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In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation and Precipitation of Posaconazole Bart Hens, Shriram M. Pathak, Amitava Mitra, Nikunjkumar Patel, Bo Liu, Sanjaykumar Patel, Masoud Jamei, Joachim Brouwers, Patrick Augustijns, and David B Turner Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.7b00396 • Publication Date (Web): 17 Aug 2017 Downloaded from http://pubs.acs.org on August 20, 2017

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Molecular Pharmaceutics

In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation and Precipitation of Posaconazole Bart Hens1,2*, Shriram M. Pathak3*, Amitava Mitra4,5, Nikunjkumar Patel3, Bo Liu3, Sanjaykumar Patel6, Masoud Jamei3, Joachim Brouwers2, Patrick Augustijns2, David B. Turner3

1

Current affiliation: College of Pharmacy, University of Michigan, Ann Arbor, MI, USA

2

Drug Delivery & Disposition, KU Leuven, Leuven, Belgium

3

Simcyp Limited (a Certara Company), Sheffield, United Kingdom

4

Biopharmaceutics, Pharmaceutical Sciences & Clinical Supply, Merck & Co., Inc., Pennsylvania, United States of America 5

Current affiliation: Sandoz, Inc., New Jersey, United States of America

6

Analytical Sciences, Pharmaceutical Sciences & Clinical Supply, Merck & Co., Inc., New Jersey, United States of America

*

Bart Hens and Shriram M. Pathak contributed equally to this work and should be considered as joint first authors

Corresponding author: David B. Turner - [email protected] Address: Blades Enterprise Centre, John Street, Sheffield, S2 4SU, United Kingdom. Tel.: +44 (0) 114 292 23236; fax: +44 (0) 114 292 2333

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Graphical Abstract:

Keywords: clinical trial simulation; mechanistic physiological model; PBPK; absorption; biopharmaceutics classification system; formulation; gastrointestinal; intestinal absorption; oral absorption; oral drug delivery; precipitation; supersaturation

Abstract: The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically-based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp® Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately two-fold


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higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semi-mechanistic modeling of a twostage media change in vitro dissolution test. The two-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a two-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation and precipitation for a weakly basic compound in part informed by modeling of in

vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage media change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost identical CSR to the clinical study value; this had no significant impact on the PBPK model predictions.

Introduction: USP 1 and 2 dissolution experiments have matured to a point that they can now be used for biowaiver of immediate release (IR) dosage forms of BCS class 1 and 3 compounds.1,2 These simple, singlecompartment in vitro dissolution setups may be inadequate for BCS class 2 compounds, as some intraluminal processes, crucial for the in vivo behavior of these poorly soluble compounds, are not incorporated. For example, a recent study clearly demonstrated the low buffer capacity of gastrointestinal (GI) fluids and alternating motility along the GI tract as a major source of intersubject variability in systemic exposure of ibuprofen (BCS class 2a compound), observed in 37 healthy volunteers in fasted and fed state.3 As a result, efforts are being made to advance the dissolution technologies as such that they become more biorelevant.4 Depending on the physicochemical and biopharmaceutical characteristics of the drug and the nature of the formulation, the drug may undergo dissolution in the stomach, followed by supersaturation, precipitation, and re-dissolution in the upper small intestine, which are often not fully captured in a simple conventional dissolution vessel. This 3 ACS Paragon Plus Environment


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interplay of dissolution, supersaturation, and precipitation is particularly important for poorly soluble weakly basic compounds and may be influenced by permeability, gastric emptying, fluid volume dynamics including dilution by ongoing GI secretions, physicochemical properties of the drug compound itself, and the ambient composition of the GI fluids.5,6 For weakly basic compounds, supersaturation can be created after transfer from the stomach to the small intestine due to a pH-shift: the acidic environment in fasting conditions (pH 1-2) will stimulate the dissolution of the predominantly ionized form of the drug (depending on its pKa), while precipitation of the predominantly unionized form may occur in the neutral environment of the small intestine (pH 6.5). In the last decade, significant efforts have been made to simulate complex intraluminal processes such as supersaturation and precipitation in vitro: several models have been proposed including (i) the twocompartmental transfer model7, (ii) the TNO Intestinal Model 1 (TIM-1)8, (iii) the Gastrointestinal Simulator (GIS)5, and (iv) the BioGIT system.9 Within the framework of OrBiTo, a European project pursuing the development of predictive tools for oral drug absorption10, a number of in vitro models have been explored to study intestinal supersaturation/precipitation and results have been compared with intraluminal in vivo data, with the aim to further optimize and validate these models and to improve their predictive power.4,11 In addition to these in vitro models, there is increasing interest in the application of physiologicallybased pharmacokinetic (PBPK) models to predict the behavior of oral dosage forms in humans.12 As commercially available PBPK simulation software, such as the Simcyp® Simulator, PK-Sim®, and GastroPlus™, permit the incorporation of supersaturation and precipitation kinetics into their models, they may contribute to a better understanding of the interplay between the obtained degree of supersaturation and the tendency to precipitate in the small intestine in order to assess the in vivo behavior of weak bases.12,13 It is, however, generally accepted that accurate a priori prediction of the

in vivo precipitation behavior of poorly soluble weak bases based directly on in vitro data is difficult.14,15 In this report, an in silico model was built using the Simcyp® PBPK Simulator to extrapolate in vitro data on supersaturation and precipitation to intraluminal concentrations in humans of the lipophilic weak base, posaconazole, and compare results against available clinical data after


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intragastric administration of two different suspensions of posaconazole, one acidified (pH 1.6) and the other neutral (pH 7.1).16 Gastric and duodenal concentrations were profiled as a function of time in parallel with systemic exposure providing three clinical reference points (AUC, Cmax, and Tmax) against which to assess the PBPK simulations. The PBPK model was informed in part by modeling of a relevant in vitro precipitation experiment using the Simcyp® In Vitro data Analysis (SIVA) toolkit to extract the second precipitation rate constant (sPRC) and the critical supersaturation ratio (CSR) was obtained from the maximum degree of supersaturation observed in the clinical study. Materials and Methods:

