Journal of Chromatography A, 1322 (2013) 97–104

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Study of organic compounds–water interactions by partition in aqueous two-phase systems Pedro P. Madeira a,∗ , Ana Bessa a , Miguel A. Teixeira a , Luís Álvares-Ribeiro b , M. Raquel Aires-Barros c , Alírio E. Rodrigues a , Boris Y. Zaslavsky d,∗∗ a Laboratory of Separation and Reaction Engineering, Dpt. de Engenharia Química, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal b Requimte, Dep. Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre 687, 4169-007 Porto, Portugal c IBB – Institute for Biotechnology and Bioengineering, Centre for Biological and Chemical Engineering, Department of Bioengineering, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001 Lisboa, Portugal d Analiza, Inc., 3615 Superior Avenue, Suite 4407b, Cleveland, OH 44114, USA

a r t i c l e

i n f o

Article history: Received 9 September 2013 Received in revised form 24 October 2013 Accepted 26 October 2013 Available online 2 November 2013 Keywords: Aqueous two-phase partitioning Organic compounds–water interactions Solute-specific coefficients Ion–dipole interactions Odor threshold

a b s t r a c t Partition coefficients of fourteen organic compounds were determined in 10 or 20 different polymer/polymer aqueous two-phase systems (ATPS) all at physiological pH (0.15 M NaCl in 0.01 M phosphate buffer, pH 7.4). Solute-specific coefficients characterizing different types of solute–water interactions for the compounds examined were determined by the multiple linear regression analysis. It is shown that (i) the partition behavior for the polar organic compounds is affected not only by dipole–dipole and hydrogen-bond interactions with aqueous environment but, notably, in most cases also by dipole–ion interactions; (ii) it is possible to predict partition behavior for compounds with pre-determined solutespecific coefficients in ATPS with characterized solvent features; and (iii) linear combinations of the solute-specific coefficients for the organic compounds might be useful in the development of quantitative structure–activity relationship (QSAR) analysis to describe their odor detection threshold. © 2013 Elsevier B.V. All rights reserved.

1. Introduction It is well known that interactions of any substance, from small organic compounds to biological macromolecules, with aqueous environment are fundamentally important for their functions in vivo [1]. Indeed, it is water as a solvent mediating protein folding, protein–protein, protein–ligand and other interactions, transport and function of biomolecules and drugs. Hence understanding of solute–water interactions is important for theoretical and applied science. Current studies of solute–solvent interactions recognize the importance of hydrophobic interactions as well as other types of interactions such as van der Waals, polar, ion–dipole, and hydrogen bonding interactions [2–4]. The solvent–solute interactions are generally described by linear free energy relationships, particularly

∗ Corresponding author. Tel.: +351 220414911; fax: +351 225081674. ∗∗ Corresponding author. Tel.: +1 2164329050x111; fax: +1 2164329050. E-mail addresses: [email protected] (P.P. Madeira), [email protected] (B.Y. Zaslavsky). 0021-9673/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chroma.2013.10.085

by the Abraham model [5,6] as a linear combination of the so-called solute descriptors and corresponding solvent descriptors: log SP = z0 + ˙(xi Xs i )

(1)

where SP is a property of a series of solutes in a given solvent system (typically solubility or the distribution coefficient in organic solvent–water biphasic system), z0 is the intercept constant, Xs i is a solute descriptor representing the ability of the solute to participate in a particular type of solute–solvent interactions; xi represents the complementary effect of the solvent in regards to this type of interactions. Solute descriptors Xs i for multiple compounds are determined by separate physicochemical measurements. Then the SP property for these compounds is examined, and solvent descriptors xi values are estimated as unknown coefficients in Eq. (1) by multiple linear regression analysis for all these compounds. It was demonstrated that the linear combinations of solute descriptors may be used successfully for describing different types of biological activity and tissue-tissue distribution of organic compounds [7–11]. The major limitation of this approach is that it is not suitable for studies of biomolecules such as proteins. An alternative approach is to characterize the solvent features xi values in different solvent systems using solvatochromic

