Accepted Manuscript Title: Thin layer chromatography in drug discovery process Authors: Krzesimir Ciura, Szymon Dziomba, Joanna Nowakowska, Michał J. Markuszewski PII: DOI: Reference:
S0021-9673(17)31334-1 http://dx.doi.org/10.1016/j.chroma.2017.09.015 CHROMA 358844
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Journal of Chromatography A
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Please cite this article as: Krzesimir Ciura, Szymon Dziomba, Joanna Nowakowska, Michał J.Markuszewski, Thin layer chromatography in drug discovery process, Journal of Chromatography Ahttp://dx.doi.org/10.1016/j.chroma.2017.09.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Thin layer chromatography in drug discovery process
Krzesimir Ciura1*, Szymon Dziomba2, Joanna Nowakowska1, Michał J. Markuszewski3
1
Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Gdansk, 107 Hallera Street, 80-416 Gdansk, Poland 2
Department of Toxicology, Faculty of Pharmacy, Medical University of Gdansk, 107 Hallera Street, 80-416 Gdansk, Poland 3
Department of Biopharmacy and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdansk, 107 Hallera Street, 80-416 Gdansk, Poland
(*) Author to whom correspondence should be addressed: Krzesimir Ciura, Department of Physical Chemistry, Faculty of Pharmacy, Medical University of Gdansk, 107 Hallera Street, 80-416 Gdansk, Poland e-mail:
[email protected] Tel./fax: +48 58 349 16 52
Highlights:
Abstract
Traditional and unconventional TLC systems applied for lipophilicity assessment were compared. Application of TLC retention constants in Quantitative Structure Activity Relationship (QSAR) studies for drug discovery were reviewed. The principles and methodology of Quantitative Structure Retention Relationship (QSRR) employed for lipophilicity prediction from TLC retention data were discussed. Bioautography using TLC systems for drug discovery was briefly discussed.
The review is mainly focused on application of thin layer chromatography (TLC) as simple, rapid and inexpensive method for lipophilicity assessment. Among separation techniques, TLC is still one of the most popular for lipophilicity measurement. The principles and methodology of Quantitative Structure Retention Relationship (QSRR) employed to lipophilicity prediction from retention data are presented. Moreover, applications of TLC retention constants in Quantitative Structure Activity Relationship (QSAR) studies were critically overviewed. The paper concerns also bioautography as a TLC method complementary to QSAR studies. In the article, the advantages and limitations of well established and less common planar chromatography modes applied for drug discovery process were discussed. Keywords: lipophilicity; Quantitative Structure Activity Relationship; Quantitative Structure Retention Relationship; salting-out thin-layer chromatography; thin layer chromatography
1. Introduction The lipophilicity is a crucial physicochemical descriptor of the molecule for its transport through biological membranes. Consequently, the lipophilicity determines absorption, distribution, as well as excretion of drugs and other xenobiotics in the body. Moreover, the lipophilicity of a compound influences its ability to undergo complexation with blood proteins and binding to receptors at the site of drug action. For this reason, the pharmacodynamic and pharmacokinetic properties of drugs or toxins are highly correlated with lipophilicity of a substance (1). The classical scale of lipophilicity is based on log P, which is the logarithm of the partition coefficient of the compound between two immiscible phases, n-octanol and water. The lipophilicity can be measured not only in liquid-liquid system (the classical shake-flash
method) but also in solid-liquid systems. According to International Union of Pure and Applied Chemistry (IUPAC) recommendation, both thin layer chromatography (TLC) and high performance liquid chromatography (HPLC) can be considered as efficient tools for prediction of lipophilicity (2). Despite the unquestionable superiority of HPLC due to higher performance and automatization, TLC has some important advantages like simplicity, significantly reduced cost and time of analysis, lower consumption of solvents and reagents, and ability to simultaneously handle dozens of samples. Therefore, this method is also attractive from “green chemistry” viewpoint. Finally, in contrary to HPLC, TLC analysis can be performed without any complicated instrumentation. For this reason, it is still widely used and is one of the most popular techniques for lipophilicity estimation, nowadays. Generally, in order to establish the correlation between chromatographic data and lipophilicity, the Quantitative Structure Retention Relationship (QSRR) approach is used. This method was originally proposed by Kaliszan in 1977 (3,4) . Since that time, the QSRR has been evolving into a powerful theoretical tool with numerous application: prediction of analytes retention time (5), investigation of molecular mechanism of separation (6), comparison of stationary phases (7,8) and evaluation of physicochemical properties (9,10), just to mention few (Figure 1). For medicinal chemistry, the two most important cases are: estimation of lipophilicity of substances and prediction of relative biological activities (11). Prediction of biological activities of molecules based on their structures is the foundation of drug discovery process. The Quantitative Structure–Activity Relationship (QSAR) links molecular properties and pharmacological activity in a quantitative manner (12). The method assumes that structurally similar molecules tend to show similar biological activity since the similar intermolecular actions determine the behavior of chemical compounds in both biological and chromatographic environments. As consequences, the chromatographic data can be used as quantitative molecular descriptors in QSAR study. This
approach is called Quantitative Retention-Activity Relationship (QRAR), but also many authors use QSRR/QSAR acronyms. Several reviews concerning the usefulness of separation methods for lipophilicity estimation were published over the last years (1,2,13,14). However, none of them was dedicated to or pay a little attention to TLC. Only one book chapter respecting TLC determination of drug lipophilicity (15) and a review dedicated to unconventional TLC systems (16) can be found in the recent literature. This fact is surprising if one consider hundreds of papers covering the topic published to date (17). The presented article aims to fill the gap in the literature in terms of TLC application in drug discovery process. The benefits and limitations of TLC methods, used for lipophilicity assessment, were compared and discussed. Moreover, applications of TLC retention constants in QSAR studies were critically reviewed. In the paper, a great emphasis was put on recent achievements in the field. Additionally, the principles of TLC hyphenated with bioautography detection mode were briefly presented. Constructive discussion on future perspectives was also performed. To the best of our knowledge, it is the first such a summary presented in the scientific literature. 2. Chromatographic parameters used for lipophilicity estimation The basic TLC parameter is retardation factor (Rf,) which can be mathematically described by the following equation:
