are film coated with an immediate release polymer.

2.2 Tablet batches used in the modelling exercise

Batches 12A015, 12E058, ELAD, MPAC and ELAB are clinical batches used in relevant studies described hereafter. Following setup and validation of the model, dissolution profiles for a number of virtual tablet batches were also generated, these are described in the Results section.

2.3 In vitro data

2.3.1 Solubility

Equilibrium solubility was determined in standard aqueous buffers across the physiological pH range, and in Fasted State Simulated Intestinal Fluid (FaSSIF) and Fed State Simulated Intestinal Fluid (FeSSIF)7. Lesinurad is a weak acid with a pKa of 3.2, a log P of 2.85 and a molecular weight of 404.3 g.mol-1. The drug substance is a crystalline powder with a melting point of 170ºC. Its solubility is low at gastric pH, but high at intestinal pH (5.3 to 7.5) as shown in Table 1.


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Lesinurad shows deviation from ideal pH solubility behavior at around pH 6 and above, due to formation of micelles from the ionized form of the drug. All aqueous and FaSSIF and FeSSIF solubility data were fitted according to the bile salt concentration and pH by assuming different partition coefficients between unionized and ionized drug and the micelles in the form using the equation below, where S0, represents the intrinsic drug solubility, K0 represents the partitioning coefficient of the unionized acid and K-1 represents the partitioning coefficient of the deprotonated acid.

, = × 1 + +

1 + 10

Since for FaSSIF and FeSSIF the amount of drug in solution in protonated form is negligible (lesinurad had an acid pKa of 3.2), only the ionized drug is expected to partition in the micelles. For lesinurad, the aqueous pH solubility profile and FaSSIF and FeSSIF data were fitted with an S0 of 6 mg/L, K0 = 0, K-1 = 0.26 and pKa = 3.2 indicating that only the ionized drug partitions with the bile salt micelles. In GastroPlusTM, FaSSIF and FeSSIF solubility values were used to fit a solubilization ratio of 5803 to be applied to the aqueous solubility profile of lesinurad. Both solubility and diffusion coefficient were adjusted for the effect of bile salts during the modelling.

2.3.2 Permeability

In vitro Caco-2 cell Apical to Basal permeability (A - B Papp) results indicate high permeability of lesinurad, and the absolute bioavailability of lesinurad in humans is 100% as 9 ACS Paragon Plus Environment


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determined by the absolute bioavailability study. Oral and intravenous plasma data obtained in the same healthy volunteers during the absolute bioavailability study were used to fit individual jejunal Peff data using GastroPlusTM. Calculated permeability values for this drug were high, with an average jejunal Peff calculated at 3 x10-4 cm/s min (range of 1.7-5.4 x10-4 cm/s) on n=10 healthy volunteers.

2.3.3 In vitro dissolution

The QC dissolution method for lesinurad tablets uses 900 mL pH 4.5 acetate buffer plus 1% sodium lauryl sulfate (SLS) as the dissolution medium, in USP Apparatus 2 at 37ºC and 75 rpm using 900mL of dissolution media. The solubility of lesinurad in this media at 37ºC is 1.77 mg/mL, lower than the expected solubility of lesinurad in the intestinal environment but enough to ensure sink conditions. Dissolution was also assessed in simple aqueous buffers at pH 3, 4 and 5 as described above. Detection was performed using standard HPLC with UV detection. In vitro dissolution of relevant batches was assessed using the QC method but also using simple buffer media of various pH. Batches 12A015 and ELAD show dissolution rates typical of commercial and clinical pivotal study batches; 12A015 was used as a reference batch in the modelling exercise. Batch ELAB was a drug product lot which showed slow and incomplete dissolution, manufactured outside of the intended commercial operating ranges for granulation. Batch MPAC and 12E058 are clinically acceptable batches which show somewhat slower dissolution than batch 12A015. Batches 12A015, ELAB and MPAC were used in the in silico modelling exercise (Figure 14).


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2.3.4 In silico dissolution

The in vitro dissolution rates of selected batches were fitted to an apparent particle size distribution using an Excel® 2013 worksheet (V15.0.4763.1002). This modelling was conducted to extract a particle size distribution representative of the in vitro dissolution for each batch that could be used as an input in GastroPlusTM for Option A (see below). The dissolution of poly-disperse particles was treated according to the film theory, i.e. controlled by the drug diffusion through a stagnant film layer surrounding the dissolving particle. The equations used are classical and already described by Okazaki at al.8. The drug diffusion coefficient D (m2.s-1) is obtained from the Stokes Einstein equation, assuming that the molecular shape is a sphere or radius Rh (m).

D=

kT 6πηR h

Where k = 1.3806504 10-23 (J.K-1) is the Boltzmann constant, T (K) is the absolute temperature in K, η (Pa.s) is the kinematic viscosity of the solvent and Rh (m) is the hydrodynamic radius of the diffusing solute. The hydrodynamic radius of the solute can be estimated assuming that the molecular shape is a sphere and that the hydration of the solute is negligible by the following equation Rh =

3

3 × M W × 10 −3 4πN A ρ S

Where MW is the molar weight of the drug (g.mol-1), NA = 6.02214179 1023 (mol-1) is the Avogadro number and ρS is the drug true density in (kg.m-3). The viscosity of water at 37°C is taken at 6.85E-04 Pa.s and the drug true density at 1200 kg.m-3. The internal Excel tool worked with n=10 bins to calculate the overall dissolution rate of a mass of drug substance in 11 ACS Paragon Plus Environment


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given dissolution conditions. The initial drug mass is distributed over the 10 bins, allowing computation of the bin particle number (assumed constant over time). At each time point, the mass of solid material remaining in each bin is calculated and a new particle radius is assigned to all particles in the bin for the next step. Numerical integration of mass dissolved is conducted over a time relevant to the measured in vitro dissolution. Measured in vitro dissolution conditions (volume, drug mass, solubility in dissolution medium) and measured dissolution results for the selected batch are entered in the Excel spreadsheet. Using the solver add-in® functionality of Excel®, the particle size distribution is optimized to fit the observed dissolution rate for each batch. The Excel® tool used to model dissolution rates is provided in the supplementary material.

2.3.5 Plasma protein binding and blood:plasma ratio

The in vitro binding of

14

C-lesinurad to human plasma proteins at concentrations of 1, 10,

and 50 µg/mL was determined by equilibrium dialysis at 37ºC. The in vitro binding of

14

C-

lesinurad to human plasma proteins at concentrations of 1, 10, and 50 µg/mL was high, with > 98% bound at 1 and 10 µM, and slightly less with 97.9% bound at 50 µM. Blood:plasma ratio was determined in the human ADME study (Study RDEA-594-112). Blood to plasma ratio for lesinurad was approximately 0.55 based on AUC ratio.