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measurements [12–15]. Then the SP properties for a given compound in the set of solvent systems are determined, and the solute-specific coefficients Xs i for the compound can be estimated as unknown coefficients in Eq. (1) by multiple linear regression analysis for all the solvent systems. It should be emphasized that in view of solvatochromic effects not being free energy related Eq. (1), in this case, should be considered empirical and not representing linear free energy relationship. A variant of this latter approach was recently developed by us and used successfully for determination of solute-specific coefficients for organic compounds and proteins [16,17]. The most important distinction of the suggested approach is that aqueous two-phase systems, suitable for analysis of biological macromolecules, are used instead of common organic solvent–water biphasic systems. Thus it is possible to study compounds in aqueous environment that simulates solvent environment in vivo much better than organic solvent–water systems. Aqueous two-phase systems (ATPS) arise when two water soluble polymers, such as dextran and poly(ethylene glycol), are mixed in water above certain concentration and/or temperature. The mixture separates into two aqueous phases, each enriched in one of the polymers. These aqueous phases have been shown to provide media suitable for biomolecules and cells [18–21]. It was established that the aqueous media in the coexisting phases differ in their solvent properties [16,17,20,22,23], and these differences govern discriminating distribution of solutes in ATPS. ATPSs formed by pairs of non-ionic polymers in water display extremely small differences between various solvent features of the media in the coexisting phases due to the same aqueous nature of the media. In organic solvent–water biphasic systems these differences are much more significant likely masking subtle and biologically important effects of various aspects of solute–solvent interactions. As an example, the free energy of transfer of a methylene group (a measure of the difference between the relative hydrophobicity of the phases) in water–octanol system is ∼720 cal/mole, while in ATPS formed by Dextran and Ficoll it amounts to just 15–25 cal/mole depending on the polymer and ionic composition of ATPS [20]. The importance of the small difference between the properties of the phases in equilibrium may be illustrated in the following example. Distribution of solutes in octanol–water biphasic system is described as [24]: log P = 0.09 + 0.56R2 − 1.05S + 0.03A − 3.46B + 3.81Vx N = 613; r 2 = 0.997; SD = 0.116; F = 23, 161.6

(2)

where P is octanol–water distribution coefficient, N – number of compounds, r2 – correlation coefficient, SD – standard deviation, F – ratio of variance; all the other parameters as defined above, while distribution of substances between rat and human blood and brain tissue is described as [25]: log BB = −0.04 + 0.20R2 − 0.69S − 0.72A − 0.70B + 0.99Vx N = 95; r 2 = 0.7; SD = 0.21; F = 41

(3)

where BB is the blood–brain distribution coefficient, all the other parameters as defined above. Comparison of coefficients representing the solvent characteristics in Eqs. (2) and (3) indicates that in the biological situation (blood–brain distribution) the solvent dipolarity/polarizability, H-bond acidity, and H-bond basicity are very close (−0.69, −0.72, and −70, correspondingly), while in octanol–water system these characteristics are quite different (−1.05, +0.03, and −3.46, correspondingly). These coefficients represent essentially the differences between the corresponding solvent features in the phases engaged in the distribution of solutes. In the case of octanol–water system these differences are more significant than in blood–brain distribution where both phases (blood and brain tissue) have the

same aqueous nature. Media in coexisting phases of aqueous twophase systems is also of the same aqueous nature. The solvent properties of the media in the phases of ATPS may be quantified with a set of solvatochromic dyes in terms of solvent dipolarity/polarizability and solvent hydrogen bond donor acidity and hydrogen bond acceptor basicity as developed by Kamlet et al. [12–15]. Additionally the differences between the electrostatic and hydrophobic properties of the phases may be quantified by analysis of partitioning of homologous series of dinitrophenylated amino acids with aliphatic side-chains [17,20,26–29]. The aforementioned solvent properties of the phases are determined in multiple ATPS formed by different pairs of water soluble nonionic polymers. It was established [17,27–29] that partition ratio of a solute (from simple organic compounds to proteins) in any ATPS of a given ionic composition is described as: log Ks = Ss  ∗ +As ˇ + Bs ˛ + Cs c

(4)

where K is the solute partition ratio; * is the difference between the solvatochromic solvent dipolarity/polarizability of the two phases, ˛ is the difference between the solvatochromic solvent hydrogen-bond donor acidity of the phases, ˇ is the difference between the solvatochromic solvent hydrogen-bond acceptor basicity of the phases; c is the difference between the electrostatic properties of the phases; Ss , As , Bs and Cs are constants (solute-specific coefficients) that describe the susceptibility of the complementary solute–solvent interactions to the changes in the corresponding solvent properties; the subscript s designates the solute. Once the partition ratios in a set of multiple ATPS of the same ionic composition for a given solute are determined, the solutespecific coefficients may be calculated as coefficients in Eq. (4) by multiple linear regression analysis. It was shown for free amino acids [28,29] that a linear combination of these solute-specific coefficients correlated with a wide variety of physico-chemical and biological properties of amino acids. It seems reasonable to suggest that these solute-specific coefficients are related to the intrinsic properties, i.e., structure, of the solute. The importance of these solute-specific coefficients in our view is that they may be determined for all types of water soluble substances from simple organic compounds to proteins and nucleic acids. Analysis of these solute-specific coefficients values therefore may lead to gaining better insight into, e.g., mechanisms of salting-in and salting-out effects, biological effects of ions in Hofmeister series, and other phenomena related to biomolecules behavior in aqueous media. It was reported in the literature that different salt additives may affect differently partitioning of polar organic compounds in PEGsodium sulfate ATPS [30]. Hence it was assumed that the dipole–ion interaction term might be necessary for satisfactory description of polar solute partition behavior in polymer/polymer ATPS. The purpose of this study was to test this hypothesis. 2. Materials and methods 2.1. Materials 2.1.1. Polymers Dextran 75 (lot 126,567), weight-average molecular weight (Mw) ∼ = 75 000 was purchased from USB (Cleveland, OH, USA). Polyethylene glycol 10,000 (lot BCBB0795), Mw = 10,000; Polyethylene glycol 8000 (lot 050M0215 V), Mw = 8000; Polyethylene glycol 6000 (lot BCBC7560), Mw = 6000; Polyethylene glycol 4000 (lot BCBD2874), Mw = 4000; Polyethylene glycol 1000 (lot 0001452731), Mw = 1000 and Polyethylene glycol 600 (lot