Rf
a b
(1)
where a is the distance from the origin to the spot center and b is the distance from the origin to mobile phase front. Despite Rf factor is directly used for lipophilicity determination by some authors, the standard procedure is based on RM parameters. The RM is recognized as
chromatographic parameter of lipophilicity in TLC and was defined by Bate-Smith and Westall (18) as:
1 R M log 1 R f
(2)
Although the chromatographic parameters obtained in a single run analysis can be successfully applied for lipophilicity estimation (Collander-type equation), the generalization of chromatographic parameters is desirable as it improves the accuracy of the assay. Considering the application of non-polar stationary phase, the retention of substances in pure water is strictly related to its lipophilicity. In practice, direct determination of chromatographic data using pure water as an eluate is impossible since under such conditions the Rf of targets are very low or equal to zero. This is why the extrapolated value of RM, called RM0 or RMW, is used for lipophilicity prediction and another reason why the generalization of chromatographic parameters is necessary. The RM0 parameter corresponds to the retention of molecule in pure water and is obtained from Soczewiński-Wachtmeister’s equation (19), which describes the relationship between RM and the volume fraction of the organic solvent (C):
R M R M mC 0
(3)
The RM0 value is intercept of presented linearity, and it is nowadays a gold standard among TLC lipophilicity parameters. The slope of the equations (3), m, represents specific hydrophilic surface area of the compound and is also frequently used for lipophilicity estimations. However, the linearity obtained with m factor is frequently inferior in comparison to RM0 value. Furthermore, based on these two parameters obtained from SoczewinskiWachtmeister’s equation, another parameter of lipophilicity, C0, introduced by Bieganowska (20), can be computed:
C0=-
RM0
m
(4) The C0 corresponds to the parameter φ0 (the isocratic chromatographic lipophilicity index), formerly defined for the HPLC. The C0 relates to the concentration of the organic component of the mobile phase for which the distribution of the analyzed substance between the mobile and stationary phase is equal (1:1) (20). The another simple way for generalizing RM established for different concentrations of organic solvent in mobile phase is calculation of its arithmetic mean value (21). Nonetheless, this parameter is not frequently used for lipophilicity assessment, since the mean value of RM can be the same for two compounds which have absolutely different chromatographic properties. The chemometric approach, based on principal components analysis (PCA) can be also utilized for RM generalization. In such case, for calculation of principal components (PC), the matrix includes RM or Rf and information about the concentration of organic solvent. Usually, the first PC (PC1), which contains the biggest percent of information included in retention matrix, is highly correlated with log P (22). Nevertheless, the approach based on PCA has some limitation as PC values cannot be used as strict surrogates of log P and the analyses of reference compounds in the same chromatographic conditions is obligatory. It should be also noticed that, according to Komsta, the PC method and the parallel factor analysis (PARAFAC) do not outperform classical extrapolation in any case (17). Moreover, excellent agreement of similar correlation power between experimental log P and RM0 or PC1 (r=0.936 vs. r=0.938) were found in the study where steroid and phenanthrene derivates were used as model substances (23). 3. Statistical and chemometric analysis of correlation between chromatographic parameters and lipophilicity
A number of works concerning newly synthesized drug candidates is published every year presenting correlation between retention data and computational log P. Freely available as well as commercial software (ACD/labs, Chemaxon, ChemDraw, Chemoffice, HyperChem, just to mention but a few) dedicated to log P calculation, are widely available. However, the computational lipophilicity indices are often not sufficiently precise and differ substantially from log P values determined experimentally (24). Therefore, the achieved experimental lipophilicity parameters (including chromatographic parameters) better reflect the physicochemical properties of studied compounds. In consequence, the experimental data are still preferred over computational. To illustrate the relationship between retention and lipophilicity of solutes, several models can be applied. The basic model is linear regression (LR) which can be used to describe relationship between chromatographic parameters such as RM0, m, C0, or PC1 and log P. The comparison and selection of the most suitable chromatographic system or computational software reflecting the octanol-water partition coefficient is a problematic task. Although numerous techniques (like: hierarchical clustering – HCA, Pearson’s correlation coefficient or PCA) might be applied for the comparison, there is no systematic or widely accepted approach. To solve this problem, Héberger and Andrić have recently proposed novel non-parametric methods for assessment of computational and chromatography lipophilicity scales: sum of ranking differences (SRD) and generalized pairwise correlation method (GPCM) (25-27). These approaches can be successfully used for selection of the most appropriate lipophilicity measures, as well as the ranking and selection of the most and the least promising candidates.
Chromatographic constants were frequently used as lipophilicity parameters of investigated substances and correlation between retention and molecular descriptors were studied. In such type of investigation, the most important information is which molecular properties influence the chromatographically established lipophilicity. The studies are frequently based on multiple linear regression (MLR) and partial least squares regression (PLS) methods (28-29). However, principal component regression (PCR) or other types of regression modes can be applied (30). The benefits of MLR method are ease in interpretation of obtained models and direct relation to the original data. In turns, MLR is incapable to treat inter-correlated variables and missing data. In contrast, PLS can be used for analysis of highly correlated data with the number of descriptors exceeding the number of analytes. In the case of MLR, at least five analytes should be used for one independent variable (31). Multivariate techniques like PCA are also used in QSRR studies (32). Nonlinear map based on the PC loadings can illustrate similarities and differences between chromatographic lipophilicity parameters and others molecular descriptors. Similar conclusion after cluster analysis (CA) can be reach (23). The classification of similar object can be visualized as tree diagrams. The application of different regression methods and multivariate technique could be recommended. This way of data analysis allows to confirm conclusions drawn after simple analysis of linear regression between chromatographic constants and log P. Graphical illustration of results of above mentioned methods was presented in Figure 2. 4. TLC modes used for lipophilicity estimation 4.1.
