Open access peer-reviewed chapter

Chromatographic Retention Parameters as Molecular Descriptors for Lipophilicity in QSA(P)R Studies of Bile Acid

By Mihalj Poša

Submitted: November 23rd 2011Reviewed: April 26th 2012Published: October 24th 2012

DOI: 10.5772/48356

Downloaded: 2518

1. Introduction

Beside of separation and identification of chemical compounds, chromatography can be used to obtain molecular parameters that reflect their structural characteristics – molecular descriptors. Most often it is a parameter of hydrophobicity (lipophilicity) of molecules (ions) which is obtained by using retention parameters of reversed phase liquid chromatography or thin layer chromatography of high resolutions (RPHPLC and RPTLC) [1,2]. Hydrophobicity of molecules is an important feature in medical chemistry, and arbitrarily connected with the logarithm of the solute partition (distribution) coefficient logP (P stands for the ratio of the equilibrium concentration of the particle of the same electronic structure in 1-octanol and its equilibrium concentration in water). Partition coefficient is determined by the traditional shake flask method, which has drawbacks: long analysis (for reaching equilibrium) and the results often do not have adequate reproducibility [1,2,3]. While hydrophobicity obtained by chromatographic methods is obtained relatively quickly, it is possible to specify a number of compounds and achieved by high precision and reproducibility of results. Lipophilicity of chemical compounds (which is expressed either as a chromatographically i.e. retention parameters or as an in silico molecular descriptors of logP) is often included in the regression equations obtained in the QSA(P)R (Quantitative Structure Activity (Property) Relationship) studies [1,2,3].

Bile acids are amphiphilic molecules that have peculiar structure, because molecular descriptors that are obtained on the basis of the molecular graph or fragmentation methods often do not reflect their true structural features [4]. Therefore, the bile acid chromatographic lipophilicity play an important application in obtaining QSA(P)R models that connect biological and pharmacological or other physical-chemical properties (solubility, critical micelle concentration, critical micelle temperature, etc.) for their structure [5]. In the QSA(P)R models chromatographic parameters are independent variables.

In the following section presents the introduction chromatographic parameters (TLC and HPLC) that are used to represent the hydrophobicity of compounds, and presents the main structural features of bile acids.

2. Chromatographic parameters

The basic characteristic of the position of analytes spot on TLC chromatograms is the Rf value (retardaction factor). Rf value is the ratio of the path length that has crossed spot of solute from the start line Ss and path length Sf of the solvent front (Fig. 1).

Rf=sssfE1

If the solute spends more time in the mobile phase, then its chromatographic spot position is closer to the front of solvent and its Rf value is even higher (maximum value of the Rf parameter is 1). Whereas if the solute spends more time in the stationary phase, then the position of his spots closer to the start line, and its Rf value is less (Fig. 1 (a)).

Figure 1.

Chromatograms: (1) TLC with Rf values of the solute over their spots, (2) HPLC.

The ratio of time spent by the solute in the stationary phase ts and the time by spend in the mobile phase tm is the capacitance factor.

k=tstmE2

The connection between Rf values and capacitive factor has the equation:

k=1Rf1E3

The logarithm of the above expression is:

logk=log(1Rf1)E4

and the retention parameter logk usually indicates with RM. In the reverse phase thin layer chromatography, where the stationary phase is the hydrophobic environment, in the course of the chromatographic process chemical compounds that are more hydrophobic spend more time in the stationary phase than in the polar mobile phase, which resulting in less crossed paths of their chromatographic spots. Therefore, the more hydrophobic is solute its Rf value is more lower or its retention parameter RM value is more higher. In reverse phase thin-layer chromatography for each compound is determined the dependence of the chromatographic parameters RM of the volume fraction φ of organic modifier in aqueous mobile phase (i.e. for each of the tested compounds from one chromatographic experiment to another experiment varies of mobile phase volume fraction of organic modifier). The most common organic modifier is methanol. If increasing the volume fraction of the organic modifier in aqueous mobile phase results of decrease in hydrophobicity of mobile phase. This is manifested as the reduction of the difference between staying time of solute in the stationary and mobile phase during the chromatographic process – increased Rf values (decreasing value of RM). Usually between the chromatographic parameters RM and the volume fraction of organic modifier φ is linear relation (usually in the interval: 0.2 ≤ φ ≤ 0.8) [3,6-8].

RM=RM0+SφE5

In the above equation RM0 is the extrapolated value of the chromatographic parameters RM which is governed to the mobile phase contain only water (or buffered aqueous solution), i.e. mobile phase without an organic modifier, while S is the slope of the right (Fig. 2). S is directly related to the specific surface of the stationary phase.

