The Determination of LogP of Anticoagulant Drugs with High-Performance Thin-Layer Chromatography

Abstract

The lipophilicity of a substance is an important physicochemical parameter for the pharmacological activity of a drug. In the current study, high-performance thin-layer chromatography was applied to determine the LogP values of the following anticoagulant drugs: warfarin, acenocoumarol, clopidogrel, and prasugrel. The mobile phase was a mixture of acetonitrile and water in mixed proportions. The content of acetonitrile varied from 50% to 80% in 5% increments. The partition coefficients were calculated with the regression curve R_m0 = f(LogP) based on the compounds with known lipophilicity. The highest LogP was observed for warfarin and the lowest for prasugrel.

1. Introduction

Lipophilicity is a very important physicochemical parameter that describes the differences in the biological activity of the substances. It depends on the permeability through the cell membranes. It is described by the partition coefficient (P) or its logarithm (LogP), which defines thsoftwaree distribution of the compound between the non-polar and aqueous phases. Analysis of a biologically active compound’s lipophilicity allows the understanding of the molecule’s absorption, distribution, metabolism, and elimination (ADME) parameters [1,2]. For a drug to act effectively, it must penetrate the general circulation and reach the receptor. It, therefore, overcomes lipid systems (cell membrane) and water systems (cell interior). The speed of transport of the medicinal substance to the receptor and the degree of its absorption depends on the partition coefficient of the substance between the water and lipid phases. In this way, the pharmacological and pharmacokinetic properties of the drug also depend to a large extent on the affinity of the substance to both of these phases. Lipophilic (hydrophobic) drugs, characterized by high partition coefficient values, have a greater affinity for lipid systems like cell membranes. In contrast, hydrophilic drugs (low partition coefficient value) are distributed in water spaces, such as blood plasma. Hydrophobic drugs bind more strongly to the receptors that determine their pharmacological action. However, they may be more toxic because they remain longer in the body, are distributed in deeper compartments, and bind strongly and non-selectively to protein. Therefore, research on the lipophilicity of drugs plays a crucial function during the early stages of drug development [2,3]. In the stage of optimizing the structure of the compound, Lipiński’s rule of five is of crucial importance. The characteristics that compounds must have to proceed to further research are that the LogP value is below 5. The other properties considered in this rule are the molar mass and the number of hydrogen bond acceptors and donors [4]. There are several useful methods in the determination of lipophilicity. One of them is a shake-flask method, which describes the distribution of the substance between two liquid immiscible phases (liquid–liquid system) [5,6]. The other one is the chromatographic method. The interaction between the liquid and solid phases is observed in this case. The chromatographic techniques that can be applied in this case are RP-HPLC (reversed-phase high-performance liquid chromatography) or RP-TLC (reversed-phase thin-layer chromatography) [7]. In the abovementioned chromatographic methods, the solid phase reflects the biological membrane, which cannot be attributed to the n-octanol/water system. The hydrocarbon chains form the hydrophobic film, and the interaction between the drug’s molecule and solid phase reflects the interaction with the body membrane rather than the static and isotropic n-octanol/water system [7,8].

The aim of this paper is the determination of the lipophilicity of the following oral anticoagulants: acenocoumarol (4-hydroxy-3-[1-(4-nitrophenyl)-3-oxobutyl]chromen-2-one), warfarin (4-hydroxy-3-(3-oxo-1-phenylbutyl)chromen-2-one), clopidogrel (methyl (2S)-2-(2-chlorophenyl)-2-(6,7-dihydro-4H-thieno [3,2-c]pyridin-5-yl)acetate), and prasugrel ([5-[2-cyclopropyl-1-(2-fluorophenyl)-2-oxoethyl]-6,7-dihydro-4H-thieno[3,2-c]pyridin-2-yl] acetate). The applied technique is reversed-phase high-performance thin-layer chromatography (RP-HPTLC). To the best of our knowledge, this is the first manuscript where this method was applied for the determination of the lipophilicity of the anticoagulant drugs. The results of HPTLC lipophilicity measurements were correlated with the computational ones. The structures of the analyzed compounds are presented in Figure 1. Warfarin and acenocoumarol are the derivatives of coumarin. They are vitamin K antagonists. Their activity is based on inhibiting vitamin K epoxide reductase complex 1. It is necessary for the biosynthesis of the following clotting factors (II, VII, IX and X) [9,10]. Clopidogrel and prasugrel are the thienopyridine derivatives and represent the second and third generation, respectively. They are both prodrugs, and they must be metabolically activated. They are irreversible inhibitors of the platelet P2Y12 adenosine diphosphate receptor [11,12,13].

