Investigation of the Intra- and Interlaboratory Reproducibility of a Small

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Investigation of the intra- and inter-laboratory reproducibility of a small scale standardized supersaturation and precipitation method Jakob Plum, Cecilie M Madsen, Alexandra Teleki, Jan Bevernage, Claudia da Costa Mathews, Eva M Karlsson, Sara Carlert, René Holm, Thomas Muller, Wayne Matthews, Alice Sayers, Krista Ojala, Konstantin Tsinman, Ram Lingameaneni, Christel A.S. Bergström, Thomas Rades, and Anette Müllertz Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.7b00419 • Publication Date (Web): 18 Oct 2017 Downloaded from http://pubs.acs.org on October 22, 2017

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Molecular Pharmaceutics

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Investigation of the intra- and inter-laboratory

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reproducibility of a small scale standardized

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supersaturation and precipitation method

4

Jakob Plum1, Cecilie M Madsen1,2, Alexandra Teleki3, Jan Bevernage4, Claudia da Costa Mathews5,

5

Eva M Karlsson6, Sara Carlert7, Rene Holm8,9, Thomas Müller10, Wayne Matthews11, Alice Sayers11,

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Krista Ojala12, Konstantin Tsinsman13, Ram Lingamaneni13, Christel AS Bergström3, Thomas Rades1,

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Anette Müllertz1,14*

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1: Department of Pharmacy, University of Copenhagen, DK-2100 Copenhagen, Denmark

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2: Analytical Research and Development, H.Lundbeck A/S, Ottiliavej 9, Valby, Denmark

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3: Department of Pharmacy, Uppsala University, SE-751 23 Uppsala, Sweden

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4: Pharmaceutical Sciences, Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, 2340

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Beerse, Belgium

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5: Drug Product Design, Pharmaceutical Sciences, Pfizer Ltd., Sandwich, Kent CT13 9NJ, UK

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6: Pharmaceutical Technology & Development, AstraZeneca R&D, Mölndal, Sweden

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7: Pharmaceutical Sciences, AstraZeneca R&D, Mölndal, Sweden

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8: Biologics and Pharmaceutical Sciences, H.Lundbeck A/S, Ottiliavej 9, Valby, Denmark

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9: Present address; Drug Product Development, Janssen Pharmaceutica, Johnson & Johnson,

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Turnhoutseweg 30, 2340 Beerse, Belgium

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10: AbbVie Deutschland GmbH & Co. KG, Knollstraße, Ludwigshafen 67061, Germany

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11: Product Development, GlaxoSmithKline R&D, Stevenage, United Kingdom

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12: Orion Pharma, P.O. Box 65, 02101 Espoo, Finland

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13: Pion Inc. Billerica, MA, USA

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14: Bioneer:FARMA, University of Copenhagen, DK-2100 Copenhagen, Denmark

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* Correspondence to: Anette Müllertz

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Department of Pharmacy,

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University of Copenhagen,

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Universitetsparken 2

29

DK-2100 Copenhagen, Denmark

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Telephone: 0045 35336440; Fax: 0045 35336001.

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E-mail address: [email protected]

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Keywords:

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Supersaturation, precipitation, oral drug delivery, variability

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Graphical abstract

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Abstract

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The high number of poorly water soluble compounds in drug development has increased the need for

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enabling formulations to improve oral bioavailability. One frequently applied approach is to induce

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supersaturation at the absorptive site, e.g. the small intestine, increasing the amount of dissolved

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compound available for absorption. However, due to the stochastic nature of nucleation,

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supersaturating drug delivery systems may lead to inter- and intrapersonal variability. The ability to

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define a feasible range with respect to the supersaturation level is a crucial factor for a successful

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formulation. Therefore, an in vitro method is needed, from where the ability of a compound to

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supersaturate can be defined in a reproducible way. Hence, this study investigates the reproducibility of

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an in vitro small scale Standardized Supersaturation and Precipitation Method (SSPM). First an intra-

48

laboratory reproducibility study of felodipine was conducted, after which seven partners contributed

49

with data for three model compounds; aprepitant, felodipine, and fenofibrate, to determine the inter-

50

laboratory reproducibility of the SSPM. The first part of the SSPM determines the apparent degrees of

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supersaturation (aDS) to investigate for each compound. Each partner independently determined the Page 3 of 31 ACS Paragon Plus Environment


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maximum possible aDS and induced 100 %, 87.5 %, 75 % and 50 % of their determined maximum

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possible aDS in the SSPM. The concentration-time profile of the supersaturation and following

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precipitation was obtained in order to determine the induction time (tind) for detectable precipitation.

