In vitro and in silico characterisation of Tacrolimus released under biorelevant conditions

International Journal of Pharmaceutics 515 (2016) 271–280

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International Journal of Pharmaceutics journal homepage: www.elsevier.com/locate/ijpharm

In vitro and in silico characterisation of Tacrolimus released under biorelevant conditions$ A. Mercuria,1, S. Wua , S. Stranzingera , S. Mohra , S. Salar-Behzadia , M. Bresciania , E. Fröhlichb,* a b

Research Center Pharmaceutical Engineering GmbH, Graz, 8010, Austria Center for Medical Research, Medical University of Graz, Graz, 8010, Austria

A R T I C L E I N F O

Article history: Received 18 August 2016 Received in revised form 6 October 2016 Accepted 8 October 2016 Available online 11 October 2016 Chemical compounds studied in this article: Tacrolimus (PubChem CID: 5282315) Keywords: Biorelevant dissolution PK modelling Tacrolimus Permeability Solubility

A B S T R A C T

This work aims to better understand the in vivo behaviour of modified release (MR) formulations (Envarsus1 tablets and Advagraf1 capsules) using in vitro properties of tacrolimus and in silico simulations. The in silico concentration profiles of tacrolimus released from the MR formulations were predicted after building a three compartments PK model with GastroPlusTM, and using the experimentally determined in vitro physico-chemical properties as input parameters. In vitro-in vivo correlations (IVIVC) were obtained after deconvolution of in vivo data from a clinical trial. The IVIVC showed that the in vitro dissolution was faster than the in vivo deconvoluted dissolution for Advagraf1, while the in vitro dissolution was slightly slower than the in vivo deconvoluted dissolution for Envarsus1. Population PK simulation showed that variability in the simulation was lower for Envarsus1 compared to Advagraf1. The in silico predicted preferential absorption sites were the proximal and distal tract for Advagraf1 and Envarsus1, respectively. The integration of experimental in vitro solubility, permeability and biorelevant dissolution data allowed to generate in silico tacrolimus concentrations for two different MR formulations. This permitted to compare the two formulations in a single PK profile, in a simulated population PK study and with respect to their absorption sites. ã 2016 Elsevier B.V. All rights reserved.

1. Introduction Tacrolimus is an immunosuppressive drug belonging to the macrolide lactone family. It possesses poor solubility in water, good permeability along the whole gastrointestinal (GI) tract (Tsunashima et al., 2014), a narrow therapeutic index (Antignac et al., 2007) and it has been classified as BCS class 2 (Biopharmaceutics Classification

System) (Tamura et al., 2002). Tacrolimus has a low oral bioavailability (Venkataramanan et al.,1995) as it is extensively metabolized by the gastrointestinal and hepatic cytochrome P450 (CYP)3A isoenzymes (mainly CYP3A5, with CYP3A4 having a lower catalytic efficiency) (Dai et al., 2006; de Jonge et al., 2012) and it is a substrate for the efflux transporter P-glycoprotein/multidrug resistance 1 (PgP/MDR1) (Hebert and Herbert, 1997).

Abbreviations: ACAT, advanced compartmental and transit; ADME, absorption, distribution, metabolism and excretion; ANOVA, analysis of variance; API, active pharmaceutical ingredient; BCS, biopharmaceutics classification system; CL, clearance; CR, controlled release; CYP, cytochrome P450; DMEM, Dulbecco’s modified eagle medium; FaSSCoF, fasted state simulated colonic fluid; FaSSGF, fasted state simulated gastric fluid; FaSSIF, fasted state simulated intestinal fluids; FPE, first pass effect; GI, gastrointestinal; HPMC, hydroxypropyl methylcellulose; HPLC, high pressure liquid chromatography; HPLC-ESI–MS, high pressure liquid chromatography electrospray ionization mass spectrometry; Ig, immunglobulin; IR, immediate release; i.v., intravenous; IVIVC, In vitro-in vivo correlations; MDR1, multidrug resistance 1; MEM, minimum essential medium; MR, modified release; MW, molecular weight; Papp, apparent permeability coefficient; PBS, phosphate-buffered saline; PgP, P-glycoprotein; PK, pharmacokinetic; PTFE, polytetrafluoroethylene; RT, room temperature; S.D., standard deviation; SIF, simulated intestinal fluid; SIR, single ion recording mode; TEER, transepithelial electrical resistance; USP, United States Pharmacopeia. $ Part of this work was presented at the 42nd Annual Meeting and Exposition of the Controlled Release Society 2015, Edinburgh, Scotland. * Corresponding author at: Stiftingtalstrasse 24, 8010 Graz, Austria. E-mail address: [email protected] (E. Fröhlich). 1 Current address: The Aptuit Center for Drug Discovery & Development, Verona, 37135, Italy. http://dx.doi.org/10.1016/j.ijpharm.2016.10.020 0378-5173/ã 2016 Elsevier B.V. All rights reserved.

