Importance of Critical Micellar Concentration for the Prediction of

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The importance of critical micellar concentration for the prediction of solubility enhancement in biorelevant media G Ottaviani, S Wendelspiess, and R Alvarez-Sanchez Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/mp5006992 • Publication Date (Web): 09 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Molecular Pharmaceutics

The importance of critical micellar concentration for the prediction of solubility enhancement in biorelevant media Ottaviani G‡, Wendelspiess S# and Alvarez-Sánchez R#*

#

Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche

Innovation Center Basel (Switzerland) ‡

Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche

Innovation Center Shanghai (China) * Corresponding author: F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland. Phone: +41 61 688 1941. Fax: +41 61 688 29 08. E-mail: [email protected]

Table of Contents/Abstract Graphic

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Abstract This study evaluated if the intrinsic surface properties of compounds are related to the solubility enhancement (SE) typically observed in biorelevant media like Fasted State Simulated Intestinal Fluids (FaSSIF). The solubility of 51 chemically diverse compounds was measured in FaSSIF and in phosphate buffer and the surface activity parameters were determined. This study showed that the compound critical micellar concentration parameter (CMC) correlates strongly with the solubility enhancement (SE) observed in FaSSIF compared to phosphate buffer. Thus, the intrinsic capacity of molecules to form micelles is also a determinant for each compound’s affinity to the micelles of biorelevant surfactants. CMC correlated better with SE than lipophilicity (log D), especially over the log D range typically covered by drugs (2 10 fold). For the compounds with lipophilicity values between 2 and 4, a poor correlation between lipophilicity and log SE was observed (r2=0.24), suggesting that logD alone is not sufficient to explain and predict SE for compounds in this lipophilic range (figure 5B).

Determination of surface active parameters The main surfactant parameters were obtained from concentration surface pressure profiles. These parameters are depicted for representative compounds in figure 3, with the complete dataset summarized in table 2. The surface properties were compared to the extent of SE (Figure 4). Among the different surface properties measured and derived, the critical micellar concentration (CMC) showed a robust inverse correlation with SE (r2=0.79), indicating that the higher the propensity of a compound to form micelles, the higher its extent of solubilization in FaSSIF. Compounds with low or no measurable CMC showed consistently SE values close to 1.

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Other surface parameters such as interfacial area, air-water partitioning coefficient and amphiphilicity showed trends when plotted against SE, though less significant than CMC.

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Discussion Low solubility frequently limits oral drug absorption. This effect has become increasingly critical as drug research programs have progressively drifted towards larger and more lipophilic compounds, translating into lower solubility entities [20]. The use of solubility data in intestinal biorelevant media such as FeSSIF and FaSSIF together with physiologically based modeling approaches allows for more predictive understanding of the extent of absorption [3]. It is well known that these media contribute to an enhanced solubilization of the compounds; however, the molecular drivers for this enhanced solubility remain poorly understood. Considering that intestinal biorelevant media are prone to the formation of supramolecular arrangements such as liposomes and micelles [6], led us to hypothesize that surfactant properties of compounds could be linked to their ability to be solubilized in such milieu. Surface properties of biorelevant media have been studied [21]; however, little attention focused on the intrinsic surfactant properties of the solubilized compounds. Indeed, the solubilization in a micellar system may happen by association of the compound to the micelles in the medium. Thus the tendency of the compound to form micelles can be a relevant property to determine the extent of solubilization. Following this working hypothesis, we characterized the surfactant properties of a diverse set of public and proprietary compounds with the aim of correlating such surface activity parameters with the degree of solubility enhancement in FaSSIF compared to aqueous medium. The compounds were characterized by measuring the main surfactant properties according to the classical Gibbs’ thermodynamics theory for air-water interfaces. The resulting data identified the critical micellar concentration (CMC) as the most influencing parameter, correlating strongly with the solubility enhancement (SE) observed in FaSSIF. 13



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Other surfactant properties such as interfacial area (As), air-water partition coefficient (Kaw) and amphiphilicity (∆∆Gam) also showed some trends when plotted against SE; yet, the correlations observed were not as clear as for CMC. In addition, as As, Kaw and ∆∆Gam are partly dependent on CMC, it is possible that the trends observed in correlating them to SE are related to the influence of CMC. These results suggest that compounds with a high propensity to form micellar systems on their own (low CMC) exhibit good ability to be solubilized by associating with the FaSSIF colloidal arrangements. Conversely, the observation that compounds with high or no observable CMC (i.e. do not form micelles) show negligible SE. This suggests that integration of the compound into the FaSSIF micellar structure is a prerequisite for solubilization and that hydrophobic interactions with the apolar micellar/vesicle domains as expressed by logD alone cannot explain SE. Examples of the latter point could be compounds #23 (logD 3.0; SE 0.6; CMC >10’000 µM) or mefenamic acid (logD 3.0; SE 2.4; CMC >10’000); compounds that despite a high lipophilicity show no or limited SE, likely linked to their inability to associate in micelles. It has been previously observed using a set of reference compounds that ionized acidic compounds showed, in general, lower SE than ionized bases. It was suggested that the solubility of ionized acids was not increased in FaSSIF due to electrostatic repulsions with the net negatively charged FaSSIF media components [8]. In this current study, for several proprietary compounds covering a broader chemical space, we have not found any significant solubility differences in SE between ionized acids or bases, suggesting that electrostatic interactions could have been masked or overridden by other factors, such as the compound’s conformation and lipophilicity

