Comparative Study of the Lipophilicity of Selected Anti-Androgenic and Blood Uric Acid Lowering Compounds

This study aimed to evaluate the lipophilicity of a series substances lowering the concentration of uric acid in blood and anti-androgen drugs by thin-layer chromatography in reversed-phase systems (RP-TLC, RP-HPTLC) and computational methods. The chromatographic parameter of lipophilicity (RMW) of tested compounds was determined on three stationary phases, i.e., RP18F254, RP18WF254 and RP2F254, using ethanol–water, propan-2-ol-water and acetonitrile–water in various volume compositions as mobile phases. The chromatographic analysis led to determining the experimental value of the lipophilicity parameter for each of the tested compounds, including those for which the experimental value of the partition coefficient (logPexp) as a measure of lipophilicity is not well described in available databases, such as febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. The chromatographic parameters of lipophilicity were compared with the logP values obtained with various software packages, such as AClogP, AlogPs, AlogP, MlogP, XlogP2, XlogP3, ACD/logP and logPKOWWIN. The obtained results indicate that, among selected chromatographic parameters of lipophilicity, both experimental and calculated logP values gave similar results, and these RP-TLC or RP-HPTLC systems can be successfully applied to estimate the lipophilicity of studied heterocyclic compounds belonging to two different pharmacological groups. This work also illustrates the similarity and difference existing between the tested compounds under study using the chemometric methods, such as principal component analysis (PCA) and cluster analysis (CA). In addition, a relatively new approach based on the sum of ranking differences (SRD) was used to compare the chromatographically obtained and theoretical lipophilicity descriptors of studied compounds.


Introduction
Among the various properties, the most important is lipophilicity, which is involved in the absorption, distribution, metabolism, elimination and toxicity (i.e., ADMET) of bioactive substances [1]. Lipophilicity is successfully used in the drug discovery process in both QSAR (quantitative structure-activity relationship) and also QSPR (quantitative structure-property relationship) studies of new drug candidates [2,3]. Therefore, numerous pieces of work are currently underway to evaluate the lipophilicity parameters of the different classes of compounds, including heterocycles [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The lipophilicity of a molecule is usually represented by partition coefficient (P) or by logP determined by an experimental method, such as shake flask in n-octanol system or in silico using different algorithms based on the structure of the appropriate molecule [1]. Recently, many commercial and free software packages are available for such calculations, e.g., ACD/ChemSketch, Hyper Chem, E-Dragon and AlogPS 2.1. These computational methods are fast and cheaper

Anti-Androgens
The second group of tested compounds were the selected anti-androgens ( Figure 2) used to treat prostate cancer. It is one of the most common cancers observed in men over

Anti-Androgens
The second group of tested compounds were the selected anti-androgens ( Figure 2) used to treat prostate cancer. It is one of the most common cancers observed in men over 50 years old worldwide [26].

Anti-Androgens
The second group of tested compounds were the selected anti-androgens ( Figure 2) used to treat prostate cancer. It is one of the most common cancers observed in men over 50 years old worldwide [26].  The discovery of the androgen receptor in the late 1960s enabled the development of anti-androgen drugs [27]. This is a chemically diverse group of drugs that plays a key role in the functioning of prostate cancer cells [28,29]. The general mechanism of anti-androgen drugs action is to compete with endogenous androgens in binding to the androgen receptor. The studied abiraterone, bicalutamide, flutamide, nilutamide and ailanthone belongs to these compounds [27][28][29][30][31][32][33]. This group of drugs is constantly being studied and new antiandrogens are being discovered, which are the subject of numerous studies of effectiveness and safety, such as ailanthone, a compound that is currently not used as a drug but is being studied for effectiveness in the treatment of prostate cancer [30,31]. For example, abiraterone is a potent and irreversible inhibitor of the CYP17 enzyme responsible for androgen synthesis. It was approved by the Food and Drug Administration Agency (FDA) in 2011 and has shown significant improvements in overall survival in patients compared to placebo in studies [32]. Next substance, namely bicalutamide, is a non-steroidal antiandrogen drug belonging to the second generation of these drugs and it was approved in 1995 [28]. In this study, we also examined the drugs leflunomide and its active metabolite teriflunomide, which are being studied as effective drugs in the chemoprevention of prostate cancer [32].
In our work, the experimental values of the lipophilicity descriptor in the form of the chromatographic parameters (R MW ) for all tested compounds belonging to both the pharma-cological groups were determined. The applied technique was thin-layer chromatography (RP-TLC/HPTLC) using different types of mobile phases and stationary phases, such as RP18F 254 , RP18WF 254 and RP2F 254 plates and a mixture of ethanol-water, propan-2-ol and acetonitrile-water. Experimental data have been compared with calculated logP, obtained using different computer software, e.g., AlogPs, AClogP, AlogP, MlogP, XlogP2, XlogP3 ACD/logP and logP KOWWIN . The conducted in silico studies allowed the calculation of the theoretical partition coefficient (logP) and other physicochemical factors important in describing the pharmacokinetic properties of studied drug substances, such as density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume. To the best of the authors' knowledge, this work is, for the first time, presenting the lipophilicity parameters, experimentally obtained by using TLC method for: febuxostat and oxypurinol, which belong to agents that lower blood uric acid, and also for abiraterone, ailanthone and teriflunomide from group anti-androgen substances. Chemometric methods, i.e., cluster analysis (CA) and principal component analysis (PCA) of obtained results were successfully applied to compare the tested compounds, taking into account both experimental and theoretical lipophilicity parameters, as well as other physicochemical descriptors. In addition, a relatively novel non-parametric method, such as the sum of ranking differences, was used to rank all applied approaches to determine of lipophilicity parameters

