Home Physical Sciences Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography
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Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography

  • Anna W. Sobańska EMAIL logo , Karolina Wanat and Elżbieta Brzezińska
Published/Copyright: February 2, 2019
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Open Chemistry
From the journal Open Chemistry Volume 17 Issue 1

Abstract

The Blood-Brain Barrier (BBB) permeability is an important factor governing a drug’s ability to act upon the Central Nervous System. The measure of the BBB permeability used throughout this study is the log BB (the blood/brain partitioning coefficient) measured in vivo or calculated. Useful yet simple models of the BBB permeability were developed by Stepwise Multiple Regression Analysis based on the chromatographic parameters Rf and Rf/PSA obtained by RP-18 TLC with acetonitrile - pH 7.4 phosphate buffered saline 70:30 (v/v) as mobile phase, combined with descriptors - the number of H-bond donors (HD), the number of H-bond acceptors (HA), energy of the highest occupied molecular orbital – (eH), energy of the lowest unoccupied molecular orbital (eL). The ability of the solutes to cross the BBB has been studied qualitatively using Discriminant Function Analysis. Almost all compounds with the known BB vivo parameter were correctly classified as CNS+/-. The classification functions based on Rf/PSA have been verified using an external group. The results of the chromatographic analysis proposed in this study (RP-18 TLC) are a source of valuable information on the BBB permeability of compounds available even on a very small scale.

Keywords: RP-18 TLC; QSAR; Blood-Brain Barrier; chemometric techniques

1 Introduction

The blood-brain barrier (BBB) is the main physical and enzymatic barrier that separates brain tissues and blood, prevents the penetration of the central nervous system (CNS) by undesired substances (peripherally acting drugs or harmful compounds) and facilitates its penetration by neurotherapeutics [1, 2, 3]. The most common measures of the BBB permeability are log BB - the blood/brain partitioning coefficient at equilibrium conditions, i.e. the ratio of the drug concentration in the brain to that in the blood plasma measured at some defined time point and the less frequently used kinetic permeability log PS, where P is the observed permeability across BBB (cm/s) and S is the surface area of brain capillary endothelium (cm2/g) [3,4]. The evaluation of the compound’s ability to cross the BBB is a vital step in assessing its therapeutic potential because many promising neurotherapeutics miss their goal due to their inability to enter the brain or, oppositely, peripherally acting drugs exhibit unwanted side effects due to their undesired brain penetration. The in vivo, in vitro and in silico methods used currently to study the drug brain penetration are discussed in a number of review papers [2,5,6]. High costs and low throughput of any in vivo experiments as well as the need to use lab animals are the reasons why several in vitro cell-based methods of the BBB permeation assessment have been developed [7]. Concurrently with the development of the in vitro cell-based techniques, some non-cell-based assays have been introduced [8]. As more data on the CNS activity of different compounds were gathered, several attempts were made to generate computational models linking the BBB permeability of compounds to their experimentally measured and/ or theoretically calculated physicochemical features [4,5].

It has been known for several years that the lipophilicity of a molecule is an important factor influencing its BBB passage. The relationship between the compound’s lipid solubility and its ability to enter the brain was observed as early as in the late nineteenth century [9]. Later studies have focused on correlations between the brain uptake and the lipophilicity of the solutes often expressed as the partition coefficient log P between water and certain water-immiscible solvents, of which 1-octanol is the most important example (log Poct). It was observed that for a congeneric series of compounds of the hypnotic activity the most active ones are those with log Poct around 2 (“the rule of 2”) [10]. Further research supported this hypothesis for a series of imidazoles injected intravenously into rats, linking the brain uptake expressed as log (Cbrain/Ci.v.) with the water-pH 7.4 buffer apparent partition coefficient log P’octvia the quadratic equation: log (Cbrain/Ci.v.) = – 0.94 + 0.574 log P’oct – 0.133 (log P’oct)2 (n = 14, R2 = 0.897) with the maximum (as it can be easily calculated) at log P’oct = 2.16 [11], and for a series of C-11 labelled radiopharmaceuticals [12].