Figure 2.

The linear dependence of RM chromatographic parameters of volume of organic modifier φ.

RM0 parameters value depends on the type of organic modifier on the basis of which is determined by the function (1.5). RM0 is usually in the good correlation of the compounds lipophilicity. In recent times to describe the molecules lipophilicity also used the chromatographic parameters φ0 which is the ratio of RM0 and the slope S (Fig. 2) [7,8].

φ0=RM0|S|E6

The chromatographic parameters φ0 corresponding volume ratio of organic modifier in aqueous mobile phase in which the same amount of solute in the mobile phase and in the stationary phase. Indeed solute in the above mobile phase composition, during the chromatographic process, spends at the same time in the stationary phase and the mobile phase, therefore capacitive factor (2) is k = 1, respectively Rf value (3) is 0.5. This Rf values corresponding to RM chromatographic parameter (4) whose value is zero. Which means that equation (5) is: 0=RM0 + Sφ0 from which follows the expression (6). If the solute is more hydrophobic then a larger quantity of organic modifier is needed to equalize the amount of solute in two phases, i.e. the chromatographic parameter φ0 has higher value. With a high efficient reverse phase liquid chromatography, stationary phase is also hydrophobic environment. Solute is characterized by retention time ts, which represents the elapsed time from injection to the occurrence of the same solute in detectors, i.e. the retention time of solute in the column. Chemical compounds in RP-HPLC analysis is usually characterized by the retention coefficient (capacity factor):

k=tst0t0E7

where t0 is the retention time of solvent from the mobile phase (Fig. 1 (a)). If a chemical compound is more hydrophobic, then more time is spent in the hydrophobic stationary phase, i.e. it takes more time to pass the column and the retention time or retention factor even greater. The logarithm of the retention coefficient logk is used as a parameter of hydrophobicity of chemical compound. An important chromatographic parameter of lipophilicity is logkw which were obtained an extrapolation of the linear equations (8) to the zero volume fraction φ of organic modifier in aqueous mobile phase.

logk=logkw+SφE8

Similar as in the RP TLC analysis, in RP HPLC also can defined the chromatographic indices φ0 (with the same meaning) as the ratio of logkw and the slope from logk = f(φ) [9,10].

Figure 3.

Application of principal component analysis on the retention data matrix D, n = number of different volume fraction of organic modifier, m = number of different compounds, n> 3.

There is a possibility of application principal component analysis (PCA) on chromatographic data to the thin layer and the liquid chromatography. PCA is applied to the data matrix D of retention parameters RM, logk (or k). The columns of the matrix D corresponding retention parameters for different volume fraction φ of organic modifier (columns represent the organic modifier)​​ while the matrix rows represent the different types of chemical compounds. PCA is usually applied directly on the covariance matrix DTD retention parameters. As a result of PCAs mathematical procedure (orthogonal diagonalization) are obtained orthonorms PC score vectors of whose number is 2 (PC1, PC2) or 3 (PC1, PC2, PC3), depending on the percentage of variance explained from the data matrix D. Accordingly objects (tested molecules) from a multidimensional space matrix of retention parameters D mapped to 2d or 3d space of PC. In the graphs of PC scores can be found congeneric group of chemical compounds by their lipophilicity. As a parameter of lipophilicity of chemical compounds in the QSA(P)R studies can be applied also to the scores of PC [3,6].

3. The hydrophobic effect

Generally, the dissolution of an amphiphilic object in water is accompanied by the disruption of the hydrogen bonds between water molecules and formation of a hydration sheath (hydration layer) around the particles of the dissolved substance. If we observe such a system (amphiphilic solution), which consists of two subsystems: hydrophobic part (a) and hydrophilic part (b), then the thermodynamic functions can be considered separately for each subsystem (Fig. 4). For both subsystems it holds that in the formation of the solvation sheaths (around the amphiphilic objects) approximately the same number of hydrogen bonds are formed as existed between the water molecules in the bulk water, i.e. before introducing the amphiphilic object. Hence the change of the enthalpy for each of the subsystems is equal to zero: ΔH(a)≈ΔH(b)≈0. Also, in the formation of the solvation sheath, the entropy (translational and rotational) of both subsystems decreases, that is: ΔS(a)≈ΔS(b)<0. However, the water molecules from the hydrophilic side (b) of the amphiphilic molecule form additional hydrogen bonds; hence for this subsystem there is an additional negative enthalpy (ΔHɛ<0), which is dissipated as heath in the environment (bulk of the solution), thus giving rise to a positive change of the entropy (of the environment). Therefore, the free enthalpy change for the hydrophilic subsystem is: ΔG(b)<0, on the basis of which the water molecules from the hydrophilic side (b) of the amphiphil can be denoted as stabilized water molecules (SWM), while the water molecules from the hydrophobic side (a) are nonstabilized water molecules (NSWM) [4,11].