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The narrow therapeutic index of tacrolimus implies that the concentration of the drug must be monitored over time, and the doses need to be carefully titrated so that the concentration is maintained within the optimal therapeutic range. In fact, if under dosed, rejection of the transplanted organ occurs, while higher doses are generally associated with nephrotoxicity and/or neurotoxicity (Venkataramanan et al., 1995). Tacrolimus is available as injectable, immediate release (IR) or modified release (MR) formulation. The MR formulations available in Europe are either capsules, containing MR pellets, or tablets, based on the MeltDose1 Technology Platform (Holm et al., 2007). The introduction in the clinics of MR formulations has lowered the burden of dosing in patients (twice daily for IR vs once daily for MR), which has been associated to increased patient compliance (Grinyó et al., 2014; Kuypers et al., 2013). However, the narrow therapeutic index of tacrolimus requires a strict control of the administered dose, which translate in the need of better understanding the relationship between in vitro properties and in vivo behaviour of these formulations. In the last years, in silico pharmacokinetic (PK) modeling has been increasingly used by the pharmaceutical industry for the prediction of the in vivo performance of oral dosage forms (Jones et al., 2009; Zhao et al., 2011). GastroPlusTM, Simcyp1 and PK-Sim1 software are the most common software commercially available (Jones et al., 2009; Zhao et al., 2011). By combining data from in vitro solubility, dissolution, permeability assay and clinical data, in silico modeling has shown to be a powerful tool to assess the in vivo dissolution, absorption and concentration profile of active pharmaceutical ingredients (API). Furthermore, using in vitro- in vivo- in silico correlations can be helpful to investigate and understand the underlying mechanism of dissolution and absorption (Kostewicz et al., 2014). The aim of this study was to simulate the in vivo behaviour of tacrolimus MR formulations based on in vitro physico-chemical properties such as solubility in biorelevant media, permeability across Caco-2 monolayers and dissolution profiles. In this way, the behaviour of tacrolimus from orally administered formulations can be better understood from a mechanistic perspective using a combination of in vitro investigations with in silico modelling. 2. Materials and methods 2.1. Materials Tacrolimus (>99%) was from Alfa Aesar GmbH & Co KG (Germany). SIF Powder Original, containing lecithin and bile salts, was from Biorelevant.com Ltd (UK). Sodium chloride, bovine serum albumin, palmitic acid, maleic acid, sodium hydroxide, sodium phosphate monobasic monohydrate, hydrochloric acid, and tris(hydroxymethyl)-aminomethane (Tris) were purchased from Sigma-Aldrich Chemie GmbH (Germany). Gastric porcine mucin and fluorescein were from Sigma-Aldrich Handels GmbH (Austria). Fetal bovine serum was from GE-Healthcare (Austria). Lglutamine and penicillin-streptomycin were from Therma Fisher Scientific (Austria). Sodium taurocholate was a kind gift from Prodotti Chimici e Alimentari S.p.A (Italy). Egg-lecithin (Lipoid E PCS, Phosphatidylcholine from egg) was from Lipoid GmbH (Germany). Envarsus1 4 mg (MeltDose1 tablets, Chiesi Farmaceutici S.p.A.) and Advagraf1 5 mg (capsules containing MR pellets, Astellas Pharma Europe B.V) were provided by Chiesi Farmaceutici S.p.A. Water was of Milli-Q grade. The different doses for Advagraf1 and Envarsus1 were chosen depending on the formulations available on the market and accounted for the differences in bioavailability between the two (Gabardi et al., 2013; Tremblay et al., 2016). All other reagents and chemicals were of

analytical grade and were used as received without further purification. 2.2. Media preparation Fasted State Simulated Gastric Fluid (FaSSGF) and Fasted State Simulated Intestinal Fluids (FaSSIF) were prepared using the SIF Original Powder. Fasted State Simulated Colonic Fluid (FaSSCoF) was prepared according to Vertzoni and co-authors (Vertzoni et al., 2010). All media were freshly prepared on the day of dissolution and degassed for at least 15 min prior to use via sonication, using an Elma Sonic S300H (Elma Schmidbauer GmbH, Germany). 2.3. Solubility studies An excess amount of tacrolimus API was placed into 1.5 mL Eppendorf Safe-Lock micro test tubes and 500 mL of water, FaSSGF and FaSSIF were added to each tube (n = 3). The solubility in FaSSCoF was not required for the simulation as Gastroplus only uses the solubility in the stomach (FaSSGF) and intestine (FaSSIF and FeSSIF). It was however measured, and since the results were found to be too variable, the results were not included in this work, as the source of the variability was not investigated into detail. Perhaps the cause can be traced to the fact that tacrolimus is highly bound to albumin (Trull et al., 2002), which is one of the ingredient in the FaSSCoF media. Blank samples without tacrolimus were also prepared. The tubes were placed in a shaker incubator at 37
C and the samples were mixed at 200 rpm for 24 h. Afterwards, the tubes were centrifuged at 15000 rpm and 37
C for 30 min. The supernatant was carefully aspirated using glass Pasteur pipettes and diluted 1:1 with mobile phase (acetonitrile containing 0.005% formic acid and purified water containing 0.005% formic acid 65:35 (v/v)) in HPLC vials. Samples were stored at 20
C. On the day of analysis, the samples were thawed at room temperature and the tacrolimus content was measured by HPLC. 2.4. Permeability studies Caco-2 cells from American Type Culture Collection (Rockville, USA) were cultured in Minimum Essential Medium (MEM), 20% fetal bovine serum, 2 mM L-glutamine and 1% penicillin-streptomycin at 37
C in humid air atmosphere containing 5% CO2 in 75 cm2 cell culture flasks. In the transport studies Caco-2 cells were seeded on transwell insert (0.4 mm pore size, translucent) on a 12-well plate (Greiner Bio-one1) at a density of 0.5  106 cells per insert. Cells were cultured with 500 mL medium in the upper compartment and 1500 mL in the lower compartment. The culture medium was changed every two or three days. Transepithelial electrical resistance (TEER) was measured with an EVOM STX-2-electrode (World Precision Instruments, Germany). When cell monolayers had reached a TEER value of >300 V cm2 (18–21 days) they were used for the experiments. Medium was removed from the apical compartment and cells coated with 100 mL of 40 mg/mL gastric porcine mucin in DMEM for 30 min according to the protocol used by Gork et al. (1999), who studied the influence of mucus on bacterial translocation across Caco-2 monolayers. In order to verify the presence of mucin on the cells, immunocytochemical detection was performed. The membranes were excised from the plastic insets and rinsed with phosphate-buffered saline (PBS), fixed with 4% paraformaldehyde for 20 min at room temperature (RT) and washed again. After blocking with 10% normal goat serum (Zymed Medical Product GmbH, Austria) for 30 min at RT, incubation with mouse antimucin 5AC ([45M1], Abcam, 1:200, UK) or normal mouse Immunglobulin G (IgG, DAKO diagnostic, Germany) for negative

