Utilization of Gastrointestinal Simulator, an in Vivo Predictive

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Utilization of Gastrointestinal Simulator (GIS), an in vivo predictive dissolution methodology, coupled with computational approach to forecast oral absorption of dipyridamole Kazuki Matsui, Yasuhiro Tsume, Susumu Takeuchi, Amanda Searls, and Gordon L. Amidon Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.6b01063 • Publication Date (Web): 23 Feb 2017 Downloaded from http://pubs.acs.org on February 24, 2017

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Molecular Pharmaceutics

Utilization of Gastrointestinal Simulator (GIS), an in vivo predictive dissolution methodology, coupled with computational approach to forecast oral absorption of dipyridamole

Kazuki Matsui1,2, Yasuhiro Tsume1, Susumu Takeuchi3, Amanda Searls1, Gordon L. Amidon1*

1

College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, Michigan 48109-1065, United

states 2

Pharmacokinetics & Safety Laboratory, Discovery Research, Pharmaceutical Research Center, Mochida

Pharmaceutical Company Limited, 722 Uenohara, Jimba, Gotemba, Shizuoka 412-8524, Japan 3

Pharmacokinetics Group, Sawai Pharmaceutical Company Limited, 5-2-30, Miyahara, Yodogawa-ku, Osaka

532-0003, Japan

* Corresponding Author: Gordon L. Amidon College of Pharmacy, University of Michigan 428 Church Street, Ann Arbor, Michigan, 48109-1065 Phone: 734-764-2464 FAX: 734-764-6282 E-mail: [email protected]

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For Table of Contents Use Only


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ABSTRACT Weakly basic drugs exhibit a pH-dependent dissolution profile in the gastrointestinal (GI) tract, making it difficult to predict their oral absorption profile. The aim of this study was to investigate the utility of the Gastrointestinal Simulator (GIS), a novel in vivo predictive dissolution (iPD) methodology, in predicting the in vivo behavior of the weakly basic drug, dipyridamole, when coupled with in silico analysis. The GIS is a multi-compartmental dissolution apparatus, which represents physiological gastric emptying in the fasted state. Kinetic parameters for drug dissolution and precipitation were optimized by fitting a curve to the dissolved drug amount-time profiles in the USP apparatus II and GIS. Optimized parameters were incorporated into mathematical equations to describe the mass transport kinetics of dipyridamole in the GI tract. Using this in silico model, intraluminal drug concentration-time profile was simulated. The predicted profile of dipyridamole in the duodenal compartment adequately captured observed data. In addition, the plasma concentration-time profile was also predicted using pharmacokinetic parameters following intravenous administration. Based on the comparison with observed data, the in silico approach coupled with the GIS successfully predicted in vivo pharmacokinetic profiles. Although further investigations are still required to generalize, these results indicated that incorporating GIS data into mathematical equations improves the predictability of in vivo behavior of weakly basic drugs like dipyridamole.



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KEYWORDS dipyridamole, GIS, in silico, intestinal absorption, in vivo predictive dissolution, modeling and simulation, precipitation, supersaturation

INTRODUCTION Poor water solubility of orally administered drug products is one of major obstacles in drug discovery and development.1 Because low drug solubility attributes to poor oral bioavailability (BA) and highly variable systemic exposure, appropriate in vitro methodologies are needed to predict in vivo oral absorption.2-4 In recent years, combinational chemistry, high-throughput screening, and structure-based drug design produced drug candidates with larger molecular weight and higher lipophilicity, reducing aqueous solubility.1 Thus, oral absorption of these drugs is typically rate limited by aqueous solubility and dissolution rate rather than by intestinal permeability.5 Therefore, appropriate dissolution tests, which can predict in vivo drug dissolution in the gastrointestinal (GI) tract, are of great interest among pharmaceutical scientists. The United States Pharmacopeia (USP) dissolution apparatuses I and II are gold standard methods for in vitro dissolution testing. However, it is often stressed that the USP-type dissolution tests do not represent GI physiologies such as intestinal fluid volume, media components, buffer capacity, pH level, or hydrodynamics.4 In consequence, these types of dissolution tests cannot capture in vivo behaviors of poorly water soluble drugs whose dissolution profile depends largely on physiological factors in the GI tract. In fact, several in vitro dissolution studies with the USP-type vessel failed to predict oral absorption potential.6, 7 In vivo predictive dissolution (iPD) methodologies have been widely recognized as in vitro novel approaches for better in vitro-in vivo correlation (IVIVC). iPD methodologies incorporate some aspects of the GI physiologies and reportedly achieve better IVIVC for certain drug products.8-15 Among them, the Gastrointestinal Simulator (GIS) is one of the most distinguished iPD methodologies for assessing the influence of gastric emptying and the physiological pH change in the GI tract on the drug dissolution profile. The GIS is 4 ACS Paragon Plus Environment