Simcyp In Vitro data Analysis toolkit (SIVA) The Simcyp In Vitro data Analysis (SIVA) toolkit is designed to model in vitro experiments with a view to confirming/improving mechanistic understanding, model confirmation/validation and estimation of parameters for input to in vivo simulations with the mechanistic PBPK Simcyp® Simulator. The toolkit enables the analysis of a broad range of in vitro assays - whole cells, tissue samples and solid dosage form testing - to assess the metabolism, passive and active transport and dissolution/solubility/precipitation of drugs. In the present study, SIVA was used for the simulation of aqueous buffer and biorelevant (in the presence of bile salts) solubility parameters of posaconazole in different conditions, estimating underlying parameters such as bile micelle partition coefficients (Km:w) and intrinsic solubility (S0) (Equation 1). () = [ ] ×

× 10 :, ! "#$ + &

! "#$ + [ ] × × 10'!(:, ! "#$ + ! "#$ &

(1)

where ())!* and [ ] are the total solubility and bile salt concentration in a given medium,

respectively; note [ ] can be replaced by any other micelle forming surfactant and micelles are

assumed to be present only where [ ] > critical micelle concentration. ! "#$ is the solubility of the ionized species in the aqueous phase, is the concentration of water in a given medium (55.56 M);

log /:, ! "#$ 012 log /:, ! "#$ refer to the surfactant (e.g., bile salts-lecithin) micelle to 5 ACS Paragon Plus Environment


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water partition coefficients for ionized and unionized species, respectively. This equation enables the

in vivo locational and inter-individual variability of pH and [BS] to be accounted for and was used to model/simulate/confirm solubility model parameter values assessed against measurements made in aqueous buffer and biorelevant media:

•

pH 1.8 Simulated gastric fluid (SGF)

•

pH 5.0 Buffer

•

pH 7.0 Buffer

•

pH 6.5 Fasted state simulated intestinal fluid (FaSSIF)

•

pH 5.0 Fed state simulated intestinal fluid (FeSSIF)

Simulated solubilities were compared with experimental values obtained from the literature.17 The solubility factor (SF) was calculated from the ratio of the solubility in the buffer at pH 1.8 and the aqueous intrinsic solubility at pH 7.0 (S0). The SF is used to account for the salt-limited solubility of a drug18 as a simple alternative to using an ionic solubility product (Ksp)-based approach. For capturing supersaturation and precipitation behavior, the Simcyp® Simulator (and the complementary SIVA toolkit) use an empirical approach based upon specifying a Critical

Supersaturation Concentration (CSC) of a drug and one or two first order rate constants for precipitation (PRC and sPRC, respectively (Figure 1)). In a given segment of the GI tract (e.g., the duodenum), drug concentration may exceed thermodynamic solubility due to the transfer of dissolved drug from a previous segment (e.g., the stomach) and/or due to dissolution within that region. Only once the CSC is reached, drug is permitted to precipitate, thus there is no precipitation in Region A, Figure 1. A two-stage (media change) in vitro dissolution test was performed to capture the first order rate constant (sPRC) for precipitation and empirically modeled using the SIVA toolkit (see below). A

sPRC can be used, if required, to capture the shape of the concentration-time curve at and above the CSC (Region B, Figure 1). At or close to the CSC there may be a variety of underlying processes defining the observed concentration profile (in vitro or in vivo) including an induction period (lag time) for nucleation and/or transit of pre-dissolved drug into the duodenal compartment, as well as (in

vivo) simultaneous transit to the jejunum and absorption into the gut wall. Thus, for example, the sPRC can be set to an extremely low value (effectively no precipitation) to capture an induction 6 ACS Paragon Plus Environment

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period. Where the sPRC is activated, the PRC is applied only at concentrations below the CSC but only once the CSC has been attained (Region C, Figure 1). Precipitation continues until dissolved concentration is equal to equilibrium solubility. The maximum degree of supersaturation (DS) is the ratio between the CSC and the equilibrium solubility and is denoted in SIVA and Simcyp as the Critical Supersaturation Ratio (CSR).

Figure 1: Schematic presentation of the interplay of supersaturation and precipitation as implemented in the Simcyp® Simulator. Region A: increasing dissolved concentrations of a drug upon reaching its critical supersaturation concentration (CSC), followed by a meta-stable time window of supersaturated concentrations (Region B; described if required by a second precipitation rate constant). In a final stage, concentrations of the drug will precipitate as a function of time following a first-order kinetic process (Region C).

As discussed in detail in the ‘Results and Discussion’ section, the SIVA Toolkit was used to estimate the empirical precipitation model parameters from the concentration-time profiles obtained from in vitro media change dissolution experiments. Other physicochemical parameters of posaconazole (i.e., pKa, intrinsic solubility, SF, and Km:w) were estimated/confirmed prior to the modeling of supersaturation experiments to minimize the identifiability issue when estimating parameters. A posaconazole suspension (acidified suspension, 40 mg, Noxafil® oral suspension 200 mg / 5 mL, Merck & Co., Inc., Kenilworth, New Jersey, USA) was tested in a two-stage biorelevant in vitro dissolution test using a USP Apparatus II paddle setup (100 RPM). After 30 minutes in 250 mL of SGF (pH 1.6), 250 mL of double-concentrated FaSSIF (pH 7.5) was instantly added to the vessel (final pH 6.5) and concentrations were monitored for an additional 90 minutes. Samples were withdrawn from the dissolution vessels at 10, 20, 30, 45, 60, 90 and 120 minutes and immediately 7 ACS Paragon Plus Environment