A. Mercuri et al. / International Journal of Pharmaceutics 515 (2016) 271–280

controls was performed at 37
C for 60 min followed by goat Alexa Fluor 488-labelled anti-mouse IgG antibody (1:400, Life technologies, Vienna, Austria) again at 37
C for 60 min. Nuclei were counterstained with 1 mg/mL Hoechst 33342 (Life technologies, Austria) for 15 min at RT. Incubations were performed under light protection and between the antibody incubations the membranes were rinsed 3  5 min in PBS. Subsequently, all membranes were mounted in fluorescence mounting medium from DAKO and stored in the dark. Cells were viewed at a 510 LSM Meta (Zeiss, Austria) at excitation at 405 nm and detection with a BP 420–480 nm filter for the nuclear stain. Binding of the anti-mucin antibody was recorded at excitation at 488 nm and detection with a BP 505–550 nm filter. Presence of mucin according to the staining with anti-mucin 5AC antibody compared to negative control with mouse IgG under the same setting is seen in Supplementary material (Fig. 1s). Three 10 mg/mL suspensions of tacrolimus were prepared in FaSSIF using the API, Envarsus1 and Advagraf1. The suspensions for each of the formulations were prepared by dispersing previously grinded Envarsus1 tablets using a mortar and pestle, and pellets of Advagraf1 capsules. The suspensions were stirred for 30 min at 37
C prior to use. The respective solutions (500 mL) were applied to the upper (donor) compartment of the transwell and 1500 mL Krebs Ringer buffer added in the lower (acceptor) compartment. Tacrolimus API was used as standard. Plates with transwells were incubated upon agitation for a total of 180 min. 100 mL samples were taken from the lower compartment after 0, 30, 60, 90, 120 and 180 min and replaced by pre-warmed KrebsRinger buffer. At the beginning and at the end of the experiment, 10 mL of solution from the upper compartment were collected for calculation of the total amount of drug applied. TEER values were measured before and after the transport study to identify potential damage of the cell layer. Permeability of the permeability marker sodium fluorescein (10 mg/mL) in Krebs-Ringer buffer across nonmucus-coated Caco-2 cells was compared to sodium fluorescein in FaSSIF across non-mucus-coated and mucus-coated Caco-2 cells to verify absence of cell damage by FaSSIF. All samples were stored at 20
C pending analysis. On the day of analysis, the samples were thawed at room temperature, diluted with mobile phase and the content was measured by HPLC. The measured transport of tacrolimus was linear (Fig. 2s, supplementary material) with coefficient of determination R2 = 0.9996 for Mucin + Envarsus1, R2 = 0.9979 for Mucin + Advagraf1, R2 = 0.9977 for Mucin + Tacrolimus standard, and R2 = 0.9971 for Tacrolimus standard. The retrieved amount of the API from the basolateral compartment was accounted for according to the following equation. Cncorr ¼

½c1  V þ c2  V þ . . . . . . þ cn  V  þ Cn  V Vbas

ð1Þ

Where Cn is the concentration measured at time n (mg/mL), V the sampling volume (mL) and Vbas the volume of the complete basolateral compartment (mL). We determined the apparent permeability coefficient (Papp) between t = 30 and t = 180 using the following equation: Papp ¼

dQ dt  A  c

ð2Þ

where dQ/dt is the flux across the cell monolayer (ng/s), A is the surface of the monolayer (cm2) and C is the initial concentration in the donor compartment (ng/mL). 2.5. Dissolution studies Two types of dissolutions were performed. One investigated the dissolution behaviour of tacrolimus in single biorelevant media

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(standard biorelevant dissolution); the other dissolution consisted in investigating the effect of changing media and pH over time on the release of tacrolimus (biorelevant dynamic dissolution). 2.5.1. Standard biorelevant dissolution Standard biorelevant dissolution profiles (n = 3) of Envarsus1 4 mg and Advagraf1 5 mg were investigated on an USP dissolution apparatus II (paddle apparatus) (Dissolution tester DT826 LH, Automatic Sampling Station, Syringe Pump SP840, Fraction Collector FRL800, Erweka, Germany). Each probe of the automatic sampling station was equipped with PTFE intake liquid-filters (10 mm). Each dosage form was placed into a stainless steel sinker (Copley Scientific, UK). All dissolutions were performed at 37
C with a paddle speed of 50 rpm. For the standard biorelevant dissolution a total volume of 500 mL biorelevant media (FaSSGF, FaSSIF or FaSSCoF) was added. 2.5.2. Biorelevant dynamic dissolution The biorelevant dynamic dissolution was performed following the sequence of media and pH shown in Table 1. Briefly, the biorelevant dynamic dissolution started in 500 mL FaSSGF, to which after 2 h were added 100 mL of a concentrated pre-warmed (37
C) FaSSIF solution. The residence time of 2 h for the two formulations was chosen based on the residence time of pellets and tablets in the fasted stomach. Locatelli and co-workers (Locatelli et al., 2009) reported variations in emptying times between 15 min up to more than 3 h for pellets. The gastric residence time of non-disintegrating tablets can be more than 1 h in the fasted stomach (Söderlind and Dressman, 2010; Wilson et al., 2010). The pH was measured and increased to 6.2 using a solution of NaOH 1N. The amount of NaOH used was measured to calculate the dissolution volumes. After 4 h from the beginning of the dissolution the pH was raised to a value of 7, and after 5.5 h the media was changed to FaSSCoF. In order to recover any dispersed particle in the media, the dissolution media at the end of the 5.5 h was filtered through Whatman glass microfiber filters GF/F (GE Healthcare Life Science, UK), which retains particles above 0.7 mm, using a vacuum filtration unit. At the end of the filtration, the volume of the filtered media was measured to check the volumes for the dissolution calculations. The filter was then placed at the bottom of the vessel (with the side without particles facing the bottom of the vessel). To each vessel, 500 mL of pre-warmed (37
C) and degassed FaSSCoF were added and the dissolution was continued for another 18.5 h. At each predetermined time point, 1 mL of media was automatically withdrawn by the autosampler and placed in HPLC vials. The sampling time points were selected based on Clinical Trial LCP-Tacro 1017, Protocol 3479 (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012), and they were: 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, 14, 16, 20, and 24 h. As the dissolution proceeded, the vials were immediately frozen to prevent degradation of tacrolimus in the biorelevant media or cooling of the autosampler was done using ice pads. All samples were stored at 20
C pending analysis. On Table 1 Dissolution sequence used for the dynamic dissolution experiments. Dissolution sequence FaSSGF FaSSIF FaSSIF FaSSCoF Total