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BCBD8607 V), Mw = 600 were purchased from Sigma–Aldrich (St. Louis, MO, USA). Ucon 50-HB-5100 (lot SJ1955S3D2), Mw = 3930 was purchased from Dow-Chemical (Midland, MI, USA). Ficoll 70 (lot 10022579), Mw ∼ = 70,000 was purchased from GE Healthcare Biosciences AB (Sweden). All polymers were used without further purification. 2.1.2. Other chemicals 4-nitrophenol, 4-nitroanisole, caffeine, 3hydroxybenzaldehyde, phenol, 4-hydroxyacetanilide, p-Anisaldehyde (4-Methoxybenzaldehyde); methyl anthranilate (methyl 2-aminobenzoate); coumarin (2H-chromen-2-one); trans-cinnamyl alcohol ((2E)-3-phenylprop-2-en-1-ol); vanillin (4-Hydroxy-3-methoxybenzaldehyde); aniline (phenylamine); indole; and acetophenone (1-phenylethanone) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Structures of all these compounds are shown in Fig. 1. All salts and other chemicals used were of analytical-reagent grade. 2.2. Methods 2.2.1. Aqueous two-phase systems Composition of aqueous two-phase systems employed is listed in Table 1 together with their solvent features established and reported previously [27]. Each ATPS was prepared as described in detail elsewhere [26–29]. Fig. 1. Structures of organic compounds examined.

2.2.2. Partitioning Solutions of each compound were prepared in water at concentrations of 1–5 mg/mL. Varied amounts (e.g., 0, 10, 20, 30, 40, and 50 ␮L) of a given compound solution and the complementary amounts (e.g., 100, 90, 80, 70, 60 and 50 ␮L) of water were added to a set of the same polymer/buffer/salt mixtures using a Multipette Xstream pipette (Eppendorf, Hamburg, Germany). Systems were vortexed and centrifuged for 30–60 min at 10,000 rpm in a minispin centrifuge (Eppendorf) to accelerate phase settling. Aliquots of 20–70 ␮L from the upper and lower phases were withdrawn with a Multipette Xstream pipette in duplicate for analysis. Two aliquots from both phases were diluted with water up to 250 ␮L

in microplate wells. Following moderate shaking at room temperature (23 ◦ C), a synergy-2 UV–vis plate reader (Bio-Tek Instruments) was used to measure optical absorbance at maximum wavelength of each compound. Phases of blank systems at corresponding dilutions were measured for comparison. The partition ratio, K, is defined as the ratio of the compound concentration in the upper phase to the compound concentration in the lower phase. The partition ratio value for each solute was determined as the slope of the plot of the solute concentration in the upper phase as a function of the solute concentration in the bottom phase, obtained from six partition experiments carried out at

Table 1 Polymer compositionsa of the phases in the aqueous two-phase systems used for partitioning and physico-chemical properties of the phasesb , c data reported previously [27]. ATPS

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 a

Polymer 1a

Dextran Dextran Dextran Dextran Dextran Dextran Dextran Dextran Dextran Dextran Dextran Dextran Ficoll Ficoll Ficoll Ficoll Ficoll Ficoll Peg8000 Ficoll

Polymer 2a

Ficoll PEG10000 PEG8000 PEG6000 PEG6000 PEG4000 PEG4000 PEG1000 PEG1000 PEG600 Ucon Ucon PEG10000 PEG8000 PEG6000 PEG6000 Ucon Ucon Ucon PEG4000

Total compositiona

Physico-chemical properties of the phasesb , c

Polymer 1

Polymer 2

*b

˛b

ˇb

cb

12.94 13.03 12.41 13.48 20.00 13.67 20.00 17.96 20.00 16.23 12.39 10.00 22.99 24.67 23.08 29.23 13.01 19.12 15.00 22.56

18.06 5.86 6.06 6.00 8.88 6.15 9.26 12.13 13.57 16.87 10.08 8.00 9.90 10.42 9.87 15.00 9.93 15.47 29.97 13.62

0.003 −0.030 −0.047 −0.035 −0.056 −0.041 −0.050 −0.035 −0.052 −0.040 −0.023 −0.026 −0.050 −0.061 −0.047 −0.106 −0.046 −0.065 −0.117 −0.050