Later studies [13] did not confirm the general nature of the log BB – log Poct parabolic relationship. It was soon observed that the octanol-water based lipophilicity is not an ideal parameter to predict the BBB permeation for larger and more diverse groups of compounds. The application of other measures of lipophilicity was proposed of which log P in other solvent systems such as cyclohexane-water (log Pcyc), chloroform-water (log Pchlor) or Δlog P = log Poct - log Pcyc seemed promising but in spite of their relatively high predictive value they have not attracted broader attention because only few log P values for these systems are available.

Attention was then turned to the partitioning of solutes in other bi-phase systems such as chromatographic systems resembling the properties of biomembranes. Thin layer chromatography is a low-cost, rapid and easy-to-perform separation technique whose main application in drug development process is at present the octanol/water lipophilicity determination [14, 15, 16]. The most important thin layer chromatographic parameter used to evaluate the lipophilicity is the RM value introduced by Bate-Smith and Westall: RM = log (1/Rf-1) [17].

Reversed-phase thin layer chromatography is particularly useful for the determination of solutes’ partition between water and the biomembrane because the partition between a non-polar sorbent and a purely aqueous mobile phase resembles that between water and the biophase. Because reversed-phase chromatography with the purely aqueous mobile phase is impractical due to very long retention times (or very low Rf), the RM0 values are obtained by extrapolation of plots of RM versus the concentration of the organic modifier to zero concentration of the modifier (pure water/buffer). This extrapolation is usually conducted by using the linear Soczewinski-Wachmeister equation (called sometimes the “Soczewiński-Matysik” equation) [18]: RM = RM0 – Sφ (φ is the volume fraction of the organic modifier in the mobile phase); some authors suggest that the relationship between RM and φ should be described with the quadratic equation RM = RM0 + bφ + aφ2 [19].

The slope S of the linear Soczewiński-Wachmeister equation is another useful measure of lipophilicity, related to the hydrophobic surface of a molecule [19]. On the basis of RM0 an alternative lipophilicity parameter, C0, has been proposed. This parameter is calculated according to the equation: C0 = RM0/S and represents the volume fraction of the organic solvent in the mobile phase for which the amount of the solute in the mobile phase is equal to that in the stationary phase [20].

The RP-thin layer chromatographic descriptors RM0 and S have been used to evaluate the brain penetration of solutes by several authors [21, 22, 23, 24, 25, 26]. The relationships between log BB and RM0 obtained by these authors are, generally speaking, linear or quadratic, depending on the group of solutes; the relationships between log BB and S described in Ref. [23] are parabolic.

Although less frequently than by RP TLC, the retention data used as the blood-brain barrier permeability predictors may be collected by other planar chromatographic techniques [24,27]. In normal-phase TLC the relationship between φ (the volume fraction of the more polar component of the mobile phase) and RM can be expressed by the equation: RM = RM0 – b log φ. NP-TLC RM0 values can be correlated with log BBvia the parabolic relationships [27]. Quite recently salting-out thin layer chromatography (SOTLC) – the separation technique involving the application of concentrated solutions of inorganic salts as mobile phases on silicagel, aluminum oxide or cellulose supports [28] – has been used to study the BBB permeability of solutes according to a novel procedure by Ciura et al. [29] utilizing the chromatographic parameter C0 = RM0/m (derived from the equation RM = RM0 + mC, where C is the salt molar concentration).

Although lipophilicity is a key factor governing the solutes’ ability to cross the BBB and hence the chromatographic lipophilicity descriptors play a dominant role in several BBB permeation models, there are other molecular parameters (related mainly to the molecule’s size and polarity) that influence the BBB passage [30]. According to some earlier research, Polar Surface Area (PSA) alone or combined with lipophilicity is an important predictor of the BBB permeability and two useful yet simple models of the blood-brain partitioning were proposed by Norinder (log BB = 0.139 + 0.152 log P – 0.0148 PSA) [31] and Clark (log BB = 0.547 – 0.016 PSA) [32].