Stomach Duodenum + Jejunum 1&2 Ileum 1 & 2 & 3 Caecum + Asc Colon

pH

time (h)

1.6 6.2 7 7.8

2 2 1.5 18.5 24

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the day of analysis, samples were thawed at room temperature, diluted with mobile phase and the content was measured by HPLC. 2.6. HPLC analysis Quantification of tacrolimus was performed via HPLC electrospray ionization mass spectrometry (HPLC-ESI–MS) on a Waters Acquity H class UPLC system coupled with a single quadrupole mass spectrometer (Waters, Milford, Connecticut). A Nucleosil C18e column (125  4 mm, 3 mm, Marcherey Nagel, Düren, Germany) served as stationary phase, whereas mobile phase A was acetonitrile and mobile phase B was purified water, both containing 0.005% formic acid. The following gradient elution program was applied: 0.00–12.00 min: 65% A, flow rate 0.5 mL/ min; 12.01–17.00 min: 100% A, flow rate 1.0 mL/min; 17.01– 21.00 min: 65% A, flow rate 0.5 mL/min. Column temperature was set to 60
C and injection volume was 1 mL. MS detection was performed in positive ion mode applying a capillary voltage of 2.5 kV, a cone voltage of 45 V, a desolvation temperature of 450
C and a source temperature of 130
C. The sodium adduct ion of tacrolimus m/z 826.9 was detected in single ion recording mode (SIR) for quantitative analysis. The retention time of the tacrolimus peak was 12.1 min. 2.7. Data analysis 2.7.1. Dissolution data analysis Values of T63.2% (Gordon et al., 2014) were calculated after fitting the dissolution data with a Weibull equation:   tb y ¼ ymax 1  e a ð3Þ where ymax is the maximum amount of tacrolimus dissolved at the end of dissolution, y is the amount of tacrolimus dissolved at time t, b is a shape parameter and a is a scale parameter. 2.7.2. Deconvolution Numerical deconvolution was performed on the individual in vivo blood concentration versus time data of tacrolimus from 21 fasted healthy volunteers after single dose of Envarsus1 2 mg or 2 capsules of Advagraf1 1 mg, Clinical Trial LCP-Tacro 1017 (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). Briefly, 21 normal, healthy, non-smoking Caucasian male subjects between the ages of 18 and 50 years were enrolled. Blood samples were taken at 0 (pre-dose), 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 20 and 24 h and analysed via HPLC. These data were used as input response. Numerical deconvolution was performed on each single in vivo profile using PCDCON (Gillespie, 1992) by using published oral solution data from (Möller et al., 1999) as impulse response.

Numerical deconvolution was preferred to compartmental deconvolution because the latter failed to provide a good fitting (when performing convolution) of the in vivo blood profiles when used in the simulation, while the former well simulated the shape and Tmax of the curve (data not shown). Both impulse and input responses were fitted via interpolating cubic spline prior to perform the deconvolution. In vitro-in vivo correlations (IVIVC) were obtained using the in vitro dissolution and the in vivo dissolved data obtained from the numerical deconvolution. When necessary, in vitro and deconvoluted data points were calculated using the linear interpolation method. 2.7.3. Statistics Data from  three independent experiments were subjected to statistical analysis. These values are represented as means  S.D. and have been analyzed with a one-way analysis of variance (ANOVA), followed by a Tukey-HSD post hoc test for multiple comparisons (IBM SPSS statistics 19 software). The results with pvalues of less than 0.05 were considered to be statistically significant. 2.8. Simulation Tacrolimus (MW 804.02; pKa 2.94; LogP 3.26 (“Tacrolimus CID 445643”) intravenous profile obtained from the literature (Möller et al., 1999) was fitted in GastroplusTM using the PK PlusTM module (Simulation Plus, Inc.) using a three compartment PK model (Åsberg et al., 2013), to calculate the PK parameters and pharnacokinetic constants (k12, k21, k13, k31) (R2 = 0.9922). The model demographic and pharmacokinetic data are shown in Table 2. A Scheme of the Advanced Compartmental And Transit (ACAT) model considering the three compartments can be seen in the supplementary material (Fig. 3s). Dose and body weight used in the PK constant calculation were kept the same as in the clinical study obtained from the literature (Möller et al., 1999). Clearance (CL) and first pass effect% (FPE%) were obtained from literature (Astellas Pharma US, 2013; Thummel et al., 1997). The input parameters used to build the (ACAT) model in Gastroplus were: the standard dissolution profiles in biorelevant media (FaSSGF, FaSSIF and FaSSCoF) and the dynamic biorelevant dissolution profiles; the Caco-2 permeability of tacrolimus from the formulations; and the solubility of tacrolimus in FaSSGF and FaSSIF. The PK profiles were compared to oral in vivo data obtained from (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). For the prediction of Envarsus1 and Advagraf1 in vivo profiles, the in vitro profiles from standard biorelevant dissolutions and in dynamic biorelevant dissolution were used as input data. The convoluted in vitro dissolution profiles of Envarsus1 and Advagraf1, were used as input for the simulation to verify the predictability of the IVIVC. From the simulations, the preferential