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filtered through a 35 µm diameter cellulose filter and 1 µm glass filter, prior to HPLC-UV analysis. Based on the particle size as presented in the United States patent application, an adequate separation between dissolved and undissolved posaconazole should be ensured.19 The rational for only testing the acidified suspension was based on the fact that no supersaturation was observed upon transfer in the duodenum for the neutral suspension in vivo, and also not in vitro (data not shown). Simcyp Population–based PBPK ADME Simulator The predictive performance of the commercially available PBPK modeling platform Simcyp® (version 15 (Release 1), Simcyp Ltd., Sheffield, UK) was judged based on the observed in vivo data for: (i) an acidified (40 mg; pH 1.6) and, (ii) a neutral suspension (40 mg; pH 7.1) of posaconazole, as examined in healthy volunteers in a previous cross-over study.16 The Advanced Dissolution Absorption and Metabolism (ADAM) model, implemented within the Simcyp® population-based simulator, was applied using physicochemical and disposition parameters of posaconazole (derived from literature data) as well as population and trial design properties matching the in vivo study (derived from Hens et al.)16 (Table 1).


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Substrate Parameters Parameter Physicochemical and Blood Binding Parameters Molecular Weight pKa(s) logPo:w B:P (Predicted) fu (User input) Absorption Related Parameters Absorption Model Papp Caco-2 in FaSSIF ( x 10-6 cm/s) Peff Predicted (x 10-4 cm/sec) Fraction of the active pharmaceutical ingredient (API) Dissolved (%) (pH 7.1)* Fraction of the API Dissolved (%) (pH 1.6)* Intrinsic Solubility (mg/ml) SF logKm:w (Unionized) logKm:w (Ionized) Particle Size Monodispersed Radius (µm) Critical Supersaturation Ratio (CSR) Precipitation Rate Constant (PRC) (h-1) Second Precipitation Rate Constant (sPRC) ( h-1) Particle heff Method API Monomer Diffusion Coefficients (x 10-4 cm2/min)

Value

Reference/Comments

700.8 3.6 (basic), 4.6 (basic) 4.6 1.146 0.02

Merck & Co., Inc., Kenilworth, NJ, USA, Internal Dataset Merck & Co., Inc., Kenilworth, NJ, USA, Internal Dataset

Advanced Dissolution Absorption Metabolism (ADAM) 48 6.41

17 ®

Predicted within the Simcyp Simulator (Version 15, Release 1) Noxafil® Suspension Label

In-house data KU Leuven Predicted using built-in Papp to Peff correlations (Simcyp® Simulator, Version 15, Release 1); calibration compounds were not available.

2.3

16

70.0 0.000981 71.35 4.52 1.0 10 2.9 4.0 7.28 Hintz-Johnson Method 3.18

16 20

Estimated using SIVA Estimated using SIVA 19 16

Default value within Simcyp Estimated using SIVA Cut off radius 30 µm Predicted within Simcyp Simulator

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Micelle Diffusion Coefficient (x 10-4 cm2/min) Distribution and Clearance Parameters Distribution Model Kp Scalar Predicted Vss Human Liver Microsomes (HLM) CLint (µL/min/mg of Protein)

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0.78

With CV of 20%

Full PBPK*1 0.047 2.00

20

88.72

Whole Organ Metabolic Clearance (HLM) back-calculated using the Retrograde Model of the Simcyp Simulator (Version 15, Release 1) from IV Clearance (6.54 L/h)21

14

Population Parameters Parameter Type of Population Gastric Emptying Time (GET, h) (%CV) Gastric pH (Suspension 7.1) (%CV) Duodenal pH (Suspension 7.1) (%CV) Gastric pH (Suspension 1.6) (%CV) Duodenal pH (Suspension 1.6) (%CV)

Trial Design16 No. of Simulated Clinical Trials in the Simulator No. of Volunteers in Each Trial Min Age of Volunteers (years) Max Age of Volunteers (years) Proportion of Females Fluid Volume taken with Dose (mL) Study Duration (h) Dose Administered (mg)

Value Healthy Volunteers (HVs) 0.175 (38) 3.28 (21.76) 6.37 (10.83) 2.34 (10.93) 5.97 (9.35)

Reference 16 22

16

, These pH values are arithmetic averages across all five individuals of values measured at different times over the duration of the clinical studies.

Value 20 5 23 25 0.6 240 8 40

Reference

16

10


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Table 1: Physiological parameters, compound parameters and trial design specifications for the simulations in the Simcyp® Simulator (Version 15, Release 1): (i) administration of an acidified suspension of posaconazole, and (ii) administration of a neutral suspension of posaconazole; * the fraction of dose dissolved in the suspension vehicle before administration to volunteers; *1 The full PBPK distribution model makes use of a number of time-based differential equations in order to simulate the concentrations in various organ compartments: the blood (plasma), adipose, bone, brain, gut, heart, kidney, liver, lung, muscle, pancreas, skin and spleen. Inter-individual variability is introduced through tissue volume prediction taking account of age, sex, weight and height.