−0.028 −0.060 −0.050 −0.071 −0.105 −0.024 −0.072 −0.037 −0.061 −0.017 −0.181 −0.158 −0.014 −0.026 −0.023 0.000 −0.098 −0.138 −0.091 −0.020

0.010 0.002 0.000 0.005 0.002 0.007 0.015 0.018 0.018 0.005 0.015 0.027 −0.029 0.000 −0.001 0.039 0.003 0.045 0.070 0.001

0.0481 ± 0.0005 −0.0058 ± 0.0001 −0.0178 ± 0.0002 −0.0372 ± 0.0007 −0.036 ± 0.002 −0.0371 ± 0.0003 −0.028 ± 0.003 −0.03 ± 0.01 −0.018 ± 0.006 −0.0148 ± 0.0004 0.041 ± 0.003 0.02 ± 0.01 −0.1262 ± 0.0003 −0.157 ± 0.005 −0.135 ± 0.002 −0.079 ± 0.002 0.005 ± 0.002 0.085 ± 0.002 0.60 ± 0.02 −0.141 ± 0.005

Polymer 1, predominant polymer in the bottom phase; polymer 2, predominant polymer in the top phase; all concentrations of polymers are in %wt. *, solvent dipolarity/polarizability; ˛, solvent hydrogen-bond donor acidity; ˇ, solvent hydrogen-bond acceptor basicity; all differences are calculated as those between values measured in the top phase and those measured in the bottom phase. c The difference between the ability of the media in the coexisting phases to participate in ion–ion and ion–dipole interactions is represented by parameter c. b

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Table 2 Partition ratios of selected nonionic compounds in aqueous two-phase systems (ATPS compositions see in Table 1). ATPS

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 a b

Partition ratios 4-Nitrophenol

4-Nitroanisole

1.264 ± 0.004 1.686 ± 0.003 1.625 ± 0.003 1.631 ± 0.003 2.55 ± 0.02 1.474 ± 0.005 2.58 ± 0.01 1.629 ± 0.008 2.33 ± 0.02 1.632 ± 0.006 3.361 ± 0.008 2.54 ± 0.02 1.400 ± 0.002 1.491 ± 0.005 1.335 ± 0.005 2.40 ± 0.02 1.930 ± 0.007 5.37 ± 0.04 7.53 ± 0.04 1.56 ± 0.01

1.143 1.441 1.415 1.392 2.05 1.336 2.066 1.324 1.70 1.333 2.603 2.094 1.240 1.255 1.166 1.69 1.745 3.52 6.50 1.245

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.002 0.005 0.003 0.005 0.01 0.002 0.006 0.006 0.01 0.005 0.004 0.008 0.006 0.007 0.004 0.01 0.007 0.04 0.06 0.007

Caffeine 1.061 1.158 1.146 1.152 1.263 1.122 1.306 1.194 1.331 1.160 1.429 1.331 1.063 1.068 1.057 1.158 1.245 1.511 1.707 1.060

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.006 0.006 0.003 0.005 0.006 0.004 0.005 0.005 0.009 0.004 0.008 0.003 0.006 0.005 0.004 0.008 0.005 0.005 0.007 0.005

3-OH-benzaldehydea

Phenol

4-OH-acetanilideb

1.173 ± 0.007 1.710 ± 0.004 1.739 ± 0.007 1.719 ± 0.005 2.672 ± 0.005 1.607 ± 0.004 2.62 ± 0.01 1.703 ± 0.009 2.236 ± 0.008 1.611 ± 0.007 3.32 ± 0.04 2.53 ± 0.02 1.668 ± 0.005 1.795 ± 0.005 1.523 ± 0.006 2.484 ± 0.005 2.102 ± 0.006 4.14 ± 0.05 4.63 ± 0.04 1.684 ± 0.001

1.21 ± 0.03 1.89 ± 0.01 1.88 ± 0.01 1.705 ± 0.008 2.78 ± 0.01 1.663 ± 0.003 2.630 ± 0.007 1.559 ± 0.006 2.38 ± 0.01 1.64 ± 0.01 3.23 ± 0.02 2.423 ± 0.009 1.71 ± 0.01 1.74 ± 0.01 1.63 ± 0.01 2.561 ± 0.009 2.146 ± 0.003 3.82 ± 0.01 4.28 ± 0.05 1.68 ± 0.02

1.237 2.019 1.905 1.86 3.01 1.765 2.832 1.85 2.332 1.780 3.76 2.80 1.963 2.200 1.755 2.89 2.22 4.20 2.99 1.813

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.007 0.002 0.003 0.01 0.01 0.007 0.007 0.01 0.007 0.005 0.02 0.02 0.006 0.009 0.004 0.01 0.01 0.02 0.02 0.002

3-Hydroxybenzaldehyde. 4-Hydroxyacetanilide.