Figure 4.

The hydrophobic effect: SWM = stabilised water molecules, NSWM = nonstabilised water molecules.

The ratio of the hydrophilic-to-hydrophobic surface area of an amphiphilic molecule determines the overall change of the Gibbs energy of formation of the hydration sheath (ΔG), and, since the hydrophobic surface of the amphiphilic molecule is larger, then ΔG>0. The larger the amount of the amphiphil present in the solution, the more water molecules participate in the formation of the hydration sheath, and the more negative is the overall entropy change. This results in the changes in the system (solution) due to the passing of NSMW from the amphiphil hydration sheath to the bulk of the solution, giving rise to the system entropy. Amphiphilic molecules (ions) are oriented so that their (formally) desolvated hydrophobic sides are to the smallest extent exposed to water molecules. This is realized either in the form of self-association of the amphiphils via their hydrophobic surfaces (Fig. 4) (entropy decrease due to the amphiphil self-association is smaller than the increase in the entropy due to their release from the hydration layer) or by binding of the amphiphil to the hydrophobic surface of the system: hydrophobic stationary phase, proteins, distribution in the organic solvent, etc. [11]. Generally, the hydrophobicity of a particle (molecule, ion) can be expressed via the logarithm of the coefficient of its partition (logP) between 1-octanol and water (P stands for the ratio of the equilibrium concentration of the particle of the same electronic structure in 1-octanol and its equilibrium concentration in water). If logP > 1, the particle is hydrophobic (lipophilic), and if logP < 1, the particle is hydrophilic [12]. Apart from the partition coefficient, the hydrophobicity of a particle can be also expressed via chromatographic parameters, either of the normal-phase or reversed-phase chromatography.

4. Structure of bile acids

Bile acid enzymatic produced in the liver of human and other mammals are the primary bile acids, and their intestinal microbial transformation to obtain the secondary bile acids. Both groups of bile acids are hydroxy derivatives of 5β-cholanic acid (cholic acid and chenodeoxycholic acid) [13]. The geometry of the 5β-cholanic acid largely determines of bile acids properties. The steroid skeleton of 5β-cholanic acid can be distinguished convex β and concave α surface (Fig. 5) [5].

Figure 5.

The bile acids general structure.

Hydroxyl groups of bile acids are mostly oriented towards α side of steroid skeleton. X ray diffraction is demonstrated that the carboxyl group of the side chain from C17 carbon is also located on the α side of the steroid ring system. A concave (α) of bile acids steroid skeleton is the polar surface – hydrophilic side, while the convex surface (β) is the nonpolar surface – hydrophobic side. Simultaneous presence of hydrophobic and hydrophilic regions in the molecule of bile acids is named as amphiphilic. Since the carboxyl group ionized under physiological conditions, bile acids in biochemical systems belong to the ionic amphiphile. Also, bile acids are a special group of amphiphilic compounds, the group of biplanar molecules [4.5]. This is most apparent in cholic acid molecules (Fig. 6) because the molecules oxygen atoms from the α OH groups are in the same plane, the polar plane. In chenodeoxycholic and deoxycholic acid cannot be talking about the true planar polarity, but the literature often uses the term molecule with a hydrophilic edge.

Figure 6.

Two different side of cholic acid.

If it oxidation of C7 or C12 α axial hydroxy group in cholic acid (C) (or deoxycolic acid (DC) or chenodeocycholic acid (CDC)) molecule leads to oxo derivative whose oxigen atom is switched for 60° (Newman projection) in relation to its' starting axial orientation i.e forms an angle of 30° with steroid skeleton mean plain (SSMP) (Newman projection, Fig. 7) [11,14]. Oxigen atom from C7 or C12 oxo group has the same steric orientation as has the equatorial C6 OH group from hyodeoxycholic acid (HDA).

Figure 7.

Introduction of oxo group in bile acids' steroid sceleton leads to rise in number of stabilised water molecules (SWM) i.e. the hydrophobic β side of the steroid sceleton decreases.

Convergence of oxigen core (from oxo group) toward steroid sceleton β side means that there is the rise in number of stabilised water molecules (SWM, stabilised by hydrogen bonds) in hydratation layer of bile acids' steroid sceleton while hydrophobic surface of the convex side of the steroid sceleton decreases (Fig. 7). Thus, the number of unstabilised water molecules (NSWM) declines [11].