Results and Discussion
Considering the importance of lipophilicity parameters in the description of the behavior of drug substances in the biological system, the present study focuses on the determination of experimental and theoretical values of lipophilicity parameters of different bioactive compounds as drug substances belonging to agents that lower blood uric acid (allopurinol, oxypurinol, febuxostat) and anti-androgenic compounds (abiraterone, bicalutamide, flutamide, nilutamide, teriflunomide, leflunomide and ailanthone), respectively. The chemical structures of the tested compounds are shown in Figures 1 and 2. Tables 1 and 2 present the results of in silico study, i.e., the theoretical partition coefficient values (logP) of studied compounds and average logP values (logP avg ) calculated by using different computer software (AlogPs, AlogP, AClogP, MlogP, xlogP2, xlogP3) by means of the Virtual Computational Chemistry Laboratory, while logP KOWWIN and ACD/logP values were obtained from the EPIWEB program. In addition, Table 1 presents the experimental values (logP exp ) available for the selected studied compounds. Table 2 presents other physicochemical descriptors of studied compounds, such as density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume. Depending on the algorithm used, a certain difference was observed between the theoretical logP values and average values of logP (logP avg ), especially for allopurinol from the group of drugs, lowering the concentration of uric acid in blood and ailanthone as the member of the second analyzed group, namely anti-androgen agents. Significant differences can be observed between the experimental logP value available for allopurinol (logP exp = −1.80) and the average theoretical logP value (logP avg = 0.17). For other compounds with a known logP exp value, the difference between both logP values is smaller. The greatest agreement between the both parameters is observed for bicalutamide. This situation indicates the predictive power of algorithms used to determine the reliable logP value of this compound. Table 2 illustrates other physicochemical parameters of the tested compounds calculated using computer programs. Differences between these parameters, such as surface tension and molecular volume in the two groups of studied compounds, can be explained by differences in their chemical structure, which was used to predict these parameters. The aim of further research was the chromatographic analysis of blood uric acid lowering and anti-androgen agents. Various systems, consisting of silica gel plates RP18F 254 , RP18WF 254 and RP2F 254 , and three mobile phases, containing ethanol, propan-2-ol, acetonitrile and water, in different volume compositions were used to determine the chromatographic parameter of lipophilicity in the form of R MW , according to the equation of Wachtmeister-Soczewiński (1). The R MW values for all compounds are listed in Table 3. The characteristics of linear relationships between chromatographic parameters, i.e., R M of each examined compound and the content of organic modifier in the mobile phase (ϕ) used to determine the R MW values in all chromatographic systems are presented in Tables S1-S10 in Supplementary Materials.  The analysis of chromatographic parameters of lipophilicity presented in Table 3 shows that in the group of the tested substances that lower blood uric acid, the highest lipophilicity parameter shows febuxostat, a compound whose experimental value of lipophilicity descriptor has not been well described so far ( Table 1). The obtained results may, therefore, be helpful in assessing the lipophilic property of this drug substance.
Data summarized in Table 3 show the R MW values for febuxostat in the range of 1.9788-2.7106 (ethanol-water), 1.2259-1.9112 (propan-2-ol-water) and 1.5489-1.9061 (acetonitrile-water). For comparison, significantly lower values of chromatographic lipophilicity parameters for febuxostat obtained by the same RP-TLC systems were observed for the two other compounds belonging to the same pharmacological class, i.e., for allopurinol and its metabolite oxypurinol. The chromatographic results of R MW values are the next group of compounds depicted in Table 3, close to their theoretical logP values (Table 1), thus indicating the similarity between the R MW and logP values of all antiandrogens, except for abiraterone and ailanthone, i.e., between bicalutamide, flutamide, nilutamide, leflunomide and teriflunomide. The first of them, i.e., abiraterone shows the highest R MW value in the range of 1.8373-4.5660. Of all the R MW results, those obtained with ethanol-water and the three chromatographic plates ranging from 3.8765-4.5660 best correspond to the theoretical logP values predicted using different algorithms, as well as to the average value of them. In the case of ailanthone, all obtained R MW results are lower compared to other anti-androgens, which is also observed between logP values. The greatest similarity can be observed between the chromatographic parameter of lipophilicity (R MW = 0.9749) measured by RP-TLC method on RP18F 254 plates and acetonitrile-water as the mobile phase and the theoretical logP value marked as MlogP = 0.99. A similar agreement indicates R MW = 0.2488 achieved on RP18W 254 plates developed by propan-2-ol and logP KOWWIN (0.25). The third examined anti-androgen whose logP exp is not available, i.e., teriflunomide, shows the best similarity of the R MW values obtained by propan-2-ol-water (2.0438) to AlogP value (2.07), as well as with other theoretical partition coefficients, such as AlogPs (2.30) and logP KOWWIN (2.25). This fact confirms the usefulness of these partition coefficients to evaluate the lipophilicity of the presented compound.