Table 2 Demographic and pharmacokinetic data for the simulation of Advagraf1 and Envarsus1. CR: controlled release; FPE%: first pass effect% calculated from (Thummel et al., 1997); CL: clearance (Astellas Pharma US, 2013). Formulation Simulation Literature formulation Weight (kg)

i.v. Envarsus1 Advagraf1

i.v. CR tablet CR dispersed

77.5 (Möller et al., 1999) 86.6 (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012)

Calculated Liver FPE%

Systemic CL (L/h/ Kg)

k12 (h1)

Dose (mg)

Intestinal FPE%

1.55 (Möller et al., 1999) 2.0 (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012)

0.040 – 63.8 0.7200 0.0760 0.3586 0.6593 (Thummel (Astellas 14.2 (Thummel et al., 1997) Pharma US, 2013) et al., 1997)

k21 (h1)

k13 (h1)

k31 (h1)

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sites of absorption from the two formulations were evaluated. A population PK simulation was performed with a sample size of 21 subjects. The physiological parameters for each individual were randomly generated by the GastroPlusTM software. 3. Results and discussion 3.1. Solubility studies The solubility of tacrolimus in biorelevant media seemed not to be so different from the solubility in water, for which values ranging from 4 to 12 mg/mL have been reported (Patel et al., 2012), Fig. 1. The solubility results showed that tacrolimus solubility was not affected by the media composition (presence and concentration of bile salts and lecithin), and it remained quite low across the whole spectra of biorelevant media used in this study. 3.2. Permeability studies Fig. 2 shows the permeability of tacrolimus as API and from Advagraf1 and Envarsus1 formulations. As it can be seen for the API, the presence of mucin is not affecting its permeability. Papp values for fluorescein were slightly lower in mucin coated than in non-mucin coated Caco-2 cells (0.48  0.08  106 cm/s vs. 0.72  0.08 106 cm/s). This indicates that similar to other permeation models (McGill and Smyth, 2010) mucus represented a barrier for the diffusion of fluorescein. The mean values of Papp were higher from formulated products than for standard tacrolimus (2.36  0.56  106 cm/s and 2.51  0.88  106 cm/s for tacrolimus standard without and with mucin, respectively), and that of tacrolimus from Advagraf1 (6.58  1.31 106 cm/s) was found to be higher than that from Envarsus1 (3.23  0.16  106 cm/s). Papp values of Advagraf1 were significantly higher than for tacrolimus API and Envarsus1. Permeability of tacrolimus API across Caco-2 monolayers determined in this study was in the same range as reported by Tamura et al. (Tamura et al., 2003) and Qin et al. (Qin et al., 2010), who reported Papp values of 1.8  106 cm/s and 3.1 106 cm/s, respectively. It cannot be excluded that excipients lead to loosening of the tight junctions, but the small, and from all formulations similar differences of TEER values before and after the transport studies make disruption of the cell monolayer unlikely. Chemical nature and amount of excipients in the formulations are more likely to cause the increased uptake. For example, surfactants such as Cremophor EL, D-alpha-tocopheryl polyethylene glycol 1000 succinate, and polyethoxylated pharmaceutical excipients are known inhibitors of PgP (Borchardt, 2010). Transport across Caco-2

Fig. 1. Solubility values of Tacrolimus API in water, Fasted State Simulated Gastric Fluids (FaSSGF), and Fasted State Simulated Intestinal Fluids (FaSSIF). Bars represent the standard deviation (n = 3).

Fig. 2. Permeability of tacrolimus in Caco-2 cells without and with mucin coating, and permeability of tacrolimus from Advagraf1 and Envarsus1 in Caco-2 cells with mucin. Bars represent the standard deviation (n = 3).

monolayers showed a linear transport for both formulations (Fig. 2s, Supplementary material) and it is possible that a delayed release from the formulation overlapped with an increased permeability due to the excipients. 3.3. Dissolution studies 3.3.1. Standard biorelevant dissolution studies Dissolution studies in all three standard biorelevant media (FaSSGF, FaSSIF and FaSSCoF) were performed for Envarsus1 4 mg and Advagraf1 5 mg, as shown in Fig. 3A and B. The impact of different technologies used to control the release of tacrolimus from the two formulations is visible from Fig. 3, with the pellet formulation releasing tacrolimus earlier than the MeltDose tablet formulation. For both formulations the dissolution profiles had a dissolution trend FaSSIF > FaSSCoF > FaSSGF. In FaSSGF, both Advagraf1 and Envarsus1 showed a plateau after circa 8 and 16 h, probably due to the low stability of tacrolimus at this pH (Gordon et al., 2014); in this case the maximum amount released in FaSSGF was about 35% for Advagraf1 and 40% for Envarsus1, and no T63.2% could be calculated. In Advagraf1 the T63.2% values were found to be 5.26 h for FaSSIF and 8.50 h for FaSSCoF. In the case of Envarsus1 the dissolution profiles in FaSSIF and FaSSCoF were comparable, and the T63.2% values were found to be 17.19 and 19.67 h, respectively. 3.3.2. Dynamic biorelevant dissolution studies In Fig. 3A and B are also shown the dissolution profiles of Advagraf1 and Envarsus1 under dynamic biorelevant conditions. As it is possible to see, the dynamic biorelevant dissolution profile for Advagraf1 was similar to that observed in FaSSIF and FaSSCoF, with a T63.2% of 5.69 h (calculated from interpolation). In the case of Envarsus1 a T63.2% of 22.47 h was calculated, and the dissolution profile under dynamic biorelevant conditions was found to be linear over 24 h. The dynamic dissolution profile of Advagraf1 reached a plateau (Fig. 3A), once the formulation was brought into contact with the colonic fluid. When FaSSIF was added to the Advagraf1 formulation dispersed in FaSSGF, the addition of the intestinal media did not induce a higher dissolution compared to the standard FaSSIF, and the dissolution curve under dynamic biorelevant conditions was found to be comparable to that in the standard FaSSCoF. Comparing the dissolution profile of Envarsus1 under dynamic conditions with that in standard biorelevant media (Fig. 3B), it was observed that there was a slight increase in dissolution in the