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The extent of solubilization has been linked in previous works to the compound’s lipophilicity [7, 22]. In particular, Mithani [7] showed that the solubilization ratio in the presence of bile salts depends directly on the lipophilicity. Although the term solubilization ratio used by Mithani does not entirely correspond to the term SE used in this work, both are directly related and expected to be dependent on lipophilicity. Interestingly, Mithani’s work reported very limited increases in solubility in the fasted conditions where bile salt levels were lower than the CMC followed by a significant increase in solubility in fed conditions, especially for lipophilic compounds. Similarly, Fagerberg et al. [22] showed an increased solubilization ratio with increased lipophilicity of the compounds. In our work, covering a significantly larger compound range, logD correlated positively with the solubility enhancement. However, our study showed that CMC is better correlated with SE than logD (Figures 5 and 6). Especially when examining compounds in the lipophilicity range often encountered in drugs (logD between 2 and 4), it becomes apparent that the logD fails to properly predict SE, so it cannot be used as a robust predictor for SE optimization purposes whereas CMC in this lipophilicity range offers a good description of the SE (figure 5). It is noteworthy that CMC and logD appear to be positively correlated (figure 6), indicating that the dependency observed between SE and logD may be the reflection of how logD influences the compound’s CMC. During compound optimization in drug research programs, different compound attributes are modulated in order to identify the molecule with highest chances of providing the best clinical PK/PD and optimal safety profile. Among others, solubility is one of the parameters routinely monitored and optimized. Often, the options to increase solubility are limited by the structural and property requirements needed for biological activity. In such cases where aqueous solubility is strongly limited, an early understanding of the molecular features that could enhance the solubility in simulated intestinal fluids could be beneficial in designing 15



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molecules with improved solubility. Although the most straightforward way to improve SE would be by testing the compounds in solubility assays including biorelevant media, it may become highly useful to determine or to predict the CMC to test hypothesis and to guide Medicinal Chemistry’s synthetic efforts. In addition the CMC (either measured or predicted) may be a valuable descriptor to build new in silico models for the prediction of solubility enhancement in biorelevant media.

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Conclusions To our knowledge, this is the first comprehensive study that clearly shows the impact of the intrinsic surface properties of drug-like compounds on solubility enhancement in a biorelevant medium compared to an aqueous medium. This study demonstrates that critical micelle concentration, a descriptor of the intrinsic capacity of compounds to self-associate in micelles, predicts the solubility enhancement in FaSSIF better than the well-established lipophilicity. Our research demonstrates that drug optimization strategies will potentially benefit by considering the compound’s tendency to associate in micelles as a way to increase intestinal absorption.

Acknowledgments The authors would like to thank Björn Wagner, Virginie Micallef, Isabelle Parilla and Sabine Pita for their technical assistance as well as Franz Schuler, Sara Belli and Jon Kyle Bodnar for the revision of the manuscript and valuable comments.

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Tables Table 1: Physicochemical properties and solubility data of the 51 compounds. Data were generated as described in Experimental Section unless otherwise stated.

Compound Amiodarone Corticosterone Felodipine Fenretinide Flurbiprofen

MW [g/mol] 645.3 346.5 384.3 391.6 244.3

Folic acid

441.4

Ketoconazole

531.4

Meclizine Mefenamic acid Mefloquin

391.0 241.3 420.4

Melphalan Miconazole Nitrendipine Ondansetron

305.2 416.1 360.4 293.4

Piroxicam Progesterone

331.4 314.5

Sulfasalazine Tamoxifen Tenidap Testosterone Torcetrapib

398.4 371.5 320.8 288.4 600.5

1 2 3 4 5

304.2 376.6 498.5 532.8 387.5

6

438.5

7 8

387.8 507.6

9 10

397.3 482.5

11

517.0

ionization constant pKa 10.24 (B)c 4.40 (B) 9.22 (A)a 4.27 (A) 3.87 (A) 4.76 (A) 7.98 (A) 2.33 (B)b 5.83 (B) 3.35 (B) 2.64 (B) 7.21 (B)a 4.22 (A) 4.87 (B)a 2.64 (A) 9.04 (B)c 6.70 (B) 6.66 (B) 2.31 (A) 5.16 (B) 2.40 (A) 7.87 (A) 10.79 (A) 8.48 (B)f