2.4 Pharmacokinetic data inputs

Pharmacokinetic data from two key clinical pharmacology studies were used to set up and validate the model, Study RDEA-594-131 and Study RDEA-594-129. Additionally, data from 12 ACS Paragon Plus Environment

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the human ADME study (Study RDEA-594-112) was used to provide disposition parameters for model setup. The studies were performed in accordance with the ethical principles that have their origin in the declaration of Helsinki and that are consistent with International Conference on Harmonization/Good Clinical Practice (GCP) and applicable regulatory requirements and the AstraZeneca policy on Bioethics.

2.4.1 Absolute bioavailability study (Study RDEA-594-131)

Study RDEA-594-131 was an IV microtracer study to determine the absolute bioavailability of lesinurad. This was an open-label study in 10 healthy adult volunteers. Batch 12A015 was dosed orally, with a concomitant IV microdose of

14

C-lesinurad timed to coincide with the

expected mean oral tmax. The geometric mean absolute bioavailability of lesinurad (batch 12A015) was 101% (90% CI: 95.4% to 106%), indicating that lesinurad is completely absorbed with minimal first pass metabolism. Lesinurad clearance and volume of distribution were 5.98 L/h and 20.3 L (0.273 L/kg), respectively. Individual Cmax observed in this study are compared to simulated values in Figure 9.

2.4.2 Relative bioavailability study (Study RDEA-594-129)

Study RDEA-594-129 was a relative bioavailability study in which batch ELAB was compared to 12A015. This was a randomized, open-label crossover study in 18 healthy adult volunteers. Data from fasted state dosing (cohort 1) were used in the modelling. This study demonstrated that, in the fasted state, batch ELAB showed reduced Cmax and AUC∞ compared to 12A015. This reflected the slow and incomplete release seen in vitro. Geometric mean 13 ACS Paragon Plus Environment


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ratios for Cmax and AUC∞ vs. the reference lot 12A015 were 80.0% (90% CI: 68.2% to 93.8%) and 88.1% (90% CI: 79.9% to 97.2%), respectively; tmax was delayed by approximately 1 hour (median 2.50 hours vs 1.67 hours).

2.4.3 Human ADME study (Study RDEA-594-112)

Study RDEA-594-112 was a human ADME study in which a 600 mg dose of 14C-lesinurad was administered as an oral solution to 6 healthy adult male volunteers in the fasted state. Mean total recovery following oral administration of a 600 mg dose of

14

C-lesinurad was

95.6% at 144 hours post dose. The major route of excretion was via urine with a mean of 63.4% of the dose excreted. Lesinurad was the major component excreted in the urine, accounting for 31.3% of the dose.

2.5 PBPK modelling approach GastroPlusTM v 9.0.0007 (SimulationsPlus, Inc. Lancaster, CA) was used as a PBPK absorption model as described in Figure 1.


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Figure 1. PBPK modelling strategy

The modelling strategy consisted in 3 steps : Model setup, validation and use.

2.5.1 Model setup

In the ACAT model of GastroPlusTM describing the GI tract, the default values for compartment volume percent occupation by water in the small intestine and colon (40% and



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10%) respectively, are reduced to 7.5% and 2% respectively to better account for measured free water content in the small intestine and colon9. Sutton10 has already questioned these default values in the software, and since the dissolution rate of lesinurad which is a weak acid is antipated to occur in the intestine and colon, the amount of water in the GI tract is a key factor to control the drug bioavailability. A sensitivity analysis was conducted to assess the impact of luminal water volume on lesinurad bioavailability.

The model set-up consisted in the individual fit of the intravenous infusion arm of each of the 10 subjects in the absolute bioavailability study using GastroPlusTM PKPlus feature. The total clearance was extracted from these individual fits. Most of lesinurad is excreted by the kidneys, as demonstrated in the human ADME study. Out of the 95.6% total mean radioactivity recovery at t=144h, 31.3 percent is excreted unchanged in the urine (Study RDEA594-112). The total clearance individually fitted from the IV profiles (CLT) was then used to calculate hepatic and renal clearances using the following formulae for each subject

CLT = CL H + CL R CL H = CL T × 0.687 CL R = CL T × 0.313

The fraction drug escaping liver first pass extraction was estimated on the basis of the following equation where CLH is the Lesinurad hepatic clearance, QH is the human liver blood flow (assumed constant to 90L/H) and B2P the blood to plasma ratio.


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 CL H   FH = 1 −  Q H × B2 P 

The absorption of Lesinurad from the GI tract is variable and displays in approximately 3040 percent of the population, multiple peaks during the absorption phase. This observation has precluded the use of average profiles to fit observed pharmacokinetic profiles in a top-down approach. Hence, the individual profiles obtained for the 10 subjects in the absolute bioavailability study who received one tablet of 400 mg lesinurad tablet (batch 12A015) were then used to fit individual effective permeability data, and gastric emptying patterns. The fraction of drug undergoing first pass gut extraction (Eg) was calculated using the Qgut model11 with individual Peff fitted to the oral profiles of Batch 12A015 and individual hepatic clearance, assuming only CYP3A4 contribution to the liver metabolism, which is a worst case scenario (Table 2).

2.5.2 Model validation

Once disposition parameters and physiological parameters were obtained for the 10 volunteers from the absolute bioavailability study the models were used to test different formulations and reproduce observed separate clinical trial outcomes of the relative bioavailability study. The model validation consisted in testing different input methodologies for drug product dissolution rates in the PBPK model and verifying if these inputs allowed to reproduce clinical outcomes.

4 options were used to integrate dissolution data in GastroPlusTM: 17 ACS Paragon Plus Environment


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A : Use of in vitro dissolution data to fit a particle size distribution that would match observed in vitro dissolution per batch factoring in the volume, solubility and doses used in the in vitro experiment. The PSD is then used as input in GastroPlusTM using support file for each subject. Formulations are switched to delayed release enteric coated tablet and the stomach residence is fitted to the observed PK profiles.

B : Use of in vitro dissolution data to fit one Weibull function per batch, where the dosage form is switched to CR dissolved and a lag time is added to the Weibull function to account for stomach residence time

C: Use of in vitro dissolution data to fit a Z-factor which accounts for the dose volume and solubility and set formulation to DR to delayed release enteric coated tablet. The stomach residence is fitted to the observed PK profiles.

D : Use of in vitro dissolution data to fit a Weibull function per batch, where the dosage form is switched to CR Undissolved and a lag time is added to the Weibull function to account for stomach residence time. In addition the drug substance particle size distribution used for each formulation batch is added as a support file to allow for the calculation of in vivo dissolution rate

Simulations were performed for batches 12A015 and ELAB using each of the above approaches, to determine whether the lower Cmax and AUC seen in the relative bioavailability 18 ACS Paragon Plus Environment

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study for batch ELAB could be reproduced using the in silico model. The predictive ability of each option was then compared, to enable the most predictive approach to be selected for the subsequent modelling work.

