European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx
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European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps
In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets Sofija Beloica a,⇑, Sandra Cvijic´ a, Marija Bogataj b, Jelena Parojcˇic´ a a b
Department of Pharmaceutical Technology and Cosmetology, University of Belgrade, Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia Department of Biopharmacy and Pharmacokinetics, University of Ljubljana, Faculty of Pharmacy, Aškercˇeva cesta 7, 1000 Ljubljana, Slovenia
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 31 March 2015 Accepted 31 March 2015 Available online xxxx Keywords: Bioperformance dissolution Gastrointestinal simulation In vitro-in vivo-in silico approach Ibuprofen
a b s t r a c t Within the last decades, physiologically based pharmacokinetic models have emerged into a biopharmaceutical toolkit that has been proven useful in understanding how physicochemical, formulation and physiological factors affect oral drug absorption. The purpose of this study was to develop a drug specific physiologically based pharmacokinetic model that will allow mechanistic interpretation of oral absorption from dosage forms exhibiting different in vitro and different in vivo performance (i.e. immediate release and sustained release tablets) and identification of bioperformance dissolution testing. Ibuprofen was chosen to be used for the ‘‘proof of concept’’ considering it is well characterised and the necessary physicochemical, biopharmaceutical and pharmacokinetic properties for model development could be found in the literature. Gastrointestinal simulation technology implemented in SimcypÒ was successful in estimating ibuprofen oral absorption. The developed model exhibited good generalisation ability for the dosage forms studied. The obtained results indicate that the model was sensitive to input kinetics represented by the in vitro drug release profiles obtained under various dissolution conditions. According to the obtained results, reciprocating cylinder apparatus with biorepresentative change in media pH might be considered as bioperformance dissolution in the case of the two ibuprofen SR products studied. These results further justify the use of integrated in vitro-in vivo-in silico approach in estimating bioperformance of oral solid dosage forms. Ó 2015 Elsevier B.V. All rights reserved.
1. Introduction Biopharmaceutical characterization of solid dosage forms involves different in vitro and in vivo studies needed in order to evaluate and understand drug substance and dosage form properties that may affect its bioperformance (i.e. the rate and extent of drug release and absorption). With the introduction and development of physiologically based pharmacokinetic (PBPK) models, emphasis in biopharmaceutical drug characterisation is shifted towards in silico modelling and simulation, mechanistic understanding and predictability of dosage form bioperformance in different stages of drug product development (Agoram et al., 2001; Dressman et al., 2011; Kostewicz et al., 2014; Parrott and Lave, 2008). PBPK models usually employ what is commonly known as a ‘‘bottom-up’’ approach in modelling and simulation. The concept is to ‘‘build’’ the drug plasma concentration profile based on drug ⇑ Corresponding author. Tel.: +381 643382758. E-mail addresses: sofi[email protected], sofi[email protected] (S. Beloica), [email protected] (S. Cvijic´), [email protected] (M. Bogataj), [email protected] (J. Parojcˇic´).
substance physicochemical, biopharmaceutical and pharmacokinetic properties, and physiological conditions to which the relevant dosage form is subjected. In the case of solid oral dosage forms, dissolution characteristics are an important product attribute and, most often, critical factor to ensure targeted drug performance in vivo. Conventional quality control dissolution methods are not always designed in a way which ensures that the obtained results will be indicative of the in vivo performance of the drug product. With the changing paradigm of drug development, aimed at Quality-by-Design and Equivalence-by-Design, there is an increased need for the dissolution test to reflect the kinetics of drug release in vivo and be discriminatory enough to predict the influence of formulation and process parameters that would affect drug release in vivo. Current approaches to bioperformance dissolution testing include different dynamic dissolution apparatuses (Blanquet et al., 2004; Garbacz et al., 2008; Koziolek et al., 2014; Stefanic et al., 2014; Vardakou et al., 2011) and physiologically based dissolution media (Bergstrom et al., 2014; Garbacz and Klein, 2012). It has been emphasized that biorelevant dissolution media should be used to generate the input drug dissolution profile requested for PBPK modelling and gastrointestinal simulation
http://dx.doi.org/10.1016/j.ejps.2015.03.027 0928-0987/Ó 2015 Elsevier B.V. All rights reserved.
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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(GIS) of drug absorption from the solid oral dosage form (Kesisoglou, 2014; Willmann et al., 2012). There are different options available to define the input drug dissolution profile: (a) drug dissolution is dynamically simulated according to the Wang-Flanagan equation for spherical particles during gastrointestinal (GI) transit (based on a user-defined particle-size distribution, pH-dependent drug solubility, particle density, drug diffusion coefficient in water, and the apparent thickness of the diffusion layer); (b) empirical kinetic model describing drug dissolution from immediate and controlled release dosage forms is employed (this implies drug release data fitted to the Weibull equation), and (c) an input table containing experimentally obtained data (SimcypÒ Simulator, 2014). In the latter case, if drug dissolution is incomplete, data extrapolation is performed automatically. Integration of experimentally obtained dissolution data with PBPK modelling is increasingly used to estimate drug product in vivo performance (Cvijic´ et al., 2014; Ilic´ et al., 2014; Kambayashi and Dressman, 2013; Kesisoglou, 2014; Shono et al., 2009; Thelen et al., 2012). Majority of the drug products investigated were immediate release (IR) dosage forms. While GIS for immediate release dosage forms may be performed either based on drug physicochemical properties, or using the experimentally obtained in vitro dissolution data, the latter is requested for sustained release (SR) dosage forms. Considering the number of dissolution media and apparatus available, multiple dissolution profiles may result from the same drug product. It is, thus, important to critically evaluate the effect of the input drug dissolution profiles on GIS model prediction. The aim of this investigation was to develop drug specific PBPK model which will allow mechanistic interpretation of the oral absorption pattern of ibuprofen administered as both IR and SR drug products, evaluate the generalisation ability of the developed model, and establish the relationship between drug dissolution in vitro and the in vivo response which would facilitate identification of bioperformance dissolution testing. 2. Experimental 2.1. In vivo data Detailed survey of the literature data available on ibuprofen pharmacokinetics and bioavailability from different drug products has been performed. The data collected have been carefully reviewed. The same set of in vivo data based on the published results reporting ibuprofen pharmacokinetic profiles following IR and SR dosage form administration was used throughout the study (Källström et al., 1988; Pargal et al., 1996). 2.2. Drug products Three commercially available ibuprofen products, one IR film tablet and two SR products with proven bioequivalence were investigated. Product SR1 were hydrophilic matrix tablets based on xanthan gum (this product corresponds to the sustained release tablet formulation which was investigated in the in vivo study by Pargal et al. (1996)), while product SR2 were lipophilic matrix tablets based on stearic acid. 2.3. In vitro study In vitro drug release studies were conducted using the compendial paddle apparatus and two types of dynamic dissolution apparatus: reciprocating cylinder and flow-through system with glass bead dissolution device (Bogataj et al., 2010). IR tablets were