Reversed phase thin layer chromatography
Among the available indirect methods for lipophilicity estimation, reserved-phase thin layer chromatography (RP-TLC) is one of the most popular techniques. The mechanism of the retention in RP-TLC is based on partitioning of the substance between hydrophobic stationary
phase and hydrophilic mobile phase, so the retention is generally strictly correlated with the lipophilicity of the solutes (33). Currently, several commercial RP-TLC plates are available like C18- (optionally wettable)- and C8-modified silica gel or less hydrophobic cyano-bonded (CN), diol- or aminomodified (NH2) beds classified as RP stationary phases. Nonetheless, the retention factors obtained using less hydrophobic stationary phases are usually less correlated with log P in comparison to C18-bonded silica gel (17). Polar stationary phases, such as silica gel, aluminum oxide and cellulose, impregnated with oil substance or coated with polymers, can be used for RP-TLC. However, this approach is not frequently applied, nowadays. Firstly, impregnation of silica gel is time consuming (frequently a whole night process) (34-35). Secondly, the impregnation of plates increase the risk of unrepeatable results, since the additional preparation process is involved. Therefore, the commercial chemically bonded stationary phases, which do not require any sophisticated initial preparation, are generally preferred. One of the RP-TLC great benefits is a wider range of applicable solvents as compared to RP-HPLC like: acetone (since a high ultraviolet absorbance is avoided in HPLC), propan2-ol, ethanol and dioxane (application of these viscous solvents results in high back-pressure in HPLC) as well as methanol and acetonitrile (often used in both TLC and HPLC). Extensive work on influence of organic modifier on lipophilicity prediction was published by Komsta and co-workers. Methanol and dioxane were found superior over other tested solvents while application of propan-2-ol and acetonitrile resulted in inferior correlation (17). Several papers describing the correlation between TLC parameters and lipophilicity of drugs, xenobiotics or important chemicals used in industry like sunscreens (36), food preservatives (37) and foodstuff dyes (38) or artificial and natural sweeteners (21) were
published in the last few years. The most important applications of TLC in lipophilicity measurements for medicines, drug candidates and other important bioactive compounds were summarized in Table 1. The RP-TLC is the most common technique used for lipophilicity assessment and, due to this fact, the most of chromatographic parameters in TLC established for QSAR studies is based on the RP-TLC. Segana et al. reported QSAR models for a group of antimalarial 1,2,4,5- tetraoxanes using C18 and CN stationary phases and water-organic mixtures as mobile phases. Although the obtained QSAR model couldn’t have been used for directed prediction of anti-malarial activity, the most important factors (polarity of compound, electron density distribution, numbers of proton donor/acceptor) influencing the antimalarial activity were elucidated (50). Schiff’s base ligands and their complexes with nickel (II) and copper (II) were studied with QSAR. C18 plates and dioxane-water mobile phase were used to establish chromatographic parameters. The parabolic dependence between RM and log 1/MIC value against Escherichia coli was found. Similar correlation was observed for antifungal properties against Sacharomyces cerevisiae. Noticeably better prediction of biological activity was achieved with chromatographically obtained lipophilicity (r=0.924, r=0.951) in respect to the computational log P (r=0.710, r=0.626)(44). The relationship between retention and antiviral activity was studied. The investigated group of thiazolidinedione featured the inhibitory effect on serine proteases (NS2B-NS3 protease of Dengue virus) which was correlated with RM0 factor (51). Hypotensive activity (determined as a measure of central alpha-adrenergic activity and expressed as pC25) of some α-adrenergic and imidazoline receptor ligands was also correlated with retention of these substances in RP-TLC (52). Good quantitative correlation of RM0 with
pC25 (r2=0.978) confirmed the theory that the interactions of drugs with imidazoline receptor mostly depends on its hydrophobic properties. Furthermore, a group of dehydroepiandrosterone derivatives was investigated using RP-TLC and NP-TLC in a view of anti-aromatase activity. In both cases the chromatographic parameters reflected its lipophilic character but the relationship between chromatographic data and biological activity was not linear (53,70). Milôseviĉ and co-workers investigated seco-androstene derivatives using QSAR/QSRR approach (54). These steroids are used for treatment of different estrogendependent diseases such as breast, prostate and endometrial cancer or prostatic hyperplasia. The relationship between the established chromatographic parameters and the theoretically calculated pharmacokinetic proprieties such as: volume of distribution, the percent of drug bound to plasma proteins, the blood-brain barrier permeability (log BBB), the effective intestinal membrane permeability in humans (Caco-2 test) was found for investigated steroids. Another papers, published by the same research group, linked RP-TLC data with in silico calculated pharmacokinetic properties for succinimide derivatives (55) or human intestinal absorption (HIA) and plasma protein binding (PPB) parameters (56). Although the investigated pharmacokinetic properties were theoretically calculated in both studies (54-56) it should be highlighted that it seems to be possible to establish similar QSAR models with typical experimental pharmacokinetic data. It is worth to emphasize that not only QSAR equations, which are based on TLC constants, but also quantitative structure–toxicity relationship (QSTR) models can be obtained. As a great example of such investigation, reports on QSTR models of natural and synthetic coumarins might be quoted (57). For calculation of the toxicity of coumarins ACD/Tox Suite was used. The possible toxic properties of compounds were expressed as the probability of a compound to cause organ-specific health effects. The established models
indicated that electric polarization descriptors, size descriptors and chromatographic lipophilicity parameters noticeably effects on toxicity of coumarins. Another report important from toxicological point of view, concerned chromatographic methods for assessment of lipophilicity of mycotoxins and alkaloids. Numerous chemically bonded stationary phase, with menthol-water mobile phase, were investigated. Simultaneously, several log P values were computed using different computer software. For lipophilicity prediction - C18, C8 and CN were recommended. The above presented methodology suggested that the obtained lipophilicity indices might be used for classification of certain natural toxins (58). 4.2.