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Fig. 4. Simulation of the blood concentration time profile of Tacrolimus after intravenous administration in healthy human. The continuous line (—) represents the simulated profile, while the squares (&) are in vivo mean values taken from the 4 h infusion obtained from the literature [13]. Bars represent the standard deviation.

especially up to 12 h, with the exception of that under dynamic conditions which better simulated the in vivo shape. The highest concentration over time was obtained with the FaSSIF, followed by the FaSSCoF. Using the dynamic dissolution profile gave a better simulation than using the dissolution in the single biorelevant

Fig. 3. Standard biorelevant dissolution profiles of (A) Advagraf1 5 mg and (B) Envarsus1 4 mg in (&) Fasted State Simulated Gastric Fluids (FaSSGF), (~) Fasted State Simulated Intestinal Fluids (FaSSIF), (^) Fasted State Simulated Colonic Fluids (FaSSCoF), and (&) dynamic biorelevant dissolution. Bars represent the standard deviation (n = 3); lines represent the fitting with the Weibull equation (Eq. (2)).

intestinal compartment when the pH was raised to 7, while the colonic release in the dynamic dissolution and the static dissolution were found to be comparable. The higher release during the intestinal step of the dynamic dissolution, compared to that in only FaSSIF, appears to be due to the previous contact of the tablet with the gastric media. 3.4. Simulation The intravenous data profile of tacrolimus in healthy subjects was obtained from the literature (Möller et al., 1999) and used to build a three-compartment PK model. The obtained simulation and the comparison of the in vivo data from the 4 h infusion obtained from the literature are shown in Fig. 4. The simulation of the blood concentration time profiles for Advagraf1 and Envarsus1 were performed using a dose of 2 mg, as for the clinical study (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). The simulated profiles for Advagraf1 after oral administration using the biorelevant dissolution profiles of the 5 mg formulation are shown in Fig. 5A. The dissolution profiles in biorelevant media produced higher simulated curves than the in vivo mean, as it can be seen in Fig. 5A. The dissolution profiles in FaSSIF, FaSSCoF, and under dynamic conditions, generated similar curves, with a lower concentration generated by the FaSSGF dissolution profile. Nevertheless, all curves were within the in vivo concentrations range, indicated as grey area in the figure. In Fig. 5B are shown the simulated profiles for Envarsus1 after oral administration. All the curves from the different biorelevant media gave a similar profile,

Fig. 5. Concentration time profiles of tacrolimus in healthy volunteers for (A) Advagraf1 and (B) Envarsus1. (&) in vivo mean; (——) simulation generated using the biorelevant dissolution in FaSSGF; (—  —) simulation generated using the biorelevant dissolution in FaSSIF; (—   —) simulation generated using the biorelevant dissolution in FaSSCoF; (—) simulation generated using the biorelevant dynamic dissolution. Shaded area represents the maximum and minimum in vivo data (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). Simulated dose was 2 mg. Dissolution profiles were for Advagraf1 5 mg and Envarsus1 4 mg.

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media, indicating that a MR tablet like Envarsus1 is more sensitive to media changes over time, which are reflective of the in vivo situation. This effect was not observed for the Advagraf1 formulation. One possible explanation is that since Advagraf1 is formed by controlled release pellets filled in a capsule, the release of the pellets from the capsule will occur predominantly in the stomach, from which they will be emptied in the intestinal tract, which is the designed site for absorption. Thus, the dissolution profile in the FaSSIF media appears to better represent the in vivo release of Advagraf1.

conditions for Envarsus1, since they were the dissolution profiles that better simulated tacrolimus in vivo concentration. In both cases a nonlinear relationship between the in vivo and the in vitro dissolution data was obtained. In the case of Advagraf1, the dissolution was found to be faster in vitro than in vivo, while in the case of Envarsus1 the dissolution was found to be faster in vivo. In both cases it was possible to fit the data with a power equation, which for Advagraf1 (R2 = 0.9194) was: y ¼ 2:3867x0:6551 and for Envarsus

3.5. IVIVC In Fig. 6A are shown the in vivo dissolved profiles obtained from the numerical deconvolution of in vivo data for Advagraf1 and Envarsus1 obtained from clinical trials (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). The in vivo dissolution profiles from Advagraf1 and Envarsus1 showed to be different, and similar in shape to those observed in vitro. Fig. 6B shows the IVIVC obtained for the two formulations using the dissolution profile in FaSSIF for Advagraf1 and the one under dynamic

277

y ¼ 3:2759x

ð4Þ 1

2

(R = 0.9916) was:

0:731

ð5Þ

It is likely that specific interactions are occurring between the dissolution media components and the tablet components. This is in line with previous observation on interactions between HPMC matrix tablets and biorelevant media (Franek et al., 2014). Furthermore, it is worth to note that in vivo the strong shear forces and low flow rate exerted by the gastrointestinal tract cannot be simulated with the current dissolution apparatuses. These two effects may be at the basis of the differences observed between the in vitro and in vivo dissolution profiles. 3.6. Convolution and simulation The biorelevant dissolution in FaSSIF and the dynamic dissolution profiles data obtained in vitro for Advagraf1 and Envarsus1 were transformed using Eq. (4) and (5), respectively. The transformed data were used as input for the simulation. For comparison also the in vivo dissolution data obtained from the deconvolution of the in vivo concentrations from the clinical trial (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012) were used as input parameter for the simulation. As it can be seen from Fig. 7A the simulations performed for Advagraf1 using the in vivo deconvoluted dissolution and the in vitro convoluted dissolution profiles were similar, although the latter produced a simulated curve closer to the in vivo mean. In the case of Envarsus1, Fig. 7B, similar results were obtained when using the in vivo deconvoluted dissolution and the in vitro convoluted dissolution profiles. The differences observed could be due to differences in gastrointestinal transit times and/or metabolism of tacrolimus from the subjects of the clinical studies used in this work, as well as permeability variations between subjects and along the GI tract. The mutual effect that the ADME processes have on the release process, play a major role especially in the case of poorly soluble drugs such as tacrolimus. 3.7. Population PK simulation

Fig. 6. (A) Mean in vivo amount dissolved profiles of (&) Advagraf1 and (^) Envarsus1 obtained after numerical deconvolution of the in vivo data from the clinical trial (EMA Committee for Medicinal Products for Human Use (CHMP), 2014; Gabardi et al., 2013; Grinyó et al., 2014; Nigro et al., 2012). The in vitro dissolution data used were those in FaSSIF for Advagraf and in the biorelevant dynamic dissolution for Envarsus1. Dose was 2 mg. Bars represent the standard deviation (n = 21). (B) In vitro-in vivo correlation (IVIVC) of the amount of tacrolimus dissolved in vitro vs the amount dissolved in vivo for (&) Advagraf1 and (^) Envarsus1. Bars represent the standard deviation (n = 3 in vitro and n = 21 in vivo).

The in vitro transformed dissolution profiles (FaSSIF for Advagraf1 and dynamic dissolution for Envarsus1) were used as input parameter to perform a population simulation. The PK profiles in the 21 simulated individual are shown in Fig. 8A and B. Both simulations were similar to the in vivo data and well approximated the in vivo data. The population simulation of Advagraf1 showed variations in Cmax ranging between 1.08 and 4.65 ng/mL (Fig. 8A), while the simulations of Envarsus1 generated variations in Cmax ranging between 0.73 and 2.75 ng/mL, Fig. 8B. The latter simulation showed lower fluctuations in the blood levels for Envarsus1 formulation compared to Advagraf1. Stability of tacrolimus blood levels is particularly important for the clinical outcome since not only too low levels but also fluctuations have been correlated to graft rejections (Sapir-Pichhadze et al., 2014). Population PK simulations are consistent with in vivo data in

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From the simulation results obtained with Gastroplus ACAT model using the in vivo % dissolved tacrolimus, it was possible to extrapolate the amount of tacrolimus absorbed along the GI tract, as shown in Fig. 9. In the case of Advagraf1 three main absorption sites were identified, and these were the proximal part of the jejunum (jejunum 1), the caecum and the ascendant colon (with amounts less than 5% of the administered dose absorbed in the other sites). On the contrary, the higher absorption sites for Envarsus1 were found to be two: the caecum and the ascendant colon. Envarsus1 showed a higher delivery in the ascendant colon, and the in silico observation was found to be in line with the results obtained from a scintigraphy study that observed the release of tacrolimus from Envarsus1 in the distal small bowel or the colon (Grinyó et al., 2014; Nigro et al., 2013). When comparing the total amount absorbed from the two formulations it was found that Envarsus1 (64.03% of the administered dose) showed overall higher absorption along the GI tract, compared to Advagraf1 (41.63% of the administered dose). This implies that the amount of tacrolimus absorbed from Envarsus1 was approximately 35% higher than that absorbed from Advagraf1, suggesting that the dose of tacrolimus can be reduced of 35% when administering Envarsus1. This finding correlates well with a recent study in kidney transplant patients converted from Prograf1 to either Envarsus1 or Advagraf1 (Tremblay et al., 2016). The work of Tremblay and co-workers has shown a reduction in the dose needed to maintain comparable exposure of approximately 36% from Advagraf1 to Envarsus1. The reduction of doses observed in

Fig. 7. Concentration time profiles of tacrolimus in healthy volunteers for (A) Advagraf1 and (B) Envarsus1: (&) in vivo mean; (—) simulation generated using the in vivo deconvoluted dissolution; (— —) simulation generated using the in vitro dissolution after convolution using the IVIVC equations. Shaded area represents the maximum and minimum in vivo data. Simulated dose was 2 mg.

showing that the extent of inter-individual variabilities was similar for Advagraf1 and Envarsus1. 3.8. Absorption sites Tacrolimus is well absorbed in both the small intestine and the colon (Tsunashima et al., 2014), and therefore any formulation able to deliver the drug mainly in these two sites is advantageous. This is due to the fact that tacrolimus is a substrate for the CYP3A enzymes and the efflux pump PgP. While CYP3A slightly increases from the duodenum to the jejunum and subsequently decreases in the ileum and the colon (Paine, 2010), PgP increases from the duodenum to the colon (Thörn et al., 2005). In particular, tacrolimus is metabolised by the CYP3A5 which is present in lower amounts in the ascending colon, compared to the descending and sigmoid colon (Bergheim et al., 2005). Therefore, while the expression of the main metabolizing enzymes, CYP3A4 and CYP3A5, decreases from duodenum to colon, the expression of the transporter PgP increases. Expression of PgP and CYP3 isoenzymes show pronounced inter-individual variations (Canaparo et al., 2007). It has been reported that typically for colon targeted delivery, the mean residence time lays around 15 h for tablets and 28 h for pellets (Abrahamsson et al., 1996). With this in mind, it is worth to consider that the dissolution of tacrolimus from Advagraf1 reached a plateau after 10 h of dissolution. At this time it can be assumed that the formulation has already released most of the API in the upper part of the gastrointestinal tract. On the contrary, the slower release observed from Envarsus1 guarantees that most of the drug is delivered at the colon, where the metabolism due to CYP3A5 is lower.