tested in the paddle apparatus using USP phosphate buffer (PB) pH 7.2 and Fasted State Simulated Intestinal Fluid without surfactants (blank FaSSIF) as dissolution media, and in glass bead dissolution device using blank FaSSIF as dissolution media. Following preliminary studies in which a range of dissolution conditions including different dissolution media, agitation intensity/flow rate have been employed, experimental settings defined for the SR tablets drug release studies were: (I) Rotating paddle apparatus (Erweka DT 70, Germany) at 37 °C and rotational speed of 50 rpm, using 500 ml of USP PB pH 7.2 and blank FaSSIF as dissolution media. The experiment was run for 12 h. (II) Reciprocating cylinder apparatus (BioDis, Varian Inc., USA), operating at 20 dpm, using the following media change pattern: 15 min blank FaSSGF (Fasted State Simulated Gastric Fluid), 3 h blank FaSSIF and 21 h SCoF (Simulated Colon Fluid), with a vessel change after 15 min and then at 1 h time intervals up to 24 h. (III) Flow-through system with glass bead dissolution device, using the following experimental setup: 50 g of glass beads, stirring rate of 50 rpm, and flow rate of 2 ml/min (Klein et al., 2013). Same media change pattern was used as with the reciprocating cylinder apparatus, and the experiment was run for 8 h: 15 min blank FaSSGF, 3 h blank FaSSIF and 5 h SCoF. The experiments were conducted at least in triplicate. Dissolution media samples were filtered through a 0.45 lm PVDF filter (25 mm GD/X, Whatman) into the test tubes and assayed for ibuprofen UV spectrophotometrically (Evolution 300 spectrophotometer, Thermo Fisher Scientific, Madison, Wisconsin) at 265 nm, or by high-performance liquid chromatography (HPLC Varian ProStar 330; column X Bridge C18 4.6 mm 50 mm; mobile phase acetonitrile: phosphate buffer pH 3.0 (30: 70 m/m) with flow rate 1.0 ml/min; injection volume was 10 lL; detection wavelength 265 nm). 2.4. Gastrointestinal simulation Mechanistic gastrointestinal simulation was performed using the commercially available software SimcypÒ Population-Based Simulator (version 13.2; Certara™, USA). The required input parameters related to ibuprofen physicochemical, biopharmaceutical and pharmacokinetic properties were taken from the literature and/or in silico estimated. Summary of the input parameters used is given in Table 1. Advanced dissolution, absorption and metabolism (ADAM) model was used as absorption model in the simulations. This model considers nine anatomically defined segments of the GI tract. Drug absorption from each segment is described as a function of release from the dosage form, dissolution, precipitation, luminal degradation, permeability, metabolism, transport, and transit from one segment to another. It is assumed that absorption from stomach is negligible, that movements of the solid and liquid drugs through each segment of the GI tract may be described by first-order kinetics, and that metabolism in the colon is negligible. Full PBPK perfusion-limited model that uses a number of time-based differential equations in order to simulate the concentrations in various organ compartments was used as distribution model. Simulations were performed for population representative subject of the Sim-Healthy Volunteers population. Automated Sensitivity Analysis (ASA) was used to assess the influence of the selected input parameters on the predicted percent of drug absorbed (Fa), peak plasma drug concentration (Cmax) and area under the concentration-time curve (AUC0t). Two SimcypÒ options were considered for developing a model for SR
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
S. Beloica et al. / European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx Table 1 Summary of ibuprofen input parameter values employed for gastrointestinal absorption simulation. Parameter
IR model
IR adjusted model
Molecular weight logPo:w Compound type pKa Blood-to-Plasma partition ratio Fraction unbound in plasma
206.3 3.68a Monoprotic acid 4.5b 0.55c 0.01d
Absorption ADAM model Human effective permeability, Peff,man (cm/s) Solubility-pH profile (mg/ml)
3.52 104e
Mean colon transit time (h) Maximum supersaturation ratio Precipitation rate constant (h1) Particle size (lm) Particle density (g/ml)
0.038 0.043 0.084 0.685 3.370 3.340
at at at at at at
pH pH pH pH pH pH
12g
1.0f 3.0f 4.5f 5.5f 6.8f 7.4f 15.8h
10g 4g 10g 1.2g
Distribution Full PBPK model, method 2 Vss (L/kg)
0.1i
Elimination CLpo (L/h)
2.9j
a
c d e f g h i j
1.8h
Literature value taken from Kasim et al. (2004). Literature value taken from Higgins et al. (2001). Literature value taken from Obach (1999). Literature value taken from Brocks and Jamali (1999). See text. Literature values taken from Potthast et al. (2005). Default Simcyp value. Estimated value using SimcypÒ Parameter Estimation option. Value calculated by Simcyp via Full PBPK distribution model. Literature value taken from Lockwood et al. (1983).
tablets. Validation of the developed models was performed based on the mean plasma concentration profile observed for ibuprofen IR tablets in the in vivo study reported by Källström et al. (1988) and ibuprofen SR tablets in the in vivo study reported by Pargal et al. (1996). The relevant percent prediction error (PE%) values between the in vivo observed and in silico predicted pharmacokinetic parameters were calculated as follows:
PE% ¼
absorption is documented by the relatively low tmax values, usually in the range of 1–3 h and relatively high absorption rate constants with the values reported being around 1.5 h1 (Ding et al., 2007; Källström et al., 1988; Müller et al., 1986; Neuvonen, 1991). When compared with IR tablets, SR preparations exhibit approximately 3–6 times lower peak plasma drug concentrations (Cmax) and notably delayed tmax (Averginos et al., 1991; Berardi et al., 1988; Parr et al., 1987; Regazzi et al., 1986). Parr et al. (1987) used external c scintigraphy to monitor gastrointestinal transit of radiolabelled SR tablets in fasted volunteers. The obtained results suggest that ibuprofen administered in the SR dosage form is absorbed throughout the entire GI tract and that the main site of drug absorption is colon (Parr et al., 1987). Plasma concentration time profiles of certain ibuprofen SR products are characterised by the appearance of double peak (Pargal et al., 1996; Wilson et al., 1989). Since the drug does not undergo enterohepatic recirculation, double peak phenomenon observed in these studies was ascribed to the loss of integrity of the dosage form, and high ibuprofen absorption in the colon (Wilson et al., 1989).
3.2. In vitro study
Trial design Population representative of the sim-healthy volunteers population Dose (mg) 600 800 Simulation time (h) 24 48 Fluid intake with dose (ml) 250 b
3
Dissolution profiles of the investigated ibuprofen IR tablets under various dissolution conditions are presented in Fig. 1. Drug dissolution in the rotating paddle apparatus was very rapid, with the plateau phase reached after 20 min. While complete dose dissolution was observed in the USP PB pH 7.2, cumulative amount of ibuprofen dissolved in blank FaSSIF (pH 6.5) was 80%, reflecting the difference in ibuprofen solubility at different pH. Ibuprofen dissolution in the glass bead device, using blank FaSSIF as dissolution media was markedly slower, leading to complete drug dissolution after 2 h of investigation. The apparent differences between dissolution profiles observed in the two apparatus reflect the differences in agitation intensity and resultant hydrodynamics. Glass beads grinding forces contributed to IR tablets disintegration, which, together with the exposure to fresh dissolution media resulted in the enhanced ibuprofen dissolution, although cumulative volume of dissolution media in the glass bead device was lower. Ibuprofen release profiles from the SR tablets obtained under various dissolution conditions are presented in Fig. 2. Drug release in the paddle apparatus using blank FaSSIF as dissolution media, was incomplete, with the cumulative amount of ibuprofen dissolved of approximately 8% and 17% after 8 h, for the products SR1 and SR2, respectively. Pronounced differences between drug release profiles for the products SR1 and SR2 were observed when USP PB pH 7.2 was employed. In this case, initial relatively slow drug release from the lipophilic matrix tablets (SR2) was followed with the abrupt release leading to complete ibuprofen dissolution
PKpredicted PKobserved 100 PKobserved
where PK denotes the relevant pharmacokinetic parameter (Cmax, time to reach peak concentration, tmax or AUC0t). In order to evaluate the effect of input dissolution profile extrapolation, a set of virtual dissolution profiles was generated based on the experimentally obtained data and used for GIS. 3. Results and discussion 3.1. In vivo data Ibuprofen absorption from IR tablets is, generally, described as well and rapid, with absolute bioavailability between 80% and 100% (Albert et al., 1984; Wagner et al., 1984). Rapid drug
Fig. 1. IR product in vitro dissolution profiles.