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The data showed that the absolute values of tind and aDS were not directly comparable between

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partners, however, upon linearization of the data a reproducible rank ordering of the three model

57

compounds was obtained based on the β-value, which was defined as the slope of the ln(tind) versus

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ln(aDS)-2 plot. Linear regression of this plot showed that aprepitant had the highest β-value, 15.1, while

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felodipine and fenofibrate had comparable β-values, 4.0 and 4.3, respectively. Of the five partners

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contributing with full data sets, 80 % could obtain the same rank order for the three model compounds

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using the SSPM (aprepitant > felodipine ≈ fenofibrate). The α-value is dependent on the experimental

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setup and can be used as a parameter to evaluate the uniformity of the data set. This study indicated that

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the SSPM was able to obtain the same rank order of the β-value between partners and, thus, that the

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SSPM may be used to classify compounds depending on their supersaturation propensity.

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Introduction

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Poor water solubility of a drug compound can result in poor oral bioavailability1. There are several

68

available formulation strategies to increase the dissolution rate and apparent solubility of the compound

69

in water, e.g. reduction in particle size2 or application of solubilizing excipients3, with the aim to

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enhance bioavailability. In the last two decades the use of supersaturation, e.g solid amorphous

71

dispersions as a drug delivery strategy, have achieved growing interest4-6. Supersaturation occurs when

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the concentration of a compound exceeds its equilibrium solubility, resulting in a thermodynamically

73

unstable system. The unstable nature of such systems eventually result in precipitation of the

74

compound and thereby a decrease of concentration towards the energetically favourable equilibrium

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solubility. A common way to describe a supersaturated system is by its apparent degree of

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supersaturation (aDS), defined as the measured concentration divided by the apparent solubility in the

77

medium4 (Eq. 1). a =

78 79 80

Eq. 1

Whether a compound is a candidate for a supersaturating drug delivery system depends on the supersaturation propensity and the dose of compound.

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The process of precipitation from a supersaturated system can be divided into nucleation and crystal

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growth. Nucleation requires energy for the formation of a stable nucleus at a critical size, which will

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continue to grow in size7. Crystal growth is the subsequent energy releasing process, incorporating

84

molecules into the energetic favorable crystal. This process will continue until the concentration of the

85

compound equals the equilibrium solubility of the compound in the given media. The induction time

86

(tind) is often defined as the appearance of the first nucleus and hence often assumed to be the reciprocal

87

of the nucleation rate.



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Several authors have previously used a linearization of the tind and the aDS, in order to describe the supersaturation propensity of different compounds, as described in Eq. 2.8, 9: ln =

+ " ln #

%$Eq. 2

Where the α-value reflects the tind at high aDS values, so that a high α-value means a long tind at high aDS levels. The β-value describes the gain in tind that is achieved when the aDS is lowered. A high βvalue will translate into a high increase in tind if the aDS is lowered. Supporting information #1 explains the concepts of α- and β-value further. Equation 2 enables a comparison of the supersaturation propensity of different compounds by linear plotting of ln(tind) vs ln(aDS)-2 and hence, to evaluate whether a supersaturating drug delivery system is a feasible formulation strategy for a specific compound. In the last decades, application of supersaturation to increase bioavailability of oral formulations has gained growing interest both in academic and industrial settings4-6,

10

. Supersaturation in the small

intestine can be obtained by several formulation strategies. Weak bases can form supersaturated solutions when they are transferred from a high solubility environment in the stomach to a low solubility environment in the small intestine. This has recently been demonstrated for the weak bases, posaconazole11 and indinavir12, in fasted humans. Several formulation strategies, such as lipid formulations13, salts14, cocrystals15, and solid dispersions16 may also create supersaturation. For posaconazole, supersaturation was recently demonstrated in humans after oral administration of an amorphous solid dispersion17. Fong et al.5 evaluated which type of compounds that may be suitable for supersaturating drug delivery strategies. The authors classified the compounds based on the pKa value. While this classification may be a good first indicator for a new chemical entity in the compound selection process, it may not contain all important elements. For instance, the weak bases dipyridamole


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and ketoconazole showed limited precipitation in the human small intestine18, whereas the weak base posaconazole precipitated to a higher degree11. Fong et al. categorize these compounds in the same class, so additional experimental data may be needed early in the drug development process to differentiate drug compounds according to their supersaturation propensity. Hence, a more complex data set would be needed to decide whether supersaturating drug delivery systems could be a viable formulation strategy for a new drug molecule. A general challenge with precipitation methods is the inherent stochastic nature of nucleation leading to high variability19. Another challenge are the differences between laboratory setups with respect to e.g. equipment5, 20, where factors such as small differences in geometry, hydrodynamics or temperature may affect the outcome19, but also with regards to selection of supersaturation levels investigated9. This makes it almost impossible to compare results from supersaturation studies carried out in different laboratories with different procedures, at different concentration, and in different apparatuses. Recently, a small scale Standardized Supersaturation and Precipitation Method (SSPM) was proposed9. The method provides a tool to assess supersaturation as a potential formulation strategy for a given compound and to rank compounds according to their supersaturation propensity using limited amount of material in a standardized way. The overall purpose of the SSPM is twofold. First, a standardized method ensures equal evaluation of a large data set of compounds, which may increase the probability of finding molecular descriptors important for supersaturation. The second purpose is to predict the in vivo situation, either directly or by using the data generated as an input to in silico modelling. However, in order to translate the data generated via the SSPM to such predictions, it is important to know the quality of the data. Therefore, this study investigates the intra- and inter-laboratory variation. It will provide a level of variation to expect when conducting the SSPM and thus qualifying the model to classify compounds based on their supersaturation propensity. Hence, the aim of the current study was Page 7 of 31 ACS Paragon Plus Environment