2.5.3 Model use

Once the method was selected, n=25 virtual trials were undertaken to compare test and reference formulations and anticipate the bioequivalence using calculated PK parameters. GastroPlusTM cross-over virtual trial feature was used where a population is first generated and then reloaded to run cross over studies with a new formulation. Population individual parameters are kept constant for a cross-over trial by default in GastroPlusTM. Only 3 dosing conditions are varied : The dose is chosen randomly at target +/-3%, particle shape factor at target +/-10%, and precipitation radius at target +/-10%. In order to add relevant within subject variability for the cross over trial, stomach pH and stomach transit times were manually varied using the following excel functions for each subject:

ℎ "#$% %&ℎ = '() × 3 − 0.09 + 0.09 ℎ ./ = '() × 4 − 1 + 1

These functions allowed to vary stomach transit time over the observed ranges of the absolute bioavailability study and stomach pH between 1 and 4. Results from these functions were copied directly in the *stc file of the virtual trial population in the relevant columns, and used for virtual cross-over trial. To assess the pertinence of the proposed drug substance particle size specifications, a virtual drug substance batch was defined at the acceptance limits 19 ACS Paragon Plus Environment


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of the proposed specifications and compared to the clinical reference using the 10 subject GastroPlusTM models resulting from The absolute bioavailability study. The PK parameters calculated with the two different drug substance particle size inputs were compared.

2.6 Statistical comparisons of exposures

Minitab® 17.1.0 software was used for the evaluation the equivalence between the test and reference means using paired observations from the simulation trials. Standard bioequivalence criteria of 0.80-1.25 were used, in terms of evaluating the ratio of the test mean to the reference mean, as modelled with a log transformation of the original data. Subject profile plots were produced which display the differences between individual pairs of observations.

Average Fold Error (AFE), Absolute Average Fold Error (AAFE) and were calculated according to equations detailed by Ring, et al.12. Whilst in the AFE the under predictions of the measured values can be balanced by the over predictions, the AAFE would treat over and under predictions equally and this parameter can be used with AFE to estimate any bias to the predictions.

3. Results and discussion 3.1 GastroPlusTM model set up and validation

3.1.1 Setting up the disposition parameters for subjects of study RDEA594-131


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The pharmacokinetics of lesinurad following microdose intra-venous infusion over 15 minutes were found to be adequately represented by three compartment PK model. The fitted disposition parameters and oral liver fixed fist pass effect for each subject are shown in Table 2.

3.1.2 Option A: Use of a fitted particle size distribution to model dissolution

For Option A, in vitro dissolution data was fitted by determining a theoretical particle size distribution that would match observed in vitro dissolution for the drug product batch factoring in the volume, solubility and doses used in the in vitro experiment. This was done using an Excel® tool described in appendix A. This particle size is then used as an input in GastroPlusTM using the adapted support file (*.psd) for each subject, to calculate in vivo dissolution using the physiologically relevant volumes, pH and transit times in the ACAT model.

Setting up the oral PK model for Option A

Using oral pharmacokinetic profiles obtained with batch 12A015 and microdose information, GastroPlus was used to calculate individual (n=10) effective permeability values for lesinurad (Table 3). In addition, the dosage form selected in GastroPlusTM was “delayed release tablet enteric coated” so as to prevent any dissolution in the stomach. Note that since the drug solubility is really low in acidic conditions, and clear lag times are observed in the PK profiles, this option consists in a worst case scenario where the drug dissolution only starts after stomach emptying. The stomach residence time and gastric emptying patterns were 21 ACS Paragon Plus Environment


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adjusted individually to the measured pharmacokinetics by fitting the gastric residence time in the ACAT model. For this purpose, a specific physiology model (ACAT model) was created for each subject of the absolute bioavailability study and the stomach residence time was fitted to the observed lag time in the in vivo PK profiles. Two cases were seen for the gastric emptying patterns in this study, subjects that exhibited single phase stomach emptying and subjects that exhibited double peaks. Double peaks are commonly observed with lesinurad; for example, in Cohort 3 of Study RDEA594-129 (batch ELAD vs 12A015 in the fasted state), multiple peaks were observed on approximately 40% of dosing occasions (Figure 2).

Figure 2. Example PK profiles from Study RDEA594-129 Cohort 3 showing multiple peaks in two subjects

There are multiple causes to the presence of double peaks in PK profiles as reported by Davies et al.13. Double peaks are seen with markers of gastric emptying which are held and released dissolved from the stomach. The presence of insoluble compound such as lesinurad 22 ACS Paragon Plus Environment

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in the stomach is bound to trigger prolonged gastric residence and emptying in multiple phases was already reported in the literature for fasted state administration14. Finally the time at which the formulation is given relative to the IMMC may trigger variability in the stomach emptying since the motor activity is highly dependent on the phase of the IMMC15. For lesinurad, a small contribution of an entero-hepatic cycle cannot be ruled out. Considering there was no peak observed after IV infusion, and since double peaks are not systematically present for all subjects after food intake, this contribution is probably small. The absolute bioavailability of lesinurad is of 101% and combined gut and liver pre-systemic extraction is estimated to range from a maximum of 9% to 30% based on the 10 subjects of the absolute bioavailability study (Table 2). A maximum of 43% of the absorbed dose can therefore be reabsorbed by entero-hepatic cycle. In addition, some individual PK profiles show secondary absorption peaks reach higher concentrations than initial absorption peak (Figure 2 and Figure 3). These observations rule out a potential entero-hepatic cycle as the contributor of multiple peaking in the absorption phase of lesinurad. Lesinurad is a weak acid that is practically insoluble in the fasted state conditions of the stomach. In the intestine, the solubility and permeability are high. Because of the high drug solubility and permeability in intestinal environment, the drug dissolves rapidly and is absorbed from the GI tract only after stomach emptying. Based on this, incomplete initial gastric emptying was considered to be the most likely explanation for the presence of a double peak in lesinurad plasma profiles. The second peak may or may not be concomitant with food administration. In the literature, before food administration, a cephalic phase is described in which released hormones trigger gastric motor activity in human16 and may be responsible for the second peak occurrence. 23 ACS Paragon Plus Environment


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In the absolute bioavailability study, subjects 106, 118 and 122 showed a double peak in the absorption phase related to partial gastric emptying. Single and multiple peaks were fitted using the lag times and gastric emptying patterns reported in Table 3. These gastric emptying patterns were used as input in the GastroPlusTM model using mixed multiple dose input option were the oral dose can be split in several fractions to match the gastric emptying patterns of Table 3. Examples for oral PK profile simulations are shown in Figure 3.

Figure 3. Simulated and observed PK profiles for oral dose for Subject 122 (A) and 123 (B) in Study RDEA594-131 using Option A.