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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products (data not shown). Certain loss of tablet integrity was noted for both SR products after five hours of investigation, which may explain slight deviation from the linear drug release profile observed at the corresponding time interval (Fig. 2b). Images of the investigated tablets after 5 h of study in the reciprocating cylinder apparatus are presented in Fig. 3. Such observation coincides with the reports of c scintigraphic studies where matrix tablet disruption upon reaching the colon was observed (Wilson et al., 1989). Drug dissolution in the glass bead device exhibited initial lag-time of up to 1 h, followed by, approximately, 25% of drug released after 8 h, from both the products investigated. The obtained results by all three methods indicate the importance of media volume available for dissolution of low solubility drugs, where complete dissolution could not be expected in physiologically based media, such as blank FaSSIF, in the experimental setting with defined media volume and non-sink conditions, as encountered in the rotating paddle apparatus. Dynamic dissolution devices provide alternate hydrodynamics, as well as the exposure to ‘fresh’ media providing the concentration difference as the driving force necessary for complete drug dissolution. Drug release profiles from the two SR products with proven bioequivalence were similar in the dynamic dissolution devices, confirming their advantages over the conventional paddle apparatus for bioperformance dissolution testing. 3.3. Gastrointestinal simulation
Fig. 2. SR products in vitro dissolution profiles in the rotating paddle apparatus (a) and dynamic dissolution devices (b); (SR1 – full lines; SR2 –dashed lines).
after 6 h. In the case of hydrophilic matrix tablets (SR1), drug release was slow and incomplete leading to total of approximately 13% dose dissolved irrespective of the increase in media pH (Fig. 2a). This could be explained by the slow hydration of xanthan gum (which was employed as hydrophilic matrix forming agent) which is independent of the media pH. The obtained results in the USP PB pH 7.2 indicate that employed dissolution method is overdiscriminative, since significant differences between two products were observed, although they have been proven bioequivalent in vivo. Drug release profiles observed in the reciprocating cylinder apparatus followed near-zero order kinetics, leading to more than 80% dose dissolved after 24 h for both the investigated
3.3.1. IR dosage form model development Development of GIS model requires that certain physicochemical and pharmacokinetic properties of the model drug substance are known. This makes ibuprofen a suitable model to be used for the ‘‘proof of concept’’, since it is widely investigated and well characterised in the literature. Ibuprofen is high permeability, low solubility drug characterized by pH dependent solubility profile in the physiological pH range. pH solubility profile reported by Potthast et al. (2005) has been employed in this study. Human effective permeability (Peff,man) was calculated using the relationship proposed by Fagerholm et al. (1996), based on the rat permeability value obtained in the in situ rat gut perfusion model (Levis et al., 2003). The resulting value of 3.52 104 cm/s is consistent with the rapid and complete ibuprofen absorption reported by Davies (1998). Scintigraphic studies in humans indicate that ibuprofen absorption from SR products occurs throughout the GI tract (Parr et al., 1987), which is in accordance with its high permeability. The volume of distribution at steady state (Vss) obtained using the Full PBPK model, and the method reported by Rodgers and Rowland (2007), was 0.1 L/kg and is in accordance
Fig. 3. Images of the investigated SR products after 5 h in the reciprocating cylinder apparatus: (a) SR1; (b) SR2.
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with the values reported by Davies (1998). Blood-to-plasma partition ratio was adopted from the study of Obach (1999), and fraction unbound in plasma from the study of Brocks and Jamali (1999). Drug clearance value was adopted from the study of
Fig. 4. Ibuprofen plasma concentration profiles predicted using GIS model for the IR product.
Lockwood et al. (1983). Model validation was performed based on the plasma concentration profile observed for ibuprofen IR tablets in the in vivo study reported by Källström et al. (1988). The percent prediction error (PE%) values for the relevant pharmacokinetic parameters were 1.91, 9.52, and 8.73 for Cmax, tmax and AUC0t, respectively, indicating that the generated PBPK model gave good estimation of the ibuprofen plasma profile. In order to evaluate the impact of input drug dissolution profiles on GIS model predictions, in vitro dissolution profiles observed for the ibuprofen IR tablets were used as inputs. Relevant plasma concentration profiles are presented in Fig. 4. The obtained Cp t profiles were characterized with respect to pharmacokinetic parameters, Cmax, tmax and AUC0t. Relevant values as well as the corresponding percent prediction errors (PE%) are given in Table 2. The pharmacokinetic parameter values estimated based on the dissolution profile in the paddle apparatus and USP PB pH 7.2 were in the best accordance with those reported in vivo (Källström et al., 1988). Predicted percent of drug absorbed (Fa) was 100%, which is in accordance with the literature reports on high ibuprofen bioavailability after oral administration (Albert et al., 1984; Wagner et al., 1984). The obtained results indicate that very rapid dissolution, such as observed in the paddle apparatus using USP PB pH 7.2 as dissolution media might be considered as bioperformance test in the case of ibuprofen IR film tablets studied.
Table 2 Pharmacokinetic parameters predicted using PBPK models developed. Input profile
Pharmacokinetic parameter
Gastrointestinal simulation model IR – IR tablets Cmax (lg/ml) (PE%)
tmax (h) (PE%)
AUC0t (lg h/ml) (PE%)
Predicted based on solubility Rotating paddle, PB pH 7.2 Rotating paddle, blank FaSSIF Glass bead apparatus, blank FaSSIF
45.76 46.01 37.44 44.34
1.90 (9.52) 1.9 (9.52) 1.9 (9.52) 2.55 (17.64)
201.98 201.96 178.95 198.75
Observed in vivo
44.90
2.10
221.3
(1.91) (2.47) (19.92) (1.26)
(8.73) (8.73) (19.13) (10.19)
Gastrointestinal simulation model IR – SR tablets Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
– 41.50 (192.04) 3.30 (76.77) – 11.33 (20.26) 9.97 (29.83) 7.91 (44.33) 8.20 (42.29)
4.16 (72.52) – – 5.54 (63.40) 9.20 (39.23) 10.39 (31.37) – –
– 5.52 – – 5.52 4.32 6.24 6.24
12.24 – – 11.04 16.32 16.32 – –
85.61 (68.14) 255.01 (5.12) 32.65 (87.85) 95.13 (64.60) 165.21 (38.53) 165.38 (38.47) 139.79 (47.99) 146.17 (45.61)
Gastrointestinal simulation model IR adjusted – SR tablets Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
– 52.78 (271.42) 4.08 (71.28) – 15.11 (6.33) 13.61 (4.22) 11.02 (22.44) –
5.93 (60.83) – – 8.36 (44.78) 14.54 (3.96) 13.09 (13.54) – 11.45 (24.37)
– 6.00 (0.84) 3.12 (47.56) – 6.00 (0.84) 6.00 (0.84) 7.2 (21.00) –
12.72 – – 11.28 16.80 18.00 – 21.75
158.92 (40.87) 420.35 (56.38) 52.9 (80.31) 167.56 (37.66) 291.59 (8.48) 288.68 (7.39) 250.98 (6.62) 262.26 (2.42)
Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
– 28.19 (98.38) 4.17 (70.65) – 14.23 (0.14) 13.39 (5.77) 10.9 (23.29) –
5.93 (60.83) – – 8.37 (44.71) 14.54 (3.96) 13.15 (13.14) – 11.41 (24.63)
– 4.32 (27.39) 3.12 (47.56) – 6.24 (4.87) 6.00 (0.84) 7.2 (17.36) –
12.72 – – 11.28 16.80 16.56 – 19.68
100.17 (62.73) 364.89 (35.75) 48.16 (82.08) 123.67 (53.99) 302.26 (12.45) 292.11 (8.67) 218.58 (18.68) 245.75 (8.57)
Observed in vivo
14.21
15.14
5.95
17.38
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
(7.22)
(7.22) (27.39) (4.87) (4.87)
(29.57)
(36.47) (6.09) (6.09)
(26.81)
(35.09) (3.33) (3.56) (25.14)
Gastrointestinal simulation model SR – SR tablets (26.81)
(35.09) (3.33) (4.71) (13.23)
268.79
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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3.3.2. Automated sensitivity analysis ASA was used to assess the influence of: (1) effective drug permeability, (2) blood-to-plasma partition ratio, (3) fraction unbound in plasma, (4) drug solubility and (5) drug clearance on the predicted percent of drug absorbed, Cmax and AUC0t. According to the default SimcypÒ settings, the selected input parameters values were varied in the range covering one tenth to 10-fold actual input value used for gastrointestinal model development. The obtained results are presented in Fig. 5. It was found that the percent of drug absorbed was rather insensitive to variation of all the selected input parameters, except when the value of effective drug permeability or drug solubility was one third or less of the input value, when a decrease in the percent drug absorbed is to be expected. Cmax was affected by the variation of all the input parameters studied, particularly change in drug clearance value. ASA showed that AUC0t is also mostly affected by variations in drug clearance value.
simulation time prolonged to 48 h. As already mentioned, SimcypÒ requires drug release profile to be entered as input in order to predict Cp t profile for sustained release formulation. Experimentally obtained drug release profiles presented in Fig. 2 were used for gastrointestinal simulation. The resultant Cp t profiles are shown in Fig. 6a. The majority of estimated profiles (except the Cp t profile based on the SR2 product dissolution in the paddle apparatus using USP PB pH 7.2 as dissolution media) exhibited lower Cmax and AUC0t values when compared to the average Cp t profile observed in the in vivo study by Pargal et al. (1996). The discrepancy between the estimated and actual in vivo data is, also, evident from the relevant percent prediction
3.3.3. SR dosage form model development The GIS model developed for ibuprofen IR tablets was subsequently employed to estimate Cp t profiles for the SR products. In accordance with the design of the actual in vivo study (Pargal et al., 1996), ibuprofen dose was adjusted to 800 mg, and
Fig. 5. Automated Sensitivity Analysis (ASA): dependence of (a) Fa, (b) Cmax and (c) AUC0t on different input parameters (the center of x-axis for each of the parameters tested represents the value that was used in model development.