different concentrations of the solute and at the fixed composition of the system. Deviation from the average K value was consistently below 5% and in most cases lower than 2%. 2.2.3. Statistical analysis The software packages utilized were Sigma plot 12.0 and JMP 7.0. All the statistics provided are for 95% confidence intervals. 3. Results and discussion The choice of compounds examined at the first stage of this study was defined by the following criteria. The compounds had to be water soluble and to have relatively high molar UV absorptivity to simplify analysis of concentrations in the phases by UV spectroscopy. Hence we selected a group of diverse structurally simple monofunctional compounds and included additionally slightly more structurally complex (more drug-like) compounds. 3.1. Use of solute-specific coefficients for prediction of solute partition behavior Distribution coefficients of polar organic compounds examined in all the ATPS listed in Table 1 are presented in Table 2. All the data for each compound were used to determine solute-specific coefficients Ss , As , Bs , and Cs in Eq. (4) by the multiple linear regression analysis. It should be noted that we followed the procedure described by Ab Rani et al. [31], using the p-value as a test for significance for each coefficient (solute-specific coefficient) in Eq. (4) for a given compound. If all 4 coefficients (Ss , As , Bs , and Cs ) proved statistically significant (p < 0.01), then the correlation was accepted. If one or more solute-specific coefficients were characterized by the p-values exceeding 0.01 the equations for different trios or pairs of coefficients were examined. The equation with a set of coefficients providing p-values for all parameters below 0.01 was accepted. The solute solute-specific coefficients determined for each compound are presented in Table 3. In order to check how Eq. (4) predicts partitioning of compounds we selected ATPS for the reference set based on the criteria described previously [27]. The criteria may be defined briefly as covering the whole spectra of the solvent features values listed in Table 1. All the data in the reference ATPS set for each compound

was used to determine the solute solute-specific coefficients Ss , As , Bs , and Cs in Eq. (4) by the multiple linear regression analysis. A backward elimination procedure similar to the one described above was followed using the p-value as a test for significance for each coefficient (solute-specific coefficient) in Eq. (4) for a given compound. The only difference was that in view of the reduced number of ATPS employed the statistical significance threshold was changed to p < 0.1. The solute-specific coefficients determined for each compound from the data obtained in the reference ATPS set are presented in Table 4 together with those determined for additional compounds. It should be indicated also that an additional condition used was that log K = 0 when all differences between solvent properties of the phases are zero [27–29]. The first and most important conclusion from comparison of the solute-specific coefficients values in Tables 3 and 4 is that the values for all solute-specific coefficients for each compound examined in both 10 and 20 ATPS are the same within the experimental error limits. The second conclusion is that as expected the uncertainties inherent to the solute-specific coefficients values usually increase when the set of ATPS is reduced. The solute-specific coefficients values determined from the data obtained in the reference ATPS set (see in Table 3) were used together with the solvent characteristics in Table 1 for calculation of K-values for each compound in the 10 ATPS not included in the reference ATPS set. The results obtained using Eq. (4) are presented in Fig. 2 as a plot of the predicted (values in ATPS not included in the reference set) and correlated (values within ATPS included in the reference set) K-values against experimentally determined K-values for the same compound. The results

Table 3 Solute-specific coefficients determined as coefficients in Eq. (4) for organic compounds indicated (calculated by multiple linear regression analysis from data in Tables 1 and 2) and calculated using 20 ATPS (p-value indicating statistical significance is below 0.0001 for each solute-specific coefficient). Compound

Ss

4-Nitrophenol 4-Nitroanisole Caffeine 3-Hydroxybenzaldehyde Phenol 4-Hydroxyacetanilide

−3.7 −2.6 −0.8 −3.7 −3.8 −3.9

Bs ± ± ± ± ± ±

0.3 0.2 0.1 0.2 0.2 0.2

−2.2 −1.8 −0.73 −2.2 −2.2 −2.5

Cs ± ± ± ± ± ±

0.2 0.1 0.07 0.1 0.1 0.2

0.46 ± 0.60 ± 0.14 ± – – −0.31 ±

0.09 0.05 0.02

0.06

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Table 4 Solute-specific coefficients determined as coefficients in Eq. (4) for organic compounds indicated (calculated by multiple linear regression analysis from data in Tables 1, 2, and 5 for the reference set of 10 ATPS: S1, S6, S9, S10, S11, S13, S14, S15, S18, and S19); p-value indicating statistical significance for each solute-specific coefficient shown in parenthesis except p < 0.0001 (not shown). Compound

Ss

4-Nitrophenolb 4-Nitroanisole Caffeine 3-Hydroxybenzaldehyde Phenol 4-Hydroxyacetanilide p-Anisaldehydea , b Methyl antranilatea , b Coumarina , b trans-Cinnamyl alcohola , b Vanillina , b Anilinea , b Indolea , b Acetophenonea , b