Incorporating dissolution data into the model for Option A

Dissolution data obtained with the QC method were fitted for Option A by adjusting a theoretical particle size distribution to the observed dissolution rates, taking into consideration the drug solubility, the volume and temperature of the medium using internal Microsoft Excel® tool as shown in Figure 4 for batch 12A015 and batch ELAB. This particle size 24 ACS Paragon Plus Environment

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information can be representative of the actual drug substance that was used to manufacture the tablet, but would most likely also reflect any impact of the process parameters like disintegration, wettability, granulation performance etc. This translational parameter can be used as direct input to the GastroPlusTM model, enabling it to calculate in vivo dissolution using the physiological conditions in the ACAT model.

Note: the value presented at the 2 hour time point for batch ELAB is from an infinity spin (15 minutes 250rpm). Figure 4. Fitting of dissolution profile for batch 12A015 (A) and ELAB (B) in the QC dissolution method with a theoretical particle size distribution

Since the particle distribution calculated from ELAB dissolution rate using the QC method is intended to be used as input parameter in GastroPlusTM to predict formulation dissolution rate along the GI tract in different conditions of pH and volume, it is important to validate that this input parameter can adequately be used to simulate the in vitro dissolution obtained on ELAB in different media. The ELAB particle size distribution calculated from the QC method (Figure 4) was therefore used as input in the Excel tool to predict the dissolution of ELAB at 25 ACS Paragon Plus Environment


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pH3, pH4 and pH5. In these media, apparent drug solubility was taken at 0.016, 0.053, and 0.385 mg/mL respectively. Figure 5 shows that the particle size distribution calculated from the in vitro dissolution profile of ELAB using the QC method is able to predict observed drug product dissolution rate over a wide range of drug solubility 0.016 to 1.77mg/mL. This approach is deemed satisfactory and the particle size distribution can be used as an input in the GastroPlusTM modelling for Option A.

Figure 5. Simulation of ELAB dissolution in pH3, pH 4, and pH 5 using particle size distribution derived from QC dissolution method profile (pH 4.5 +1%SLS) vs observed data +/-1 SD

Impact of luminal volume on exposure to Lesinurad


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Using Option A and Subject 112, the amount dissolved and absorbed in the different compartments of the GI tract following the administration of 400 mg tablets 12A015 were simulated with 3%, 7.5% and 100% small intestinal volume occupation in the ACAT model. The volume of luminal water is limiting the absorption in the duodenum through limitation of the amount drug dissolved but not in the jejunum 1 (Figure 6).

Figure 6. Simulation of amount lesinurad absorbed from the small and large intestine out of 400 mg 12A015 tablet administered to subject S112 using 3%, 7.5% and 100% water volume occupation of the small intestinal compartments.

Increasing the amount of water in the lumen of the GI tract leads to a decrease in tmax and to a bell shape relationship to the Cmax. With 100% duodenal volume occupation, the amount absorbed in the duodenum is maximal and reduces the amount which will be absorbed in the



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jejunum leading to an earlier but more prolonged absorption. This translates in reduced Cmax as shown in Error! Reference source not found..

Figure 7. Simulated PK profile vs measured plasma concentrations for S112 following administration of 400 mg 12A015 tablet using Option A

We chose to use physiological values of 7.5% for the small intestine knowing that the drug formulation and drug substance will only have access to the lumen to dissolve.

3.1.3 Option B : Weibull function as an input for dissolution

For Option B, in vitro dissolution data obtained with batch 12A015 using the QC method was fitted using a Weibull single phase function. For each subject a lag time is then added to the Weibull function to prevent the drug from dissolving in the stomach during the simulation as seen in vivo. The lag times are directly extracted from the simulation exercise of Option A


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above (Table 3). Weibull functions with individual lag times are used as input for each subjects and the dosage forms are set to “controlled release dissolved” in GastroPlusTM. In addition, since Weibull functions release drug as a solute in the model, precipitation can happen in the GI tract, which would not be physiologically relevant. In order to fix the amount dissolved in vivo to that observed in vitro, the precipitation time is set to 100,000 seconds to prevent precipitation for all subjects. Subjects 106, 118 and 122 which showed apparent double peaks in the absorption phase are discarded for the evaluation of Option B. Indeed, when using the mixed multiple dose module of GastroPlusTM only one Weibull function can be loaded for several doses of controlled release formulations. This would have led same lag times being applied to the first and second doses and a need to alter the gastric emptying patterns to run this option. The conclusions on Option B are not impacted by not considering these 3 subjects in the analysis.

3.1.4 Option C: Use of the Z-factor

In Option C, the dissolution curves obtained for batches 12A015, ELAB and MPAC using the QC method are used together with the in vitro dissolution conditions (volume, solubility in the dissolution medium and dose) to calculate Z-factors on the basis of Takano et al.17. The Z-factor calculation tool of GastroPlusTM was used and the results of fit of Z-factors from in vitro data are shown in Figure 8.



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Figure 8. Z-factor fit for batches 12A015 (Z=1E-3 mL/mg/s), ELAB (3.74E-4 mL/mg/s) and MPAC (Z=5e-4 mL/mg/s) Z-factor based dissolution rates do not adequately capture the measured profiles for 12A015, ELAB and MPAC (Figure 8). This is related to the fact that the in vitro dissolution profiles for the above-mentioned batches clearly show multiphasic phenomena, which are not captured by a single Z-factor.

3.1.5 Simulations using the subjects from Study RDEA594-131 with Option A, B and C for batches 12A015

Simulations were performed to determine whether the model options could adequately reproduce the observed Cmax and AUC0-t for batch 12A015 dosed in the absolute bioavailability study which was rapidly and completely dissolving in vitro. Results for predicted Cmax for batch 12A015 are shown in Figure 9 for options A, B and C.


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Figure 9. Measured vs predicted Cmax for batch 12A015 in the absolute bioavailability study using Options A, B and C

Model options A, B and C all seem to reasonably well predict observed Cmax in the absolute bioavailability study. Summary statistics about the prediction ability of Cmax are shown in Table 4.

The Option B gave slightly worse results for the prediction of batch 12A015 Cmax compared to Options A and C. In addition, contrary to Option A or C, it cannot be applied to subjects showing partial gastric emptying. In order to explain these equivalent model performances, a Parameter Sensitivity Analysis was run using permeability, solubility and particle size distribution for Option A for a representative subject (S103) at the dose of 400 mg (Figure 10).



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Figure 10. Parameter sensitivity analysis for batch 12A015

We can see from this parameter sensitivity analysis that the main factor driving the Cmax for this rapidly dissolving batch is the drug permeability. Solubility and particle size (which govern dissolution rate) are not limiting over a wide range around the baseline parameters. Since the Peff and gastric emptying patterns were adjusted to each subject of the absolute bioavailability study, this explains why the models, since using the same Peff inputs perform equally well for Cmax prediction of 12A015 oral data. All model options gave similar results of predicted AUC for batch 12A015. The rest of the analysis focuses on the ability of the model to integrate the in vitro dissolution difference and how these link to different in vivo absorption rates, i.e. focusing primarily on model ability to predict Cmax ratios.