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Relevant in vitro experimental information (i.e., aqueous pH-solubility profile, biorelevant solubility in FaSSIF, FeSSIF etc.) was used for sequential fitting/verification of the physicochemical parameters (intrinsic solubility (So), pKa(s), bile salt micelle partition coefficients) of posaconazole using the SIVA Toolkit, before using these directly within the PBPK simulation. Particle size distribution was set at the value of 10 µm particle size monodispersed radius and was kept the same for simulation of both suspensions. It should be stated that differences in particle size may exist between both suspensions and could lead to differences in dissolution rate along the intestinal tract. However, as the particle size distribution was not experimentally measured in the two suspensions, the same value was used for both suspensions. The ADAM Model of the Simcyp® Simulator has been described by Hens et al.22 particularly in relation to gastric emptying of the non-absorbable marker paromomycin. All physiological parameters of the GI tract were kept at the default Healthy Volunteer (HV) population values in the Simcyp® Simulator, except for gastric emptying time (GET) and gastric and duodenal pH values (Table 1). The clinical study was simulated by administering posaconazole as two different suspensions - a suspension of 40 mg in 240 mL water at pH 1.6 (70.0% dissolved) versus a suspension of 40 mg in 240 mL water at pH 7.1 (2.3% dissolved) - to a virtual population of 100 HVs (20 virtual trials with 5 volunteers each; two men and three women, aged between 23 and 25 years) using the standard fasting state conditions. The gastric and duodenal pH values were matched to arithmetic averages across all five individuals of values measured at different times over the duration of the clinical studies. Standard gastric emptying time (0.4 h) was reduced to 0.175 h based on clinical intraluminal data from a previous study.22 The clinical study itself involved only five subjects which implies the possibility that these subjects were not fully representative of the underlying population, given the poor solubility of posaconazole and the known variability of physiological parameters such as pH, gastric emptying, bile salts, buffer capacity, fluid volumes etc.23,24. Therefore, the simulation trials were repeated 20 times (20 trials, each with 5 individuals) to help assess the potential impact of inter-individual variability on the results obtained. The simulated concentrations in duodenal luminal fluid and plasma were compared with previously reported clinically obtained values in HVs. Virtual trials were built to closely match clinical study design in terms of dose/dosage form administered, duration of the study, the proportion of males and females, the age range, fluid intake and luminal 12 ACS Paragon Plus Environment

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sampling time points. The performance of the model to simulate in vivo dissolution/precipitation was evaluated by comparing the extent and duration of drug dissolved/precipitated in the luminal fluids by calculating the area under the curve (AUC0-3h) of the total and dissolved concentration-time profiles of posaconazole in the stomach and duodenum. Additionally, the plasma concentrations of posaconazole measured simultaneously in the same study subjects, were also compared to the PBPK simulated plasma profiles. Data presentation and analysis Simulated intraluminal dissolved and total concentration-time profiles of posaconazole were plotted as a function of time and were compared with the observed intraluminal profiles. Also, the calculated luminal Cmax and AUC0-3h were compared with the observed clinical data. Similar comparisons of simulated and observed data were also conducted for the systemic exposure to posaconazole. In order to express the fraction of solid posaconazole in the aspirated duodenal samples of the clinical study after intragastric administration of both suspensions, the parameter Ɣ is used (Equation 2): Ɣ=1−

*

(2)

where is the dissolved concentration of posaconazole at a specific time point and * is the total concentration; i.e., dissolved and solid amount (precipitated and never dissolved) of posaconazole at the same time point.

Results and Discussion Mechanistic Modeling of In Vitro Data (SIVA Toolkit) Mechanistic modeling, based on Equation 1, of solubility measurements in five different media was performed to confirm and/or estimate posaconazole intrinsic solubility and bile salt micelle partition coefficients; simulated and observed solubility values are given in Table 2.



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Media Used

Experimental Solubility (mg/mL)

Simulated Solubility (mg/mL)

pH 1.8 SGF Media

0.07000

0.07000

pH 5.0 Buffer Media

0.00100

0.00143

pH 7.0 Buffer Media

0.00098

0.00099

pH 6.5 FaSSIF Media

0.00280

0.00275

logKm:w,unionized = 4.52

pH 5.0 FeSSIF Media

0.01020

0.01021

logKm:w,ionized = 1.00

Model Parameter Optimized

!'AB C.D EFG

Solubility Factor (SF) = !'

AB H.I JKLLMN

= 71.35

Table 2: Experimental (provided by Merck & Co., Inc., Kenilworth, NJ, USA, Internal Dataset and taken from the literature) and estimated solubility related parameters values (derived using the SIVA toolkit) for posaconazole. ‘log /:, ! "#$ ′ and ′ log /:, ! "#$ ′ refer to the surfactant (in this case bile salt–lecithin) micelle to water partition coefficients for ionized/unionized species, respectively. The Solubility Factor (SF) expresses the salt-limiting factor for posaconazole, calculated from the ratio of its ionized solubility at pH 1.8 (678 9.; . ??#@ . Ionized solubility at pH 1.8 takes into account both ionized species of posaconazole.

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First, experimentally determined aqueous solubility in pH 1.8 SGF, pH 5.0 buffer and pH 7.0 buffer were used to confirm intrinsic solubility and pKa and estimate the salt-limiting SF which accounts for the salt-limited solubility region of a pH-solubility profile. The authors acknowledge that the chloride salt of the drug may be solubility limiting at the low pH of the stomach due to the presence of endogenous chloride ions and a SF is used to capture this limit. The SF was estimated from measured solubility in simulated gastric buffer. However, this in vitro chloride ion concentration may be much higher than in vivo, especially immediately after taking a drink and immediate dilution takes place in the stomach. Thus, the general expectation is that the SF derived from the in vitro study is likely to be too low and may incorrectly limit the solubility at low pH. However, the clinical data for the dose studied indicate that the gastric posaconazole concentrations do not reach the solubility defined by the SF and therefore there was no need to refine the SF. For higher doses of API this value may be a limiting factor and would need to be reconsidered. Second, having fixed the aqueous phase solubility parameters, biorelevant solubility in pH 6.5 FaSSIF and pH 5.0 FeSSIF were used to estimate the bile-micelle partition coefficient (Km:w) of the drug (Equation 1). These estimated values were then used in the SIVA Toolkit dissolution module and, ultimately, the Simcyp® simulator to simulate luminal and systemic plasma profiles of posaconazole. Having evaluated and fixed the solubility-related parameters, the next step was to assess the supersaturation and precipitation related parameters. To investigate the precipitation of posaconazole upon transfer from the stomach to the small intestine (pH-shift), a two-stage (media change) in vitro dissolution test was performed for a 40 mg dose of posaconazole (Noxafil® oral suspension). The obtained in vitro time-concentration profiles in duodenal conditions (Figure 2) were then modeled in SIVA and parameter estimation tools were used to estimate the required empirical precipitation model parameters.