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composed of three chambers which represent the stomach, duodenum, and jejunum. After the drug product is dosed into the gastric chamber, gastric contents will be pumped into the duodenal and subsequent jejunal chambers through a transfer tube. The transfer rate was previously found in the literature to approximate physiological gastric emptying in fasted state.12 Gastric emptying in the fasted state triggers a drastic pH change when drugs come from the stomach (pH 1-3) to the small intestine (pH 4-7), affecting the dissolution and precipitation profiles of weakly basic drugs, in particular. Weakly basic drugs with poor intrinsic solubility (BCS class II with basic property, BCS class IIb) quickly dissolve at gastric pH but not at intestinal pH.16 Therefore, dissolved BCS class IIb drugs supersaturate and precipitate when transferred from the stomach to the small intestine. Our previous experiments revealed that the GIS adequately captured in vivo dissolution as well as precipitation of BCS class IIb drugs such as dipyridamole, dasatinib, and itraconazole.13-15 IPD methodologies, including the GIS, are designed to capture in vivo dissolution and, ultimately, to predict oral drug absorption quantitatively. For this purpose, computational mass transport analysis is helpful because oral drug absorption is governed by many processes such as dosage form disintegration, drug dissolution, precipitation, transit to the distal position, permeation through intestinal epithelial cells, and first-pass metabolism.17, 18 Therefore, incorporating appropriate in vitro dissolution data into computational analysis may enable us to capture both in vivo dissolution behavior and in vivo pharmacokinetics in systemic circulation. In this study, a simplified mass transport model was constructed with numerical integrations of GI kinetics of a typical BCS class IIb drug, dipyridamole (log P 2.74, pKa 6.24).19 Kinetic parameters which describe drug dissolution and precipitation behaviors of dipyridamole in the GI tract were obtained from in vitro dissolution with USP apparatus II and the GIS, respectively. These parameters were introduced into differential equations, constructing in vitro data-based computational prediction of oral absorption. Since the GIS adequately mimics the GI physiology, mass transport kinetics such as gastric emptying and GI transit were assembled based upon experimental settings of the GIS. The purpose of this study is to clarify whether the in vitro-in silico approach using the GIS data will provide quantitative information on in vivo dissolution as well as in vivo pharmacokinetics of dipyridamole. 5 ACS Paragon Plus Environment


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MATERIAL AND METHODS Chemicals Dipyridamole immediate release tablets (25 mg) (Zydus Pharmaceuticals USA, Pennington, NJ) were obtained through University of Michigan Hospital. Dipyridamole, potassium phosphate monobasic, hydrochloric acid, and sodium chloride were purchased from Sigma-Aldrich Chemicals Co. (St. Louis, MO) and used as received. Acetonitrile, trifluoroacetic acid (TFA), and methanol were purchased from Fisher Scientific Inc. (Pittsburgh, PA). All chemicals were either analytical or HPLC grade. Fasted state simulated intestinal fluid (FaSSIF) was prepared by dissolving FaSSIF/FeSSIF/FaSSGF Powder (Biorelevant.com, Croydon, Surrey, UK). FaSSIF contains 3 mM sodium taurocholate and 0.75 mM lecithin in 28.7 mM potassium phosphate buffer with 103.4 mM potassium chloride at pH 6.5, which composition is derived from Galia and co-workers.20 In vitro dissolution with USP apparatus II The USP apparatus II used to test the dissolution of dypridamole tablets was a Hanson SR6 Dissolution Test Station (Chatsworth, CA). Two 25 mg dipyridamole immediate release tablets (50 mg dipyridamole) were dosed in 300 mL of dissolution media, which is either simulated gastric fluid at pH 2.0 (SGFpH2.0, 10-2 N hydrochloric acid (HCl) with 34.2 mM sodium chloride) or FaSSIF at pH 6.5. Dissolution tests were conducted with an overhead paddle revolution speed of 50 rpm at a temperature of 37°C. The dissolution media volume (300 mL) was chosen to reflect in vivo intestinal fluid volume.21 At predefined time points, 200 µL of sample were withdrawn for up to 120 min. All samples were immediately filtered through a 0.22 µm pore size PVDF membrane (Millipore, Milford, MA) and the filtrate was mixed with equal volume of methanol to measure dissolved drug concentration by HPLC. In silico dissolution model 7 ACS Paragon Plus Environment


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Dissolution kinetics was calculated using the Noyes-Whitney dissolution model described by the following equation (1).22

=

X ⁄ X ⁄ C −

(1)

where Xdis is the mass of dissolved drug at time t, Deff is the diffusion coefficient, r0 is the initial particle radius, hp is the thickness of the diffusion layer, ρ is the particle density, Xtot is the total mass of undissolved and dissolved drug at time t, Xud is the mass of drug remaining to be dissolved (undissolved drug) at time t, Cs is the equilibrium solubility of the drug, and V is the fluid volume. To simplify equation (1), 3Deff/r0hpρ is replaced by zeta (z value), which is a dissolution parameter introduced by Takano et al.23 Here, this dissolution model assumes that spherical and isometric drug particles dissolve with decreasing surface area of particles, and diffusion coefficient and diffusion layer thickness were regarded as constant during dissolution.