Fig. 6. Ibuprofen plasma concentration profiles predicted for the SR products using (a) IR model; (b) IR adjusted model and (c) SR model; (SR1 – black lines; SR2 – gray lines).
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
S. Beloica et al. / European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx
error values presented in Table 2. Taking into account the results of ASA performed, SimcypÒ Parameter Estimation option was employed to optimize drug clearance value. The SimcypÒ Parameter Estimation option allows the estimation of parameter values in order to fit a selected model to the observed data by minimising the value of the errors for each observation. This is achieved by defining an objective function which is a measure of the overall difference between the predicted model outcome and the observed data. Each algorithm starts with an initial set of parameters which are used to generate predicted outcomes, which are then compared with observed data using the objective function. The initial parameter values are changed iteratively to determine parameter values which optimise the value of the objective function. The final parameter estimates obtained give predicted model values that overall better represent the observed data. The objective function used for all estimations is Weighted least square and Nelder–Mead method as minimisation method, which are default Simcyp options. The Nelder–Mead is a powerful classical optimisation method, suitable for estimating many parameters. This method is local optimisation method which is sensitive to the initial parameter value and boundary values and therefore this values should be assigned carefully (SimcypÒ Simulator, 2014). The estimated value of 1.8 L/h is in the range of values reported in the literature (Davies, 1998; Garsía-Martin et al., 2004; Geisslinger et al., 1989), and was adopted for model development. Predicted Fa values were lower when compared to the in vivo observed (data not shown). Taking into account that the largest percent of ibuprofen from SR formulations is absorbed in the colon (Parr et al., 1987), colon transit time was recognized as parameter that may affect ibuprofen absorption. This parameter was also estimated using the SimcypÒ Parameter Estimation option and the resulting value of 15.8 h which is in accordance with the value observed by Ghoshal et al. (2012), was adopted for model development. The estimated Cp t profiles using the IR model with adjusted values for drug clearance and colon transit time (IR adjusted model) are shown in Fig. 6b. Relevant pharmacokinetic parameter values, as well as the corresponding percent prediction error values (PE%) are given in Table 2. The percent prediction error values for the investigated pharmacokinetic parameters varied from 0.84% to 271.42%. The best estimation was obtained based on the input dissolution profile observed in the reciprocating cylinder apparatus using biorepresentative media change. In order to evaluate the applicability of SimcypÒ Controlled/ Modified (CR/MR) Release option intended for gastrointestinal simulation of relevant dosage forms bioperformance, experimentally obtained dissolution profiles were employed as the requested inputs for Cp t profile simulation. The obtained results are presented in Fig. 6c and Table 2. It can be noted that the values of Cmax and AUC0t obtained using SimcypÒ Controlled/Modified (CR/MR) Release option were somewhat lower when compared to the values obtained using IR adjusted model. The underlying reason is that, in SimcypÒ Simulator, the profile entered under the CR/MR option is considered as percent of total mass released from a formulation but not dissolved (SimcypÒ Simulator, 2014), and this two profiles are different for low solubility drugs, such as ibuprofen. Therefore, the use of IR option may be considered as more appropriate mode of operation in the case of ibuprofen, as well as other low solubility drugs. SimcypÒ generated cumulative fractions of ibuprofen absorbed over time for IR and SR tablets are shown in Fig. 7a. It can be noted that the two profiles differ significantly. In the case of IR tablets, cumulative fraction of ibuprofen absorbed over time profile reflects relatively fast absorption, which is complete after approximately 3 h. Such results are in accordance with complete ibuprofen absorption from IR tablets which predominately occurs in the proximal part of the small intestine. Ibuprofen absorption from
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Fig. 7. Cumulative fractions of ibuprofen absorbed (a) and regional ibuprofen absorption distribution (b) estimated by GIS.
the SR tablets is significantly slower, with a plateau phase reached after, approximately, 20 h and the total amount of ibuprofen absorbed estimated to be 65% of the dose administered. Regional absorption distribution indicates that majority of ibuprofen from the IR product is absorbed in jejunum (64% of the total amount of dose absorbed, Fig. 7b), which is in accordance with the data reported in the literature (Gura, 2012). Maximum absorption of ibuprofen from the SR products occurs in colon (74% of the total amount of dose absorbed), which is in agreement with the findings of Parr et al. (1987). Such data indicate that the GIS model developed demonstrates generalisation ability to predict bioperformance of various ibuprofen products with different drug release rates. 3.3.4. Effect of input profile extrapolation In the case of incomplete drug dissolution, linear extrapolation is performed automatically in the SimcypÒ Simulator. One of the drawbacks of linear extrapolation is that the resulting profile might be, practically unrealistic. To assess the impact of small deviations from the linearly extrapolated profiles on simulation outcomes, a set of virtual in vitro data was constructed based on the dissolution profile obtained in the glass bead apparatus using biorepresentative media change. Virtual in vitro dissolution profiles were generated to reflect linear extrapolation of the amount of drug dissolved (profile a), and situations where certain disruption of the tablets and rise in the amount of ibuprofen dissolved occurs after 8 h (profile b), 10 h (profile c), 12 h (profile d) and 14 h (profile e) (Fig. 8a). The corresponding Cp t profiles estimated using the adjusted IR model are shown in Fig. 8b. It can be observed that the obtained profiles differ significantly, and that profile c is very close to the in vivo situation. This results indicate that data extrapolation might be misleading and that it is necessary to record the overall dissolution profile, until complete drug dissolution is achieved.
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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S. Beloica et al. / European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx
Fig. 8. Virtual ibuprofen dissolution profiles (a) and the corresponding simulated in vivo data.
4. Conclusions The obtained results indicate that dynamic dissolution devices exhibit certain advantages over the compendial rotating paddle apparatus, which tended to be over-discriminative, indicating differences between the two ibuprofen SR formulations which were not observed in vivo. It was shown that GIS technology could be successfully used in estimation of oral ibuprofen absorption using ‘‘bottom up’’ approach. The developed model exhibited good generalisation ability, providing good estimations for the two dosage forms studied. The model was sensitive to the input kinetics represented by the in vitro profiles obtained under various experimental conditions. Best estimation of the actual in vivo profile was obtained based on the in vitro dissolution in the reciprocating cylinder apparatus with biorepresentative change in media pH and might be considered as bioperformance dissolution testing in the case of the two ibuprofen SR products studied. The results of this study justify the use of in vitro-in silico-in vivo approach in biopharmaceutical characterisation of oral solid dosage forms. Acknowledgement This work was performed under the Project TR-34007 supported by the Ministry of Education, Science and Technological Development, Republic of Serbia. References Agoram, B., Woltosz, W.S., Bolger, M.B., 2001. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Deliv. Rev. 50, S41–S67.