100

Dissolved (%)

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Observed Simulated

80 60 40 20 0 0.0

0.5

1.0

1.5

2.0

2.5

Time (h) Figure 2: Dissolved concentrations of posaconazole obtained from a two-stage dissolution test for a 40 mg dose. Data are presented by the blue dots (mean ± SD; n=3). Simulated concentrations of posaconazole are depicted by the dotted line. The vertical black line indicates the pH shift and the red horizontal dotted line indicates the thermodynamic solubility of posaconazole in FaSSIF.

In a first step, the SIVA parameter estimation tools were used to estimate sPRC. The objective function reflects the differences between the simulated and experimental values at the sampled time points under duodenal conditions. The input value for CSR was taken from in vivo data where a maximum degree of supersaturation of 2.9 was observed after intragastric administration of the acidified posaconazole solution to 5 healthy volunteers. This value was implemented in the simulation software as the CSR: this is a fixed value, regardless of type of suspension, as it describes the maximum ratio of supersaturation specifically for posaconazole. Having established the CSR value, the SIVA parameter estimation (PE) tools provided a final value of 7.28 h-1 for the sPRC based on modeling the in vitro study. The value of the PRC did not seem to be an influential factor as assessed by the objective function of the PE tools. This lack of sensitivity can be attributed to the fact that the supersaturated concentrations of posaconazole obtained in vitro are significantly higher than the CSC and, therefore, the sPRC is the main controlling factor within the prescribed empirical framework. In parallel with the in vitro modeling, automated sensitivity analysis (ASA) was performed with the Simcyp® simulator using the simulated plasma profiles as the endpoint against which to assess the sensitivity of the models to the supersaturation and precipitation parameters and to the gastric emptying time. Consistent with the in vitro modeling, the pharmacokinetic (PK) profiles obtained from the ASA (Figure 3) indicate sensitivity to the values of CSR and the sPRC (Figures 2A and 2C


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respectively) but minimal sensitivity to the PRC (Figure 2B). Therefore, the PRC was fixed at its default value of 4 h-1 while CSR and sPRC were 2.9 and 7.28 h-1 respectively.



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Figure 3: Sensitivity analysis results expressed as plasma concentration-time profiles from Simcyp® PBPK model simulations for the parameters of the empirical first order precipitation model – A. CSR; B. PRC; C. sPRC; the parameter ranges assessed were: CSR 1-10; PRC and sPRC 0.1-10 h-1.

Population–based PBPK Modeling of Two Posaconazole Suspensions using Simcyp® Simulator Neutral Suspension (pH 7.1) After intragastric administration of the neutral suspension (pH 7.1) to HVs, the clinical data indicated that posaconazole concentrations upon entry to the upper small intestine were (sub)saturated on average in the presence of significant amounts of solid posaconazole (consisting of both precipitated posaconazole upon entry to the duodenum and solid particles of posaconazole from the dosed suspension).16 On average, (sub)saturated concentrations of posaconazole were observed upon entry in the small intestine. The reason for not showing any supersaturated concentrations, as observed for the acidified suspension, may be due to two specific reasons: 1) the concentration of drug leaving the stomach is much lower than that of the acidified suspension, and 2) there are likely to be significant numbers of undissolved posaconazole particles transiting from the stomach which may act as nuclei/seeds likely resulting in rapid precipitation such that even if supersaturated conditions arise they may be transient and not readily observed. An average fraction of 0.61 of posaconazole present in the aspirated intestinal fluids was in solid form with a maximum fraction of 0.97 for all five HVs.16 Using the parameters listed in Table 1, PBPK simulations were performed to gain mechanistic insight into the GI behavior of posaconazole. Figure 4 depicts the simulated and observed gastric and duodenal profiles of posaconazole (dissolved and total concentrations) when administered as a neutral suspension (pH 7.1).


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Figure 4: (A) Mean (± SD) gastric profiles, expressed as total concentrations (i.e., dissolved plus solid posaconazole), as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 7.1 in 240 mL of water. (B) Mean (± SD) gastric profiles, expressed as dissolved concentrations, as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 7.1 in 240 mL of water. (C) Mean (± SD) duodenal profiles, expressed as total concentrations (i.e., dissolved plus solid posaconazole), as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 7.1 in 240 mL of water. (D) Mean (± SD) duodenal profiles, expressed as dissolved concentrations, as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 7.1 in 240 mL of water.

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Intragastric administration of the neutral suspension resulted in incomplete gastric dissolution of posaconazole, which was surprising considering the low dose (40 mg). The extent of gastric dissolution depends upon at least the gastric pH and buffer capacity, fluid volume, gastric residence time and the extent of mixing. The observed clinical data demonstrated a mean gastric pH of 3.28. The generally accepted value for the HV population mean fasted gastric pH is about 2 or slightly lower, as reported by Kalantzi and colleagues.25 The higher observed average gastric pH can be explained by the neutral pH and relatively large volume (240 mL) of the suspension vehicle compared to the residual gastric fluids, assumed to be on average 50 mL in these simulations. Also, it may be that the limited number of individuals (n = 5) are not fully representative of the HV population as a whole. It is known that pH is quite variable among individuals;26 the default population coefficient of variation (CV) on gastric fasted pH in the Simcyp® Simulator HV population library is 38%. Besides gastric pH, gastric emptying of liquids occurs on average faster compared to solid dosage forms (GET in average HV is assumed to be 0.175 h in these simulations with a CV of 38%).22,26 Based on the total gastric fluid concentrations (Figure 4A), it can be stated that gastric emptying of the administered suspension was rapid since posaconazole could not be detected in the aspirated gastric fluids after approximately one hour. Clinical gastric profiles demonstrated fluctuations in the observed gastric fluid concentrations as a function of time, especially in the first 30 minutes post-dosing. A previous intraluminal study with paromomycin suggested poor mixing of gastric contents in some volunteers22,27, which may be related to the time of intragastric administration of posaconazole relative to the interdigestive migrating motor complex (IMMC)3,24,28,29, in which phases of quiescence alternate with (shorter) phases of medium to very strong contractions. The impact of GI motility on oral drug exposure has recently been demonstrated by Hens et al., who found a significant association (p < 0.05) between plasma Cmax and the appearance of post-dose phase III contractions (i.e., very strong active motility patterns) after oral administration of an 800 mg ibuprofen tablet to 37 healthy volunteers in fasted and fed state.3 Overall, the incomplete gastric dissolution, the mass transport of posaconazole from the stomach to the duodenum (based on total concentration-time profiles) and the solid amount present in the duodenum 20 ACS Paragon Plus Environment