= X ⁄ X ⁄ (C −

)

(2)

This z value has different unit from its original manuscript proposed by Nicolaides et al., where the dissolution parameter (z value) includes fluid volume as a constant.24 In this manuscript, Takano’s z value which does not incorporate fluid volume is suitable since the fluid volume (V) changes with time when this dissolution model is applied to the GIS experiment. The z values in SGFpH2.0 and FaSSIF were estimated from USP II dissolution test with a multipurpose nonlinear least-squares fitting computer program Napp (version 2.31) developed by Hisaka and Sugiyama.25 In vitro dissolution with GIS GIS dissolution test of dipyridamole was conducted as previously described with slight modification.12, 13

The GIS consists of three chambers. The first chamber which represents the stomach (GISstomach) has 50 mL

of SGFpH2.0 with 250 mL of distilled water as dosing volume (total 300 mL). The second chamber is filled with 8 ACS Paragon Plus Environment

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50 mL of FaSSIF to mimic the duodenum (GISduodenum). The third chamber, representing the jejunum (GISjejunum), is left empty initially. As soon as the 50 mg dipyridamole were dosed into the GISstomach, contents in the GISstomach were pumped to the GISduodenum through a transfer tube. The fluid transfer rate was controlled by a computer program which was set to decrease the gastric fluid volume at first-order rate. To mimic gastric and duodenal secretions, gastric secretion fluid (SGFpH2.0) and duodenal secretion fluid (4-times concentrated FaSSIF) were introduced to the GISstomach and the GISduodenum at constant flow rates.15, 26 Duodenal contents were also pumped into the GISjejunum to maintain a constant fluid volume in the GISduodenum. All fluid transfers were conducted by peristaltic pumps (Ismatec® REGLO pump; IDEX Health and Science, Glattbrugg, Switzerland). Fluid volume in each chamber can be described as following equations: V = V,# × e&

'(()) , *+

(3)

V = V,# V- = V,# × .1 − e&

(4) '(()) , *+

0 + (k 34 () + k 34 () ) × 5

(5)

where Vs, Vd, Vj are the fluid volume at time t in the GISstomach, GISduodeum, and GISjejunum, respectively. Vs,0 and Vd,0 are the initial fluid volume in the GISstomach (300 mL) and the GISduodenum (50 mL), respectively. GE is the gastric half emptying time (8 min), ksec(s) and ksec(d) are the fluid secretion rates to the GISstomach and the GISduodenum (each 1 mL/min), respectively. Since all pumps were stopped 45 min after dosing, fluid volumes remained constant after this time point (if t>45 min, t was equal to 45 min in equations (3) and (5)). The CM-1 overhead paddles (Muscle Corp., Osaka, Japan) in the GISstomach and the GISduodenum were appropriately controlled to simulate relevant gastric and duodenal motility.14 The GISjejunum was stirred at a constant speed with a stir bar. All dissolution tests were conducted at a temperature of 37°C. At predefined time points, 200 µL of samples were withdrawn for up to 180 min. Immediately after filtration with a 0.22 µm pore size PVDF membrane, the filtrate was mixed with the equal volume of methanol to measure dissolved drug concentration 9 ACS Paragon Plus Environment


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by HPLC. Dissolved drug amount in each chamber was calculated by multiplying dissolved concentration by fluid volume. Mass transport analysis: GIS Mass transport equations for the GIS were constructed based on the drug dissolution, precipitation, and transit kinetics as shown in Figure 1. Dissolution kinetics were described by equation (2), assuming that the dissolution rate in the GISstomach (dXdis(s)/dt) is equal with the dissolution in USP apparatus II with SGFpH2.0. The dissolution rates in the GISduodenum and the GISjejunum (dXdis(d)/dt and dXdis(j)/dt) are estimated from the USP apparatus II dissolution test with FaSSIF. Mass transit kinetics were applied to both undissolved and dissolved drug. Mass transfer rate between the chambers was calculated as a function of fluid transfer rate and drug amount per volume in the previous chamber. For instance, mass transfer rates for undissolved drug (dXud(s-d)/dt) and dissolved drug (dXd(s-d)/dt) from the GISstomach to the GISduodenum can be expressed as: 6(7)

(7)

= −

= −

+ k 34() (

+ k 34() (

6()

()

)

(if t < tlag,

6(7)

)

= 0)

(6)