Micellar liquid chromatography
Micellar liquid chromatography (MLC) is a mode of reversed-phase liquid chromatographic (RPLC) where the mobile phase contains surfactant above its critical micellar concentration (CMC). Typical RP stationary phases (C8, C18 and CN) are used in MLC, whereas the anionic sodium dodecyl sulphate (SDS), cationic cetyltrimethylammonium bromide (CTAB) and non-ionic Brij-35, are commonly used as modifiers of mobile phase. It is worth to emphasize that surfactants in MLC not only affects the properties of mobile phase but also adsorb on the stationary phase influencing its characteristics like reduction of silanophilic interactions. In the ideal situation, the mobile phase should be free of organic solvent. However, the addition of small amounts of organic solvent to micellar, aqueous solution provides some advantages like shorter time of analysis, improved peak shape and efficiency as well as resolution (71). The MLC have unique advantages, since in this chromatographic system, solutes retention is governed by three different equilibriums. Those are solutes distribution between the micelles and the bulk phase, partition between the stationary phase and the bulk phase and
direct transfer between surfactant-modified bed surface to the micelles. The basis and application of MLC technique was insightfully reviewed by several research teams and for details on MLC the reader is referred to these works (71-75). The presence of surfactant in chromatographic system is expected to result in additional advantageous for QSAR studies. The micelles have amphiphilic character, so both nonpolar and polar interactions between micelles and analytes take place. As a consequence, MLC is supposed to be more similar to biomembranes in comparison to classical reversedphase chromatography (11). Stępnik et al. published investigation where micellar TLC showed excellent prediction of oral absorption of fatty acids and polyphenols. The proposed MLC method was discussed to be significantly cheaper than cell cultures for in vivo models, typically used for absorption studies (59). The application of the over-pressured-layer chromatography (OPLC) with micellar mobile phases to evaluate the lipophilicity is worth to be mentioned. The lipophilicity prediction of 21 newly synthesized 1,2,4-triazoles was presented. During the study micellar TLC, OPLC and HPLC were compared with classical RP-TLC using QSRR approach. According to the obtained LR between calculated lipophilicity and chromatographic parameters, chromatographic systems with surfactants were more suitable for estimation of the lipophilicity of triazoles than RP-TLC (60). However, Andrića and Héberger analyzed the obtained data using PCA, SRD and GPCM and demonstrated that “OPLC and HPLC micellar data are outliers from the rest of the lipophilicity parameters; therefore, they cannot “outperform” lipophilicity measures obtained from the typical reversed-phase systems as well as computationally assessed log P values.” (25). Different conclusions, indicating that lipophilicity parameters obtained by the use of indirect methods (like TLC) and computational methods should be analyzed not only with LR but also by chemometric tools.
4.3.
Normal phase thin layer chromatography (NP-TLC)
NP-TLC is a less frequently applied technique for lipophilicity estimation. In contrary to above discussed techniques, NP-TLC is related to adsorption chromatography, so different molecular mechanism of retention is involved. Usually, the mobile phase contains two organic solvents, polar and non-polar. The non-polar solvents, such as benzene, cyclohexane, carbon tetrachloride or toluene, are highly toxic and should be eliminated from analytical protocols. Silica gel, Florisil® or polyamide are exemplary stationary phases used in NPTLC. Generally, the most articles demonstrated linearity between RM0 and calculated log P value (64,65). The relationship between NP-TLC parameters and in silico absorption, distribution, metabolism, and excretion (ADME) properties was also described. The assay performed for seco-androstene derivatives has shown that both RP (RM0) and NP (C0) chromatographic parameters can be considered as a reliable alternative for traditional lipophilicity scales. The chromatographic lipophilicity parameters were highly correlated with theoretically computational pharmacokinetic parameters (Caco-2 permeability test or constant of absorption, plasma protein binding (PPB), volume of distribution (Vd) and logarithm of blood-brain partitioning (log BB) (54). Reports concerning NP-TLC parameters of bile acids and their oxo-derivatives were presented. Correlation between molecular parameters (total polar surface area, molecular weight) and chromatographic data of bile acids derivatives with in silico determined human intestinal absorption, PPB, skin permeability and log BB (61). Furthermore, in silicodetermined bioactivity (protein coupled receptor ligand, ion channel modulator, nuclear receptor inhibitor, protease inhibitor) depended strongly on the same descriptors.
Several QSTR models concerning acute toxicity of bile acids and their oxo-derivatives as well as their influence on aquatic ecosystems were provided using NP-TLC. Apart from chromatographic lipophilicity parameters, topological polar surface area (TPSA) and molecular volume (V) played vital role in establishing QSTR equations (62). Several binary mobile phases including benzene and polar solvents (ethyl acetate, acetone, dioxane and tetrahydrofuran) were tested for lipophilicity estimation of carbohydrate derivatives (64). Estradiol derivatives were studied using benzene-acetone and benzene-ethyl acetate eluents (65). In both quoted reports linear correlation of chromatographic parameters and computational log P values were found. The RP-TLC and NP-TLC approaches were compared as potential tools for lipophilicity estimation for compounds with a phenanthrene skeleton. Linear regression (r=0.820), PCA and CA tests showed that despite NP-TLC can be used for this purpose, RPTLC was found more suitable (r=0.938) (23). 4.4.