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(based on dissolved vs. total concentrations) were all simulated within two-fold of the observed values by the Simcyp® Simulator. Differences between observed and simulated concentrations may be attributed to multiple physiological factors all of which, as noted above, are expected to exhibit interindividual variability and therefore may be different in the simulated vs. the actual subjects in the clinical study. There may also be mechanistic considerations such as the presence of discontinuous liquid pockets within the intestinal tract as observed in humans which are not currently handled by the model and have an unknown impact on intraluminal concentrations as a function of time.30 In addition, inter-subject variability in effective gut wall permeability has been shown for several compounds in vivo which may result in differences in systemic outcome on the one hand and differences in intraluminal concentrations, on the other hand.31 Table 3 depicts the observed and simulated descriptive pharmacokinetic (PK) parameters (AUC and Cmax) of posaconazole luminal concentration profiles after administration of the 40 mg neutral suspension.



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Parameter Cmax (µg/ml) AUC (µg.h/ml) Parameter Cmax (µg/ml) AUC (µg.h/ml)

Stomach Dissolved Observed Stomach Dissolved Simulated 52.1 ± 30.1 42.4 ± 25.4 26.1 ± 15.1 28.71 ± 16.7

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Duodenum Dissolved Observed 9.09 ± 3.80 6.14 ± 2.64

Duodenum Dissolved Simulated 15.6 ± 9.23 10.7 ± 5.7

Stomach Total Observed 151 ± 153 53.6 ± 29.44

Stomach Total Simulated 133 ± 7.32 63.4 ± 16.0

Duodenum Total Observed 83.8 ± 26.8 33.7 ± 17.6

Duodenum Total Simulated 107 ± 8.89 68.6 ± 12.6

Stomach Observed

Stomach Simulated

Duodenum Observed

Duodenum Simulated

Cmax (µg/ml)

0.35

0.32

0.11

0.15

AUC (µg.h/ml)

0.49

0.45

0.18

0.16

OPQQRSTUV WRXYS

Table 3: Descriptive parameters (mean ± SD) AUC (µg.h/mL) and Cmax (µg/mL) of posaconazole for the observed and simulated gastric & duodenal profiles after administration of a 40 mg neutral suspension of posaconazole (pH 7.1). The lower two rows describe the ratio of dissolved AUC and Cmax versus total AUC and Cmax, in order to express the fraction of dissolved posaconazole relative to the total concentration (i.e., dissolved plus solid posaconazole).

22


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The ratios of gastric and duodenal AUC and Cmax obtained from observed and simulated profiles were almost identical, indicating that the fractions dissolved in the stomach and duodenum were well simulated by the model. In addition, the calculated fraction of solid posaconazole (Ɣ; Equation 2) present in the aspirated duodenal fluids was on average 0.61 with a mean maximum of 0.97.16 In comparison, the average simulated Ɣ by the Simcyp® Simulator was 0.58, with a mean maximum of 0.89. Overall, incomplete gastric dissolution and limited supersaturated intestinal concentrations in the presence of a high amount of posaconazole were adequately reflected by the PBPK model which was able to explain the observed in vivo data. Acidified Suspension (pH 1.6) To assess the impact of pH on in vivo dissolution, supersaturation and precipitation, a second suspension (40 mg, pH 1.6) was intragastrically administered to five HVs.16 Similar to the first test condition, gastric and duodenal aspirates were collected and posaconazole concentrations (i.e., dissolved versus total versus thermodynamic solubility) were determined in order to assess gastric dissolution, intestinal supersaturation, and precipitation. A PBPK model was built in the Simcyp® Simulator for the acidified suspension using similar inputs to those used for the neutral suspension, with the exception of the initial fraction of posaconazole dose dissolved in the administered acidified suspension (70%; Table 1). In addition, the pH values in the stomach and duodenum were adjusted to reflect the mean and inter-individual variability of pH observed in the clinical studies (Table 1). Figure 5 depicts the total versus dissolved gastric concentration-time profiles as observed in the clinical study (squares) and as simulated by Simcyp® Simulator (dotted lines).



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Figure 5: (A) Mean (± SD) gastric profiles, expressed as total (i.e., dissolved and solid posaconazole) concentrations, as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 1.6 in 240 mL of water. (B) Mean (± SD) gastric profiles, expressed as dissolved concentrations, as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 1.6 in 240 mL of water. (C) Mean (± SD) duodenal profiles, expressed as total (i.e., dissolved and solid posaconazole) concentrations, as observed in the clinical study (blue squares) and simulated by Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 1.6 in 240 mL of water. (D) Mean (± SD) duodenal profiles, expressed as dissolved concentrations, as observed in the clinical study (blue squares) and simulated by the Simcyp® Simulator (dotted line) after administration of 40 mg posaconazole at pH 1.6 in 240 mL of water.