BA[NSWM]n(aq)BA(s)+nNSWME9
(R1)

If bile acids' hydrophobic surface deceases (B), i.e. the number of NSWM is lower, the observed molecule bonds to the hydrophobic stationary phase with lower intensity, i.e. reaction (R1) is moved to the right. This change in hydrophobic surface reflects in the values of retention coefficient k (RPHPLC) and retention parameter RM0 (RPTLC).

5. Discussion: bile acids and the hromatographic parametars (indices)

In the RPHPLC(TLC), there is an equilibrium between the bile acid molecules (ions) from the polar mobile phase BA(aq) and the bile acid BA(s) adsorbed on the hydrophobic stationary phase.

BA(aq)BA(s)E10
(R2)

This equilibrium is characterized by the equilibrium constant Kad = [BA(s)] / [BA(aq)], which is connected with the retention factor (capacity factor) (7) via the following relation [15]:

k=KadVhcVpf=KadφE11

where Vhc represents the volume of hydrocarbon in the stationary phase, whereas Vpf is the volume of the polar phase. Their ratio is denoted as φ, and it represents a characteristic of the column. The change of the standard Gibbs energy ΔGadof the reaction (R2), taking also into account (9), is:

ΔGad=RTlnKad=RTlnk+RTlnφE12

In both RPHPLC and RPTLC, in the adsorption of bile acids on the hydrophobic stationary phase, the water molecules from the solvation sheath that are not stabilized by hydrogen bonds (NSWM) pass to the hydrophilic mobile phase, giving a positive entropy contribution. Beside that, during the return of these water molecules in the bulk solution, NSWMs surface energy is released, which is then dissipated as a heat in the environment (solvophobic theory: according to this theory change in surface energy is a key factor in the reversed phase chromatographic processes energy). The formally dehydrated hydrophobic surfaces of bile acid molecules (ions) bind to the surface of the adsorbent by hydrophobic interactions (London dispersion forces, and dipol-induced dipole interactions). This makes a negative enthalpic contribution to the adsorption process, which is also dissipated as heat into the mobile phase (environment), giving rise to a positive change of the entropy of the environment.

Hence, the overall entropy change is positive, i.e. the Gibbs energy is lowered. The decrease in the free enthalpy of adsorption (ΔadGº) of bile acids (molecules or ions) on the hydrophobic stationary phase is more pronounced if the solvation layer of the steroid skeleton contains a greater number of NSWM, since then, there exists a largest increase in the overall entropy. In the binding of bile acids (molecules or ions) to the hydrophobic stationary phase, their steroid skeletons orient in such a way that the formally desolvated surfaces are to the smallest possible extent exposed to the solvent molecules from the hydrophilic mobile phase (methanol and water). Since bile acids are biplanar compounds, their molecules bind to the hydrophobic stationary phase by the side of the steroid skeleton that is most hydrophobic. With natural bile acids and their oxo derivatives, this is usually the convex β side of the steroid skeleton (an exception is the enantiomer of cholic acid with β configurations of all three OH groups, where α side is more hydrophobic than the β side of the steroid skeleton). Thus, the lnk chromatographic parameter has the liner dependence of ΔadGº, which means that lnk has also linear depending on the hydrophobic surface of the β side of steroid nucleus. Similar is true for RM chromatographic parameter (RPTLC). Therefore, lnk could serve as a molecular descriptor for the hydrophobicity of bile acids. But the studies dealing with the hydrophobicity of bile acids as expressed via the retention coefficients (k), there is a problem of mutual comparability of the lnk values. Because of that it is usually accustomed to give an order of hydrophobicity of the molecules, while the lnk values, though determined under identical experimental conditions, may vary because of the characteristic of the column (RPHPLC), which is in equation (9) denoted with φ. Because of that, Heuman introduced for bile acids a relative retention coefficient (kr) which is defined as the ratio of the retention coefficient of the given bile acid (k(BA)) and the retention coefficient of taurocholic acid (TC) (k(TC)) (Fig. 8) [16].

kr(BA)=k(BA)k(TC)E13

Taurocholic acid (37) was chosen as a reference because its retention coefficient in the pH range of the mobile phase from 2.9 to 9.0 is practically constant (complete ionization) [16]. By combining equations (10) and (11), the free enthalpy of adsorpion of bile acid (BA) on the hydrophobic stationary phase is:

ΔGad(BA)=RTlnkr(BA)+ΔGad(TC)E14

In this way are eliminated the individual characteristics of the column, so that the relative retention coefficient depends only on the composition of the mobile phase and the degree of hydrophobicity of the stationary phase.

Figure 8.

Tauro conjugated bile acids.