Salting-out thin-layer chromatography (SOTLC)
The inorganic salts are quite common ingredients of mobile phase in liquid chromatography. In most cases, they are added as a buffer components to adjust the pH value of the solution. The inorganic salt at low concentration can act similarly to ion-pairing reagents, increasing the retention time of ionized compounds. On the other hand, aqueous solution of inorganic salts at high concentrations are used as mobile phases in SOTLC. The mechanism of SOTLC is not completely explained but several theoretical mechanisms were comprehensively discussed (76). The commonly accepted theory assumes that under these conditions, non-specific hydrophobic interactions govern the chromatographic mechanism. Bij and coworkers proposed “solvophobic interactions” term for general description of this phenomenon (77). According to this theory, it can be expected that the retention of solutes is
associated with its lipophilicity. However, several inorganic salts can be applied (calcium chloride, magnesium chloride, ammonium sulfate, ammonium chloride, sodium bromide, sodium chloride and many others) as modifiers of mobile phase among which ammonium sulfate is the most widely used addition in SOTLC (because of its strong salting-out properties). Highly polar sorbents such as silica gel, cellulose, or aluminum oxide, are commonly utilized as stationary phases (76). Tostic and co-workers applied SOTLC for lipophilicity assessment of macrolide antibiotics. The chromatographic experiments were carried out on cellulose plates with aqueous solution of ammonium sulfate as eluent. The linearity between retention parameter (C0) obtained in SOTLC with calculated log P values was found (r=0.814)(66). Several SOTLC systems, containing different inorganic salts and three types of stationary phases: silica gel, cellulose plates and basic aluminum oxide, were compared as potential tools for lipophilicity estimation of macrolide antibiotics. The established QSRR equations and PCA analysis showed that basic aluminum oxide plates can be successfully used for estimation of logarithm of distribution coefficient (log D) value. Obtained results suggested that basic aluminum oxide stabilizes the compounds in non-ionized form which improved the prediction of log D. Moreover, the statistically significant correlations between microbial activities against S. pyogenes, S. pneumoniae, and L. monocytogenes and different chromatographic parameters were found (r > 0.834)(67). Fliger and co-workers published extensive work on relationship between retention, molecular structures and biological activity of chosen sulphonamides which are an important class of compounds featuring several pharmacological activities. The obtained 3D scatterplot of the chromatographic parameter RM0 (NaCl), molecular molar refractivity and calculated log P, were used for rapid classification of sulphonamides. In the first cluster the bacteriostatic
sulphonamide derivates were grouped. The second cluster contained sulphonamides typically used as a locally administered drugs. To better visualize the results it is worth to highlight that the latter group of substances feature distinct physicochemical properties as compared to the former group like poor water solubility and ionization under physiological pH (68). Correlations between SOTLC retention constants and intestinal absorption of angiotensin-converting enzyme (ACE) inhibitors were described by Odović and co-workers. Topological polar surface area (TPSA) and RM0 parameters were used for construction of QSAR models which were proposed as an alternative way for prediction of intestinal absorption for angiotensin-converting enzyme inhibitors (69). 5. Other planar chromatographic techniques 5.1.
Magnetochromatography
The beginnings of magnetochromatography can be linked with report presented by Barrado and his group (78). They proposed high-performance liquid magnetochromatography (HPLMC) as a new chromatographic technique. Later, the magnetic field was successfully connected with TLC method by Malinowska’s group (79). It was discussed that application of external magnetic force during TLC separation induce the evaporative flow that can influence the composition of mobile phase and, thus, affects the retention of the solutes. The recent research also showed that magnetic field can affects the sorption of ionic substances on solid supports (80). Thus, the influence of magnetic field on solutes affinity to stationary phase might play crucial role in observed phenomenon. An exemplary scheme of TLC chromatographic system for magnetochromatography was presented in Figure 3 (81). The magnetochromatography method for determination of lipophilicity of carboxylate complexes with transition metals or rare earth elements should be primarily mentioned. This class of compounds is important not only for its biological activities but also some industrial
applications like magnetic or electrical materials production and technology. Computational assessment of its lipophilic character is frequently impossible since commonly used softwares usually miss a central metal atom during calculation. As a consequence, the experimental data better reflected its physiochemical properties of studied complexes than computational models (82). Another report concerned changes of 1,2,4-triazole chromatographic parameters (on RP-TLC and MLC-TLC) in the presence of an external magnetic field (83). Obtained results indicated that the magnetic field can affects retention of compounds, mostly determined by the structures of analytes. Additionally, the strong influences of organic modifier were observed both in the RP-TLC and MLC-TLC. The lower retention parameter on MLC-TLC can be explained by stronger interaction between stationary phase and surfactant. Only for MLC-TLC with magnetic field the calculated Mlog P was better correlated with chromatographic data (R2=0.884) in comparison to some experimental conditions without magnetic field (R2 = 0.768). Thus, this observation supports the theory that magnetic field can modify the action of drugs or other substances which cross human biological barriers. However, further investigations are required to address this issue. 5.2.