2. Material and Methods

2.1. Reagents

Methanol (MeOH) and acetonitrile (ACN) were of HPLC grade and were purchased from Merck (Darmstadt, Germany). Glacial acetic acid and p-nitrophenol were purchased from POCH (Gliwice, Poland). 2,6-dichloroaniline and 3,4-dichloroaniline were purchased from Sigma-Aldrich (Steinheim, Germany). Isatin was purchased from Fluka (Steinheim, Germany) and N-(2,4-dichlorophenyl) acetamide was purchased from Sigma-Aldrich. 2,3,9,10-tetramethoxy-12-oxo-12H-indolo[2,1-a] isoquinoline chloride was synthesized according to the procedure described in [14]. Warfarin was purchased from Orion Pharma (Espoo, Finland). Acenocoumarol was purchased from WZF (Warsaw, Poland). Prasugrel was purchased from Eli Lilly (Honten, The Netherlands). Clopidogrel was purchased from Instytut Farmaceutyczny (Warsaw, Poland).

2.2. The Chromatographic Procedure

The HPTLC separation was carried out on HPTLC RP-18 F254s glass plates (10 × 10 cm, Merck, Germany). The mobile phase was a mixture of water and acetonitrile (v/v). The content of acetonitrile varied from 50% to 80% and varied in 5% increments. The experiments were conducted at ambient temperature, set at 22 °C. All measurements were carried out in triplicate.

The concentration of the analytes dissolved in methanol was 1% (w/v). An amount of 10 µL of the solutions was transferred to the chromatographic plate with a micropipette on the start point. The separation started when the organic solvent evaporated. The chromatograms were developed in the horizontal chamber (Chromdes, Lublin, Poland) and visualized with UV light at λ = 254 nm.

2.3. The Calculation of the Data

The R_f values were calculated with the following equation:

$$R_f={\displaystyle \frac{x}{y}}$$

(1)

where x is the distance of the solute traveled from the baseline and y is the distance of the solvent traveled from the baseline.

The R_f values were used for the calculation of R_m values, which characterize the retention in TLC according to the Bate–Smith and Westall equation [15,16]:

$$R_m=log\left({\displaystyle \frac{1-R_f}{R_f}}\right)$$

(2)

where R_f is the retardation factor, which is the ratio of the distance covered by the center of the spot to the distance simultaneously covered by the mobile-phase front.

The R_m values were calculated for each concentration of ACN for each analyte, and using the linear function R_m = f(C_ACN), the extrapolated value of R_m0 was calculated using the intercept:

$$R_m=a\times C+R_{m0}$$

(3)

where a is the slope of the curve, which indicates the rate at which the solubility of the compound increases in the mobile phase; C is the concentration of ACN [%]; and R_m0 is the R_m of the compound when the concentration of ACN is zero—it is extrapolated to pure water as a mobile phase [17].

The precision (coefficient of variation) was calculated for R_m0 for each compound according to the following equation:

$$CV[\%]={\displaystyle \frac{SD}{\overline{X}}}\times 100$$

(4)

where SD is the standard deviation and $\overline{\mathrm{X}}$ is the mean value.

2.4. The LogP_HPTLC Calculation

The lipophilicity presented as a LogP_HPTLC value was calculated with the following equation:

$$R_{m0}=a\times logP+B$$

(5)

where ‘a’ and ‘B’ are the slope and intercept, respectively. R_m0 is the R_m0 value of the compounds with known LogP, such as isatin, N-(2,4-dichlorophenyl) acetamide, 3,4-dichloroaniline, 2,6-dichloroaniline, p-nitrophenol, and 2,3,9,10-tetramethoxy-12-oxo-12H-indolo[2,1-a] isoquinoline chloride [8,11]. The LogP values are listed in

Table 1. The R_m0 and LogP values for the compounds with known LogP.

Compound	Rm0 ± SD	logP [15,18]
Isatin	0.89 ± 0.03	0.83
2,6-dichloroaniline	2.06 ± 0.04	2.82
p-nitrophenol	1.30 ± 0.04	1.38
3,4-dichloroaniline	1.82 ± 0.05	2.69
N-(2,4-dichlorophenyl) acetamide	1.53 ± 0.06	2.18
2,3,9,10-tetramethoxy-12-oxo-12H-indolo[2,1-a] isoquinoline chloride	2.40 ± 0.04	3.28

.