(7)

where Xud(s) and Xd(s) indicate the amount of undissolved and dissolved drug in the GISstomach, respectively. Since the transfer of undissolved drug from the GISstomach to the GISduodenum is performed by a tube with an inner diameter of 2 mm, undissolved drug cannot be transferred until tlag. The tlag indicates the time required to produce particles with diameter less than 2 mm in the GISstomach. For precipitation model, first-order kinetics was employed using the precipitation rate constant, kpre. 9

= k : 3 (X − C3; V)

(8) 10 ACS Paragon Plus Environment

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This precipitation model was applied to the GISduodenum (kpre(d)) and the GISjejunum (kpre(j)). In the GIS experiment, the observed drug concentration at the end of the study (180 min) was much higher than equilibrium solubility in FaSSIF, indicating a sustained supersaturation. Therefore, dissolved drug concentration at 180 min (Cend) was incorporated into the equations for the precipitation model. By integrating these parameters, tlag, kpre(d), and kpre(j) were estimated from the dissolved drug amount-time profile in the GIS by a simultaneous nonlinear least-squares regression. Full mathematical equations were included in the Supporting Information. Mass transport analysis: GI tract In the GIS, neither the drug transit from the intestine to the colon nor the intestinal permeation is taken into account. Therefore, these processes were incorporated to represent the entire mass transfer kinetics in the GI tract (Figure 2). In this analysis, the GISjejunum was referred to as the small intestinal compartment in order to predict in vivo behavior of dipyridamole. This is because the total fluid volume in the GISjejunum at the end of the study was approximately 390 mL, which accounts for total aqueous volume in the small intestine.21 The drug dissolution rate and precipitation rate in the small intestinal compartment were assumed to be the same as the values in the GISjejunum obtained by curve fittings. To represent mass transfer rate from the small intestine to colon (dX(i-c)/dt), compartmental absorption and transit (CAT) model based kinetics were applied as follows: (7

= DF × X P3CC

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(10)

?

where R is the radius of the small intestine, DF is the degree of flatness of the intestinal tube, and Peff is the effective intestinal membrane permeability coefficient. The above equation was applied only to the duodenal and small intestinal compartments (dXperm(d)/dt, dXperm(i)/dt) as drug absorption from the stomach and colon was regarded to be negligible. The Peff value for dipyridamole was estimated based on a gastrointestinal unified theoretical framework (GUT framework) theory as previously proposed by Sugano et al.18, 27. Equations for other GI kinetics such as dissolution, precipitation, and mass transit were the same as the equations used for those of the GIS. Full mathematical equations were included in the Supporting Information. In silico prediction of in vivo drug behavior in the GI tract By using mass transport equations for the GI tract with optimized parameters, dissolved drug amount in the duodenum was predicted. Psachoulias et al. reported the intraluminal drug concentration-time profile after an intake of 30 mg or 90 mg of dipyridamole in HCl solution (pH 2.7) in human.28 Therefore, in this study, 30 mg or 90 mg of dipyridamole were hypothetically dosed into the gastric compartment as a complete solution, and then the dissolved drug concentration-time profile in the duodenal compartment was predicted up to 80 min after dosing. Luminal concentration plots of dipyridamole were extracted from figures in the literature using Digit version 1.0.4 (SimulationPlus, Inc., Lancaster, CA). In silico prediction of in vivo pharmacokinetics In vivo pharmacokinetic parameters and a median plasma concentration-time profile following oral administration of 50 mg dipyridamole were taken from the literature.29 In the study, six healthy subjects over the age of 65 (including three men and three women) with gastric pH less than 2.1 and five achlorhydric subjects over the age of 65 (including four men and one woman) with gastric pH between 4.4 and 6.6 were given single oral dose of 50 mg dipyridamole in fasted state. 12 ACS Paragon Plus Environment

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To predict the pharmacokinetic profile of dipyridamole, drug absorption rate (dXabs/dt) was calculated as follows: DE

=

9>()

+

9>()

FF F =

G ?

X () P3CC +

G ?

X (=) P3CC 1 −

HIJKJ LM

(11)

where Fg is the fraction escaped from gut metabolism, and gut metabolism is regarded to be negligible (Fg=1). Fh is the fraction escaped from liver metabolism, assuming that systemic elimination is governed only by liver metabolism. Accordingly, Fh can be expressed by the total body clearance (CLtot=2 mL/min/kg) and the blood flow rate (Qh=20 mL/min/kg).30 The plasma concentration-time profile after an oral dose of 50 mg dipyridamole was predicted using systemic pharmacokinetic parameters estimated from the intravenous data.30 To predict the pharmacokinetics in achlorhydric subjects using a simulation, drug dissolution rate in the stomach was assumed to be the same as the rate in the duodenum and small intestine. HPLC Analytical method The concentration of dipyridamole was measured by a gradient HPLC method using a Water HPLC system (Waters Inc., Milford, MA) as previously described.13 Two Waters pumps (model 515), a Waters autosampler (WISP model 712), and a Water UV detector (996 photodiode arraydetector) were controlled by Waters Millennium 32 software (Version 3.0.1) to compose the HPLC system. Peak separation was performed by a ZORBAX Eclipse XDB-C18 column (3.5 µm, 4.6 × 150 mm) equipped with a guard column. The mobile phases were 0.1% TFA containing water (Solvent A) and 0.1% TFA containing acetonitrile (Solvent B) and the flow rate was set to 1.0 mL/min at room temperature. The solvent B gradient changed from 20% to 65% at a rate of 11.5%/min in a total 12 min run. The wavelength of the UV detector was set at 290 nm.