The analysis of the R MW values of bicalutamide, nilutamide, flutamide and leflunomide presented in Table 3 confirms the usefulness of almost-tested chromatographic systems for the evaluation of the lipophilicity of these compounds. However, the best are those consisting of ethanol-water and acetonitrile-water, as well as the chromatographic plates RP18F 254 and RP18WF 254 . These R MW results are very similar to logP exp and to the average value of theoretical logP given in Table 1. In the further parts of our study, we used the two chemometric methods, such as cluster analysis and principal component analysis, to compare the studied compounds belonging to different pharmacological classes on the basis of all the parameters presented in this work, including the experimental, i.e., chromatographic parameters of lipophilicity, theoretical logP values calculated using different algorithms, as well as other physicochemical descriptors predicted on the basis of their structure, i.e., density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume. All data are presented in Tables 1 and 2, respectively. These data were used in the similarity analysis of analyzed compounds. The first analysis concerned the similarity of these compounds in terms of lipophilicity values obtained experimentally, as shown in Figure 3A, and theoretical values, as shown in Figure 3B.
Next, Figure 3 presents the similarity analysis of tested compounds based on their experimentally obtained lipophilicity parameters (R MW ) and theoretical logP. The analysis of both dendrograms indicates that three clusters can be distinguished in Figure 3A, consisting of oxypurinol and allopurinol; teriflunomide and febuxostat; and nilutamide, leflunomide, bicalutamide and abiraterone, respectively. On the other hand, the analysis of only theoretical logP values ( Figure 3B) allows the separation of two clusters, including, respectively, oxypurinol, allopurinol and ailanthone, as well as other compounds, except for abiraterone. As well as basing on these analyses, it is possible to determine the differences between the experimental and theoretical values of lipophilicity. The theoretical ones only take into account the structure of the compounds, hence, for example, the very high similarity of compounds from a larger cluster, where all (except febuxostat) have fluorine atoms in their composition. Considering the Euclidean distance, these two groups of compounds (two clusters) are also far apart, while the experimental values are much more similar to each other. In Figure 3A, three visible clusters can be seen, which include, respectively, oxypurinol and allopurinol; febuxostat and teriflunomide; and nilutamide, leflunomide, flutamide and bicalutamide. The first cluster includes compounds that are similar both in terms of structure and action because both belong to the group of drugs used in gout. The second cluster includes compounds containing a carbon-nitrogen triple bond in the structure, which may cause similar interactions between the compound and the mobile or stationary phase during TLC analysis used to determine logP. When data on the values of other physicochemical properties were added to the cluster analysis, the analysis allowed us to distinguish three clusters, although the Euclidean distances, in this case, are much larger than in the case of the logP similarity analysis alone (Figure 4). of their structure, i.e., density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume. All data are presented in Tables 1 and 2, respectively. These data were used in the similarity analysis of analyzed compounds. The first analysis concerned the similarity of these compounds in terms of lipophilicity values obtained experimentally, as shown in Figure 3A, and theoretical values, as shown in Figure 3B. Next, Figure 3 presents the similarity analysis of tested compounds based on their experimentally obtained lipophilicity parameters (RMW) and theoretical logP. The analysis of both dendrograms indicates that three clusters can be distinguished in Figure 3A, consisting of oxypurinol and allopurinol; teriflunomide and febuxostat; and nilutamide, used in gout. The second cluster includes compounds containing a carbon-nitrogen triple bond in the structure, which may cause similar interactions between the compound and the mobile or stationary phase during TLC analysis used to determine logP. When data on the values of other physicochemical properties were added to the cluster analysis, the analysis allowed us to distinguish three clusters, although the Euclidean distances, in this case, are much larger than in the case of the logP similarity analysis alone (Figure 4). The resulting clusters, including bicalutamide and ailanthone; flutamide and teriflunomide; and nilutamide and febuxostat, were based on the similarity of their physicochemical properties and they have the final influence on the resulting graph. To describe the variability of the tested system, an analysis of principal components was also carried out, during which all data obtained for the tested compounds were taken into account, namely theoretical and experimental values of lipophilicity and values of physicochemical properties. All data have been standardized. First, the eigenvalues were extracted (Table 1). Based on the Kaiser criterion, it was found that the first three eigenvalues are sufficient to describe the examined set of compounds because their value exceeds 1. They describe 92.68% of the variability of the system.