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3.1.6 Relative bioavailability of ELAB vs 12A015 with Options A, B and C

The Cmax ratios between ELAB and 12A015 were simulated and compared to the corresponding exposure ratios measured during the relative bioavailability study using the population of the absolute bioavailability study and the model options described above. During the calculation of exposure ratios, for batch ELAB and options A and C, it was necessary to reduce the dose in the GastroPlusTM simulation to account for the incomplete in vitro release observed. This enabled both the dissolution rate (controlled by in vivo dissolution input options) and the maximum amount dissolved in vivo (controlled by the dose) to be handled during the simulation. The maximum amount dissolved with batch ELAB was used to adapt the dose for the GastroPlusTM modelling. For options A and C, the dose was hence reduced to 352 mg in the simulation and the dosage form was set to “Delayed Release enteric coated tablet” so as to prevent any dissolution in the stomach. For option B, for reasons highlighted below, the dose was left to 400 mg and individual stomach transit times were used to add a lag time to the in vivo release as shown below. Results for predicted (ELAB/12A015) Cmax ratios using options A, B and C are shown in Figure 11.



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Figure 11. Predicted Cmax ratios (ELAB/12A015) using Options A, B and C vs measured (solid line) and confidence interval (dashed lines) in the relative bioavailability study

Despite the fact that 100% dissolution was hypothesized for Option B and no precipitation was allowed, the Cmax ratio simulated for ELAB/12A015 is much lower the one observed during the clinical trial RDEA594-129, predicted at 0.699 vs observed at 0.80 (Figure 11). Weibull functions only capture in vitro dissolution profiles at a given pH and with given volumes and agitation conditions. Over or underestimations of the in vivo situations can be frequent depending on the limiting factors for in vivo absorption. One reason why the Cmax was underestimated using Option B for ELAB, could be that the dissolution data for this formulation are obtained at pH 4.5 +1% SLS where the drug solubility is 1.77 mg/mL. In vivo, in lumenal pH conditions will be of about 6.5 to 6.8 where lesinurad is much more soluble. In addition, the high drug permeability would also contribute to increase drug absorption rate from the GI tract, both parameters leading to a greater in vivo dissolution rate than that


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measured in vitro with the QC method. A further disadvantage of Option B is that it “releases” drug in the lumen as a solute which may lead to local supersaturation owing to the low free fluid content of the GI tract. If the precipitation time is not set to a high enough value for the modelling, a drug precipitation can be simulated. In vivo for a weak acid, and specifically for lesinurad, there is no envisaged supersaturation or precipitation. The drug would move from stomach conditions where it is almost insoluble to lumen of the GI tract where the drug dissolution would be driven by the solubility and permeability. For these simulations, the dose for Option B was left at 400 mg. A lower dose for ELAB would have led to even lower predicted Cmax ratios. Option B is not considered as valid for lesinurad absorption.

Option D is a variant of Option B, in that the in vitro release profile is used to control the in vivo release of drug substance in an undissolved state, using the formulation option “controlled release undissolved”. Option D, which releases undissolved particles, will therefore lead to lower predicted absorption rates from the GI tract compared to Option B. Since Option B already under-estimates the Cmax ratio observed during Study RDEA 594-129 comparing ELAB and 12A015, the use of controlled release undissolved option in GastroPlusTM will lead to an even lower anticipated exposure and be less predictive of the in vivo study results than Option B. Option D was therefore not considered to remain a valid option for the modelling of Lesinurad absorption rate.

As for Option C, It can be seen that the use of Z-factor does not allow prediction of the 80% Cmax exposure ratio observed in clinic. The simulated exposure ratio over estimates by 10% the ratio observed in vivo. Based on this, Option C is not retained for modelling. Z-factors 35 ACS Paragon Plus Environment


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cannot capture the biphasic release observed for ELAB batch in vitro at pH above 1.2, leading to an over-estimation of the later time points as the fit goes through the experimental dissolution data points. This would translate in over-estimated in vivo dissolution rates (Figure 8).

In conclusion, the best option for integration of dissolution data in the in silico model is demonstrated to be Option A. Option A, takes into consideration the solubility and volumes used to generate the dissolution data and therefore, using particle size distribution as an input in GastroPlusTM, allows the simulation to dynamically adapt the dissolution rate to the local conditions of volume and pH in the gastrointestinal tract. This is not feasible with options B or C. In order to explore the better results obtained with Option A, a Parameter Sensitivity Analysis was run at higher particle size distribution range for Option A, representative of the inputs used to model the lower performing batch ELAB (Figure 12).


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Figure 12. Parameter sensitivity analysis for batch ELAB

At the particle size range representative of batch ELAB, solubility and particle size (i.e. dissolution rate) control Cmax equally as the human permeability. This demonstrates why, while all of the models adequately predicted Cmax for a well performing batch such as 12A015, not all of them performed well for a poorly dissolving batch such as ELAB, as the predictions obtained are much more dependent on how well the dissolution rate has been simulated.

3.2 GastroPlusTM model Use

3.2.1 Definition of dissolution space for bioequivalence

The GastroPlusTM model using Option A was used to predict the likely in vivo performance of batches with dissolution performance at the limit of the proposed specification (Q=80% in 30 minutes), such as MPAC and 12E058. The dissolution profile for batch MPAC was used for the simulations, as this has the lower dissolution profile compared to 12E058, and was manufactured at the intended commercial site (AstraZeneca AB) at the 800L granulation scale. Figure 13A shows the virtual particle size distribution generated in Excel to use as input for the GastroPlusTM model.



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B

A

Figure 13. Fitting of dissolution profile for batch MPAC (A) and virtual batch A (B) in the QC dissolution method with a theoretical particle size distribution

As MPAC is a 200mg tablet batch, simulations were performed at a 400mg dose to facilitate comparison to the simulations previously performed for ELAB and the reference batch 12A015. A simulated cross-over trial using n=25 virtual population was performed for batch MPAC, ELAB and 12A015. Since lesinurad is a weak acid highly soluble and permeable in the intestine but practically insoluble in the stomach, the simulated tmax is found closely related to the gastric residence time. This observation made it more important to vary randomly the gastric residence time within and between each subject for the virtual cross-over study as indicated in the methods section. Paired equivalence comparisons were performed using Minitab® software for the subjects in the virtual trial, separately comparing the performance of batches MPAC and ELAB vs. the reference batch 12A015. Table 5 clearly demonstrates that batch MPAC is anticipated to be bioequivalent to batch 12A015, as the geometric mean ratios for both AUC and Cmax are very close to one. In an appropriately 38 ACS Paragon Plus Environment

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powered bioequivalence study these batches would be expected to give confidence intervals that fall within the standard limits of 0.8-1.25.