Biochromatography
The principle of biochromatography is imitation of interactions of studied compounds with biological environment. It is mainly achieved through the impregnation of stationary phase with biocomponents typical for targeted structures like binding sites of receptors. As a consequence, the established data from biochromatography experiments have prognostic value for medicinal chemistry. It is worth to mention that MLC with Brij-35 as a modifier of mobile phase is also classified as biochromatography by some authors (59). Structure–activity relationship (SAR) study of thiazole and benzothiazole derivatives with antihistamine activity was reported. C2 bonded silica gel (84) and silica gel (85)
impregnated with amino acids (L-Asp, L-Asn, L-Thr and L-Lys), which are an integral structural element of histamine H1 receptor, were used. Simultaneously, the controlled experiments based on non-impregnated plates were carried out. The determination coefficients for predicting H1 receptor activity for presented models were statistically significant and ranged between 0.91-0.94 while no correlation between chromatographic data and antihistamine activity was found in the case of control. In further studies five discriminating models for the screening of antihistamine drug candidates were developed (86). The silica gel plates impregnated with L-aspartic acid were used to study the compounds activity toward dopaminergic, serotoninergic and muscarinic receptors (87). Stepwise Discriminant Analysis (SDA) based on biochromatographic retention data as well as calculation of molecular descriptors were carried out. The established models indicated that such analyses do not have to be limited to structurally similar compounds. The results from all the examples of SDA demonstrated the usefulness of biochromatographic data for prediction of action on the selected metabotropic receptors. 5.3. Bioautography detection in TLC Oppositely to quantitative approach (QSAR/QSRR), planar chromatography hyphenated with the biological detection provides “yes/no” response. This method is called bioauthography and basically it is classified as a screening microbiological method. Usually, microbes are cultured in contact with chromatographic plate on which the separation of complex mixture of biocompounds was conducted. Observation of microorganisms’ growth in a presence of tested substances enables to screen these chemicals in a view of e.g. antimicrobial properties (88). However, in this way not only antimicrobial activity (89,90) could be verified but also antifungal (91), antitumour (92), antiprotozoae (88), antioxidant
(93) activities or inhibitory toward selected enzymes like acetylcholinesterase and butyrylocholinesterase (94) and others biological activities (Figure 4). This biological detection mode could be combined with different chromatography techniques like TLC, OPLC and planar electrochromatography (PEC) . Three basic types of bioautography can be distinguished: contact bioautography (TLCCB), direct bioautographic (TLC-DB) and immersion bioautography (TLC-IB) (Figure 5) among which the former technique (TLC-CB) is the most commonly used. The method consists in immersion of a developed TLC plate in a suspension of microorganisms. Next, the microbes are incubated in a humid atmosphere in a broth medium directly on the silica surface. The presence of antimicrobial substances in the analyzed mixture results in the creation of zones where the growth of microorganisms is inhibited. To visualize these zones, dehydrogenase activity-detecting reagent (usually tetrazolium salts) is used. The living microorganisms converts tetrazolium salt into formazan which have very intensive purple color. Finally, cream-white spots are formed on the surface of silica plates which indicates the antimicrobial agents’ positions on chromatogram (Figure 6). Bioautography was shown to be not only efficient tool for plant extracts activity screening but also for analysis of newly synthesized structures. TLC-DB was used for testing of crude antimicrobial peptides (95). Application of this method caused significant reduction of expensive reagents dedicated for purification of peptides. Additionally, small amounts of crude peptides were used. The presented strategy meets the assumptions of green chemistry. Readers interested in the more accurate details and application of bioautography are referred to comprehensive and exhaustive reviews published by Choma (88), Dewanjee (93) or Cieśla (96), just to mention few. 6. Conclusion
The separation techniques are nowadays more frequently used for lipophilicity prediction as compared to classical liquid-liquid system, since they have many significant advantages like lower cost, shorter time of analysis and smaller amounts of investigated compounds needed per assay. Moreover, analytical quantification is not required as only retention factors must be determined. Direct application of chromatographic parameters in QSAR study is a great alternative for frequently complicated, labor and time-consuming biological experiment. TLC can be also considered as economical and environmentally friendly analytical technique as it does not require either an expensive equipment investments or intensive analytical staff training. This method can be simply implemented in virtually every laboratory. Although the recommendation of one TLC chromatographic system for lipophilicity prediction is difficult, the RP-TLC can be advised as the most universal. Among several available RP-TLC stationary phases, C18 bonded silica gel seems to be a “gold standard”. CN stationary phase and C8 bonded silica gel are also commonly used for lipophilicity determination with satisfactory results. According to the literature review, two organic modifiers: methanol and dioxane, provided most accurate lipophilicity prediction (17). The usefulness of acetonitrile is questionable, since in some cases, like artificial and natural sweeteners, this organic modifier of mobile phase provided the best correlation of lipophilicity with chromatographic retention (21) while in other cases, like oxicams and coxibs, its use delivered inferior results (24). Additionally, GPCM and SRD indicated that there are no significant differences between chromatographic lipophilicity parameters obtained when methanol and acetonitrile as organic modifiers were used (27). NP-TLC mode was also found to be useful for lipophilicity measurements (54,64). However, diluents used in NP-TLC are highly toxic and due to this fact more green, chromatographic modes should be considered instead of NP-TLC.
Therefore, modification of mobile phase with surfactants and salts (MLC and SOTLC, respectively) seems to be very promising. These methods have significantly lower environmental impact and, in some cases, were found to provide better prediction of lipophilicity as compared to RP-TLC (67). Despite of significant benefits of SOTLC, some limitations must be quoted. This method cannot not be applied for compounds poorly soluble in water. It should be stressed that there are no studies covering set of chemically diverse solutes that might indicate for which type of compounds the SOTLC and micellar TLC methods can overcome classical RP-TLC in lipophilicity determination. It can be suspected that lipophilicity determination of relatively hydrophilic, ionic compounds using MLC might be found advantageous as such substances are expected to feature low or no retention in RPTLC. Comparative assays facing this issue should be delivered in near future. According to the number of reports concerning comparison of retention constants and calculated log P values, chemometric tools used for comparative studies should be discussed. TLC and computational methods are frequently applied to assess lipophilicity of systematically synthesized drug candidates. The conclusion of these type of investigation should be confirmed not only using LR but also with multivariable methods, since LR give only information how strong the correlation between calculated and chromatographic lipophilicity is. The multivariable methods (like PCA, CA, GPCM and SDR) give additional information about grouping of different lipophilicity scales and support inference which methods reflect the real lipophilic characters of investigated compounds. Special attention should be also paid to less common chromatographic techniques. Despite there is still a long way to establish the position of these chromatographic modes in drugs screening process, they carry some advantages like possibility of estimation of ligand receptor interactions in a simple and low cost manner. In such way, biochromatography can serves as an incomparably faster and high through-put drug candidates screening method
complementary to commonly used and well established computational studies like molecular mechanics and docking experiments. Although the precise mechanism of magnetochromatography has not been completely understood (84), the presented results indicated that magnetochromatography can be useful in estimation of lipophilicity of selected molecules like metal ion complexes (82-83). Interestingly, it seems to be possible that TLC system, which can mimic the biological environment, can provide new information and contribute to better understanding of how magnetic field can modify drugs or xenobiotics transport across cell membranes and change its bioavailability. Moreover, the application of biological detection hyphenated with planar chromatography provides rapid “yes/no” response for potential biologically active compounds. Considering this approach, TLC is a leading chromatographic technique for preliminary screening of potential drug leads in natural sources. Nevertheless, the main drawback of this method should be stressed, the noticeably lower separation capacity in comparison to high and utlra-high performance separation techniques. The observed activity can be derived not from the single compound but be a result of synergism of poorly separated substances. Consequently, the obtained data might be exaggeratedly interpreted (96). On the other hand, application of bioautography as high throughput screening method for drug candidates seems to be very promising as activity assessment can be performed without purification of substances. It is noteworthy that autobiography can be extended to virtually any bioassay if only proper biodetection tool is provided like specific probes or transgenic microbes. Thus, this method is expected to be rapidly developed in near future. Conflict of interest statement The authors declare no conflict of interest.