Using the principal components, a graph of the projection of cases onto the plane of factors was prepared. The graph is shown in Figure 5. The resulting clusters, including bicalutamide and ailanthone; flutamide and teriflunomide; and nilutamide and febuxostat, were based on the similarity of their physicochemical properties and they have the final influence on the resulting graph. To describe the variability of the tested system, an analysis of principal components was also carried out, during which all data obtained for the tested compounds were taken into account, namely theoretical and experimental values of lipophilicity and values of physicochemical properties. All data have been standardized. First, the eigenvalues were extracted (Table 1). Based on the Kaiser criterion, it was found that the first three eigenvalues are sufficient to describe the examined set of compounds because their value exceeds 1. They describe 92.68% of the variability of the system.
Using the principal components, a graph of the projection of cases onto the plane of factors was prepared. The graph is shown in Figure 5. The clusters marked in Figure 6 confirm what the cluster analysis showed for all the analyzed data. These clusters contain the same compounds as the clusters in Figure 5. This indicates the high usefulness of the PCA analysis, which allows only three principal components to be taken into account instead of many data describing the system, and the con- The clusters marked in Figure 6 confirm what the cluster analysis showed for all the analyzed data. These clusters contain the same compounds as the clusters in Figure 5. This indicates the high usefulness of the PCA analysis, which allows only three principal components to be taken into account instead of many data describing the system, and the conclusions of the analysis remain the same. Similar conclusions can also be observed when analyzing all logP data, experimental and theoretical. PCA for these values would reduce the amount of data to two eigenvalues. On the basis of these two eigenvalues, it is possible to draw a graph of the projection of cases onto the plane of factors, which would confirm, as in the previous case, the similarity analysis for these data. The figure below shows the graphs prepared based on PCA ( Figure 6A) and based on CA ( Figure 6B). The clusters marked in Figure 6 confirm what the cluster analysis showed for all the analyzed data. These clusters contain the same compounds as the clusters in Figure 5. This indicates the high usefulness of the PCA analysis, which allows only three principal components to be taken into account instead of many data describing the system, and the conclusions of the analysis remain the same. Similar conclusions can also be observed when analyzing all logP data, experimental and theoretical. PCA for these values would reduce the amount of data to two eigenvalues. On the basis of these two eigenvalues, it is possible to draw a graph of the projection of cases onto the plane of factors, which would confirm, as in the previous case, the similarity analysis for these data. The figure below shows the graphs prepared based on PCA ( Figure 6A) and based on CA ( Figure 6B). Continuing this study, we used novel non-parametric methods, such as ranking (SRD analysis), to obtain a reliable comparison of lipophilicity parameters of examined compounds. The inspiration of this analysis was the recently published papers on the determination of lipophilicity of various bioactive compounds by chromatographic and theoretical methods, such as acetylenequinoline derivatives [19], purine-2,6-dione based compounds [20] and quaternary (fluoro)quinolones [21]. However, it should be highlighted that the best guide regarding the procedure for conducting the SRD analysis are the pieces of work published by Héberger and Andrić [34][35][36]. The SRD ranking of several chromatographically estimated lipophilicity descriptors expressed as RMW values and computationally calculated logP values of all tested compounds shown in Figure 7, confirming the Continuing this study, we used novel non-parametric methods, such as ranking (SRD analysis), to obtain a reliable comparison of lipophilicity parameters of examined compounds. The inspiration of this analysis was the recently published papers on the determination of lipophilicity of various bioactive compounds by chromatographic and theoretical methods, such as acetylenequinoline derivatives [19], purine-2,6-dione based compounds [20] and quaternary (fluoro)quinolones [21]. However, it should be highlighted that the best guide regarding the procedure for conducting the SRD analysis are the pieces of work published by Héberger and Andrić [34][35][36]. The SRD ranking of several chromatographically estimated lipophilicity descriptors expressed as R MW values and computationally calculated logP values of all tested compounds shown in Figure 7, confirming the best lipophilicity measures and the closest to the zero shows the theoretical partition coefficient MlogP. Thus, it can be concluded that the theoretical parameter of lipophilicity MlogP may be considered a suitable lipophilicity measure for studied compounds, whereas the calculated XlogP2 and XlogP3 variables have the highest SRD scores of all theoretically obtained logP values. It can be also seen that the chromatographic parameter of lipophilicity denoted as RP18W(EtOH/H 2 O), which was determined using ethanol-water on chromatographic plates precoated with silica gel RP18WF 254 , falls in the same range as MlogP, i.e., near zero. According to the SRD values, ethanol would be suggested as the most suitable organic modifier for the determination of chromatographic parameters of lipophilicity, i.e., the R MW of studied compounds.