It can also be observed that the predicted confidence intervals from the simulated trial are smaller than those observed in the clinic. This is due to additional sources of variability being present in the clinical setting which are not simulated by the virtual trial. In particular, intrasubject variability in gastric residence time between dosing occasions can be expected in the clinic, as there is no way to standardize the timing of the tablet dosing in relation to the IMMC (Figure 2). For lesinurad this translates into lag times and partial gastric emptying patterns in approximately 40% of the population. Although we were able to easily integrate variable lag times in our modelling, current commercial PBPK tools do not allow partial gastric emptying patterns to be integrated in virtual trials. However this within subject variability is anticipated to contribute less than partial emptying where, in particular for Cmax, observed values would be linked to the fraction of the dose delivered by the stomach in each emptying phase.

Conclusions for batches MPAC and 12E058

Batches MPAC and 12E058, which pass the proposed dissolution specification, are anticipated to be bioequivalent to the reference clinical batch 12A015 according to the in silico modelling described above. This demonstrates that the proposed dissolution specification of Q=80% at 30 minutes is justified, as it is able to pass batches which are anticipated to have suitable clinical performance, and rejects batches such as ELAB, which have been shown to have reduced exposures in vivo (Figure 14).



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Further simulations exploring potential edge of failure for dissolution

To further explore the dissolution space, it was decided to search for the edge of failure, i.e. for a dissolution profile that would keep the Cmax exposure ratio to 12A015 between approximately 1 and 0.9 (which would be expected to pass an adequately powered bioequivalence study). A Virtual Batch A was generated for this purpose, with a dissolution profile that reaches complete release within 2 hours. For input into the GastroPlusTM model, the dissolution profile of virtual batch A was fitted to a theoretical particle size distribution as previously described for Option A (Figure 13B). A simulated trial was performed for Virtual Batch A as described above using 25 subjects, and equivalence comparisons performed in Minitab®. As shown in Table 6, the simulations demonstrate that Virtual Batch A would be anticipated to be bioequivalent to batch 12A015, as the geometric mean ratios for Cmax and AUC are close to one. This exemplifies the robust in vivo performance of lesinurad, driven by high intestinal solubility and good permeability. Although the edge of failure for dissolution was not found, these data demonstrate that the proposed dissolution specification sits comfortably within a region of dissolution performance where bioequivalence is anticipated, and is not near an edge of failure for dissolution. This provides additional confidence in the proposed specification.

Proposed bioequivalence dissolution space for lesinurad IR tablets

The proposed dissolution space for lesinurad IR tablets, within which batches are anticipated to be bioequivalent to the clinical reference 12A015, is shown below in Figure 14, represented by the shaded area.


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Figure 14. Dissolution space for anticipated bioequivalence to lesinurad IR tablets using the QC dissolution test

The lower limit is proposed based on the simulation results for Virtual Batch A, which GastroPlusTM modelling indicates will be bioequivalent to standard clinical batches such as 12A015 despite having a dissolution profile which would fail the proposed dissolution specification of Q=80% in 30 minutes. For the upper limit, any batch showing dissolution quicker than 12A015 is also anticipated to be bioequivalent to standard clinical batches. As shown in the sensitivity analysis in Figure 10, any batch with dissolution rate higher than 12A015 would be absorbed at a rate limited by the drug effective permeability and, therefore is anticipated to be bioequivalent to 12A015. 41 ACS Paragon Plus Environment


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3.2.2 Justification for drug substance particle size distribution specifications

A two-point specification with upper limits for D(v, 0.5) and D(v, 0.9) is proposed for the drug substance particle size distribution: D(v, 0.5) NMT 70 µm; D(v, 0.9) NMT 159 µm.

In order to justify this drug substance particle size specification, the GastroPlusTM model developed above for the 10 subjects of the absolute bioavailability study was used to predict the in vivo dissolution rate of drug substances that would be at the limit of this specification. A virtual drug substance batch (VSD1) was generated exactly at the limit of these specifications, and compared to the measured particle size distribution data for 12A015 (D (v, 0.5) 23.2, D (v, 0.9) 45.9). The particle size distributions used in GastroPlusTM for 12A015 were a log normal distribution with mean particle radius of 11.5 microns and standard deviation of 3, whereas that of Virtual Drug Substance 1 were a log normal distribution with mean particle radius of 35 microns and standard deviation of 10. These particle size inputs were used in the GastroPlusTM model Option A developed above to calculate drug in vivo dissolution for the 10 subjects of the absolute bioavailability study. No appreciable difference was observed between the calculated Cmax, tmax and AUC for 12A015 vs. VDS1 in any of the subjects. A virtual drug substance batch with a particle size distribution at the proposed particle size specification limits is therefore anticipated to be bioequivalent to batch 12A015.


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4 Conclusions and perspectives

An in silico PBPK model was successfully built for lesinurad tablets by using intravenous and oral PK data and fitting individual subject profiles to account for differences in physiological factors such as gastric emptying. The model was validated using clinical data from a batch which was bio-inequivalent in vivo (batch ELAB). Several methods were evaluated to input in vitro dissolution data into the model. Option A provided the most accurate prediction of the observed in vivo performance of batch ELAB; this involved fitting of a theoretical particle size distribution to dissolution data, which is then used as a translational input in GastroPlusTM to predict formulation dissolution along the GI tract.

The alternative approaches for integration of dissolution data in PBPK models were shown not to perform well for Lesinurad for various reasons highlighted previously. However, their use in this context for other products would be recommended and a comparison of their predictive performance should be undertaken before choosing the right method. Indeed for BCS class I drugs, where solubility and permeability are not limiting, Weibull functions may apply as good inputs to predict in vivo dissolution, examples of this are found for paracetamol, diltiazem or zolpidem modified release formulations18. Z-factors have the potential to handle differences in volume and pH conditions found in vitro and in vivo. However, since they describe single release phases, they are not be able to capture the biphasic in vitro release that



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is sometimes observed with given formulations. This could potentially be overcome by defining multiple Z-factors associated to fractions of doses within the formulation.

Using the model described in this paper, it was shown that the proposed dissolution specification for lesinurad tablets Q=80% at 30 minutes will ensure bioequivalence to the clinical reference batch 12A015. Furthermore it was shown that this specification lies well within the anticipated bioequivalent space for lesinurad tablets, giving additional confidence in the proposed limit. Finally, a virtual drug substance batch with a particle size distribution at the limit of the proposed specifications was shown to behave similarly to the drug substance used in the clinical reference batch 12A015 in terms of Cmax, tmax and AUC, demonstrating that the proposed specification limits for particle size distribution would give product bioequivalent to the pivotal clinical batches.