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Gastric dissolution of posaconazole was enhanced compared to the first test condition due to the more acidic average pH of the aspirated gastric fluids (pH 2.34) added to which there is a higher predissolved fraction of posaconazole in the acidified suspension (70% versus 2.3%). However, complete gastric dissolution was not observed suggesting that posaconazole was rapidly transferred to the small intestine rather than adequately mixed by gastric motility which would be expected to allow the drug to fully dissolve.22,28 In vivo concentrations of posaconazole are on average slightly lower than the predicted concentrations which may be due to a variety of reasons as discussed below. It should be noted that it is quite possible to optimize the model against the clinical data using parameter estimation tools but this was not the main purpose of this study. The high dissolved gastric concentrations led to an approximately 45-minute time window of supersaturated concentrations of posaconazole upon transfer to the duodenum (Figure 4D). Besides intestinal supersaturation, the fraction of solid posaconazole (Ɣ; Equation 2) present in the aspirated duodenal fluids was on average 0.54 with a maximum of 0.85 based on the observed data.16 The average simulated fraction of solid posaconazole matched quite closely with the observed in vivo solid fraction (average of 0.51, maximum of 0.82). The simulated profiles closely matched the observed profiles and concentrations were in the same range (i.e., the total stomach concentrations were approximately 1.4 times higher than the total duodenal concentrations, both observed for simulated and clinical data). As mentioned before, differences in simulated and observed intraluminal concentrations (Table 4) may be explained by the unknown residual fluid volumes at the time of aspiration, inter-subject variability in motility and gastric emptying all of which may impact the degree of supersaturation and the precipitation kinetics.7,15 As demonstrated in vitro, the rate of gastric emptying may affect duodenal supersaturation and precipitation tremendously.7 As this variable is not integrated in present in vitro models that explore the potential of supersaturation and precipitation for weak bases/enabling formulations32, an ASA was performed in the Simcyp® Simulator to assess the impact of gastric emptying on systemic exposure of posaconazole for both suspensions (Figure 6).



B - Suspension pH 1.6

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Figure 6: Sensitivity analysis results expressed as plasma concentration (Cp) vs. time profiles from Simcyp® PBPK model simulations for the gastric emptying time (GET) parameter: (A) suspension pH 7.1 and (B) suspension pH 1.6; the parameter ranges assessed were 0.1-1.75 h with the most rapid GET corresponding to the profile with the earliest plasma Tmax.

These results suggest that rapid emptying from the stomach is favorable for the acidified suspension, as this will likely result in higher intestinal concentrations to promote faster intestinal absorption; the faster the drug will be transferred from the stomach to the small intestine, the higher the initial duodenal concentrations will be, resulting in a higher systemic exposure. The reason for this phenomenon is presumably due to the higher predissolved state of the drug in the stomach for the acidified suspension compared to the lower predissolved state for the neutral suspension; for the neutral suspension, differences in gastric emptying rates seem to have no major impact on systemic exposure. Future studies may rely on combining intraluminal profiling of drug concentrations with other techniques (e.g., MRI, manometry etc.) in order to study the impact of physiological variables (e.g., gastric emptying) on precipitation.3 Such additional data can help to improve the mechanistic simulation models and provide extra information for input into the programs to increase predictive power. Aside from correct parameterization of the physiological parameters of existing models, it may be that additional mechanisms play also an important role. For example, particles of the suspension may embed into the mucus lining the wall of the GI tract where dissolution may be significantly slower than in the free luminal fluids.


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Cmax (µg/mL) AUC (µg.h/mL)

Stomach Dissolved Observed 67.4 ± 40.3 44.9 ± 24.2

Stomach Dissolved Simulated 93.1 ± 5.1 50.0 ± 12.9

Duodenum Dissolved Observed 18.0 ± 6.6 12.1 ± 3.0

Duodenum Dissolved Simulated 53.1 ± 7.9 20.6 ± 5.2

Parameter Cmax (µg/mL) AUC (µg.h/mL)

Stomach Total Observed 79.8 ± 37.9 47.5 ± 25.2

Stomach Total Simulated 132.9 ± 7.3 63.4 ± 16.0

Duodenum Total Observed 56.3 ± 21.7 37.7 ± 17.7

Duodenum Total Simulated 96.4 ± 13.4 64.4 ± 11.1

Stomach Observed

Stomach Simulated

Duodenum Observed

Duodenum Simulated

0.85 0.95

0.70 0.79

0.32 0.32

0.55 0.32

Parameter

OPQQRSTUV WRXYS Cmax (µg/mL) AUC (µg.h/mL)

Table 4: Descriptive parameters (mean ± SD) describing AUC (µg.h/mL) and Cmax (µg/mL) of posaconazole as mean ± SD for the observed and simulated gastric & duodenal profiles after administration of a 40 mg acidified suspension of posaconazole (pH 1.6). The lower section presents the ratio of dissolved AUC and Cmax versus total AUC and Cmax, in order to express the fraction of dissolved posaconazole in comparison to the total concentration (i.e., dissolved and solid posaconazole).

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Although the same dose was administered for both formulations, the pH difference of the suspensions resulted in large differences in drug behavior in the GI tract. The observed and simulated dissolved posaconazole concentrations in the duodenum were two-fold higher for the acidified suspension compared to the neutral suspension, discriminating between the intraluminal behavior of both formulations (Tables 3 and 4). This suggests that the driving force (i.e., duodenal concentrations) for intestinal absorption from the duodenum is twice as high for the acidified suspension compared to the neutral suspension, which in turn results in a close to two-fold difference in systemic exposure over 8 hours. The observed and simulated mean plasma concentration-time profiles are depicted in Figure 7 for both suspensions.


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Figure 7: (A) Observed (blue squares) and simulated (dotted line) systemic plasma concentration-time profiles of posaconazole after administration of 40 mg suspension of posaconazole (pH 7.1); (B) Observed (blue squares) and simulated (dotted line) systemic plasma concentration-time profiles of posaconazole after administration of 40 mg suspension of posaconazole (pH 1.6).

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Descriptive PK parameters plasma Cmax, Tmax, AUC0-8h and AUC0-inf are listed in Table 5.