Chromatographic Analysis
Chromatographic analysis was conducted on RP-TLC plates (RP2F 254 , RP18F 254 , RP18WF 254 ). Five microliters of standard solutions of analytes were spotted onto the chromatographic plates. The chromatographic chamber of 20 cm × 10 cm (Camag, Muttenz, Switzerland) was saturated with the mobile phase vapors for 20 min. The mobile phases were prepared by mixing appropriate organic modifiers (ethanol, propan-2-ol, acetonitrile) and water in different volume compositions in a range from 20% to 90% (v/v). The content of the organic modifier in the mobile phase used was changed every 5% (v/v). Chromatograms were developed at room temperature (21 ± 1 • C) to the solvent distance of 7 cm. Next, the chromatograms were dried for 24 h at 21(±1 • C) in a fume cupboard. The identification of studied compounds were carried out under a UV lamp at 254 nm (Camag, Switzerland). The values of R f (retardation factor) are the average values of three independent measurements in each case. To determine the chromatographic parameter of lipophilicity of studied compounds in terms of R MW value, the Soczewiński-Wachtmeister's [1] equation, which shows the linear relationship between the chromatographic factor R M and volume fraction of organic modifier in the mobile phase (ϕ), was used [1]:

In Silico Study
Various software packages with different calculation algorithms were useful for the prediction of the logP values, as well as other physicochemical parameters of studied compounds. The source of the following physicochemical descriptors of studied compounds, such as density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume, given in Table 2, were EPI-WEB 4.1 program (Estimation Programs Interface) Suite TM Version 4.1 and ChemSpider (http://www.chemspider.com), accessed on 19 October 2022. Six different logP values (AlogPs, AlogP, AClogP, MlogP, xlogP2, xlogP3) and average logP value (logP avg ) were predicted by using Virtual Computational Chemistry Laboratory http://www.vcclab.org./ (accessed on 20 October 2022), while logP KOWWIN and ACD/logP values were obtained from ChemSpider (http://www.chemspider.com), accessed on 20 October 2022. In addition, the experimental value (logP exp ) available for selected studied compounds, such as allopurinol, bicalutamide, flutamide, nilutamide and leflunomide, are presented in this work ( Table 1). The logP exp values were derived from a drug database online, namely Drug-Bank (https://www.drugbank.com/), accessed on 20 October 2022. All the experimental and calculated logP values were summarized in Table 1.