−3.4 −2.5 −0.8 −3.6 −3.6 −3.7 −2.6 −3.4 −2.2 −3.2 −3.5 −2.0 −3.3 −2.6

a b

Bs ± ± ± ± ± ± ± ± ± ± ± ± ± ±

−2.7 −2.0 −0.8 −2.6 −2.4 −2.9 −2.15 −2.7 −2.0 −2.5 −2.6 −1.24 −3.4 −1.9

0.4 0.2 0.1 (p = 0.0007) 0.2 0.2 0.2 0.1 0.3 0.2 0.2 0.2 0.1 0.3 0.1

0.3 0.1 0.1 0.1 0.1 0.2 0.08 0.2 0.1 0.1 0.1 0.07 0.2 0.1

0.44 ± 0.58 ± 0.13 ± – – −0.35 ± 0.17 ± 0.23 ± 0.28 ± 0.10 ± −0.08 ± 0.18 ± 0.36 ± 0.12 ±

0.08 (p = 0.0009) 0.04 0.03 (p = 0.004)

0.05 0.03 (p = 0.0002) 0.06 (p = 0.004) 0.04 0.04 (p = 0.06) 0.04 (p = 0.1) 0.02 0.06 (p = 0.0004) 0.03 (p = 0.005)

Partition ratios for the compound are listed in Table 5. Compounds with known odor detection threshold (see Table 6).

obtained indicate that it is possible to predict partition behavior of a compound with established solute-specific coefficients in any ATPS with experimentally characterized solvent features of the phases provided the ionic composition of the ATPS is the same as in those used for solute-specific coefficients determination. It remains to be explored if the ionic composition may affect the solute-specific coefficients for polar organic compounds. 3.2. Solute-specific coefficients Analysis of the solute-specific coefficients Ss and Bs values in Table 4 shows that both these solute-specific coefficients have negative values for all compounds examined here. For all dinitrophenylated (DNP) amino acids the reported Bs and Ss values are also negative (except for DNP-glycine with Ss value of 0.05 ± 0.02)[27], while for zwitterionic free amino acids Ss and Bs values are positive (except for free tyrosine and tryptophan) [27–29]. It is possible that the negative signs for both solute-specific coefficients Bs and Ss values originate from the differences between the corresponding solvent features of the phases (* and ˛) being negative in essentially all ATPS employed (see in Table 1) but this hypothesis does not explain the data reported previously for free amino acids [27–29]. Another explanation may be that the dipole–dipole

Predicted/Calculated partition ratio, logKcalc.

Cs ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1,5

1,0

0,5

0,0

-0,5 -0,5

0,0

0,5

1,0

1,5

Experimental partition ratio, logKexp Fig. 2. Correlated and predicted (see the text) partition ratios values plotted versus experimentally determined values of partition ratios for compounds examined in all ATPS ( – predicted values;  – correlated values).

and hydrogen bonding solute–water interactions in the vicinity of two oppositely charged groups (free amino acids) are both different from those occurring for uncharged compounds and compounds with a single charged group. The number of compounds examined so far is clearly insufficient for any general conclusion. The solute-specific coefficient Cs values for dinitrophenylated amino acids are positive and for free amino acids are negative similarly to 4-hydroxyacetanilide and vanillin (see in Table 4), while this solute-specific coefficient values for the remaining organic solutes are positive. It is well known from the data obtained with the LSER model that contributions of different types of solute–solvent interactions may be positive or negative depending on the particular solvent system (see above in Eqs. (2) and (3)). All the solutes descriptors are considered positive via application of the normalization procedure described by Abraham [5]. In our empirical solvatochromic comparison model (not strictly free energy related) we assume the positive contribution for each type of interactions and hence for each individual compound examined in a series of different ATPS we find the solute-specific coefficients being positive or negative depending on the contribution of the particular type of solute–solvent interactions into distribution of the compound between the aqueous phases with different solvent features. Normalization procedures might take care of the negative signs of the solute-specific coefficients but it seems to us premature in view of a vast variety of possible ATPS compositions and their corresponding solvent features and limited number of solutes examined so far. The solute hydrogen-bond donor acidity is not manifested in ATPS used except for few cases [29] likely due to the minute differences between solvent hydrogen bond basicity of the coexisting phases (see ˇ values in Table 1). Studies of salt effects on polar organic compounds in aqueous solutions are commonly discussed from the viewpoint of the mechanism of salting-out and salting-in effects of different salts. Actually, solubility of inorganic salts in water exemplifies an important role of ion–dipole interactions [32]. The role of direct contact and/or possibly solvent separated ion–dipole interactions in different salt effects on solubility of organic compounds and polymers including proteins was suggested by Boström et al. [33,34]. Hydrogen-bond interactions of anions with amide group in Nmetylacetamide in aqueous solution was reported by Algaer and van der Vegt [35]. According to the data reported by Rembert et al. [36] SCN− ion is capable of interaction with polypeptide backbone via a hybrid binding to the amide nitrogen and the adjacent ␣carbon more strongly than Cl− ion, while SO4 2− ion is repelled from the backbone as well as from the hydrophobic side-chains of