Acknowledgments The study was supported by the Polish Ministry of Science and Higher Education Grant for Young Investigators, no. 01-0309/08/518. S.D. gratefully acknowledge the scholarship of the Ministry of Science and Higher Education of the Republic of Poland funded from the quality – promoting subsidy under the Leading National Research Centre (KNOW) program for the years 2012 –2017. All authors acknowledge Bartosz Wielgomas for comments that greatly improvement the manuscript.
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Figures: Figure 1. Applications of QSRR Figure 2. Graphical illustration of result after: A) MLR; B) PCA (3D loading plot); C) PCA (2D loading plot); D) PARAFAC; E) CA; F) relative colored heatmap representation of Pearson correlation matrices; G) SRD; H) GPCM. The figure was based on [ 27, 42, 69]. Figure 3. A) Scheme of a system for TLC in magnetic field. B) Computer simulation of magnetic field induced by pair of permanent magnets used in experiment, with field lines, local values of flux density, and local direction of B (magnetic field) vector represented by arrows. The figure was based on [81]. Figure 4. Application of bioautography Figure 5. Scheme of different bioautography methods coupled with TLC. The figure was based on [93]. Figure 6. Bioautography assay of propolis samples against six bacterial strains. A) phenolic profile of propolis extract, B) E. faecalis, C) B. subtilis, D) S. aureus, E) L. monocytogenes, F) A. hydrophila and G) S. flexneri. The figure was based on [90].
Figure 1. Applications of QSRR
Figure 2. Graphical illustration of result after: A) MLR; B) PCA (3D loading plot); C) PCA (2D loading plot); D) PARAFAC; E) CA; F) relative colored heatmap representation of Pearson correlation matrices; G) SRD; H) GPCM [27, 42, 69].
Figure 3. A) Scheme of a system for TLC in magnetic field. B) Computer simulation of magnetic field induced by pair of permanent magnets used in experiment, with field lines, local values of flux density, and local direction of B (magnetic field) vector represented by arrows. The figure was based on [81].
Figure 4. Application of bioautography
Figure 5. Scheme of different bioautography methods coupled with TLC. The figure was based on [93].
Figure 6. Bioautography assay on TLC plates of propolis samples against six bacterial strains. A) phenolic profile of propolis extract, B) E. faecalis, C) B. subtilis, D) S. aureus, E) L. monocytogenes, F) A. hydrophila and G) S. flexneri. The figure was based on [90].
Table 1. The summary of selected papers on lipophilicity estimation of drugs and bioactive substances with various TLC modes.
RP-TLC Drugs
Biological activity
Chromatographic system
Results
Ref.
mobile phase: methanol-water, acetonitrilewater, acetone-water, 1,4-dioxane-water, 2propano-water.
Relationship between chromatographic data and calculated lipophilicity.
(41)
stationary phase: C18 C8, C2, CN, NH2 and Diol;
Identification of the best chromatographic system and chromatographic data processing method to predict log P values.
(42)
Relationship between chromatographic data and calculated lipophilicity.
(43)
Investigation of influences of molecular descriptors on chromatographically determined lipophilicity parameters.
(32)
stationary phase: C18;
Cephalosporins
antibiotics
mobile phase: methanol-water, acetone-water. stationary phase: C18; Fluoroquinolones
antibiotics
Polydentate Schiff bases
competing affinity to some toxic metals, antibacterial, anti-fungicidal properties
mobile phase: methanol-water, acetonitrilewater, acetone-water, tetrahydrofuran-water, 2propano-water. stationary phase: C18; mobile phase: methanol-water, tetrahydrofuran-water, acetonitrile-water.
herbicides, antibacterial, antifungicidal properties
stationary phase: C18;
Complexes of carboxylates with metals Amine neurotransmitters and derivatives
monitoring of treatment of some diseases
stationary phase: C18, C8, C2, CN and Diol;
stationary phase: C18;
Oxicams and coxibs
non-steroidal antiinflammatory drugs
5β-cholic acid derivatives
potential drug absorption modifiers
Bile acids and their absorption derivatives promoters
mobile phase: dioxane-water, acetone-water, acetonitrile-water.
mobile phase: methanol-water.
mobile chase: methanol-water, acetone-water, dioxane-water, acetonitrile-water, 2-propanolwater. stationary phase: C18; mobile phase: methanol-acetic acid. stationary phase: C18, CN; mobile phase: methanol-water.
Determination of lipophilicity of metal— complexes.
(44)
The developed methodology allowed estimation of log P values even for highly hydrophilic compounds.
(45)
Relationship between chromatographic data and calculated lipophilicity.
(24)
The relationships between the lipophilicity and absorption enhancement effect.
(46)
Relationship between chromatographic data and calculated lipophilicity as well as QSRR study based on 2D and 3D Dragon descriptors.
(47)
N-acyl-2-amino-1cyclohexanol derivatives
stationary phase: C18; anticonvulsant
mobile phase: methanol-water.
stationary phase: C18; Thioquinoline derivatives
anticancer
mobile phase: acetonitrile-water.