3. Results

3.1. The Determination of R_m0 of the Analyzed Compounds

The mean values of R_f for chromatographic measurements of the compounds with known lipophilicity and the tested compounds are presented in Tables S1 and S2 in the Supplementary Information. The measurements were performed in triplicate. The precision did not exceed 9%.

The R_m0 values for the compounds with LogP values are presented in Table 1, and the regression curve R_m0 = f(LogP) is presented in Figure 2. The R_m = f(acetonitrile content) graphs and the data for the determination of R_m0 of the analyzed compounds are presented in the Supplementary Data (Figures S1–S10, Tables S3–S12).

The following equation describes the regression curve in Figure 2:

$$R_{m0}=0.57logP+0.4134,\mathrm{r}=0.9801.$$

(6)

The R_m0 values of the analyzed anticoagulant drugs are presented in

Table 2. The R_m0 values for the anticoagulant drugs.

Compound	Rm0 ± SD
Warfarin	2.26 ± 0.09
Acenocoumarol	2.20 ± 0.08
Clopidogrel	2.15 ± 0.07
Prasugrel	1.36 ± 0.01

.

3.2. The Results of the Determination of Lipophilicity

The values of LogP are listed in

Table 3. The values of LogP determined with experimental (LogP_HPTLC) and computational methods.

Compound	LogPHPTLC	ALOGPs a	xlogP (clogP)	miLogP d	Chemaxon e
Warfarin	3.23	2.41	2.70 b	3.03	2.74
Acenocoumarol	3.13	2.53	2.53 b	2.99	2.68
Clopidogrel	3.03	3.84	2.50 c	3.60	4.03
Prasugrel	1.60	3.67	1.97 c	3.61	4.31

^a [19], ^b [20], ^c [21], ^d [22], ^e [23].

.

4. Discussion

This study aimed to determine the lipophilicity of the anticoagulant drugs utilizing RP-HPTLC (LogP_HPTLC − LogP determined experimentally with HPTLC). The determination of LogP is followed by the determination of R_m0 (Table 1 and Table 2). The R_m0 values for the compounds with known lipophilicity correlate very well with their lipophilicity (LogP) (Table 1, Figure 2). This is proven by the high value of the correlation coefficient, which is higher than 0.9 (Equation (6)). The determined LogP_HPTLC defines the affinity to the lipophilic and aqueous phases. Lipophilicity affects the activity of the drug in the human body. It enables the prediction of the absorption, permeability, protein binding, and toxicity. The analyzed anticoagulant drugs are lipophilic. The LogP_HPTLC values are higher than 0 (P > 1), indicating the affinity to the organic phase. According to the literature data, the analyzed drugs (warfarin, acenocoumarol) and prodrugs (clopidogrel and prasugrel) are slightly soluble in water [20,21]. According to the PubChem database, the solubility in water of warfarin is 17 mg/L (20 °C), that for acenocoumarol is 9.39 mg/L (20–25 °C), that for clopidogrel is 15.1 mg/L (mean results at pH 7.4), and that for prasugrel is 2.73 mg/L. However, in the case of prodrugs, their solubility increases as they are metabolically activated, and their LogP decreases [21].

The LogP_HPTLC measurements (Table 3) show that the most lipophilic drugs are warfarin and acenocoumarol. Acenocoumarol and warfarin have a similar structure. The presence of an -NO₂ group is observed in the case of acenocoumarol. It is an electron-donor substituent, which decreases the LogP value. A similar trend was observed when the lipophilicity of azathioprine was compared with 6-mercaptopurine. Azathioprine has a lower LogP_HPTLC than 6-mercaptopurine [24].

Clopidogrel and prasugrel are the representatives of thienopyridine derivatives. Clopidogrel possesses a chlorine atom and a methoxy group in the structure. When compared to acenocoumarol and warfarin, clopidogrel has a slightly lower value of LogP_HPTLC than the two previously mentioned drugs (Table 3). In the prasugrel fluorine atom, cyclopropyl and acetate groups are in the structure. The introduction of the acetate group results in the decrease in the lipophilicity of prasugrel. The lower value of LogP_HPTLC for prasugrel when compared to clopidogrel is caused by the fact that it possesses the additional oxygen atom in the acetyl groups. This atom is an acceptor of hydrogen bonds, which may result in a decrease in the lipophilicity.