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3.96 5.92 3.92 4.51 3.85 2.79 8.02 3.46 3.11 4.81 2.94 3.87 3.98 2.10 5.12 2.86 1.324 1.478 1.17 1.447 1.57 1.18 1.367 1.283 1.544 1.694 1.402 1.73 1.90 1.33 1.70 1.570 1.43 1.613 1.298 1.63 1.75 1.258 1.59 1.399 2.84 3.52 2.51 3.23 3.37 1.87 5.05 2.481 0.008 0.02 0.01 0.02 0.006 0.01 0.01 0.01 ± ± ± ± ± ± ± ± 1.435 1.49 1.37 1.53 1.677 1.27 1.55 1.35

the elastin-like polypeptide (VPGVG)120 . Interactions of different anions with tertiary butyl alcohol in aqueous solution according to data reported by Bowron and Finney [37] may include direct and water separated pairing with polar group of the solute. The results obtained by Chen et al. [38] indicate that different ions may interact with poly-(N-isopropylacrylamide) and affect organization of water adjacent to the macromolecule. Endo et al. [39] indicated that Na+ and Cl− ions increase the polar interactions of neutral organic solutes with water. The data obtained here does not provide the mechanism of polar organic solute interactions with ions. This mechanism may involve direct or solvent separated ion–solute pairing. It seems possible to suggest that the solute-specific coefficient Cs values in Table 4 may serve as representing the strength of ion–dipole interactions for the organic compounds studied and that the approach presented here, when extended, e.g., to analysis at different ionic composition of the media may lead to gaining better insight into mechanisms of salting-in and salting-out effects, biological effects of ions in Hofmeister series, transport in vivo, structure–function relationships for biomolecules and multiple other phenomena related to biomolecules behavior in aqueous media. It should be mentioned that in contrast to the case with free amino acids where the relationship between the octanol–water partition coefficients log P and solute-specific coefficients was established [28,29] we did not find one for the organic compounds examined in this work. The presence of the electrostatic ion–dipole solute-specific coefficient Cs for most of the compounds used in the study implies that their log P values calculated or even experimentally measured and reported in the literature [40] may be judged as unreliable. It was established previously [41] that octanol–water partitioning of polar organic compounds may vary over several orders of magnitude with ionic composition of the aqueous phase and that explains the multitude of log P values reported [40] for the same compound by different authors who often indicated only pH but not the composition of the buffer utilized. The data obtained up to the present including those reported here and previously [27–29] indicate that the properties of the polar and charged compounds displayed in terms of their different types of interactions with aqueous media appear to depend on the ionic composition of the media. How this dependence is related to the compounds structures remains to be explored. The work in this direction is currently in progress in our laboratories.

p-Anisaldehyde Methyl anthranilate Coumarin trans-Cinnamyl alcohol Vanillin Aniline Indole Acetophenone

1.144 1.253 1.164 1.273 1.19 1.10 1.42 1.082

± ± ± ± ± ± ± ±

0.004 0.006 0.007 0.007 0.01 0.01 0.04 0.007

1.434 1.623 1.34 1.59 1.69 1.34 1.84 1.458

± ± ± ± ± ± ± ±

0.005 0.008 0.01 0.01 0.01 0.01 0.02 0.008

1.755 2.037 1.67 2.07 2.15 1.486 2.29 1.71

± ± ± ± ± ± ± ±

0.005 0.006 0.01 0.02 0.01 0.009 0.01 0.01

S10 S9 S6 S1

Partition coefficients

Table 5 Partition ratios of polar compounds in selected aqueous two-phase systems (ATPS compositions see in Table 1).

S11

± ± ± ± ± ± ± ±

0.04 0.02 0.05 0.03 0.04 0.01 0.01 0.009

S13

± ± ± ± ± ± ± ±

0.01 0.008 0.008 0.01 0.02 0.007 0.01 0.008

S14

± ± ± ± ± ± ± ±

0.009 0.007 0.004 0.01 0.02 0.01 0.01 0.007

S15

± ± ± ± ± ± ± ±

0.006 0.007 0.02 0.008 0.01 0.01 0.006 0.007

S18

± ± ± ± ± ± ± ±

0.04 0.08 0.05 0.03 0.05 0.02 0.03 0.02

S19

± ± ± ± ± ± ± ±

0.03 0.07 0.02 0.03 0.04 0.01 0.08 0.02

102

3.3. Use of solute-specific coefficients in structure–activity relationship analysis It has been suggested some years ago [42] that the distribution of a compound (e.g., drug) in vivo, in the absence of active transport mechanisms, may be approximated by equilibrium partitioning between different aqueous environments. This hypothesis was later confirmed in studies of human intestinal drug absorption [7] and distribution of drugs between blood and tissues, such as liver [8], brain [9,10] and skin [11]. Since the solute-specific coefficients obtained in the present work represent the susceptibilities of the complementary solute–solvent interactions to the changes in the corresponding solvent properties in aqueous environment, it is reasonable to expect that these coefficients might be useful in Quantitative Structure–Activity Relationship (QSAR) analysis. Previously we showed that linear combinations of the solute-specific coefficients for free amino acids might be used to describe their biological effects [29] and we tested the same hypothesis here. The odor detection threshold (ODT) represents the minimum concentration of an odorant in the air that can be detected by the human nose. Some of the compounds studied in the present work are used as odorants in perfumery industry and Table 6 presents