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Suspension pH 7.1

TMax (h)

CMax (ng/mL)

AUC0-8h (ng/mL.h)

AUC0-inf (ng/mL.h)

Simulated (ng/mL) Observed (ng/mL)

1.68 ± 2.19 3.70 ± 0.97

59.3 ± 26.4 63.0 ± 41.4

293.8 ± 120.9 298.1 ± 186.2

522.4 600.8

Suspension pH 1.6

TMax (h)

CMax (ng/mL)

AUC0-8h (ng/mL.h)

AUC0-inf (ng/mL.h)

Simulated (ng/mL) Observed (ng/mL)

0.36 ± 0.06 2.00 ± 0.71

132.6 ± 26.1 105.7 ± 32.0

508.4 ± 135.9 579.4 ± 202.2

914.5 841.9

Table 5: Mean ± SD descriptive parameters of the simulated and observed systemic concentration-time profiles following intragastric administration of 40 mg posaconazole as a neutral suspension (upper section) and acidified suspension (lower section). The AUC0-inf was calculated as- AUC0-t + Cplast/Ke. .

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Regarding plasma Cmax, a two-fold difference was simulated between the neutral and acidified suspension (59.3 ng/mL vs. 132.6 ng/mL, respectively), which was in reasonable agreement with the observed data (54.9 ng/mL vs. 98.2 ng/mL, respectively). Moreover, the same trend was seen for the simulated and observed systemic exposure, expressed as AUC0-8h, after administration of the acidified and neutral suspension (Table 5). Differences in observed and simulated systemic AUC, Cmax and Tmax can be attributed to the underlying anatomical and physiological variables of the GI tract.33 The dynamic and complex composition of GI fluids along the GI tract as a function of time is challenging to integrate into PBPK programs to accurately and precisely capture the dissolved fraction of drug available for intestinal absorption. Moreover, precipitated drug may easily (depending on the solid state and therefore thermodynamic solubility of the precipitate) re-dissolve in the intestinal fluids and thus become available for absorption; the solid state of the precipitate has to be either assumed by the modeler or assumed to match that of a precipitate in an in vitro experiment. The wide range of relevant physiological parameters which can impact upon dissolution, supersaturation and precipitation behavior require further characterization particularly in relation to their possible inter-dependence (covariation) and time-dependence (e.g., pH, bile salt concentrations). Integration of such information into PBPK models is expected to improve simulations and is of great interest as increasing numbers of clinical studies are being simulated with PBPK modeling programs.

Conclusion and Future Directions This study demonstrates the predictive power of PBPK modeling to assess the impact of formulation pH for two suspensions of a 40 mg dose of posaconazole on systemic exposure. Simulation results with the Simcyp® Simulator, informed in part by parameters derived from the modeling of relevant in

vitro dissolution and supersaturation experiments, were compared with previously described clinical data and found to match reasonably well in terms of gastric dissolution, intestinal supersaturation and the solid amount of posaconazole present in the upper small intestine. Supersaturated concentrations of posaconazole led to a higher systemic exposure for the acidified suspension compared to the neutral suspension. The simple empirical supersaturation and precipitation model based on first-order rate constants and specification of the critical supersaturation ratio (CSR) was found to be able to capture


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the in vivo supersaturation and precipitation behavior of the drug and, coupled with the PBPK model as a whole, captured the associated plasma concentration-time profiles. For these simulations the CSR was taken directly from the clinical studies because it was not found possible to estimate this value from two-stage media change experiments. As such this may be seen as a severe limitation of the in

vitro- in vivo extrapolation approach described. However, subsequent to the completion of this study data from a BioGIT, a three-compartment media transfer in vitro, experiment became available from which a CSR value of 3.14 was obtained which is very close to the clinical value of 2.9. Substitution of this value into the PBPK models resulted in no significant difference to the outcomes (data not shown). More importantly, it suggests that the transfer style experiment may be more informative than a media change approach although this assertion remains to be confirmed through further in vitro experiments. Further developments of this posaconazole study could include consideration of whether the model can be extrapolated to different doses, something that it is not established herein. In addition, the study could be extended to formulated dosage forms such as amorphous solid dispersions34 which may contain excipients to inhibit precipitation or crystal growth for example. In this regard there are various algorithmic improvements required of the PBPK models but which require support from appropriate in vitro experiments and better mechanistic understanding and characterization of in vivo processes and physiology, including its variability. Additional in vitro experiments could include characterization of the solid state of a precipitate and information on the particle size (distribution) of the precipitate as it evolves over time. The solid state of a precipitate cannot be predicted but can have a major impact on solubility and therefore subsequent re-dissolution rate. As alluded to in the main text it is very often the case that in vivo undissolved fine particles of API may transit from the stomach to the small intestine and act as nuclei/seeds for precipitation; with the exception of Koyama et al.,35 this is something which has not been widely considered in terms of its characterization by in vitro experiment but can have a significant impact on precipitation behavior. At the current time, there is no evidence in the literature that more complex models based on Classical Nucleation Theory (e,g., Lindfors and colleagues36) or alternatives such as the Two-Step Nucleation 33 ACS Paragon Plus Environment


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model (e.g., Vekilov 201037) perform any better than a simple empirical model such as that described herein. This is largely because these models are difficult to parameterize. In terms of the physiology of the gut there are various factors which are poorly understood or characterized including but not limited to: 1) the impact of treating luminal water as a population of discontinuous liquid pockets of varying size as observed in humans rather than as a single pocket albeit of dynamic volume; 2) the impact of the mucus layer as mentioned above. Overall, this research provides a pragmatic approach to the extrapolation of information gained from the modeling of in vitro supersaturation and precipitation experiments to in vivo predictions of these properties and, ultimately, the systemic exposure/clinical efficacy of basic drug compounds and/or supersaturating drug delivery systems.5,38,39

Acknowledgments This work has received support from (1) the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT) and (2) the Innovative Medicines Initiative Joint Undertaking (http://www.imi.europa.eu) under Grant Agreement No. 115369, resources of which are composed of financial contribution from the European Union's Seventh Framework Program and EFPIA companies' in kind contribution.

References (1) (2) (3)

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