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Contents lists available at ScienceDirect
European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps
In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets Sofija Beloica a,⇑, Sandra Cvijic´ a, Marija Bogataj b, Jelena Parojcˇic´ a a b
Department of Pharmaceutical Technology and Cosmetology, University of Belgrade, Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia Department of Biopharmacy and Pharmacokinetics, University of Ljubljana, Faculty of Pharmacy, Aškercˇeva cesta 7, 1000 Ljubljana, Slovenia
a r t i c l e
i n f o
Article history: Received 7 January 2015 Received in revised form 31 March 2015 Accepted 31 March 2015 Available online xxxx Keywords: Bioperformance dissolution Gastrointestinal simulation In vitro-in vivo-in silico approach Ibuprofen
a b s t r a c t Within the last decades, physiologically based pharmacokinetic models have emerged into a biopharmaceutical toolkit that has been proven useful in understanding how physicochemical, formulation and physiological factors affect oral drug absorption. The purpose of this study was to develop a drug specific physiologically based pharmacokinetic model that will allow mechanistic interpretation of oral absorption from dosage forms exhibiting different in vitro and different in vivo performance (i.e. immediate release and sustained release tablets) and identification of bioperformance dissolution testing. Ibuprofen was chosen to be used for the ‘‘proof of concept’’ considering it is well characterised and the necessary physicochemical, biopharmaceutical and pharmacokinetic properties for model development could be found in the literature. Gastrointestinal simulation technology implemented in SimcypÒ was successful in estimating ibuprofen oral absorption. The developed model exhibited good generalisation ability for the dosage forms studied. The obtained results indicate that the model was sensitive to input kinetics represented by the in vitro drug release profiles obtained under various dissolution conditions. According to the obtained results, reciprocating cylinder apparatus with biorepresentative change in media pH might be considered as bioperformance dissolution in the case of the two ibuprofen SR products studied. These results further justify the use of integrated in vitro-in vivo-in silico approach in estimating bioperformance of oral solid dosage forms. Ó 2015 Elsevier B.V. All rights reserved.
1. Introduction Biopharmaceutical characterization of solid dosage forms involves different in vitro and in vivo studies needed in order to evaluate and understand drug substance and dosage form properties that may affect its bioperformance (i.e. the rate and extent of drug release and absorption). With the introduction and development of physiologically based pharmacokinetic (PBPK) models, emphasis in biopharmaceutical drug characterisation is shifted towards in silico modelling and simulation, mechanistic understanding and predictability of dosage form bioperformance in different stages of drug product development (Agoram et al., 2001; Dressman et al., 2011; Kostewicz et al., 2014; Parrott and Lave, 2008). PBPK models usually employ what is commonly known as a ‘‘bottom-up’’ approach in modelling and simulation. The concept is to ‘‘build’’ the drug plasma concentration profile based on drug ⇑ Corresponding author. Tel.: +381 643382758. E-mail addresses: sofi[email protected], sofi[email protected] (S. Beloica), [email protected] (S. Cvijic´), [email protected] (M. Bogataj), [email protected] (J. Parojcˇic´).
substance physicochemical, biopharmaceutical and pharmacokinetic properties, and physiological conditions to which the relevant dosage form is subjected. In the case of solid oral dosage forms, dissolution characteristics are an important product attribute and, most often, critical factor to ensure targeted drug performance in vivo. Conventional quality control dissolution methods are not always designed in a way which ensures that the obtained results will be indicative of the in vivo performance of the drug product. With the changing paradigm of drug development, aimed at Quality-by-Design and Equivalence-by-Design, there is an increased need for the dissolution test to reflect the kinetics of drug release in vivo and be discriminatory enough to predict the influence of formulation and process parameters that would affect drug release in vivo. Current approaches to bioperformance dissolution testing include different dynamic dissolution apparatuses (Blanquet et al., 2004; Garbacz et al., 2008; Koziolek et al., 2014; Stefanic et al., 2014; Vardakou et al., 2011) and physiologically based dissolution media (Bergstrom et al., 2014; Garbacz and Klein, 2012). It has been emphasized that biorelevant dissolution media should be used to generate the input drug dissolution profile requested for PBPK modelling and gastrointestinal simulation
http://dx.doi.org/10.1016/j.ejps.2015.03.027 0928-0987/Ó 2015 Elsevier B.V. All rights reserved.
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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S. Beloica et al. / European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx
(GIS) of drug absorption from the solid oral dosage form (Kesisoglou, 2014; Willmann et al., 2012). There are different options available to define the input drug dissolution profile: (a) drug dissolution is dynamically simulated according to the Wang-Flanagan equation for spherical particles during gastrointestinal (GI) transit (based on a user-defined particle-size distribution, pH-dependent drug solubility, particle density, drug diffusion coefficient in water, and the apparent thickness of the diffusion layer); (b) empirical kinetic model describing drug dissolution from immediate and controlled release dosage forms is employed (this implies drug release data fitted to the Weibull equation), and (c) an input table containing experimentally obtained data (SimcypÒ Simulator, 2014). In the latter case, if drug dissolution is incomplete, data extrapolation is performed automatically. Integration of experimentally obtained dissolution data with PBPK modelling is increasingly used to estimate drug product in vivo performance (Cvijic´ et al., 2014; Ilic´ et al., 2014; Kambayashi and Dressman, 2013; Kesisoglou, 2014; Shono et al., 2009; Thelen et al., 2012). Majority of the drug products investigated were immediate release (IR) dosage forms. While GIS for immediate release dosage forms may be performed either based on drug physicochemical properties, or using the experimentally obtained in vitro dissolution data, the latter is requested for sustained release (SR) dosage forms. Considering the number of dissolution media and apparatus available, multiple dissolution profiles may result from the same drug product. It is, thus, important to critically evaluate the effect of the input drug dissolution profiles on GIS model prediction. The aim of this investigation was to develop drug specific PBPK model which will allow mechanistic interpretation of the oral absorption pattern of ibuprofen administered as both IR and SR drug products, evaluate the generalisation ability of the developed model, and establish the relationship between drug dissolution in vitro and the in vivo response which would facilitate identification of bioperformance dissolution testing. 2. Experimental 2.1. In vivo data Detailed survey of the literature data available on ibuprofen pharmacokinetics and bioavailability from different drug products has been performed. The data collected have been carefully reviewed. The same set of in vivo data based on the published results reporting ibuprofen pharmacokinetic profiles following IR and SR dosage form administration was used throughout the study (Källström et al., 1988; Pargal et al., 1996). 2.2. Drug products Three commercially available ibuprofen products, one IR film tablet and two SR products with proven bioequivalence were investigated. Product SR1 were hydrophilic matrix tablets based on xanthan gum (this product corresponds to the sustained release tablet formulation which was investigated in the in vivo study by Pargal et al. (1996)), while product SR2 were lipophilic matrix tablets based on stearic acid. 2.3. In vitro study In vitro drug release studies were conducted using the compendial paddle apparatus and two types of dynamic dissolution apparatus: reciprocating cylinder and flow-through system with glass bead dissolution device (Bogataj et al., 2010). IR tablets were