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RESULTS USP apparatus II dissolution and parameter fitting for dissolution kinetics Figure 3 shows the dissolution profiles of 50 mg dipyridamole in USP apparatus II with 300 mL of either SGFpH2.0 or FaSSIF. The dissolution parameter, z value, was obtained by equation (2). Estimated parameters were shown in Table 1. Each dissolution profile was well fitted with the calculated dissolution curve by the equation. GIS dissolution and parameter fitting for precipitation kinetics An in vitro dissolution test of 50 mg of dipyridamole was conducted with the GIS. Drug amount in solution-time profiles were shown in Figure 4. Dipyridamole rapidly dissolved in the GISstomach, which was consistent with the dissolution profile in USP apparatus II in SGFpH2.0 (Figures 3 and 4). Dipyridamole exhibited 8.1-fold (160.3 µg/mL) and 3.5-fold (68.6 µg/mL) higher drug concentrations in the GISduodenum and the GISjejunum compared to its equilibrium solubility in FaSSIF (19.7 µg/mL). This suggests supersaturation occurred. The onset of precipitation appeared as a sudden cloudy solution in the entire chambers, and floating small precipitates came to be visually observed. The high drug concentrations were observed at the end of the experiment (76.0 µg/mL in the GISduodenum and 54.2 µg/mL in the GISjejunum at 180 min), indicating that supersaturation was maintained in some extent. Therefore, sustained supersaturation was considered when constructing mathematical equations for the GIS dissolution test. Precipitation rate constants (kpre(d) and kpre(j)) and lag time (tlag) for producing small particles that can be transferred from the GISstomach to the GISduodenum were optimized by curve fitting analysis. Experimental and compound-related parameters for GIS mass transport equations were shown in Table 2. kpre(d) and kpre(j) were estimated to 0.0330 (min-1) and 0.137 (min-1), respectively. tLag was optimized to 5.8 min. Fitted curves were represented as dotted lines in Figure 4. Prediction of in vivo drug behavior in the GI tract 14 ACS Paragon Plus Environment

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The dissolved drug concentration-time profile in the duodenal compartment was predicted using the mass transport equations for the GI tract. Physiological and compound-related parameters used in the equations were shown in Table 3. In vivo dissolved drug concentration-profiles in the duodenum after administration of 30 mg or 90 mg of dipyridamole as a complete solution were compared with the predicted profiles. As shown in Figure 5, predicted curves adequately captured the observed data. Prediction of in vivo pharmacokinetics The plasma drug concentration time profile after oral administration of 50 mg of dipyridamole was predicted using the mass transport equations for the GI tract. The drug amount which reaches the systemic circulation was calculated by equation (11). In the systemic circulation, the drug is distributed according to the pharmacokinetic parameters. These parameters were estimated from intravenous administration data as shown in Table 4.30 Figure 6 compares the predicted plasma drug concentration-time profile with observed data. The mass transport equations for the GI tract appropriately describe the observed pharmacokinetic profile of dipyridamole. Pharmacokinetic parameters of dipyridamole in both subject groups, those with normal and elevated gastric pH levels, were predicted and summarized in Table 5. These predicted parameters, Cmax, Tmax, AUC0-36hr, and AUC reduction ratio caused by an acid-reducing agent were in agreement with the observed parameters.



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Discussion The dissolution profile of poorly soluble drugs is deeply affected by the GI physiologies such as gastric emptying time, fluid volume, fluid components, buffer capacity, pH level, and intestinal motility. Therefore, evaluating in vivo dissolution with iPD methodologies is crucial to comprehend the characteristics of the drug candidates and/or drug products in early stages of drug discovery and development. Specifically, pH change from an acidic environment to a neutral environment caused by gastric emptying in the fasted state induces supersaturation and precipitation of weakly basic drugs, making it difficult to predict their oral absorption profiles. To overcome this challenge, an in vitro dissolution methodology, which adequately mimics physiological gastric emptying, may be a useful tool. In the past decade, several types of multi-compartmental dissolution apparatuses have been developed such as artificial stomach-duodenum (ASD) model, TNO GI tract model (TIM), in vitro biorelevant gastrointestinal transfer (BioGIT) system, and GIS.10, 12, 31, 32 These apparatuses have individual characteristics and each of them represents some aspects of the GI physiologies, providing insights to oral absorption potentials. In our previous work, the GIS was able to capture the supersaturation and precipitation of dipyridamole.13 In that study, the results from the GIS experiments managed to reveal the reduction in Cmax caused by the elevation of gastric pH. However, further investigation has to be conducted to characterize the reduction in AUC. This is because in vivo drug behavior in the GI tract is not only affected by gastric emptying, drug dissolution, and precipitation, but also by intestinal permeation and intestinal transit time to the distal position. Although the GIS is a well-designed iPD system to capture the drug behavior in the upper GI tract, intestinal drug permeation as well as drug transit to the distal small bowel like the colon cannot be represented in the current GIS system, resulting in unsuitable prediction of AUC reduction. Overall, further refinements of the GIS or a different type of epoch-making iPD methodology that improve in vivo predictability are still awaited.