Cluster Analysis (CA)
Cluster analysis, in the simplest terms, allows you to group objects that are similar to each other [37], an accessible course in statistics with the use of Statistica PL. Thanks to such grouping of objects (data), we found that the data in one cluster or group (cluster) indicate some regularity. Cluster analysis in the presented work was performed using Statistica 13.3 software. During the analysis, the calculations were based on Euclidean distances and the single linkage distance.

Principal Component Analysis (PCA)
Principal component analysis is useful when a given system is described by many variables. Through PCA, the number of variables can be reduced to the minimum number necessary to describe the variability of the system [38]. The analysis of principal components in the presented work was carried out using the Statistica 13.3 software. The number of eigenvalues was determined based on the Kaiser criterion and scree plot. All data for principal component analysis were previously standardized. The purpose of such standardization is to change the value of data in such a way that it will allow for their analysis when they have different dimensions.

Sum of Ranking Differences (SRD)
Sum of ranking difference is a non-parametric method useful for performing a reliable comparison of methods for the measurements or calculations of the same property, such as lipophilicity descriptors [34][35][36]. The SRD analysis of both chromatographically obtained values using various TLC systems (different stationary and mobile phases) and calculated lipophilicity parameter values of examined compounds was performed using Microsoft Excel macro program downloaded at http://aki.ttk.mta.hu/srd/ (accessed on 15 December 2022).

Conclusions
The chromatographic studies presented in this paper emphasize the importance of thinlayer chromatography in the reversed-phase system for the evaluation of the lipophilicity of selected compounds that lower blood uric acid and anti-androgen compounds, including those for which there is no experimental value of the partition coefficient as a measure of their lipophilicity. The following stationary phases, such as RP18F 254 , RP18WF 254 and RP2F 254 and three mobile phases consisting of ethanol-water, propan-2-ol-water, as well as acetonitrile-water, as mobile phases, were successfully applied in this work. The chromatographic parameters of lipophilicity were compared with logP values obtained with different software packages, such as AClogP, AloGPs, AlogP, MlogP, XlogP2, XlogP3, ACD/logP and logP KOWWIN .
The similarity between the R MW values obtained onto three chromatographic plates with ethanol-water and acetonitrile-water as mobile phases with logP exp or with average values of theoretical logP confirms the usefulness of the proposed chromatographic method for measuring this important parameter in the point of view of pharmacokinetic properties of drug substances, such as lipophilicity. The results obtained also show that chemometric methods, such as PCA and CA, can be a good tool to illustrate the similarity and differences existing between the investigated compounds based on their both, i.e., experimentally and theoretically obtained lipophilicity parameters, as well as other physicochemical descriptors, including, e.g., density, boiling point, index of refraction, molar refractivity, polar surface area, polarizability, surface tension and molar volume. It has been found that the SRD analysis is very useful in obtaining the reliable comparison of chromatographically determined and theoretical parameters of all studied compounds belonging to compounds that lower blood uric acid and anti-androgenic compounds. The SRD analysis confirms that of all calculation methods, the method based on the MlogP algorithm is the most useful for determining the best lipophilicity measures of the tested compounds. According to SRD scores, ethanol would be suggested as the most suitable organic modifier for the determination of chromatographic parameters of lipophilicity, i.e., R MW of studied compounds using thin-layer chromatographic method in a reversed-phase system.
The lipophilicity parameters obtained in this work can be successfully applied in further study, i.e., in quantitative structure-property relationship and quantitative structureactivity relationship, as well as to facilitate the process of designing new derivatives of the examined compounds that lower blood uric acid or anti-androgens, respectively.

Supplementary Materials:
The following materials can be downloaded at: https://www.mdpi.com/ article/10.3390/molecules28010166/s1, Table S1: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for allopurinol. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S2: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for oxypurinol. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S3: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for febuxostat. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S4: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for abiraterone. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S5: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for bicalutamide. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S6: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for flutamide. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S7: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for nilutamide. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S8: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for leflunomide. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S9: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for teriflunomide. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ); Table S10: Data for linear correlation (Equation (1)) between R M values and the content of organic modifier in the mobile phase for ailanthone. Where: correlation coefficient (R 2 ), standard error of estimation (SEE); F-factor; significance level (p), volume fraction of organic modifier in mobile phase (ϕ).