tested in the paddle apparatus using USP phosphate buffer (PB) pH 7.2 and Fasted State Simulated Intestinal Fluid without surfactants (blank FaSSIF) as dissolution media, and in glass bead dissolution device using blank FaSSIF as dissolution media. Following preliminary studies in which a range of dissolution conditions including different dissolution media, agitation intensity/flow rate have been employed, experimental settings defined for the SR tablets drug release studies were: (I) Rotating paddle apparatus (Erweka DT 70, Germany) at 37 °C and rotational speed of 50 rpm, using 500 ml of USP PB pH 7.2 and blank FaSSIF as dissolution media. The experiment was run for 12 h. (II) Reciprocating cylinder apparatus (BioDis, Varian Inc., USA), operating at 20 dpm, using the following media change pattern: 15 min blank FaSSGF (Fasted State Simulated Gastric Fluid), 3 h blank FaSSIF and 21 h SCoF (Simulated Colon Fluid), with a vessel change after 15 min and then at 1 h time intervals up to 24 h. (III) Flow-through system with glass bead dissolution device, using the following experimental setup: 50 g of glass beads, stirring rate of 50 rpm, and flow rate of 2 ml/min (Klein et al., 2013). Same media change pattern was used as with the reciprocating cylinder apparatus, and the experiment was run for 8 h: 15 min blank FaSSGF, 3 h blank FaSSIF and 5 h SCoF. The experiments were conducted at least in triplicate. Dissolution media samples were filtered through a 0.45 lm PVDF filter (25 mm GD/X, Whatman) into the test tubes and assayed for ibuprofen UV spectrophotometrically (Evolution 300 spectrophotometer, Thermo Fisher Scientific, Madison, Wisconsin) at 265 nm, or by high-performance liquid chromatography (HPLC Varian ProStar 330; column X Bridge C18 4.6 mm 50 mm; mobile phase acetonitrile: phosphate buffer pH 3.0 (30: 70 m/m) with flow rate 1.0 ml/min; injection volume was 10 lL; detection wavelength 265 nm). 2.4. Gastrointestinal simulation Mechanistic gastrointestinal simulation was performed using the commercially available software SimcypÒ Population-Based Simulator (version 13.2; Certara™, USA). The required input parameters related to ibuprofen physicochemical, biopharmaceutical and pharmacokinetic properties were taken from the literature and/or in silico estimated. Summary of the input parameters used is given in Table 1. Advanced dissolution, absorption and metabolism (ADAM) model was used as absorption model in the simulations. This model considers nine anatomically defined segments of the GI tract. Drug absorption from each segment is described as a function of release from the dosage form, dissolution, precipitation, luminal degradation, permeability, metabolism, transport, and transit from one segment to another. It is assumed that absorption from stomach is negligible, that movements of the solid and liquid drugs through each segment of the GI tract may be described by first-order kinetics, and that metabolism in the colon is negligible. Full PBPK perfusion-limited model that uses a number of time-based differential equations in order to simulate the concentrations in various organ compartments was used as distribution model. Simulations were performed for population representative subject of the Sim-Healthy Volunteers population. Automated Sensitivity Analysis (ASA) was used to assess the influence of the selected input parameters on the predicted percent of drug absorbed (Fa), peak plasma drug concentration (Cmax) and area under the concentration-time curve (AUC0t). Two SimcypÒ options were considered for developing a model for SR
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
S. Beloica et al. / European Journal of Pharmaceutical Sciences xxx (2015) xxx–xxx Table 1 Summary of ibuprofen input parameter values employed for gastrointestinal absorption simulation. Parameter
IR model
IR adjusted model
Molecular weight logPo:w Compound type pKa Blood-to-Plasma partition ratio Fraction unbound in plasma
206.3 3.68a Monoprotic acid 4.5b 0.55c 0.01d
Absorption ADAM model Human effective permeability, Peff,man (cm/s) Solubility-pH profile (mg/ml)
3.52 104e
Mean colon transit time (h) Maximum supersaturation ratio Precipitation rate constant (h1) Particle size (lm) Particle density (g/ml)
0.038 0.043 0.084 0.685 3.370 3.340
at at at at at at
pH pH pH pH pH pH
12g
1.0f 3.0f 4.5f 5.5f 6.8f 7.4f 15.8h
10g 4g 10g 1.2g
Distribution Full PBPK model, method 2 Vss (L/kg)
0.1i
Elimination CLpo (L/h)
2.9j
a
c d e f g h i j
1.8h
Literature value taken from Kasim et al. (2004). Literature value taken from Higgins et al. (2001). Literature value taken from Obach (1999). Literature value taken from Brocks and Jamali (1999). See text. Literature values taken from Potthast et al. (2005). Default Simcyp value. Estimated value using SimcypÒ Parameter Estimation option. Value calculated by Simcyp via Full PBPK distribution model. Literature value taken from Lockwood et al. (1983).
tablets. Validation of the developed models was performed based on the mean plasma concentration profile observed for ibuprofen IR tablets in the in vivo study reported by Källström et al. (1988) and ibuprofen SR tablets in the in vivo study reported by Pargal et al. (1996). The relevant percent prediction error (PE%) values between the in vivo observed and in silico predicted pharmacokinetic parameters were calculated as follows:
PE% ¼
absorption is documented by the relatively low tmax values, usually in the range of 1–3 h and relatively high absorption rate constants with the values reported being around 1.5 h1 (Ding et al., 2007; Källström et al., 1988; Müller et al., 1986; Neuvonen, 1991). When compared with IR tablets, SR preparations exhibit approximately 3–6 times lower peak plasma drug concentrations (Cmax) and notably delayed tmax (Averginos et al., 1991; Berardi et al., 1988; Parr et al., 1987; Regazzi et al., 1986). Parr et al. (1987) used external c scintigraphy to monitor gastrointestinal transit of radiolabelled SR tablets in fasted volunteers. The obtained results suggest that ibuprofen administered in the SR dosage form is absorbed throughout the entire GI tract and that the main site of drug absorption is colon (Parr et al., 1987). Plasma concentration time profiles of certain ibuprofen SR products are characterised by the appearance of double peak (Pargal et al., 1996; Wilson et al., 1989). Since the drug does not undergo enterohepatic recirculation, double peak phenomenon observed in these studies was ascribed to the loss of integrity of the dosage form, and high ibuprofen absorption in the colon (Wilson et al., 1989).
3.2. In vitro study
Trial design Population representative of the sim-healthy volunteers population Dose (mg) 600 800 Simulation time (h) 24 48 Fluid intake with dose (ml) 250 b
3
Dissolution profiles of the investigated ibuprofen IR tablets under various dissolution conditions are presented in Fig. 1. Drug dissolution in the rotating paddle apparatus was very rapid, with the plateau phase reached after 20 min. While complete dose dissolution was observed in the USP PB pH 7.2, cumulative amount of ibuprofen dissolved in blank FaSSIF (pH 6.5) was 80%, reflecting the difference in ibuprofen solubility at different pH. Ibuprofen dissolution in the glass bead device, using blank FaSSIF as dissolution media was markedly slower, leading to complete drug dissolution after 2 h of investigation. The apparent differences between dissolution profiles observed in the two apparatus reflect the differences in agitation intensity and resultant hydrodynamics. Glass beads grinding forces contributed to IR tablets disintegration, which, together with the exposure to fresh dissolution media resulted in the enhanced ibuprofen dissolution, although cumulative volume of dissolution media in the glass bead device was lower. Ibuprofen release profiles from the SR tablets obtained under various dissolution conditions are presented in Fig. 2. Drug release in the paddle apparatus using blank FaSSIF as dissolution media, was incomplete, with the cumulative amount of ibuprofen dissolved of approximately 8% and 17% after 8 h, for the products SR1 and SR2, respectively. Pronounced differences between drug release profiles for the products SR1 and SR2 were observed when USP PB pH 7.2 was employed. In this case, initial relatively slow drug release from the lipophilic matrix tablets (SR2) was followed with the abrupt release leading to complete ibuprofen dissolution
PKpredicted PKobserved 100 PKobserved
where PK denotes the relevant pharmacokinetic parameter (Cmax, time to reach peak concentration, tmax or AUC0t). In order to evaluate the effect of input dissolution profile extrapolation, a set of virtual dissolution profiles was generated based on the experimentally obtained data and used for GIS. 3. Results and discussion 3.1. In vivo data Ibuprofen absorption from IR tablets is, generally, described as well and rapid, with absolute bioavailability between 80% and 100% (Albert et al., 1984; Wagner et al., 1984). Rapid drug
Fig. 1. IR product in vitro dissolution profiles.