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Furthermore, this situation raises another issue of how to utilize iPD methodologies to precisely predict oral drug absorption behavior. In recent years, a combination of in vitro dissolution test with in silico analysis has been reported to be a powerful tool for quantitative prediction of oral drug absorption.23, 33, 34 Particularly, several studies which utilized in vitro tests to assess the precipitation, successfully simulated intraluminal behavior as well as pharmacokinetics of weakly basic drugs.35-37 In those studies, to mimic gastrointestinal transit, a simple “dumping” test, an immediate pH shift test, or a transfer model with a variable flow rate was employed to capture the kinetics of precipitation. In line with these in vitro systems, the GIS represents the GI physiology, especially captures the physiological GI transit in the fasted state.12 Therefore, in this study, in silico mathematical equations for dipyridamole were constructed using conventional in vitro dissolution test data and in vitro precipitation data obtained in the GIS with the mass transport kinetics of the GIS system as it is. Dissolution kinetics of dipyridamole in the USP apparatus II with SGFpH2.0 and FaSSIF were well characterized by the Noyes-Whitney dissolution model with optimized dissolution parameters, equilibrium solubilities and z values in each dissolution media (Figure 3). The z value is independent of the dosed drug amount and the fluid volume, thus, it can be applied to the dissolution in the GIS where the total drug amount and fluid volume at each chamber are variable over time. In our previous study, the GIS dissolution test of dipyridamole was conducted by utilizing bile salts free media. However, it was revealed that biorelevant media is advantageous to provide better in silico prediction of fraction absorbed.23 Thus, in this GIS experiment, FaSSIF was employed in line with our recent study, in which itraconazole was tested in the GIS with FaSSIF.15 Although acidic fluid was transferred from the GISstomach to the GISduodenum, pH level and bile salt concentration in the GISduodenum and the GISjejunum were maintained at a physiological relevant level due to the secretion of 4-times concentrated FaSSIF into the GISduodenum.15 As shown in Figure 4, 50 mg of dipyridamole immediately dissolved in the GISstomach. The dissolution curve was sufficiently fitted by mass transport analysis which was performed using dissolution 17 ACS Paragon Plus Environment


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parameters obtained in USP apparatus II and the lag time which measured the time it took to start transferring the undissolved drug particles into the next chamber. Optimized lag time was 5.8 min and was consistent with the visual observation. This lag time may represent in vivo gastric disintegration and distribution processes, and the difference in these processes between formulations is known to initiate different pharmacokinetic profiles.38 Thus, it is worth investigating whether the GIS can distinguish between two oral drug products that failed to exhibit bioequivalence probably due to the different gastric emptying of solid particles. Rapid dissolution of dipyridamole in the GISstomach caused the transfer of high concentrated drug into the subsequent chambers, which induced the supersaturation and precipitation (Figure 4). The drug concentrations that were higher than its equilibrium solubility should result in enhanced drug absorption as previously demonstrated by in situ mouse intestinal infusion study in combination with the GIS.13 Interestingly, dipyridamole showed prolonged supersaturation even at the end of the study (180 min). It was also clarified that after overnight incubation, drug concentrations of dipyridamole in the GISduodenum and the GISjejunum significantly dropped and those values were similar with the saturated solubility of dipyridamole products in FaSSIF (data not shown). This observation indicated that it requires a long time to completely precipitate out as the same crystal form with the one contained in dipyridamole tablets. Thus, It can be hypothesized that dipyridamole exhibits slow precipitation rate even when it precipitates as crystalline. Here, it should be acknowledged that precipitated dipyridamole was claimed to be amorphous in human intubation study when dipyridamole solution, without solid particles, was dosed into the stomach of healthy subjects.28 In comparison, in this study, dipyridamole tablets, instead of solution, were put into the GISstomach and small particles were appeared to be transferred into the intestinal chambers where dipyridamole precipitated in presence of these particles as nuclei. The crystal form of the precipitants was not determined in our GIS study, thus, the characterization of the precipitants will provide meaningful information to elucidate this hypothesis, enabling further discussion.