Besides, Heuman defined also the hydrophobicity index HI as the ratio of the logarithm of the relative retention coefficient of the given bile acid (BA) and of the relative retention coefficient of taurolithocholic acid (TL) (lnkr(TL)) (Fig. 7):

HI(BA)=lnkr(BA)lnkr(TL)E15

By introducing equation (13) into (12), one obtains the following equation:

ΔGad(BA)=HI(BA)×(RTlnkr(TL))+ΔGad(TC)E16

from which is evident that there is a linear relation between the hydrophobicity index of a bile acid and the Gibbs energy of adsorption on the hydrophobic stationary phase.

The HI value for taurocholic acid (TC) is zero since its relative retention coefficient equation (11) is equal to one, and the same value (one) holds also for taurolithocholic acid (TL). If a certain bile acid is more hydrophilic than taurocholic acid (TC), its retention constant (k) is smaller than the k value of taurocholic acid (TC), so that the relative retention coefficient of that bile acid is smaller than unity (lnk<0), which means that, according to equation (13), the HI(BA) is negative. In the opposite case, if a certain bile acid is more hydrophobic than taurocholic acid (TC), then the value of its retention coefficient is greater than the retention coefficient of taurochollic acd (37), and the relative retention coefficient for that bile acid is larger than unity (lnk>0), that is HI>0 (Fig. 9).

Figure 9.

Heumans scale of bile acids hydrophobicity.

Besides of Heumans hydrophobic index (based on data retention) logk is used to obtain predictive equations where logk is the independent variable while bile acid properties associated with its hydrophobicity is the dependent variable. Armstrong and Carey were given a linear regression equation, where logk is associated with the solubilized molar fraction of cholesterol ϑ (solubilized by micellar solutions of bile acid salts in the equilibrium conditions) [17]:

logk=Slogϑconst.E17
In the plane of ϑ and logk is obtained of three linear congeneric groups of examined bile acids, a particular group form the non-conjugated bile acids, whereas the glyco- and tauro-conjugated bile acids are also in separate groups. For each of the derived linear congeneric group is determined equation (15) whose regression coefficients (R) is range in 0993-0999. Armstrong and Carey were predicted the capacity of cholesterol solubilization by bile acids oxo derivatives using the equations (15) for each congeneric group of bile acid [17]. It can be shown that logk is indeed associated with a number of hydration layers non-stabilized water moleculesfrom β side of steroid skeleton:
dlnkd(ΦmNaCl)=nNSWMMH2OE18

where ΦmNaClis the product of the osmotic coefficient and the molality of NaCl, and MH2Ois the molecular weight of water [11]. Therefore with the increasing of the number of the α axial OH groups which is replacement with equatorial OHs or oxo groups resulting in a decrease of NSWM number from the hydratation shell of bile acids, which are then reflected in the increase of logk values i.e. increasing hydrophobicity of bile acids. However, the cholesterol solubilization depends on the size of hydrophobic domain of micelles, which depends on the hydrophobic surface of the β side of steroid nucleus, that’s all mean that the logk adequately describes of cholesterol (or any other hydrophobic guest molecules) solubilization. However it should be noted that the use of logk as molecular descriptors, logk for each molecule, as the molecules from teaching set and as the molecules from the control set must be obtained in an identical column. Contrary to the different values of φ (9) increases the error of prediction.

Figure 10.

Micellar solubilization of cholesterol: cholesterol is in the contact with the hydrophobic surfaces of β side of bile acids steroid skeleton.

Natalini et al. as an experimental molecular descriptor used chromatographic parameter φ0 (6) for modeling the negative logarithm of the critical micellar concentration (pCMC = -logCMC) [9,10]. The study was included 20 non-conjugated and conjugated bile acids. For the full set of bile acids were obtained following linear regression equation:

pCMC=3.85φ00.82E19

R = 0.8246

Regression coefficient is improved if the equations of linear regression obtained separately for conjugated (18) and for non-conjugated bile acids (19):

pCMC=4.78φ01.57E20

R = 0.9643

pCMC=1.05φ0+1.32E21

R = 0.9380

Equations (18) and (19) have a good predictiveness in their congeneric groups, also with φ0 is well described hydrophobicity of the bile acids steroid skeleton β side. The mutual association of bile acid salts, especially around the first critical micellar concentration, which is through to their hydrophobic β side of steroid skeleton (Fig. 11) indicated that the chromatographic parameter φ0 adequately be used to predict its critical micellar concentration. Advantages of chromatographic parameters φ0 (6) with respect to the parameters RM0 (5) and logkw (8) is reflected in the absence of appropriate extrapolation of retention coefficients. Extrapolation error can be large if the linear area of equations (5) and (8) are away from the mobile phase without an organic modifier.