Please cite this article in press as: Beloica, S., et al. In vitro-in vivo-in silico approach in biopharmaceutical characterization of ibuprofen IR and SR tablets. Eur. J. Pharm. Sci. (2015), http://dx.doi.org/10.1016/j.ejps.2015.03.027
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products (data not shown). Certain loss of tablet integrity was noted for both SR products after five hours of investigation, which may explain slight deviation from the linear drug release profile observed at the corresponding time interval (Fig. 2b). Images of the investigated tablets after 5 h of study in the reciprocating cylinder apparatus are presented in Fig. 3. Such observation coincides with the reports of c scintigraphic studies where matrix tablet disruption upon reaching the colon was observed (Wilson et al., 1989). Drug dissolution in the glass bead device exhibited initial lag-time of up to 1 h, followed by, approximately, 25% of drug released after 8 h, from both the products investigated. The obtained results by all three methods indicate the importance of media volume available for dissolution of low solubility drugs, where complete dissolution could not be expected in physiologically based media, such as blank FaSSIF, in the experimental setting with defined media volume and non-sink conditions, as encountered in the rotating paddle apparatus. Dynamic dissolution devices provide alternate hydrodynamics, as well as the exposure to ‘fresh’ media providing the concentration difference as the driving force necessary for complete drug dissolution. Drug release profiles from the two SR products with proven bioequivalence were similar in the dynamic dissolution devices, confirming their advantages over the conventional paddle apparatus for bioperformance dissolution testing. 3.3. Gastrointestinal simulation
Fig. 2. SR products in vitro dissolution profiles in the rotating paddle apparatus (a) and dynamic dissolution devices (b); (SR1 – full lines; SR2 –dashed lines).
after 6 h. In the case of hydrophilic matrix tablets (SR1), drug release was slow and incomplete leading to total of approximately 13% dose dissolved irrespective of the increase in media pH (Fig. 2a). This could be explained by the slow hydration of xanthan gum (which was employed as hydrophilic matrix forming agent) which is independent of the media pH. The obtained results in the USP PB pH 7.2 indicate that employed dissolution method is overdiscriminative, since significant differences between two products were observed, although they have been proven bioequivalent in vivo. Drug release profiles observed in the reciprocating cylinder apparatus followed near-zero order kinetics, leading to more than 80% dose dissolved after 24 h for both the investigated
3.3.1. IR dosage form model development Development of GIS model requires that certain physicochemical and pharmacokinetic properties of the model drug substance are known. This makes ibuprofen a suitable model to be used for the ‘‘proof of concept’’, since it is widely investigated and well characterised in the literature. Ibuprofen is high permeability, low solubility drug characterized by pH dependent solubility profile in the physiological pH range. pH solubility profile reported by Potthast et al. (2005) has been employed in this study. Human effective permeability (Peff,man) was calculated using the relationship proposed by Fagerholm et al. (1996), based on the rat permeability value obtained in the in situ rat gut perfusion model (Levis et al., 2003). The resulting value of 3.52 104 cm/s is consistent with the rapid and complete ibuprofen absorption reported by Davies (1998). Scintigraphic studies in humans indicate that ibuprofen absorption from SR products occurs throughout the GI tract (Parr et al., 1987), which is in accordance with its high permeability. The volume of distribution at steady state (Vss) obtained using the Full PBPK model, and the method reported by Rodgers and Rowland (2007), was 0.1 L/kg and is in accordance
Fig. 3. Images of the investigated SR products after 5 h in the reciprocating cylinder apparatus: (a) SR1; (b) SR2.
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with the values reported by Davies (1998). Blood-to-plasma partition ratio was adopted from the study of Obach (1999), and fraction unbound in plasma from the study of Brocks and Jamali (1999). Drug clearance value was adopted from the study of
Fig. 4. Ibuprofen plasma concentration profiles predicted using GIS model for the IR product.
Lockwood et al. (1983). Model validation was performed based on the plasma concentration profile observed for ibuprofen IR tablets in the in vivo study reported by Källström et al. (1988). The percent prediction error (PE%) values for the relevant pharmacokinetic parameters were 1.91, 9.52, and 8.73 for Cmax, tmax and AUC0t, respectively, indicating that the generated PBPK model gave good estimation of the ibuprofen plasma profile. In order to evaluate the impact of input drug dissolution profiles on GIS model predictions, in vitro dissolution profiles observed for the ibuprofen IR tablets were used as inputs. Relevant plasma concentration profiles are presented in Fig. 4. The obtained Cp t profiles were characterized with respect to pharmacokinetic parameters, Cmax, tmax and AUC0t. Relevant values as well as the corresponding percent prediction errors (PE%) are given in Table 2. The pharmacokinetic parameter values estimated based on the dissolution profile in the paddle apparatus and USP PB pH 7.2 were in the best accordance with those reported in vivo (Källström et al., 1988). Predicted percent of drug absorbed (Fa) was 100%, which is in accordance with the literature reports on high ibuprofen bioavailability after oral administration (Albert et al., 1984; Wagner et al., 1984). The obtained results indicate that very rapid dissolution, such as observed in the paddle apparatus using USP PB pH 7.2 as dissolution media might be considered as bioperformance test in the case of ibuprofen IR film tablets studied.
Table 2 Pharmacokinetic parameters predicted using PBPK models developed. Input profile
Pharmacokinetic parameter
Gastrointestinal simulation model IR – IR tablets Cmax (lg/ml) (PE%)
tmax (h) (PE%)
AUC0t (lg h/ml) (PE%)
Predicted based on solubility Rotating paddle, PB pH 7.2 Rotating paddle, blank FaSSIF Glass bead apparatus, blank FaSSIF
45.76 46.01 37.44 44.34
1.90 (9.52) 1.9 (9.52) 1.9 (9.52) 2.55 (17.64)
201.98 201.96 178.95 198.75
Observed in vivo
44.90
2.10
221.3
(1.91) (2.47) (19.92) (1.26)
(8.73) (8.73) (19.13) (10.19)
Gastrointestinal simulation model IR – SR tablets Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
– 41.50 (192.04) 3.30 (76.77) – 11.33 (20.26) 9.97 (29.83) 7.91 (44.33) 8.20 (42.29)
4.16 (72.52) – – 5.54 (63.40) 9.20 (39.23) 10.39 (31.37) – –
– 5.52 – – 5.52 4.32 6.24 6.24
12.24 – – 11.04 16.32 16.32 – –
85.61 (68.14) 255.01 (5.12) 32.65 (87.85) 95.13 (64.60) 165.21 (38.53) 165.38 (38.47) 139.79 (47.99) 146.17 (45.61)
Gastrointestinal simulation model IR adjusted – SR tablets Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
– 52.78 (271.42) 4.08 (71.28) – 15.11 (6.33) 13.61 (4.22) 11.02 (22.44) –
5.93 (60.83) – – 8.36 (44.78) 14.54 (3.96) 13.09 (13.54) – 11.45 (24.37)
– 6.00 (0.84) 3.12 (47.56) – 6.00 (0.84) 6.00 (0.84) 7.2 (21.00) –
12.72 – – 11.28 16.80 18.00 – 21.75
158.92 (40.87) 420.35 (56.38) 52.9 (80.31) 167.56 (37.66) 291.59 (8.48) 288.68 (7.39) 250.98 (6.62) 262.26 (2.42)
Cmax1 (lg/ml) (PE%)
Cmax2 (lg/ml) (PE%)
tmax1 (h) (PE%)
tmax2 (h) (PE%)
AUC0t (lg h/ml) (PE%)
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
– 28.19 (98.38) 4.17 (70.65) – 14.23 (0.14) 13.39 (5.77) 10.9 (23.29) –
5.93 (60.83) – – 8.37 (44.71) 14.54 (3.96) 13.15 (13.14) – 11.41 (24.63)
– 4.32 (27.39) 3.12 (47.56) – 6.24 (4.87) 6.00 (0.84) 7.2 (17.36) –
12.72 – – 11.28 16.80 16.56 – 19.68
100.17 (62.73) 364.89 (35.75) 48.16 (82.08) 123.67 (53.99) 302.26 (12.45) 292.11 (8.67) 218.58 (18.68) 245.75 (8.57)
Observed in vivo
14.21
15.14
5.95
17.38
Rotating paddle, blank FaSSIF, SR1 Rotating paddle, blank FaSSIF, SR2 Rotating paddle, PB pH 7.2, SR1 Rotating paddle, PB pH 7.2, SR2 Reciprocating cylinder, media change, SR1 Reciprocating cylinder, media change, SR2 Glass bead apparatus, media change, SR1 Glass bead apparatus, media change, SR2
(7.22)
(7.22) (27.39) (4.87) (4.87)
(29.57)
(36.47) (6.09) (6.09)
(26.81)
(35.09) (3.33) (3.56) (25.14)
Gastrointestinal simulation model SR – SR tablets (26.81)
(35.09) (3.33) (4.71) (13.23)
268.79
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3.3.2. Automated sensitivity analysis ASA was used to assess the influence of: (1) effective drug permeability, (2) blood-to-plasma partition ratio, (3) fraction unbound in plasma, (4) drug solubility and (5) drug clearance on the predicted percent of drug absorbed, Cmax and AUC0t. According to the default SimcypÒ settings, the selected input parameters values were varied in the range covering one tenth to 10-fold actual input value used for gastrointestinal model development. The obtained results are presented in Fig. 5. It was found that the percent of drug absorbed was rather insensitive to variation of all the selected input parameters, except when the value of effective drug permeability or drug solubility was one third or less of the input value, when a decrease in the percent drug absorbed is to be expected. Cmax was affected by the variation of all the input parameters studied, particularly change in drug clearance value. ASA showed that AUC0t is also mostly affected by variations in drug clearance value.