Fig. 8. Population simulations of the concentration time profile of tacrolimus in healthy volunteers for (A) (&) Advagraf1 and (B) (*) Envarsus1. Symbols represent the mean in vivo data, (—) individual simulations generated using the convoluted biorelevant dissolution in FaSSIF (Advagraf1) or the convoluted dynamic dissolution (Envarsus1). Shaded area represents the maximum and minimum in vivo data. Simulated dose was 2 mg.

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Fig. 9. In silico predicted absorption sites of tacrolimus from Advagraf1 and Envarsus1. Data were obtained from the simulations using the in vitro % dissolution profiles after applying the IVIVC equation (dashed lines in Fig. 7).

vivo further reinforce the reliability of the results of the simulation model. 4. Conclusions In this work it was found that the solubility of tacrolimus was not affected by the presence of lecithin and bile salts. Furthermore, while the permeability of tacrolimus in Caco-2 cells was not affected by the presence of mucus, excipients present in the formulation were found to possibly increase the permeability, as observed for tacrolimus released from Advagraf1. The dissolution profiles of the two formulations containing tacrolimus were tested under standard and dynamic biorelevant conditions and they showed different behaviour, in line with the type of release mechanism belonging to each specific technology (MR tablet and MR pellets filled capsule). The best dissolution model for the simulation of Advagraf1 in healthy subjects was the dissolution profile of the 5 mg formulation in FaSSIF. Through the simulation it was possible to define the best in vitro conditions that allowed the prediction of the in vivo behaviour of tacrolimus depending on the type of the applied drug delivery technology used. The simulations were further improved by transforming the in vitro dissolution profiles with the IVIVC equations. This allowed to perform a population PK simulation and to investigate the different absorption sites for tacrolimus released from the two formulations. In summary, the model of Advagraf1 in healthy subjects was optimized by using the biorelevant dissolution in FaSSIF of the 5 mg formulation and that of Envarsus1 using the dynamic dissolution profile of the 4 mg formulation. Based on this study it may be concluded that for tacrolimus formulations showing predominant absorption in the distal gastrointestinal tract, the simulation is improved when data obtained by biorelevant dynamic dissolution are used (especially when the formulations are MR tablets). Furthermore, less fluctuation of blood levels were observed for Envarsus1 compared to Advagraf1, as the former guarantees that most of the drug is released along the colonic tract, where the metabolism of CYP3A5 is lower. The use of experimental data from biorelevant dissolution and permeation allowed a realistic simulation of tacrolimus blood levels and degree of inter-individual variations in two different

formulations. This approach allowed the comparison between two different MR formulations. This was done by comparing the simulation of their PK profiles in a single healthy individual, a population PK and to the absorption sites. This type of approach can be used to further support the formulation design, especially for new drugs. Acknowledgments The authors would like to acknowledge Reingard Sattler for the solubility and dissolutions experiments, and Claudia Meindl for the permeability studies. The authors would like to thank Chiesi Farmaceutici S.p.A. (Italy) for providing the tacrolimus formulations, availability of clinical data and financial support for the project, and Prodotti Chimici e Alimentari SpA (Italy) for providing sodium taurocholate. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. ijpharm.2016.10.020. References Åsberg, A., Midtvedt, K., van Guilder, M., Størset, E., Bremer, S., Bergan, S., Jelliffe, R., Hartmann, A., Neely, M.N., 2013. Inclusion of CYP3A5 genotyping in a nonparametric population model improves dosing of tacrolimus early after transplantation. Transpl. Int. 26, 1198–1207. Abrahamsson, B., Alpsten, M., Jonsson, U.E., Lundberg, P.J., Sandberg, A., Sundgren, M., Svenheden, A., Tölli, J., 1996. Gastro-intestinal transit of a multiple-unit formulation (metoprolol CR/ZOK) and a non-disintegrating tablet with the emphasis on colon. Int. J. Pharm. 140, 229–235. Antignac, M., Barrou, B., Farinotti, R., Lechat, P., Urien, S., 2007. Population pharmacokinetics and bioavailability of tacrolimus in kidney transplant patients. Br. J. Clin. Pharmacol. 64, 750–757. Astellas Pharma US, I., 2013. Prograf prescribing informations in the U.S.: tacrolimus capsules, injection: 13H057-PRG-PI-WPI. Bergheim, I., Bode, C., Parlesak, A., 2005. Distribution of cytochrome P450 2C, 2E1, 3A4, and 3A5 in human colon mucosa. BMC Clin. Pharmacol. 5, 1–7. Borchardt, R.T., 2010. Absorption barriers in the rat intestinal mucosa. 3: Effects of polyethoxylated solubilizing agents on drug permeation and metabolism. Mudra DR1. J. Pharm. Sci. 99, 1016–1027. Canaparo, R., Finnström, N., Serpe, L., Nordmark, A., Muntoni, E., Eandi, M., Rane, A., Zara, G.P., 2007. Expression of CYP3A isoforms and P-glycoprotein in human stomach: jejunum and ileum. Clin. Exp. Pharmacol. Physiol. 34, 1138–1144.

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