Correlation between RM0 and calculated log P. Additionally, for active compounds, according to Anticonvulsant Screening (48) 0 Project (ASP) class, the RM values were in the 1.66–3.38 range
Correlation between chromatographic data and in vitro activity against human breast cancer cell lines.
(49)
QSAR models presented the main molecular factors influencing the antimalarial activities
(50)
The parabolic dependence between chromatographic constants and log 1/MIC value against Escherichia coli and Sacharomyces cerevisiae.
(44)
Correlation between chromatographic data calculated lipophilicity as well as molecular descriptors
(51)
stationary phase: C18, CN ; 1,2,4,5tetraoxanes
antimalarial
Schiff’s base ligands and their complexes with nickel (II) and copper (II)
antibacterial, antifungicidal
mobile chase: methanol-water, acetone-water, dioxane-water. stationary phase: C18; mobile chase: dioxane-water. stationary phase: C18;
Thiazolidinedione
antiviral
mobile chase: methanol-water, acetone-water, dioxane-water, acetonitrile-water, 2-propanolwater, ethanol-water.
α-adrenergic and imidazoline receptor ligands
stationary phase: C18; hypotensive
Dehydroepiandrost erone derivates
anti-aromatase
Seco-androstene derivatives
treatment of different estrogendependent diseases
mobile chase: dioxane-water. stationary phase: C18; mobile chase: acetone-water, dioxane-water. stationary phase: C18; mobile chase: acetonitrile -water, dioxane-water, stationary phase: silica gel
Relationship between RM0 and experimental log P as well as hypotensive activity
(52)
The correlation between the chromatographic lipophilic parameters and the calculated log P values.
(53)
The correlation between the chromatographic lipophilic parameters and the calculated log P values and pharmacokinetic proprieties.
(54)
mobile chase: acetonitrile - toluene, dioxane- toluene. stationary phase: C18; mobile chase: acetonitrile -water, dioxane-water. Succinimide derivatives
(55)
anticonvulsant stationary phase: C18; mobile chase: methanol-water, acetone-water, dioxane-water.
Natural and synthetic coumarins
The correlation between the chromatographic and in silico biological descriptors.
antiproliferative, antiviral and antibacterial properties.
The correlation between the chromatographic lipophilic parameters and the calculated log P values and pharmacokinetic proprieties.
(56)
Correlation between chromatographic parameters and computational log P and QSTR models.
(57)
stationary phase: C18; mobile chase: acetonitrile-water, methanol-water, tetrahydrofuran-water.
Mycotoxins and alkaloids
certain natural toxins
stationary phase: C18 C8, C2, CN, NH2 and Diol; mobile phase: methanol-water.
The correlations between chromatographic data and the calculated log P values.
(58)
Micellar TLC stationary phase: CN;
Fatty acids and polyphenols
1,2,4-triazoles
anti-inflammatory antioxidants
fungicides
mobile phase: SDS – water – tetrahydrofuran/acetone/dioxane
CTAB-water- acetonitrile Brij35-water- acetonitrile. Micellar HPLC: C8/buffered SDS*— acetonitrile Micellar OPLC: CN/buffered SDS*— tetrahydrofuran Micellar TLC: CN/buffered SDS— tetrahydrofuran RP TLC: C8/buffer—acetonitrile RP TLC: C8/buffer—tetrahydrofuran
Chromatographic parameters have been used for prediction of oral absorption descriptors and correlated with lipophilicity properties.
(59)
Linear correlation between chromatographic data and calculated log P.
(60)
Correlation between molecular parameters and chromatographic data with in silico determined pharmacokinetic parameters, ADME properties or computational toxicity.
(61,6 2)
Relationship between chromatographic data and log P determined in shake-flash method and QSRR equations.
(23, 63)
NP-TLC Bile acids and their oxo derivatives
cholagogic activites surface active substances
Steroid and phenantrene derivatives
antiinflammatory, anti-asthmatic, synthetic hormones
stationary phase: silica gel; mobile phase: toluene-ethanol, toluene-butanol. stationary phase: C18,CN, silica gel; mobile phase: acetonitrile-water, acetonewater, acetone-petroleum ether
stationary phase: silica gel; Carbohydrate derivatives
precursor in the synthesis of important biomolecules
mobile phase: benzene-ethyl acetate, benzeneacetone, benzene-dioxane benzenetetrahydrofuran.
Relationship between chromatographic data and calculated lipophilicity
(64)
The liner regression analysis shows correlation between chromatographic retention (RM) and calculated lipophilicity
(65)
stationary phase: silica gel; Estradiol derivatives
synthetic hormones
mobile phase: benzene-acetone, benzene-ethyl acetate.
SO-TLC Macrolide antibiotics
antibiotics
stationary phase: cellulose;
Macrolide antibiotics
Sulphonamides
Angiotensinconverting enzyme (ACE) inhibitor’s
mobile phase: aqueous solution of (NH4)2SO4. stationary phases: silica gel, cellulose plates and basic aluminum oxide; antibiotics mobile phase: aqueous solution of (NH4)2SO4, CaCl2, NH4Cl, NaCl, stationary phases: silica gel, cellulose plates and basic aluminum oxide; bacteriostatic,
hipotensive
** - 0.01 M Na2HPO4/0.01 M citric acid
mobile phase: aqueous solution of (NH4)2SO4, CaCl2, NaCl, MgCl2,NH4Cl, NH4SCN,NaH2PO4,CH3COONH4,NH4NO3, Mg(NO3)2. stationary phases: silica gel; mobile phase: aqueous solution of (NH4)2SO4, NH4NO3, NH4Cl, NaCl.
Linear correlation of chromatographic data and calculated lipophilicity
(66)
Correlations between microbial activities against S. pyogenes, S. pneumoniae, and L. monocytogenes and calculated log D and log C.
(67)
Relationship between retention, molecular structures and biological activity of chosen sulphonamides
(68)
MLR models for estimation of intestinal absorption based on salting-out thin-layer chromatographic hydrophobicity parameters and molecular descriptors
(69)