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to challenge the precision and reproducibility of the SSPM suggested by Palmelund et al.9 In a first step, the intra-laboratory variation for the model compound felodipine was investigated by assessing the day-to-day reproducibility of the SSPM and the obtained tind. In a second step, the reproducibility of the SSPM between laboratories was evaluated, by investigating three model compounds, i.e. aprepitant, felodipine, and fenofibrate, in seven different laboratories using the SSPM protocol. The partners include two university laboratories, a contract research organization and four laboratories in medium and large pharmaceutical companies.

Materials and Methods Materials Aprepitant (Merck Sharp Dome, Kenilworth, NJ, USA), felodipine (AstraZeneca, Mölndal, Sweden), and fenofibrate (Veloxis Pharmaceuticals, Hørsholm, DK) were donated by the respective companies. The same batch of compound was used for all inter-laboratory and intra-laboratory experiments. SIF powder original (Biorelevant.com, South Croydon, UK) was used for the preparation of fasted state simulated intestinal fluid (FaSSIF). The media were prepared following the instruction of the manufacturer. All other chemicals were of analytical grade or higher. Methods All experiments were conducted using a µDiss Profiler™ (Pion Inc., Billerica, MA) with in situ UV probes. The SSPM used to assess supersaturation propensity has previously been published9. In brief, to determine the maximum possible concentration, and hence the maximum possible DS, that does not immediately precipitate, aliquots of a stock with high concentration of the model compound in an organic solvent (e.g. methanol or dimethyl sulfoxide (DMSO)) were spiked into 10 mL of FaSSIF, preheated to 37°C. After each addition a UV spectrum was recorded. At aliquot addition nprecipitate,


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precipitation was immediately detected either by a baseline shift in the UV spectrum or visually in the vial. It was assumed that the maximum possible concentration, and thereby the maximum possible DS, corresponded to the concentration at aliquot nprecipitate-1. The protocol specified that tind of the maximum possible DS should be < 10 min. A concentration-time profile for 1 hour was measured for 100 % 87.5 %, 75 % and 50 % of the maximum possible DS, in order to determine tind as a function of DS. The background of organic solvent was kept constant for all concentration-time profiles. The model compound was always introduced in 200 µL of organic solvent into 10 mL of FaSSIF at 37°C. The concentration-time profile was measured using the 2nd derivative of the in situ UV measurements. To minimize the inter-run variation, the protocol prescribed an incomplete block design with 4 treatments, 4 blocks and a block size of 3. The experimental design and the protocol for the SSPM are further described in the Supporting Information #2. Intra-laboratory investigation of variation The first part of the intra-laboratory variation study investigated the day to day variation of the tind. Supersaturation of felodipine was induced at fixed aDS (2.8; 3.7; 5.0; 5.7) and the concentration-time profile was monitored for 1 hour. The fixed aDS values were based on aDS values obtained the first day the experiment was conducted. The fixed aDS experiment was conducted by the same person (person #1) on three different days (> 6 days between each repetition). The second part of the intralaboratory variation study was an execution of the SSPM protocol (Supporting Information #2) for felodipine by person #1 on three different days (> 6 days between each repetition), i.e. the DS was determined daily and the experiment conducted accordingly. A second person (person #2) who was unaware of the results obtained by person #1 also conducted the SSPM using the same piece of