The modelling package described in this manuscript was submitted to US FDA in support of the proposed control strategy, and resulted in acceptance of the proposed specifications for dissolution and particle size. In the absence of the validated in silico model, an in vivo relative bioavailability or bioequivalence study would have been required to demonstrate a lack of impact on clinical performance. This demonstrates the value of investing in PK studies to support the development of a robust in silico model at the right stage of the development value chain, so that the model is available to support discussions with regulatory authorities in a timely manner.


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Indeed, the authors believe that a single well-designed relative bioavailability study, incorporating an IV micro-tracer dose, mechanistically selected formulation variants and markers for gastric pH and emptying such as paracetamol19, SmartPills®20, breath tests21, should be sufficient to support the development and validation of a robust in silico model which can be used to support future formulation site and scale up changes and establishment of the control strategy especially with regards dissolution and particle size specifications. A strategy for mechanistic IVIVe development and in vitro dissolution method development is proposed in Figure 15.

PoC or first read on efficacy = +

IR product with rapid dissolution is rapid >80% in 15 min

MR product envisaged

Develop formulation variants n=3 with different release profiles Prototype dissolution using Preclinical methods

BCS I or III

Waive BE requirements using BCS based biowaiver guidance

Run IVIVe human study in vivo human evaluation with IV microdosing, markers of gastric emptying, pH probe in stomach.

Extract in vivo absorption profiles from PBPK modelling Use PBPK model for QbD, safe space, specs justification (dissolution and DS PSD), support changes within safe space

Clinical relevant dissolution method development

Use vitro and vivo data + refined PK-PD or PK-Tox to maximize therapeutic index, define best commercial formulation or LCM opportunities through PBPK

Figure 15. IVIVe and biowaiver strategy

A mechanism based IVIVe using absorption modelling gives increased confidence to understand the impact of different dissolution profiles on pharmacokinetics as compared to



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traditional mathematical IVIVc. Indeed, mechanism based IVIVe factors in physiological variables such as pH, transit times, volumes, gastric emptying patterns, all factors being key in determining the in vivo dissolution rates and absorption profiles. Methods for integrating dissolution data should enable the in vivo dissolution rate calculation to be related to these physiological parameters. This would bridge the gap between in vitro dissolution and in vivo dissolution and allow better understanding of the effect of within and between subject variability related to GI tract physiology. In contrast, for traditional mathematical IVIVc the in vitro dissolution data are usually very reproducible for the chosen test conditions and variability in the in vivo absorption rates may confound the correlation.

Broader adoption of mechanistic modelling integrating in vivo physiological parameters would enable to distinguish the true product performance from the impact of physiological GI variables in the measured human oral pharmacokinetics. This would increase the confidence with which we assess product performance along drug development and commercial manufacture, and is likely to save several human bioequivalence studies during the life of the product.


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ABBREVIATIONS AAFE, Absolute Average Fold Error; ACAT , Advanced Compartmental Absorption and Transit; ADME, Absorption Distribution Metabolism and Excretion; AFE, Average Fold Error; API , Active Pharmaceutical Ingredient; AUC, Area Under the plasma Concentration vs time curve; CR, Controlled Release; CI, Confidence Interval; Cmax, Maximum Plasma Concentration; CV, Coefficient of Variation; DR, Delayed Release; FaSSIF, Fasted State Simulated Intestinal Fluid; FeSSIF, Fed State Simulated Intestinal Fluid; GI, Gastro-Intestinal; IMMC, Inter-digestive Migrating Myoelectric Complex; IV, Intra-Venous; IVIVE, In vitro-in vivo extrapolation; MMD , Mixed Multiple Dose profile; NMT, Not More Than; Papp, Apparent permeability; PBPK, Physiologically based pharmacokinetic; Peff, Effective jejunal human permeability; PK, Pharmacokinetics; PSA , Parameter Sensitivity Analysis; PSD , Particle Size Distribution; QC, Quality Control; SGF, Simulated Gastric Fluid; SD, Standard Deviation; SLS , Sodium Lauryl Sulphate; Tmax, Time to reach maximum plasma concentration; VDS1, Virtual Drug Substance batch 1, which has a particle size distribution at the limit of the proposed specification (D(v, 0.5) NMT 70 µm; D(v, 0.9) NMT 159 µm)



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Responders (CLEAR 1 and 2). Abstract Number: L10 ACR/ARHP Annual Meeting 2014. http://acrabstracts.org/abstract/lesinurad-a-novel-selective-uric-acid-reabsorption-inhibitor-intwo-phase-iii-clinical-trials-combination-study-of-lesinurad-in-allopurinol-standard-of-careinadequate-responders-clear-1-and-2/ (accessed 05.05.16). (6)

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ZURAMPIC®

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label, (accessed

05.05.16). (7) Jantratid, E.; Janssen, N.; Reppas, C.; Dressman, J.B. Dissolution media simulating conditions in the proximal human gastrointestinal tract: an update. Pharm. Res. 2008, 25(7), 1663–1676. DOI: 10.1007/s11095-008-9569-4. (8) Okasaki, A.; Mano, T.; Sugano, K. Theoretical dissolution model of polydisperse drug particles in biorelevant media, J. Pharm. Sci. 2008, 97(5), 1843-1852. (9) Schiller C.; Fröhlich, C.P.; Giessmann, T.; Siedmund, W.; Mönnikes, H.; Hosten, N.; Weitschies, W. Intestinal fluid volumes and transit of dosage forms as assessed by magnetic resonance imaging. Aliment. Pharmacol. Ther. 2005, 22(10), 971-979. DOI: 10.1111/j.13652036.2005.02683. (10) Sutton, S.C. Role of Physiological Intestinal Water in Oral Absorption. The AAPS Journal 2009, 11(2), 277-285. DOI: 10.1208/s12248-009-9087-2. (11) Yang, J.; Jamei, M.; Yeo, K.R.; Tucker, G.T.; Rostami-Hodjegan A. Prediction of intestinal first pass drug metabolism. Current Drug Metabolism 2007, 8, 676-684. DOI: 10.2174/138920007782109733. 49 ACS Paragon Plus Environment