Figure 11.

Dimeric micelles of cholic acids anion.

Sarbu et al. in RPTLC experiments examined which chromatographic parameter the best describes the hydrophobicity of bile acids. In the studied bile acids (Table 1) between RM0 parameter and the slope S (Equation 5) there is a good correlation, suggesting the presence of congeneric groups among chemical compounds in the tested assembly [3].

Bile acidsAbbreviationPosition and orientation of OHs
Lithocholic acidLC
Deoxycholic acidDC3α, 12α
Chenodeoxycholic acidCDC3α, 7α
Ursodeoxycholic acidUDC3α, 7β
Hyodeoxycholic acidHDC3α, 6α
Hyocholic acidHC3α, 6α, 7α
Cholic acidC3α, 7α, 12α
Glycolithocholic acidGLC
Taurolithocholic acidTLC
Glycochenodeoxycholic acidGCDC3α, 7α
Taurochenodeoxycholic acidTCDC3α, 7α
Glycodeoxycholic acidGDC3α, 12α
Taurodeoxycholic acidTDC3α, 12α
Glycocholic acidGC3α, 7α, 12α
Taurocholic acidTC3α, 7α, 12α

Table 1.

Studied bile acids

Application of PCA on the matrix of RM values (D) ​​of the studied bile acids are obtained space of principal components with smaller dimensions then matrix D. Screen plot showed that the significant first and second principal components (PC1 and PC2) that explained 99.75% of variance in the starting matrix of retention parameters. Bartlett’s statistic shows that PC1, PC2 and PC3 are significant which explained of 99.92% of the total variance. In the set of studied bile acids (Table 1) there is a good correlation between RM0 chromatographic parameters and scores of PC1:

RM0=5.3792.624PC1E22

R = - 0.9203

CompoundRM0PC1PC2PC3Rodas data:
scoreslogPHAlogPHA
1LC5.330.1950.1220.080
2DC4.780.3460.1790.1113.502.65
3CDC5.010.3400.1770.0823.282.25
4UDC3.650.5450.2260.1143.002.20
5HDC3.880.4880.2080.1103.082.28
6HC3.660.5590.2290.1072.801.84
7C3.840.5330.2170.1052.021.10
8GLC3.390.6740.2460.119
9TLC2.121.0000.2490.077
10GCDC2.870.8190.2370.0882.120.45
11TCDC2.271.2490.1940.082
12GDC2.900.8500.2470.0872.250.80
13TDC2.301.1650.1910.090
14GC2.301.1020.1860.0901.65-0.40
15TC2.291.5240.1370.125

Table 2.

Chromatographic parameters

Sarbu for assessing whether RM0 or score of PC adequate to describe lipophilicity of bile acids using the experimental data of particion coefficients between 1-octanol and water of bile acids in molecular (HA) and ionized (A) forms. Between the logarithm partition coefficients and chromatographic parameters on the basis of Table 1 are obtained by the following equation of linear regression:

logPHA=0.366+0.620RM0E23

R = 0.8559

logPA=2.255+1.017RM0E24

R = 0.8751

logPHA=3.9952.195PC1E25

R = 0.8675

logPA=3.8243.807PC1E26

R = 0.9379

logPHA=4.4072.122PC12.168PC2E27

R = 0.8715

logPA=3.3873.885PC1+2.290PC2E28

R = 0.9395

Linear regression equations (23 and 24) in which the chromatographic parameter RM0 replaced with a score of PC1 has correlation coefficients with larger values ​​compared to the equations (21 and 23). Correlation coefficient is slightly increased in the multiple linear regression equation for logP (molecular and ionized form) when the equations involved scores of PC1 and PC2. Increasing the coefficient of correlation in the above equations can be explained with Wolds assumption according to which measurement errors are contained in the principal components with small eigenvalues​​, so the scores of PC1 is purified from the errors. In Fig. 12 bile acids are shown from the Table 2 in the plane of the scores ​​of principal components. It can be seen to form three linear congeneric groups: non- conjugated bile acids (BA), glyco-conjugate (G-BA) and tauro-conjugated (T-BA). Grouping of bile acids on the basis of the scores of PC as the appropriate regression equation for logP (from 23 to 26), indicating that the PC1 scores is adequate replacement for RM0 chromatographic parameters to describe the lipophilicity (hydrophobicity) of bile acids, namely the scores of PC1 can be used as molecular descriptors for hydrophobicity of the steroid skeleton β (bile acids).

Figure 12.

Sore of PC: bile acids from Table 1.