P.P. Madeira et al. / J. Chromatogr. A 1322 (2013) 97–104 Table 6 Odor detection thresholda and polarizabilityb for aroma compounds. Compound

Log (1/ODT)a

Polarizabilityb (×1023 cm3 )

p-Anisaldehyde Methyl anthranilate Coumarin trans-Cinnamyl alcohol Vanillin Aniline Indole Acetophenone 4-Nitrophenol

8.646 8.332 9.113 9.562 9.835 5.518 9.563 7.569 5.638

1.5732 1.6754 1.5765 1.7315 1.6478 1.2087 1.5274 1.4383 1.3748

the approach described in the present work may be used for QSAR analysis. Acknowledgments

a

Data from van Gemert, L. J.[43] (for explanation see the text. ODT units – mg/m3 ). ChemSpider. Database of Chemical Structures and Property Predictions – Royal Society of Chemistry, 2013, from http://www.chemspider.com/Default.aspx. b

their ODT values. In order to reduce variability from literature data (sample presentation, methodology bias, panelists, among others) the geometric mean values are listed as recommended in sensory analyses [43,44]. The ODT values in Table 6 were found to correlate with the solute-specific coefficients values for the compounds examined as: log(1/ODT) = 2.7±0.3 Ss + 6.6±0.4 Ps − 3.0±0.3 Bs − 5.4±0.7 Cs N = 9; SE = 0.27; F = 2129.1, and p values for each independent variable < 0.001

103

(5)

where Ps is the polarizability of a compound, N is the number of compounds, SE is the standard deviation, F is the ratio of variance, and all the other parameters are as defined above. The olfactory detection of chemically distinct odorants presumably results from their binding to specific olfactory receptors [45,46]. The general validity of the relationship described by Eq. (5) remains to be proven over an extended variety of chemical structures but it seems to provide useful information about the likely mechanism of odorant(s)–olfactory receptor(s) interaction(s). Apparently the receptor (or receptors) involved in the mechanism of perception of these compounds is strongly charged or has a strong dipole moment as implied by the large coefficients at both Cs and Ps solute-specific coefficients. It should be mentioned that it was shown by Abraham et al. [47,48] that the odor detection threshold is related to the solute descriptors and even coefficients at the corresponding solute descriptors are close to those in Eq. (5). The limited number of compounds examined here prevents any general conclusion from Eq. (5). It shows a trend remaining to be proven for the extended set of compounds. It confirms, however, the aforementioned assumption that the solute-specific coefficients determined by the approach used are related to the structures of the compounds and hence may be used in QSAR analysis for biomolecules including macromolecules, such as proteins. Further studies in this direction are in progress in our laboratories.

Financial support for this work was in part provided by national research grant PTDC/EQU-EQU/112812/2009 for which the authors are thankful. This work was co-financed by FCT and FEDER under Programe COMPETE (Project PEst-C/EQB/LA0020/2013). A. Bessa acknowledges the scholarship within the Project PTDC/EQUEQU/112812/2009 from Fundac¸ão para a Ciência e a Tecnologia (FCT). P.P. Madeira acknowledges the financial support (Grant SFRH/BPD/45055/2008) from FCT. M.A. Teixeira acknowledges his post-doctoral grant from FCT (SFRH/BPD/76645/2011). The authors thank Prof. Manuel Coelho and Profa . Maria do Carmo Pereira (Faculdade de Engenharia da Universidade do Porto) for the use of their UV–vis plate reader equipment. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]

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4. Conclusions

[27]

The results obtained indicate that solute-specific coefficients representing the susceptibilities of the complementary solute–solvent interactions to the changes in the corresponding solvent properties, in particular, solute dipole–dipole, hydrogen bonding and electrostatic interactions with the aqueous environment determined in a set of selected ATPS allow one to predict the partition ratios values for the solute in other ATPS with established solvent features provided the ionic composition of the ATPS is the same as the one used for solute-specific coefficients determination. The data obtained indicate also that polar solute–solvent interactions in aqueous media may be affected by the ionic composition of the media. These effects appear to be solute structure dependent. It is also shown that linear combination of the solute-specific coefficients characterizing solute–water interactions obtained by

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Study of organic compounds-water interactions by partition in aqueous two-phase systems.

Partition coefficients of fourteen organic compounds were determined in 10 or 20 different polymer/polymer aqueous two-phase systems (ATPS) all at phy...
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