4.52 (A) 2.45 (B) 4.97 (A)a 3.51 (A) 4.09 (A) 3.84 (A) 4.00 (A) 11.25 (A) 4.00 (A) 11.25 (A) 7.16 (A) 6.45 (A) 3.16 (B) 7.17(A) 7.14 (A) 2.81 (B)

logD6.5 4.59b 2.01 4.46e 8.04e 1.79

Fraction ionized at pH 6.5h 1 0 0 0 -0.99

phosphate solubility (µg/ml) 110.0 1.0

A

-3.1b

-2

481

421

0.9

B

3.49

0.18

5

11

2.2

B A B

5.63b 2.96 4.26

0.84 -0.99 0.02

49.0 2.4 25.0

Z B N B

10000 151 43 >10000 >10000 >1000 32 >10000 268 551 27 236 >10000 >10000 654 >10000 191 >10000 >2500 10 2500 26 >5000 41 >5000 488 >10000 47 69 124 174 >10000 >10000 180 >10000 13 15 14 >10000 178 >10000 >10000 1209 1065 147 179 131 250 65 251

logCMC 0.8 4.0 2.2 1.6 4.0 4.0 3.0 1.5 4.0 2.4 2.7 1.4 2.4 4.0 4.0 2.8 4.0 2.3 4.0 3.4 1.0 3.4 1.4 3.7 1.6 3.7 2.7 4.0 1.7 1.8 2.1 2.2 4.0 4.0 2.3 4.0 1.1 1.2 1.1 4.0 2.2 4.0 4.0 3.1 3.0 2.2 2.3 2.1 2.4 1.8 2.4

∆Pi [mN.m-1] 17.5

Kaw [µM-1] 0.721 0.003 0.060 0.041

As [Å2] 44 103 65 21

78 128

-3.00

18.3 12.2 16.5 12.5

0.042 0.101 0.000 0.213 0.032 0.224 0.018

94 103 56 64

-10.00 -7.10 -4.50 -3.60

11.3

0.014

88

-5.50

27.8

0.225

57

-9.40

96 26 47 80 66 49 83 49 44 78 63 59 42 53 140

-2.40 -8.74 -3.20 -4.11

10.2

0.005 0.106 0.001 0.548 0.006 0.132 0.012 0.009 0.000 0.056 0.482 0.029 0.030 0.002 0.001 0.150

14.1 12.4 14.1

0.414 0.497 0.067

64 80 27

-4.25 -5.11 0.15

10.7

0.012 0.013

49 95

-1.83

27.9 8.8 10.5 21.6 12.2 9.1 29.6 11.0

0.074 0.004 0.025 0.125 0.013 0.008 0.815 0.010

68 89 68 62 37 56 58 53

-11.22 -3.60 -3.25 -7.73 -1.26 -1.75 -9.95 -2.42

16.0 26.1

5.4

16.1 12.0 14.8 16.8 17.6 16.1 14.1 19.1 11.5 14.6

∆∆Gam [kJ/mol] -3.70 -5.50 -1.40

-0.04 -2.23 -6.63 -4.23 -10.30 -3.84

-8.25

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Figures

Figure 1: Equilibrium solubility determined for 51 compounds at pH 6.5 in phosphate and FaSSIF buffers. Compounds are classified as acids (■), bases (▲), neutrals (x) and zwitterions (●). Line corresponds to the unity line. Values below detection limit are plotted at the detection limit. 10000

1000 S_Fassif µg/mL

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100

10

1 0.1

1

10

100

1000

10000

S_aqueous µg/mL

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Figure 2: Solubility enhancement (SE = S_FaSSIF/S_aqueous) for 51 compounds as a function of the octanol/waterpartition coefficient at pH 6.5. Compounds are classified as acids (■), bases (▲), neutrals (x) and zwitterions (●).Values above or below detection limit are plotted at the detection limit.

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Figure 3: Representative examples of surface pressure profiles for A) compound 29 and B) nitrendipine. Dotted lines and equations depict the derivation of the surface tension parameters.

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Figure 4: Solubility enhancement plotted against different surface parameters: A) logCMC in µM; B) interfacial area (As); C) Air-water partition coefficient (Kaw) and D) Amphiphilicity (∆∆Gam). Compounds are classified as acids (■), bases (▲), neutrals (x) and zwitterions (●).Values below or above detection limit are plotted at the detection limit.

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Figure 5: Solubility enhancement plotted against logCMC (µM, A) and the octanol partition coefficient at pH 6.5 (logD6.5; B). Dataset restricted to the compounds showing logD6.5 comprised between 2 and 4. Open circles in plot B correspond to logD values retrieved from literature, predicted or extrapolated from logD at pH 7.4. Values above or below detection limit are plotted at the detection limit.

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Figure 6: logCMC (µM) plotted against and the octanol partition coefficient at pH 6.5 (logD6.5). For the full compound set (A) classified as acids (■), bases (▲), neutrals (x) and zwitterions (●) and for the compounds in the range between logD 2 and 4 (B). Open circles in plot B correspond to logD values retrieved from literature, predicted or extrapolated from logD at pH 7.4. Values above or below detection limit are plotted at the detection limit.

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