If increase the set of bile acids from Table 1 by including oxo and acetoxy derivatives, RM0 chromatographic parameter also included in the multiple linear regressions for the logPHA and the logPA with molecular descriptors such as polarizability and molar specific sum of absolute values of the charges on each atom of the molecule (electronic parameter), molecular volume, the third order of the connectivity index and Wiener index (shape parameter). Based on the loadings of PC can be concluded that RM0 carries different information about the structure of the studied bile acids than the above in silico molecular descriptors [6].

Effect of temperature on the chromatographic process in reverse phase chromatography can also be applied to obtain chromatographic parameters that have properties of molecular descriptors [15]. Namely, the reaction (R2) is exothermic (Had <0), then based on the van' t Hoff equation, the equilibrium constant of reaction (R2) decreases

dlnKaddT=dHadRT2E29

This means that the retention factor k (9) also decreases. Thus, the increase in temperature has the same effect on the value of k as there are increasing the volume fraction of organic modifier in aqueous mobile phase (reversed phase chromatography). In the experiment, the influence of temperature with an the retention factor at constant mobile phase included 25 different bile acids (conjugated and non-conjugated) (Table 3) [18].

For each tested bile acids between retention factor k and temperature t there is a linear relation:

k=a+btE30

The linear model (28) explains from 96% to 99% of the variance at tested bile acids (Table 4). In the set of the studied bile acids (Table 3) between the parameters of equation (28) (a and b) there is a good correlation, indicating the formation of congeneric groups in the plane of a and b:

a=0.7683+58.8522bE31

R=0.9996

Bile acidsAbbreviationPosition and orientation of OH and oxo groups
Lithocholic acidLC3α-OH
Deoxycholic acidDC3α-OH, 12α-OH
Chenodeoxycholic acidCDC3α-OH, 7α-OH
Cholic acidC3α-OH, 7α-OH, 12α-OH
Ursodeoxycholic acidUDC3α-OH, 7β-OH
Hyodeoxycholic acidHDC3α-OH, 6α-OH
Hyocholic acidHC3α-OH, 6α-OH, 7α-OH
Glycochenodeoxycholic acidGCDC3α-OH, 7α-OH
Taurodeoxycholic acidTDC3α-OH, 12α-OH
Glycocholic acidGC3α-OH, 7α-OH, 12α-OH
Glycodeoxycholic acidGDC3α-OH, 12α-OH
Taurolithocholic acidTLC3α-OH
Taurochenodeoxycholic acidTCDC3α-OH, 7α-OH
Glycolithocholic acidGLC3α-OH
12-Monoketocholic acid12-MKC3α-OH, 7α-OH, 12α-Oxo
7-Monoketocholic acid7-MKC3α-OH, 7α-Oxo, 12α-OH
7,12-Diketocholic acid7,12-DKC3α-OH, 7α-Oxo, 12α-Oxo
3,7-Diketocholic acid3,7-DKC3α-Oxo, 7α-Oxo, 12α-OH
3,12-Diketocholic acid3,12-DKC3α-Oxo, 7α-OH, 12α-Oxo
Dehydrocholic acidTKC3α-Oxo, 7α-Oxo, 12α-Oxo
12-Monoketodeoxycholic acid12-MKD3α-OH, 12α-Oxo
Diketodeoxycholic acidDKD3α-Oxo, 12α-Oxo
7-Monoketochenodeoxycholic acid7-MKCD3α-OH, 7α-Oxo
Diketochenodeoxycholic acidDKCD3α-Oxo, 7α-Oxo
6-Monoketohyodeoxycholic acid6-MKHD3α-OH, 6α-Oxo

Table 3.

Tested bile acids in the experiment the influence of temperature on the values ​​of retention factor k

Fig. 13 shows that the tested bile acids in the plane of parameters of equation (23) formed by three congeneric groups. The first group (I) formed by bile acids with oxo groups in the steroid nucleus and the bile acids that in addition to C3 have one more equatorial OH groups. The α equatorial OH groups have the same position relative to the steroid skeleton mean plane such have the α oxo groups. However, in this group also includes the cholic acid and the glycocholic acid which is structurally not fit into the above mentioned group. The second group (II) formed deoxycholic acid and chenodeoxycholic acid with its conjugates. The third group (III) forms lithocholic acid and its conjugates. Question may arise as whether can be improved with grouping of bile acid gained by in the plane of a and b, especially in terms of group I which provides the vendor cholic acid? The answer may be the application of methods of principal components of the matrix of the data of retention factor dependence on temperature. The first principal component (PC1) explains 99.99715% of variance in the starting data matrix, while the remaining variance explained by PC2. In Table 4 presents the scores of principal components of bile acids studied.