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equipment. This part of the study enabled an assessment of the inter-person differences when collecting results using the same equipment in the same laboratory. Inter-laboratory investigations of variation The three model compounds, aprepitant, felodipine, and fenofibrate (see Table 1), were distributed amongst the partners, along with a protocol for the SSPM precipitation method (Supporting Information #2). The protocol specified the use of cross bar magnets set at 100 rpm, 10 mL FaSSIF at 37°C prepared from commercially available SIF powder. All partners used DMSO as organic solvent, except for felodipine for partner #2, where methanol was used. Each partner determined the maximum possible DS, as described above. In order to mimic the use of the method for an unknown compound in a drug development situation, the protocol specified to use approximately 100x the expected solubility in FaSSIF for the organic solvent stock, not a specific concentration. If the compound did not precipitate at the lowest aDS (50 % of maximum possible aDS) within 80 min, the protocol prescribes to use of 60 % of the maximum possible DS for the following runs. Partner #1 to #4 completed the experiment as described by the protocol. Partner #5 did not run the incomplete block design for aprepitant, and partner #6 did not comply with the incomplete block design for any of the three compounds. The protocol described six replicates, however, differences in the number of probes in the local µDiss Profiler™ system lead to 3-8 replicates per partner. For partner #1, the data for the interlaboratory study has previously been published9. Furthermore, the SSPM data sets for aprepitant and fenofibrate were excluded for partner #6 and partner #7, respectively, due to saturation of the UV spectrum during the measurement of the concentration-time profile. Table 2 gives an overview of the contribution of data from the partners as well as the compliance to the SSPM protocol.


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Table 1. Physico-chemical data and solubility of the model compounds in FaSSIF (37°C). Compound Aprepitant

Physico-chemical Properties Solubility (µg/mL) in FaSSIF MW: 534.4 18.6 ± 0.8 * * pKa: 2.4 (basic) / 9.15 (acidic) LogP: 4.8□ MW: 384.3 41.2 ± 0.1 Felodipine pKa: N/A logP: 4.46● MW: 360.9 14.9 ± 1.0 Fenofibrate pKa: N/A logP: 4.6# * Data from Sjögren21, □data from Takano et al.22 and Wu et al.23, ●data from der Lee et al.24, #data from Law et al.25, all solubility data from Palmelund et al.9

Data analysis Data handling was carried out in Excel, and visualization and regression analysis were performed in GraphPad Prism version 7.0 (GraphPad software Inc. Ca, USA). All data analysis was performed at the University of Copenhagen, Denmark. As described above, the induction time is the time to appearance of the first nucleus. In order to apply this in a UV-spectroscopic method, tind has been defined as the time point where the supersaturated concentration has decreased by 2.5 %, in accordance with Palmelund et al.9: ≈ #'()* ) + :-..0%

Eq.4

#'()* ) + ,:-..0% = 97.5% ∙ 7#'*)* ) + − #'*+9):9 ; < + #'*+9):9 ; Eq. 5 The combined linear regression was done separately on all included data. The coefficient of variation (cv) was calculated using the equation (?

=> = @ ∙ 100%

Eq. 6



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where SD is the standard deviation of the mean. The cv of the α- and β-values refers to the cv of the individually determined α- and β-values for each partner. All calculations of DS were based on the solubility in Table 1. All data is presented as mean ± SD, unless otherwise stated. Table 2 Overview of data contribution from the partners

Partner

1

2

3

4

5

6

7

Max DS

+

+

+

+

+

+

+

Aprepitant

+

+

+

+

+

NO*

+

Felodipine

+

+¤

+

+

+

+

+

Fenofibrate

+

+

+

+

+

+

NO*

Incomplete Block design

+

+

+

+

NO

NO

+

Number of replicates

6

3

8

6

6

6

6

Compound

*

Excluded due to saturation of the UV detection.

¤

The SSPM was conducted using methanol instead of DMSO

Results & Discussion Part 1: Intra-laboratory variation The first part of the study was the fixed DS experiment, to assess the day to day variation of the tind measurements. Figure 1A shows the linearized results for the concentration-time profile of the fixed aDS (2.8; 3.7; 5.0; 5.7) of felodipine induced on three different days as described above. There was


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generally a variation in the absolute value of tind, however the individual β-values (the slope factor from Eq. 3) of each replicate were consistent (varying from 4.44-5.78). The α-values (the intercept from Eq. 3) ranged from -1.2 to -0.43. The second part of the intra-laboratory variation study assessed the reproducibility of the SSPM. The SSPM protocol, as described in Supporting Information #2, was performed independently at three different days by the same person, and one time by a second unbiased person. Four repetitions of the full experiment for felodipine can be seen in Figure 1B. The maximum possible DS for the individual data sets varied from 3.9 to 5.7 for person #1 and was 3.6 for person #2. Even though there was a rather large difference between the individual data sets in Figure 1B, a corresponding change in tind ensured that the individual data points were positioned on the same straight line, except for one point in the second data set by person #1. This point was at very low DS and was excluded from the regression analysis. The effect of experimental conditions, such as sites for heterogeneous nucleation will have a large effect at low DS for experiments measuring tind26, hence it can be expected that outliers may occur predominately at low DS (high ln(aDS)-2). The α-values varied from -1.3 to -0.4. This corresponds well to the α-values found in Figure 1A. Because the system was the same in the intra-laboratory studies, it served as a measure of similarity between runs. The β-values ranged from 3.3 to 4.8 (Figure 1B). The β-value describes the susceptibility of tind to a change in aDS. The β-values of Figure 1A and 1B were statistically different (p < 0.05), but still within the same range (3.4 and 4.7, respectively).