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The optimization analysis by curve fittings provided different first-order precipitation rates for dypirdamole in the GISduodenum and the GISjejunum. It can be explained by the fact that the GISduodenum is more acidic than the GISjejunum due to the direct influx of acidic media from the GISstomach. In the GIS, the pH level in the GISduodenum ranges from 5.7 to 6.5, whereas the pH in the GISjejunum was more stable, between 6.2 and 6.5.15 This experimental setting captures the reported pH value in human.21 Thus, this favorable condition for BCS class IIb drugs in the GISduodenum may have prevented supersaturated solution from excessive precipitation, providing smaller precipitation rate constant in the GISduodenum than in the GISjejunum. In order to mathematically describe the mass transport kinetics in the GI tract, intestinal permeability should be appropriately predicted. In the present study, intestinal permeability of dipyridamole was estimated based on GUT framework.18, 27 This theoretical framework divides intestinal absorption process into several intrinsic steps such as transcellular membrane permeation, paracellular permeation, diffusion across the unstirred water layer, and bile micelle solubilization.39, 40 The intestinal permeability of dipyridamole under saturated conditions in the fasted state was calculated to 3.0×10-4 cm/sec.18 This permeability provided appropriate prediction of the pharmacokinetics in subjects with elevated gastric pH level (Table 5), emphasizing the adequacy of the employed permeability since the oral absorption rate of dipyidamole under elevated gastric pH condition is mainly determined by intestinal solubility and permeability. Here, the permeability of dipyridamole was regarded as a constant value regardless of the drug concentration in the GI tract. This assumption is appropriate because the membrane permeability for dipyridamole through Caco-2 cell monolayer was not reduced even when highly supersaturated drug solution was applied to the apical side of cell monolayer (data not shown). Several reports suggested that mechanistic precipitation kinetics models such as classical nucleation theory and particle growth model were able to precisely capture the precipitation of weakly basic drugs.41, 42 However, in this study, a simple first-order precipitation kinetics model was employed to describe the precipitation behavior as a starting point for mathematical analysis coupled with the GIS. In the case of 19 ACS Paragon Plus Environment


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dipyridamole, intraluminal behavior after dosing 30 mg or 90 mg in solution was well predicted by this simple equation (Figure 5). It can be speculated that precipitation of dipyridamole had minor contribution to the intraluminal concentration-time profile since the fraction precipitated in aspirated intestinal fluids was less than 9% in humans.28 Therefore, further investigation may be required to assess the suitability of this first-order precipitation kinetics for another weakly basic drug that shows intensive precipitation in the upper intestine such as posaconazole.43 However, the current in vitro setting of GIS is the starting point to predict in vivo drug precipitation. A more accurate prediction might be necessary in the future and can be achieved by adjusting the experimental conditions such as buffer type, capacity, and fluid secretion rate to conclude in vivo phenomena. Computational approach based on the GIS adequately predicted the plasma concentration-time profile after a single oral administration of 50 mg of dipyridamole (Figure 6). In addition, predicted pharmacokinetic parameters with normal and elevated gastric pH level were consistent with the observed data (Table 5). It should be pointed out that employed mathematical equations are simply based on mass transport kinetics of the GIS with additional equations for intestinal permeation and drug transit to the colon. This fact strongly suggested that in vitro experimental settings of the GIS represented in vivo GI physiology and pH-induced supersaturation of dipyridamole was reproduced in the GIS. In other words, the GIS can serve as an appropriate iPD methodology to forecast in vivo behavior of BCS class IIb drugs quantitatively. As demonstrated in this study, in vitro-in silico approach using the GIS data implemented meaningful information on in vivo behavior of dipyridamole. Together with our previous work, iPD methodology in combination with mathematical analysis will improve in vivo predictability of the oral drug products with weakly basic properties.12-14 The oral BA and pharmacokinetic profile of weakly basic drugs typically fluctuate depending on the extent and duration of supersaturation which can be observed upon drug entry into the upper small intestine. These phenomena can be observed in iPD methodology like the GIS which adequately reflects gastric emptying, pH change, and intestinal transit. Although further studies are required to conclude the usefulness of the GIS and to generalize this result, the GIS dissolution coupled with in silico approach yielded 20 ACS Paragon Plus Environment

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quantitative in vivo prediction, emphasizing the utility of the GIS in the characterization of a new drug candidate, the development of oral drug formulations, and the determination of bioequivalence and bioinequivalence based on the formulation differences.