Table 3 lists the computational values of LogP calculated with different algorithms. These algorithms consider the impact of atoms or groups of atoms. xlogP is an atom additive method with correction factors considering intramolecular interaction [25]. It has XLOGP2 and XLOGP3 versions. XLOGP2 uses a total of 90 atom types to classify neutral atoms [26]. XLOGP3 uses the scheme of 87 atoms/groups and two correction factors for internal hydrogen bonds and amino acids [26]. The miLogP method is based on the group contribution and is based on 35 small basic fragments and 185 larger fragments characterizing the intramolecular hydrogen bonding contribution and charge interactions [26]. AlogPs considers the hydrogen and nonhydrogen atoms and is useful in determining the octanol–water partition coefficient. It is based on associative neural networks [25,26]. Chemaxon takes into consideration atomic LogP contributions [27].

The comparison of the determined logP_HPTLC with the calculated logP with the software led to interesting results. The calculated values of logP with the software differed between the algorithms. It was observed that the lowest differences between the experimental values of logP_HPTLC and computational miLogP were noted for warfarin and acenocoumarol (3.23 vs. 3.03 for warfarin and 3.13 vs. 2.99 for acenocoumarol). For thienopyridines, the lowest differences between the experimental values of logP_HPTLC and the calculated ones were noted for the xlogP algorithm (3.03 vs. 2.50 for clopidogrel and 1.60 and 1.97 for prasugrel). The correlation analysis showed that the lowest correlation was observed for AlogPs, R² = 0.3339. Higher correlations were observed for milogP (R² = 0.4288) and Chemaxon (R² = 0.5526). The highest correlation was observed between the experimental values and xlogP (R² = 0.9650). When analyzing the computational values, the xlogP algorithm indicated a similar trend in lipophilicity changes. This is shown in Figure 3. This indicates that this program might better predict lipophilicity parameters and, therefore, might be a valuable tool for the early stages of designing new analogs of the analyzed compounds.

The very low correlation between LogP_HPTLC and AlogPs, milogP, and LogP determined by Chemaxon is caused by the fact that the experimental value of LogP_HPTLC for prasugrel is lowest (1.60). The LogP values calculated with the algorithms are higher.

The lowest value for AlogPs might be caused by the fact that this algorithm is useful in predicting the n-octanol/water LogP.

The differences between the values of LogP determined experimentally and computationally were observed by other research groups [26,28]. In the study of Mannhold et al. [26], xLogP3 provided the highest correlation between the experimental and theoretically predicted values. The differences in the estimated values of LogP between the algorithms are caused by the presence of nonhydrogen atoms. According to Tetko et al. [29], these atoms make the parts of the molecules unavailable for the interaction, and they will be unavailable for the interaction, which decreases prediction availability.

The determined values of LogP_HPTLC for the analyzed drugs are higher than 0, which implies that the partition coefficient is higher than 1 (Table 3). The lipophilic compounds permeate the tissues easily. However, according to the ‘rule of 5’, the LogP value of the bioactive compound should not exceed 5 [4]. Above this value, the compounds may permeate from a hydrophilic environment (which might be, for example, the intestines) to cell membranes that are lipophilic. Too high a lipophilicity can cause the compounds to stop in the cell membrane and not reach the circular system. In our study, none of the analyzed compounds exceeded the value of 5. Acenocoumarol, warfarin, and clopidogrel have similar lipophilicity. The LogP_HPTLC of prasugrel is lower, but its value still implies the lipophilic properties.

5. Conclusions

The lipophilicity of the tested compounds is expressed by experimental R_m0 parameters using HPTLC and by the calculated LogP values using computation methods. The results show that the HPTLC method is suitable for the determination of the LogP of anticoagulant drugs. Warfarin has the highest lipophilicity, and the lowest is observed for prasugrel. The analysis of the correlation between the compounds’ experimental and theoretically calculated LogP parameters with different algorithms resulted in R² values in the range of 0.3339–0.9650. These results may suggest that the theoretically calculated LogP value depends on the conformation of molecules and is one of the many factors directly influencing biological activity [30]. It might be caused by the fact that the analyzed software takes into consideration different approaches—some of them analyze the atoms (XLOGP, AlogPs), large fragments (miLogP), or atomic LogP (Chemaxon). The highest correlation of the results with the computational algorithms is observed for xlogP. It indicates that this program might better predict lipophilicity parameters and, therefore, might be a valuable tool for the early stages of designing new analogs of the analyzed compounds.

The lipophilicity of the compound impacts its ADME properties, such as binding to plasma proteins, the blood–brain barrier, and absorption in the human intestine, which may provide the opportunity to more fully understand and reliably predict properties indicative of the biological activity of anticoagulants and their possible analogs.