simulation time prolonged to 48 h. As already mentioned, SimcypÒ requires drug release profile to be entered as input in order to predict Cp t profile for sustained release formulation. Experimentally obtained drug release profiles presented in Fig. 2 were used for gastrointestinal simulation. The resultant Cp t profiles are shown in Fig. 6a. The majority of estimated profiles (except the Cp t profile based on the SR2 product dissolution in the paddle apparatus using USP PB pH 7.2 as dissolution media) exhibited lower Cmax and AUC0t values when compared to the average Cp t profile observed in the in vivo study by Pargal et al. (1996). The discrepancy between the estimated and actual in vivo data is, also, evident from the relevant percent prediction
3.3.3. SR dosage form model development The GIS model developed for ibuprofen IR tablets was subsequently employed to estimate Cp t profiles for the SR products. In accordance with the design of the actual in vivo study (Pargal et al., 1996), ibuprofen dose was adjusted to 800 mg, and
Fig. 5. Automated Sensitivity Analysis (ASA): dependence of (a) Fa, (b) Cmax and (c) AUC0t on different input parameters (the center of x-axis for each of the parameters tested represents the value that was used in model development.
Fig. 6. Ibuprofen plasma concentration profiles predicted for the SR products using (a) IR model; (b) IR adjusted model and (c) SR model; (SR1 – black lines; SR2 – gray lines).
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error values presented in Table 2. Taking into account the results of ASA performed, SimcypÒ Parameter Estimation option was employed to optimize drug clearance value. The SimcypÒ Parameter Estimation option allows the estimation of parameter values in order to fit a selected model to the observed data by minimising the value of the errors for each observation. This is achieved by defining an objective function which is a measure of the overall difference between the predicted model outcome and the observed data. Each algorithm starts with an initial set of parameters which are used to generate predicted outcomes, which are then compared with observed data using the objective function. The initial parameter values are changed iteratively to determine parameter values which optimise the value of the objective function. The final parameter estimates obtained give predicted model values that overall better represent the observed data. The objective function used for all estimations is Weighted least square and Nelder–Mead method as minimisation method, which are default Simcyp options. The Nelder–Mead is a powerful classical optimisation method, suitable for estimating many parameters. This method is local optimisation method which is sensitive to the initial parameter value and boundary values and therefore this values should be assigned carefully (SimcypÒ Simulator, 2014). The estimated value of 1.8 L/h is in the range of values reported in the literature (Davies, 1998; Garsía-Martin et al., 2004; Geisslinger et al., 1989), and was adopted for model development. Predicted Fa values were lower when compared to the in vivo observed (data not shown). Taking into account that the largest percent of ibuprofen from SR formulations is absorbed in the colon (Parr et al., 1987), colon transit time was recognized as parameter that may affect ibuprofen absorption. This parameter was also estimated using the SimcypÒ Parameter Estimation option and the resulting value of 15.8 h which is in accordance with the value observed by Ghoshal et al. (2012), was adopted for model development. The estimated Cp t profiles using the IR model with adjusted values for drug clearance and colon transit time (IR adjusted model) are shown in Fig. 6b. Relevant pharmacokinetic parameter values, as well as the corresponding percent prediction error values (PE%) are given in Table 2. The percent prediction error values for the investigated pharmacokinetic parameters varied from 0.84% to 271.42%. The best estimation was obtained based on the input dissolution profile observed in the reciprocating cylinder apparatus using biorepresentative media change. In order to evaluate the applicability of SimcypÒ Controlled/ Modified (CR/MR) Release option intended for gastrointestinal simulation of relevant dosage forms bioperformance, experimentally obtained dissolution profiles were employed as the requested inputs for Cp t profile simulation. The obtained results are presented in Fig. 6c and Table 2. It can be noted that the values of Cmax and AUC0t obtained using SimcypÒ Controlled/Modified (CR/MR) Release option were somewhat lower when compared to the values obtained using IR adjusted model. The underlying reason is that, in SimcypÒ Simulator, the profile entered under the CR/MR option is considered as percent of total mass released from a formulation but not dissolved (SimcypÒ Simulator, 2014), and this two profiles are different for low solubility drugs, such as ibuprofen. Therefore, the use of IR option may be considered as more appropriate mode of operation in the case of ibuprofen, as well as other low solubility drugs. SimcypÒ generated cumulative fractions of ibuprofen absorbed over time for IR and SR tablets are shown in Fig. 7a. It can be noted that the two profiles differ significantly. In the case of IR tablets, cumulative fraction of ibuprofen absorbed over time profile reflects relatively fast absorption, which is complete after approximately 3 h. Such results are in accordance with complete ibuprofen absorption from IR tablets which predominately occurs in the proximal part of the small intestine. Ibuprofen absorption from
7
Fig. 7. Cumulative fractions of ibuprofen absorbed (a) and regional ibuprofen absorption distribution (b) estimated by GIS.
the SR tablets is significantly slower, with a plateau phase reached after, approximately, 20 h and the total amount of ibuprofen absorbed estimated to be 65% of the dose administered. Regional absorption distribution indicates that majority of ibuprofen from the IR product is absorbed in jejunum (64% of the total amount of dose absorbed, Fig. 7b), which is in accordance with the data reported in the literature (Gura, 2012). Maximum absorption of ibuprofen from the SR products occurs in colon (74% of the total amount of dose absorbed), which is in agreement with the findings of Parr et al. (1987). Such data indicate that the GIS model developed demonstrates generalisation ability to predict bioperformance of various ibuprofen products with different drug release rates. 3.3.4. Effect of input profile extrapolation In the case of incomplete drug dissolution, linear extrapolation is performed automatically in the SimcypÒ Simulator. One of the drawbacks of linear extrapolation is that the resulting profile might be, practically unrealistic. To assess the impact of small deviations from the linearly extrapolated profiles on simulation outcomes, a set of virtual in vitro data was constructed based on the dissolution profile obtained in the glass bead apparatus using biorepresentative media change. Virtual in vitro dissolution profiles were generated to reflect linear extrapolation of the amount of drug dissolved (profile a), and situations where certain disruption of the tablets and rise in the amount of ibuprofen dissolved occurs after 8 h (profile b), 10 h (profile c), 12 h (profile d) and 14 h (profile e) (Fig. 8a). The corresponding Cp t profiles estimated using the adjusted IR model are shown in Fig. 8b. It can be observed that the obtained profiles differ significantly, and that profile c is very close to the in vivo situation. This results indicate that data extrapolation might be misleading and that it is necessary to record the overall dissolution profile, until complete drug dissolution is achieved.
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Fig. 8. Virtual ibuprofen dissolution profiles (a) and the corresponding simulated in vivo data.
4. Conclusions The obtained results indicate that dynamic dissolution devices exhibit certain advantages over the compendial rotating paddle apparatus, which tended to be over-discriminative, indicating differences between the two ibuprofen SR formulations which were not observed in vivo. It was shown that GIS technology could be successfully used in estimation of oral ibuprofen absorption using ‘‘bottom up’’ approach. The developed model exhibited good generalisation ability, providing good estimations for the two dosage forms studied. The model was sensitive to the input kinetics represented by the in vitro profiles obtained under various experimental conditions. Best estimation of the actual in vivo profile was obtained based on the in vitro dissolution in the reciprocating cylinder apparatus with biorepresentative change in media pH and might be considered as bioperformance dissolution testing in the case of the two ibuprofen SR products studied. The results of this study justify the use of in vitro-in silico-in vivo approach in biopharmaceutical characterisation of oral solid dosage forms. Acknowledgement This work was performed under the Project TR-34007 supported by the Ministry of Education, Science and Technological Development, Republic of Serbia. References Agoram, B., Woltosz, W.S., Bolger, M.B., 2001. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Deliv. Rev. 50, S41–S67.
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