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Figure 1. Linearization of the intra-laboratory study conducted by partner #1. A: The fixed aDS experiment for felodipine were executed on three different days (more than six days between experiments) by person #1, in order to investigate the reproducibility of the induction times. The black line is the linear regression of all three experiments with f(x) = 4.73 x – 0.76, r2=0.65. The individual data sets had linear regressions with an r2 of 0.84-0.88, n=4-6. B: The experiment performed for felodipine according to the protocol (Supporting Information #2) by person #1 (three different days, > six days between experiments) and by person #2. The black line is a linear regression of all four experiments with f(x) = 3.41 x – 0.45, r2=0.65. The individual data sets had a linear regression with an r2 of 0.76-0.91, N = 5 - 6. tind are given in minutes. Data is presented as mean ± SD.


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The fixed DS experiments had individual r2-values of 0.84-0.88 and a linear regression of the entire dataset produced an r2-value of 0.65. Due to the novelty of the SSPM it is not possible to compare these α- and β-values to other studies, as the µDiss has not been used with the SSPM in other laboratories, than the ones included in this study. However, for the SSPM, the individual regressions had an r2-value of 0.76 – 0.91. Palmelund et al. reported r2-values of 0.75 – 0.96 for the analysis of aprepitant, felodipine, fenofibrate, and three additional model compounds9, consistent with the results obtained in the present study. The SSPM can therefore be considered reproducible within one laboratory. Part 2: Inter-laboratory variation In the first part of the inter-laboratory study, the maximum possible aDS to be used for the precipitation method was determined by spiking aliquots of concentrated organic solvent stock of compound into FaSSIF, as described in the methods section. All partners used DMSO as organic stock, except for partner #3 for the study of felodipine, where methanol was used as organic solvent. The identified maximum possible aDS (aDS after the addition of aliquot nprecipitation-1) for each partner is shown in Figure 2.



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Figure 2. Maximum possible aDS for aprepitant (grey), felodipine (white), and fenofibrate (black) for all partners, found by adding aliquots of an organic solvent stock of the compound into FaSSIF. Neither the concentration of the stock nor the aliquot volume had been specified in the protocol in order to give a more realistic pattern of variation. The data of partner #1 is from Palmelund et al9. N = 1. The mean shows the mean of all seven partners ± SD. For all partners, except partner #4, aprepitant reached the highest aDS with a mean and standard deviation of 9.6 ± 1.7. For felodipine and fenofibrate, a similar aDS was achieved for all partners (5.1 ± 0.9 and 6.4 ± 1.2, respectively), except for partner #7, where a markedly higher aDS was achieved for fenofibrate compared to felodipine. The coefficients of variation (cv) of the maximum possible aDS for the three model compounds were comparable (17.3 %, 18.0 %, and 19.5 % for aprepitant, felodipine and fenofibrate, respectively). The apparent solubility of aprepitant and felodipine has recently been determined in an inter-laboratory study using a similar experimental setup27. Here the cv was found to be 23.2 % and 24.6 %, respectively27, thus slightly higher than in the present study. This is surprising, as the study of a thermodynamically stable system, such as a saturated solution, would be generally expected to have a lower cv compared to a study of an unstable kinetic phenomenon, such as supersaturation and precipitation. A cv below 20 % can be seen as an acceptable variation in a screening setup like the one investigated in the present study, especially as the method did not describe specific organic stock concentrations or the aliquot volumes to be used for the initial experimental work, defining the maximum possible aDS. The amount of organic solvent used differed between partners when investigating the maximum possible DS, which may have contributed to variation in the concentration of onset of precipitation between partners. Conversely, the volume of organic solvent was kept constant for concentration-time profiles of the actual supersaturation and precipitation


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experiment (Figure 3. Hence, the results in Figure 2 provided a more realistic pattern of variation between laboratories for the SSPM, than if the SSPM protocol had prescribed the organic solvent stock concentration and aliquot volumes. The only acceptance criterion to fulfill was that tind should be below 10 min for the maximum possible aDS in the subsequent concentration-time profile, as specified in the protocol (Supporting Information #2). The concentration-time profiles for aprepitant for each partner are shown in Figure 3, except for partner #6, where saturation of the UV spectrum obscured the measurements. Considerable differences can be observed between the different partners. The large standard deviations seen during precipitation correspond to differences in the induction time between runs (Figure 3). For all partners the onset of precipitation, and hence the induction time, started within 5 minutes for the highest concentration. The overall trend was in accordance with crystallisation theory, as a lower aDS showed a prolonged induction time. For partner #1 and #7 the lowest concentration of aprepitant did not precipitate within 1 hour (Figure 3A and B), whereas for partner #2, #3, #4, and #5 the lowest concentration precipitated within 1 hour (Figure 3C, D and E).