CompoundabR2PC1PC2
score
1LC43.05010.72000.98795.376370.023150
2DC21.88930.35180.99061.79015-0.051081
3CDC18.42600.30210.98071.055750.020646
4C8.48730.13190.9828-0.662450.016292
5UDC4.48840.06800.9843-1.406680.031446
6HDC5.62030.08040.9538-1.11512-0.034642
7HC5.54770.08640.9836-1.232050.041543
8GCDC16.68620.26510.99460.85970-0.024730
9TDC19.55030.31650.98021.30724-0.001736
10GC7.85690.12130.9841-0.774430.014749
11GDC20.62860.33360.97861.51098-0.012582
12TLC39.16280.65440.97284.658360.029036
13TCDC16.38080.26710.97960.706680.007651
14GLC41.10160.69260.97504.950530.026326
1512-MKC3.13400.04020.9704-1.58139-0.004996
167-MKC2.82020.03530.9676-1.64238-0.002356
177,12-DKC0.71410.00780.9769-2.129060.056420
183,7-DKC0.61000.00540.9974-2.117150.056756
193,12-DKC0.62370.00600.9752-2.129810.056122
20TKC0.50420.00560.9738-2.170860.066570
2112-MKD7.76590.10400.9652-0.57871-0.116428
22DKD4.14900.05110.9681-1.32315-0.048688
237-MKCD6.17460.08170.9665-0.91824-0.078586
24DKCD3.49440.04230.9704-1.46618-0.031508
256-MKHD6.36140.09130.9667-0.96809-0.039373

Table 4.

The parameters of the linear model (28) and scores of principal components

Figure 13.

Grouping the bile (Table 4) acid on the basis of linear regression parameters of the retention factor temperatures dependence.

In the plane of principal component scores are obtained by analogous groups such as Fig. 14. However in the group I the oxo derivatives and the derivatives with α equatorial OH groups forming linear congeneric group, while based on Cooks distance cholic acid and glycocholic acid may be considered as outliners. With loadings of principal component can be determined by mutual correlation of different in silico descriptors (topological and electronic) and obtained experimental descriptors a, b, and PC1 on the set of 25 bile acids (Table 4). It may be concluded that chromatographic parameters a and PC1 are more or less orthogonal to most topological descriptors (Winers index, Balabans index, shape attributes, konectivities, etc.), while PC1 and a are mutually parallel so that explain the same structural characteristics of bile acids. Namely, as flat molecules nitrazepan guest molecule is incorporated in the hydrophobic domain of micelles. Therefore, if the β side of bile acids steroid skeleton more hydrophobic then nitrazepans partition coefficient has a higher value. Using the method of partial least square (PLS) regression equation was obtained for the nitrazepans partition coefficients in which experimentally determined variables a and PC1 are the most significant with respect to in silico descriptors. Which means that PC1 and a can be used as molecular descriptors of bile acids lipophilicity, and that the temperature dependence of retention factors may obtain information regarding the structure of bile acids (lipophilicity).

Figure 14.

Grouping of bile acids (Table 4) based on the scores of principal component.

6. Conclusions

Chromatographic data (parameters) can be used for prediction of following feaatures of bile acids:

  • Critical Micellar Concentration

  • Haematolityc potentitial

  • Binding for Albumin

  • Solubilization of cholesterol, lecitin, vitamin E etc.

  • Mixed micelles parameters (for example partition coefficient of nitrazepan in bile acids' micelles)

Application of retention parameters for prediction of bile acids' properties is convenient for finding the size of the starting set of molecules. If the set is too small (less than ten molecules), by using in silico descriptors multiple regression equations are obtained which are overfitted, while by using chromatographic parameters that posses appropriate structural informations about bile acids, simple linear equations are derived with acceptable predictive power.

Acknowledgement

This study was supported by the Secretariat for Science and Technological Development of AP Vojvodina, Republic of Serbia (Grant No. 114-451-2113/2012).

Abbreviations

QSA(P)R = Quantitative Structure Activity (Property) Relationship

RPHPLC = Reversed Phase High Pressure (Resolutions) Liquid Chromatography

RPTLC = Reversed Phase Thin Layer Chromarography

PCA = Principal Component Analysis

PC = Principal Component

SWM = Stabilised Water Molecules

NSWM = Non Stabilised Water Molecules

SSMP = steroid skeleton mean plane

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mihalj Poša (October 24th 2012). Chromatographic Retention Parameters as Molecular Descriptors for Lipophilicity in QSA(P)R Studies of Bile Acid, Chromatography - The Most Versatile Method of Chemical Analysis, Leonardo de Azevedo Calderon, IntechOpen, DOI: 10.5772/48356. Available from:

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