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(12) Ring, B.J.; Chien, J.Y.; Adkinson, K.K.; Jones, H.M.; Rowland, M.; Do Jones, R.; Yates, J.W. T.; Ku, M.S.; Gibson, C.R.; He, H.; Vuppugalla, R.; Marathe, P.; Fischer, V.; Dutta, S.; Sinha, V.K.; Björnsson, T.; Lavé, T.; Poulin, P. PhRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, Part 3: Comparative Assessment of Prediction Methods of Human Clearance. J. Pharm. Sci. 2011 100(10), 4090-4110. DOI: 10.1002/jps.22552. (13) Davies, N.M.; Takemoto, J.K.; Brocks, D.R.; Yáñez, J.A. Multiple Peaking Phenomena in Pharmacokinetic Disposition. Clin. Pharmacokinet. 2010, 49(6), 351-377 DOI: 10.2165/11319320-000000000-00000 (14) Langguth, P.; Lee, K.M.; Spahn-Langguth, H.; Amidon, G.L. Variable gastric emptying and discontinuities in drug absorption profiles: dependence of rates and extent of cimetidine absorption on motility phase and pH. Biopharm. Drug Disp. 1994, 15(9), 719-746. DOI: 10.1002/bdd.2510150902. (15) Janssen, P.; Vanden Berghe, P.; Verschueren, S.; Lehmann, A.; Depoortere, I.; Tack, J. The role of gastric motility in the control of food intake, Aliment. Pharmacol. Ther. 2011, 33, 880-894. DOI: 10.1111/j.1365-2036.2011.04609.x. (16) Katschinski, M. Nutritional implications of cephalic phase gastrointestinal responses. Appetite 2000, 34(2), 189-196. DOI:10.1006/appe.1999.0280. (17) Takano, R.; Sugano, K.; Higashida, A.; Hayashi, Y.; Machida, M.; Aso, Y.; Yamashita, S. Oral Absorption of Poorly Water-Soluble Drugs: Computer Simulation of Fraction


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Absorbed in Humans from a Miniscale Dissolution Test. Pharm. Res. 2006, 23(6), 1144-1156. DOI: 10.1007/s11095-006-0162-4. (18) Pepin, 2014, Use of IVIVc and IVIVe to support formulation development. Industrial case

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https://zerista.s3.amazonaws.com/item_files/2943/attachments/31737/original/249.pdf (accessed 05.05.16). (19) Heading, R. C.; Nimmo, J.; Prescott, L.F.; Tothill, P. The dependence of paracetamol absorption on the rate of gastric emptying, Br. J. Pharmac. 1973, 47, 415-421. DOI: 10.1111/j.1476-5381.1973.tb08339.x. (20) Koziolek, M.; Schneider, F.; Grimm, M.; Modeβ, C.; Seekamp, A.; Roustom, T.; Siegmund, W.; Weitschies, W. Intragastric pH and pressure profiles after intake of the highcaloric, high-fat meal as used for food effect studies. J. Cont. Rel. 2015, 220, 71-78. DOI: 10.1016/j.jconrel.2015.10.022. (21) Leodolter, A.; Domínguez-Muñoz, J.E.; Von Arnim, U.; Malfertheiner, P. Citric acid or orange juice for the 13C-urea breath test: the impact of pH and gastric emptying. Aliment Pharmacol. Ther. 1999, 13, 1057-1062. DOI: 10.1046/j.1365-2036.1999.00557.x.



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Table 1. Solubility of lesinurad free acid at 37°C Media

Final pH

mg/mL

FaSSIF pH 6.5

5.6

3.2

FeSSIF pH 5.0

5.0

1.7

SGF pH 1.6 (HCl 30 mM, I = 0.1 M)

1.5

0.0061

pH 4 buffer (Citrate 25 mM, I = 0.1 M)

4.0

0.045

pH 5 buffer (Acetate 25 mM, I = 0.1 M)

5.1

0.60

pH 6 buffer (Citrate 22 mM, I = 0.1 M)

5.9

5.2

pH 6.5 buffer (Phosphate 25 mM) + NaOH

6.0

17

pH 6 buffer (Citrate 22 mM) + NaOH

5.9

43

FaSSIF pH 6.5 + NaOH

5.9

21


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Table 2. PK disposition model parameters fitted to observed intravenous microdose data for subjects in Study RDEA594-131 Subject Liver FPE (%) CLR (L/h/kg) CLH(L/h/kg) Vc(L/kg) k12 (1/h) k21 (1/h) k13 (1/h) k31 (1/h)

Fg

S101

5.74

0.01371

0.03009

0.1267

0.80841

1.0356

0.01346

0.01841 0.95

S102

15.3

0.04368

0.09587

0.09234

0.75967

0.60576

0.04079

0.03797 0.81

S103

9.10

0.02679

0.05879

0.11884

0.147

0.18627

0.01925

0.01739 0.89

S106

6.31

0.02257

0.04955

0.11908

0.20127

0.22838

0.01164

0.02119 0.89

S112

12. 9

0.03666

0.08046

0.09729

0.37606

0.33304

0.03327

0.02739 0.88

S115

10.8

0.03485

0.07648

0.12036

0.30142

0.29502

0.02396

0.02078 0.86

S116

7.21

0.02223

0.0488

0.10991

0.3016

0.29513

0.02396

0.02078 0.90

S118

8.78

0.02842

0.06237

0.10414

0.99019

0.57865

0.02759

0.02018 0.83

S122

6.47

0.01975

0.04335

0.10624

0.16883

0.26526

0.01508

0.01889 0.88

S123

5.12

0.01654

0.0363

0.07871

0.40437

0.36691

0.0131

0.02662 0.95



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Table 3. Gastric emptying patterns observed in Study RDEA594-131

Subject

Peff (cm/s x 10-4)

Lag time first peak (h)

Dose first peak (mg)

Gastric residence time second peak (h)

Dose second peak (mg)

05003-101

2.8121

3.17

400

05003-102

2.4187

0.81

400

05003-103

2.8765

1.73

400

05003-106

2.9872

1.32

200

4.25

200

05003-112

3.7031

0.72

05003-115

3.1256

0.09

05003-116

2.7388

0.62

05003-118

1.8112

0.01

150

2.01

250

05003-122

1.7419

1.37

200

5.37

200

05003-123

5.4409

0.54

Table 4. AFE, AAFE for Cmax calculations using Options A, B and C in the absolute bioavailability study Model option

n

AFE

AAFE

Option A

10

0.966

1.073

Option B

7

0.919

1.126

Option C

10

0.975

1.087

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Table 5. Paired comparisons of ELAB and MPAC vs. 12A015 Cmax and AUC using the values obtained from the virtual trial Predicted Cmax

Predicted AUC (0-96)

Geomean Ratio

90% CI

Geomean Ratio

90% CI

ELAB vs. 12A015

0.805

(0.796, 0.814)

0.876

(0.869, 0.883)

MPAC vs. 12A015

0.987

(0.977, 0.998)

1.000

(0.990, 1.01)

Table 6. Paired comparison of Virtual Batch A vs. 12A015 Cmax and AUC using the values obtained from the virtual trial. Predicted Cmax Geomean Ratio Virtual Batch A vs. 12A015 0.992

Predicted AUC (0-96) 90% CI

Geomean Ratio

90% CI

(0.990, 0.993)

0.989

(0.988, 0.990)

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Justification of Drug Product Dissolution Rate and Drug Substance

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