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Figure 3. Concentration-time profiles of aprepitant from A) partner #1, B) partner #2, C) partner #3, D) partner #4, E) partner #5, F) partner #7. Data for partner #6 was excluded due to saturation of the UV detector. The black line shows the solubility of aprepitant ‘in FaSSIF. N = 3-8 of aprepitant. The data of partner #1 is from Palmelund et al9. Data is presented as mean ± SD Direct comparison between the precipitation curves across partners is difficult, because of the different concentrations used for the maximum possible aDS. In order to compare the data between partners, the data were, hence, linearized, as described above9, using the tind as a proxy for the start of nucleation. As mentioned above, tind was defined as a decrease of 2.5 % in the supersaturated concentration (see Eq. 4 and Eq. 5). In Figure 4A, the relationship between the induction time and the DS for aprepitant is shown. All partners obtained a linear relationship between ln(tind) and ln(aDS)-2 with individual β-values (Eq. 3). Five out of the six partners had β-values in the range from 11.6 to 26.6 (r2 = 0.37 - 0.92, cv 35.3 %). Partner #2 obtained a markedly different β-value of 4.9 with an r2 of 0.71, and was less than half the size of the other β-values. The regression of the 5 partners was f(x) = 15.1x 1.5, r2 = 0.44, when partner #2 was excluded. Interestingly, the lowest r2 value was observed for partner #5, but the average values of partner #5 were located on the combined linear regression (See Figure 4A). The individual α-values ranged from -3.9 to 0.4 (cv 79 %). The spread in α-values was high when compared to the variation in the α-values found in the intra-laboratory study.


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Figure 4. Linearization of the dissolution curves of A) aprepitant, B) felodipine and C) fenofibrate. The black line is a linear regression of the combined data for each compound with A) f(x) = 15.1x - 1.5, r2 =



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0.44 B) f(x) = 4.0x - 0.2, r2 = 0.58 C) f(x) = 4.3 x - 0.2, r2 = 0.62. For all points, N = 3 – 8. Data is presented as mean ± SD. The high DS values found in Figure 2 corresponds to the low ln(aDS)-2 values. For partner #7, the UV spectrum was saturated for the fenofibrate measurement and was not shown. The data of partner #1 is from Palmelund et al9 For felodipine, the linear relationship between ln(tind) and ln(aDS)-2 was also observed with a regression of f(x) = 4.0x - 0.2, r2 = 0.58 (Figure 4B). Compared to aprepitant, markedly lower β-values were observed and the precipitation of felodipine seemed less affected by a lower degree of supersaturation than aprepitant. The individual β-values were between 2.2 and 6.3 (r2 = 0.47 - 0.90, cv 42.1 %). The range of β-values found in the inter-laboratory study was comparable to the results found in the intra-laboratory study, with β-values of 3.4 and 4.7 (Figure 1A & B). The individual α-values ranged from -1.6 to 1.8 (cv 701 %). The difference in α-values between partners was larger when compared to the intra-laboratory study. One reason for this would be that the average value was close to zero, hence the cv was high. However, while the β-values were comparable to the intra-laboratory study, the α-values were less consistent between partners. As discussed above, a large difference in the α-values may suggest differences in the experimental setup, both with respect to equipment and procedure, between the partners.

For fenofibrate, the same trend as for felodipine was observed. The linear regression was f(x) = 4.3x - 0.2, r2 = 0.62. The individual β-values were between 3.8 and 5.6 (r2 = 0.19 – 0.99, cv 14.4 %). The individual α-values ranged between -0.7 to -0.2 (cv 50 %). Fenofibrate had the lowest variation between the linear regression results obtained by the different partners. In another inter-laboratory study, using a similar experimental setup, the cv for the intrinsic dissolution rate of aprepitant and


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felodipine was 35.9 % and 48.9 %, respectively27. Hence, the cv values of the β-values found in this study were comparable to Andersson et al.27 and could be considered acceptable for a precipitation method, taking the inherent sources of variation into account, as discussed above.

Based on Figures 3 and 4, it may be difficult to compare absolute values of tind and aDS between different partners. There was no general trend in the differences observed in the data sets between partners. In Figure 5, the β-values for the 3 compounds from each partner are shown. The individual βvalues permit comparison of the similarity of the data sets between partners. Even though there were differences between partners, a general trend of a β-value for aprepitant > 10 for five out of six partners and comparable β-values for felodipine and fenofibrate of around 5 for all seven partners was found. As mentioned previously, partner #2 used methanol to induced supersaturation of felodipine (see Table 2). The used of methanol compared to DMSO as organic solvent for the induction of supersaturation seems to have no effect on the β-value. A higher β-value indicates a higher supersaturation propensity for aprepitant than for felodipine and fenofibrate.