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AUTHOR INFORMATION

Corresponding Author: Gordon L. Amidon

Phone: 734-764-2464. FAX: 734-764-6282. E-mail: [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENT

The authors would like to thank Gail Benninghoff for her excellent secretarial work, and Naoto Igawa for his valuable comments. This work was supported by FDA Grant HHSF223201310144C. SUPPORTING INFORMATION Mass Equations for GIS Mass Equations for the GI tract

ABBREVIATIONS AUC, area under the curve; BA, bioavailability; BCS, biopharmaceutics classification system; CAT, compartmental absorption and transit; FaSSIF, fasted state simulated intestinal fluid; GIS, Gastrointestinal Simulator; GI tract, gastrointestinal tract; HPLC, high performance liquid chromatography; iPD, in vivo


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predictive dissolution; IVIVC, in vitro-in vivo correlations; NaCl, Sodium chloride; SGF, simulated gastric fluid; TFA, trifluoroacetic acid; USP, United States Pharmacopeia;



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Figure 1. Mass transport kinetics for the GIS.


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Figure 2. Mass transport kinetics for the GI tract.



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Figure 3. Dissolved drug amount-time profiles and % dissolved-time profiles of 50 mg dipyridamole in USP apparatus II with 300 mL of SGFpH2.0 (a) and FaSSIF (b). White circles represent the experimental data, and dotted lines indicate the fitted curves. Experimental data was shown as mean ± SD (n=3).


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Figure 4. Drug amount in solution-time profiles of 50 mg dipyridamole in the GISstomach (a), GISduodenum (b), and GISjejunum (c) of the GIS. White circles represent the experimental data, and dotted lines indicate the fitted curves. Experimental data was shown as mean ± SD (n=3).



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Figure 5. Drug concentration-time profiles in the duodenum after an administration of 30 mg (a) and 90 mg (b) dipyridamole as a complete solution. Box plots indicate the observed data, and dotted lines represent the predicted curves, respectively. Observed data was taken from the literature.28


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Figure 6. Plasma concentration-time profile after a single oral administration of 50 mg dipyridamole in healthy subjects with normal gastric pH level. White circles indicate the observed data, and dotted line represents the predicted data. Observed data is median values, taken from the literature.29



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Table 1. Dissolution parameters for dipyridamole in USP apparatus II with different media. Parameter

Value

Cs,SGFpH2.0 (mg/mL)

5

Cs,FaSSIF (mg/mL)

0.0197

z(SGFpH2.0) (mL/mg/min)

1.6

z(FaSSIF) (mL/mg/min)

0.45


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Table 2. Experimental and compound-related parameters for the GIS mass transport equations Parameter

Value

Reference

Dosed amount (mg)

50

Russell et al.29

ksec(s) (mL/min)

1

Takeuchi et al.12

ksec(d) (mL/min)

1

Takeuchi et al.12

GE (min)

8

Takeuchi et al.12

Vs (mL)

300~5

equation (3)

Vd (mL)

50

equation (4)

Vj(mL)

0~390

equation (5)

Cs,stomach (mg/mL)

5

Solubility in SGFpH2.0

Cs,duodenum (mg/mL)

0.0197

Solubility in FaSSIF

Cs,jejunum (mg/mL)

0.0197

Solubility in FaSSIF

z(s) (mL/mg/min)

1.6

z value in SGFpH2.0

z(d) (mL/mg/min)

0.45

z value in FaSSIF

z(j) (mL/mg/min)

0.45

z value in FaSSIF

Cend(d) (mg/mL)

0.076

Experimental data

Cend(j) (mg/mL)

0.054

Experimental data

tlag (min)

5.8

Optimized by fitting

kpre(d) (min-1)

0.0330


kpre(j) (min-1)

0.137




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Table 3. Physiological and compound-related parameters for GI tract mass transport equations Parameter

Value

Reference

k(i-c) (min-1)

0.00556

α

DF

1.7

Sugano et al.18

R (cm)

1.5

Sugano et al.18

Peff (×10-4 cm/sec)

3.0

Sugano et al.18

α

Mean small intestinal transit time = 180 min


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Table 4. Systemic pharmacokinetic parameters for dipyridamole. Pharmacokinetic parameterβ

Value

Vc (mL)

6160

ke (min-1)

0.0275

k12 (min-1)

0.666

k21 (min-1)

0.0447

k13 (min-1)

0.01

k31 (min-1)

0.00135

β

: Estimated from reference: Mahony et al.30



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Table 5. The comparison of observed and predicted pharmacokinetic parameters after a single oral administration of 50 mg dipyridamole. AUC0-36hr (µg*hr/mL) Cmax (µg/mL)

Tmax (hr) (AUC ratio)

Subjects Observedγ Predicted

Normal gastric pH

Elevated gastric pH γ

1.58

0.333

1.54

0.446

Observedγ

Predicted

0.58

0.64

2.25

Observedγ

Predicted

4258

4728

(1.00)

(1.00)

2692

3145

(0.63)

(0.67)

2.08

Reference: Russell et al.29


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Utilization of Gastrointestinal Simulator, an in Vivo Predictive

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