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Figure 5. β-values of the linear regression of Figure 4 for aprepitant (grey), felodipine (white), and fenofibrate (black) by all partners. The data of partner #1 is from Palmelund et al9. Data is presented as mean ± SD. Mean is the combined mean ± SD of all partners, except partner # 2 for aprepitant. In Figure 6, the α-values of the linear regressions of the individual partners are shown. Overall, the αvalue was less consistent than the β-value between partners. For aprepitant, four out of six data sets had a negative α-value, but for the two last data sets, the α-value was zero. For fenofibrate, the α-values were consistently below zero, at comparable levels, with an average of -0.44. The high variation was to some extent not a surprise, as the α-value includes the pre-exponential factor, which can be sensitive towards even minor differences in the experimental setup28. A high α-value means that the tind at high aDS is long. The current method is designed to have a short tind, hence the α-value can be used as a parameter for the similarity in the data set. The α-value is further explained in the Supporting Information #1


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Figure 6. α-values of the individual linear regressions of Figure 4 for aprepitant (grey), felodipine (white), and fenofibrate (black) by all partners. The data of partner #1 is from Palmelund et al9. Data is presented as mean ± SD. Mean is the combined mean ± SD of all partners, except partner # 2 for aprepitant. In a perfect setup, the absolute values of tind and aDS would be similar between laboratories and predictive for the in vivo behaviour. However, precipitation is a stochastic phenomenon, and, hence, even small differences in the experimental setup may cause differences in tind. Thus, there is a need for a method to compare compounds supersaturation propensity. The SSPM is designed to determine the tind at maximum aDS and 3 lower levels of aDS for a compound. This means that if compound shows an increased tind at a relatively small decrease in aDS then this compound would have an inherent ability to stay supersaturated longer than a compound where little or no effect on tind is observed by a decrease in DS. The compound with an inherent ability to stay supersaturated would most likely be a better candidate for a supersaturation drug delivery system. However, it can be difficult to describe the supersaturation propensity in a single term. Using



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the SSPM and subsequent linearization of data, it was possible to obtain reproducible β-values between the partners. Using the β-value for comparison between compounds, it seems possible to have a representative descriptor to compare compounds in a drug selection process and use this descriptor to evaluate the feasibility of a supersaturation drug delivery strategy. Although the α-value is also important to explain correlation between tind and aDS, it seems subjected to more inter-laboratory variation. Only one of the three compounds had comparable α-values between partners. Hence, the use of the β-value seems most appropriate to compare data from different partners. More compounds and in vivo studies are needed in order to verify exactly how these data would relate to an in vivo supersaturating drug delivery system. However, having an in vitro method, which allows for a reasonable reproducibility between laboratories, and especially within the same laboratory, gives a good basis for establishing an in vitro in vivo relation. Table 3 shows the cv of both the intra- and interlaboratory study. The intra-laboratory study overall has the lowest cv. The larger cv for the interlaboratory study highlights the difficulty in transferring methods between laboratories.


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Table 3 Overview of the cv for the inter-laboratory and intra-laboratory study

Coefficient

Intra-laboratory study¤

Inter-laboratory study

of variation

Aprep

Felo

Feno

Same DS

Full experiment

Maximum

17.3 %

18.0 %

19.5 %

N/A

21.3 %

α*

79.0 %

701.0 %

50.0 %

33.5 %

40.1 %

β

35.3 %

42.1 %

14.4 %

8.9 %

7.2 %

possible DS

* The α-values are close to zero, which causes the cv to be large. ¤

The intra-laboratory study was conducted using felodipine.

The inherent uncertainty in supersaturation measurements makes it a difficult task to work with. Some measures were taken to ensure coherency in the data in the SSPM protocol. Commercially available FaSSIF should ensure similar media between partners. The magnets have been shown to have a large impact on the hydrodynamics and the precipitation process of danazol in the same setup29. The magnet bar geometry and the speed of the magnet bar were specified in the protocol; however, there may still be small differences between partners. Another important factor is the temperature. While the temperature was specified in the protocol to be 37°C, there may still be slight variation between partners with regard to the actual temperature in the vial. This is an important parameter as the temperature is inversely related to the nucleation rate to the power of 3 (Eq. 2). Changes in the temperature will thus have a great effect on the β-value. Some other important factors which were difficult to control, e.g. dust particles or minor scratches in the glassware, may further act as heterogeneous nucleation sites30, hence, increasing the variability in the method. The use of high DS



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values should minimize the effect of heterogeneous nucleation31. On the other hand, measures were also taken to ensure a more realistic pattern of variation. Each partner had to determine the maximum possible aDS with no prescribed organic solvent concentration or aliquot volume to use for the determination. This was performed to reflect the scenario of an unknown compound in a drug development process. If the aDS to investigate had been described in the protocol, it seems reasonable to expect less variation. Another factor potentially affecting the reproducibility of the results is liquid-liquid phase separation (LLPS). It was demonstrated that low soluble compounds in aqueous solutions could undergo LLPS if their concentration exceeds amorphous solubility in such solutions32, 33. Additional analytical caution is needed when distinguishing whether compound is dissolved in one (aqueous) phase or second drugrich phase is formed in the system. For example, change in the shape of the second derivative spectrum can be used to identify whether LLPS occurred. Inspecting data for this effect was beyond of the scope for the current study. The regression analyses conducted in this study had r2-values ranging from 0.19 to 0.99. For the combined data sets of both the intra- and inter-laboratory study, r2-values ranged from 0.44 to 0.65. However, in Figure 3, it is possible to see a trend with a higher ln(tind) when ln(aDS)-2 increased. Furthermore, using a decrease of 2.5 % in the supersaturated concentration (Eq. 4 and Eq. 5) as a proxy for nucleation has its limitations, as a decrease in concentration is a rough estimate of the appearance of the first nucleus above the critical size. This will also add to the variation, as the tind was not only governed by nucleation, but also has contributions from crystal growth. However, the aim of the SSPM was not to determine the precise induction time or nucleation rate, but to enable a practical method to compare the supersaturation propensity of different compounds. Hence, despite these inherent differences and sources of variation, it was possible to achieve the same rank order of the β-value for Page 26 of 31 ACS Paragon Plus Environment

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80 % of the partners completing a full data set. Comparing this to the complex nature of supersaturation and the aim of the SSPM to be used as a screening tool in early drug development, 80 % is considered acceptable. Hence, the SSPM could rank order compounds reproducible based on the β-value between different partners.

Conclusion This study demonstrated the level of variation to expect when conducting the SSPM. The intralaboratory data showed that three repetitions of the same aDS of felodipine on different days resulted in comparable β-values of the ln(tind) vs ln(aDS)-2 plot. Conducting the SSPM on different days showed a satisfactory reproducibility within one laboratory, which confirmed the use of SSPM to screen compounds for their supersaturation propensity in a given laboratory set-up. Seven partners participated in the inter-laboratory study. This was the first study to compare results between partners using the SSPM to investigate supersaturation and precipitation kinetics. The standardized way of determining the maximum possible aDS for the concentration-time profile gave an aDS of 9.6 ± 1.7, 5.1 ± 0.9 and 6.4 ± 1.2 for the aprepitant, felodipine, and fenofibrate, respectively. For all three compounds, the cv was < 20 %, which was comparable to the cv previously reported for solubility experiments in an inter-laboratory study using a similar setup(ref). The data showed that it may be difficult to compare absolute concentrations and tind between partners; however, by linearizing the data and the resulting β-value, it was possible to rank order the compounds as a function of their supersaturation propensity. Aprepitant had a β-value of 15.1 (cv 35.3 %) and felodipine and fenofibrate had β-values of 4.0 (cv 42.1 %) and 4.3 (cv 14.4 %), respectively. The cv was considered reasonable given the complexity of the experimental setup and the stochastic nature of nucleation and precipitation. Four out of the five partners completing the entire data set found the highest β-value for



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aprepitant, whereas felodipine and fenofibrate had comparable β-values. The SSPM was, thus, considered to be reasonably reproducible between partners, when used to rank order supersaturation propensity of compounds based on the β-value. Therefore, the model may be useful for future studies classifying compounds according to their supersaturation propensity.

Acknowledgement Henrik Palmelund, Juliane F. Christfort, and Lene Olsson are acknowledged for their laboratory work. Kasper R. Jensen is acknowledged for his excel expertise to optimize the analysis of the data. This study was conducted as part of the Oral Biopharmaceutics Tools (ORBITO) project (http://www.orbitoproject.eu). This work has received support from the Innovative Medicines Initiative Joint Undertaking (http://www.imi.europa.eu) under grant agreement no. 115369, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. Abbreviations: aCsolubility

Apparent concentration at the thermodynamic solubility

aCsupersaturation

Apparent concentration of the supersaturated state

cv

Coefficient of variation

DMSO

Dimethyl sulfoxide

aDS

Apparant degree of supersaturation

FaSSIF

Fasted state simulated intestinal fluid


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LLPS

Liquid-liquid phase separation

SSPM

Standardized supersaturation and precipitation method

SD

Standard deviation

tind

induction time

Supporting Information Describition of how to interpret the linearized data has been included in Supporting Information #1. The SSPM protocol for the inter-laboratory studies has been included as Supporting Information #2.



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Investigation of the Intra- and Interlaboratory Reproducibility of a Small

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