Milosevic et al. [24] proposed the multiple regression BBB permeation models involving chromatographic descriptors (RPTLC RM0 or NPTLC C0) combined with topological polar surface area (TPSA) and according to Ciura [29] the SOTLC C0 parameter should be combined with several other descriptors, including TPSA.

In our earlier research we created some useful models, capable of the quantitative prediction of the BBB permeability of solutes [33,34] involving the retardation factor Rf (RP-18 TLC) determined for a single composition of the mobile phase (without the need to extrapolate as in the approaches based on RM0 or C0). The single chromatographic run approach, although used less frequently than the extrapolation technique, is recommended by some authors for lipophilicity determination [35,36] and the chromatographic data obtained via this approach proved useful in the BBB permeability estimations presented in Ref. [33,34]. The Rf values were combined with some easily calculable molecular descriptors, selected by stepwise multiple regression analysis (MR) and traditionally linked to good brain penetration (log D, PSA, the number of hydrogen bond donors and acceptors HD and HA, dipole moment DM). The same RP-chromatographic descriptors (Rf, Rf/ PSA) were used in our earlier research to study the solutes that cross the BBB (CNS+) and the compounds that do not exhibit good brain penetration (CNS-) [33,34].

Initially the efficiency of our methodology based on a RP 18 TLC model was confirmed for a group of CNS-active drugs [33]; then the applicability of our approach was acknowledged for some sunscreens and excipients in pharmaceutical and cosmetic preparations, whose CNS bioavailability should be limited [34]. In this paper we are proving a high predictive value and a universal applicability of our analytical model for a large group of cases of significant structural diversity and different sites of action.

In our current study we managed to demonstrate that the proposed analytical and mathematical models can be applied to any chemical compound of both CNS+ and CNS- activity. We confirmed the retardation factor Rf and the chromatography-derived descriptor Rf/PSA proposed in our previous paper [33] as universal predictors of good brain absorption and suggested adding them as novel parameters to the list of the currently recognized conditions of the BBB permeability [30].

2 Experimental

2.1 Materials

154 drugs analyzed during these investigations (Figure 1, Supplementary Materials) were isolated from pharmaceutical preparations (1, 4, 5, 7-10, 12-21, 23, 24, 26, 28-44, 47-51, 54-69, 71, 73-111, 131, 132, 144-154), purchased from Sigma-Aldrich (2, 3, 6, 11, 22, 25, 27, 52, 53, 72, 114) or donated as free samples by CIBA (113), Polfa-Pabianice (115, 116, 133-143), BASF (70, 120, 121, 127, 129), Merck (117-119, 122, 123, 126, 130) and Symrise (124, 125, 128). The purity of drugs isolated from pharmaceutical preparations was assessed by Thin Layer Chromatography and densitometry (Section 2.2.). All isolated drugs gave single chromatographic spots (densitometric peaks) and were used without further purification. Drugs purchased from Sigma-Aldrich were of analytical or pharmacopeial grade. Distilled water used for chromatography was from an in-house distillation apparatus. Analytical grade acetonitrile and methanol were from Avantor Performance Materials (formerly Polskie Odczynniki Chemiczne). pH 7.4 phosphate buffered saline was from Sigma-Aldrich. Some of the analyzed compounds (52 cases): 3-4, 6, 9, 11, 16, 18-19, 22-26, 29-33, 36-37, 40-41, 43-44, 48-50, 52-53, 62, 64-66, 69, 70 and 114-130 were investigated chromatographically and presented in previously defined populations [33,34]. The chromatographic data and physicochemical descriptors of these compounds were introduced into the analysis.

2.2 Thin layer chromatography

Thin layer chromatography was performed on 10x20 cm glass-backed TLC plates coated with an RP-18 F254s layer from Merck, Germany (layer thickness 0.25 mm). Before use, the plates were pre-washed with methanol-dichloromethane 1:1 (v/v) and dried overnight in ambient conditions. Solutions of compounds 1-154 in methanol (1 μg·μL-1, spotting volume 1 μL), were spotted with the Hamilton microsyringe, 15 mm from the plate bottom edge, starting 10 mm from the plate edge, at 8 mm intervals. The chromatographic plates were developed in a vertical chromatographic chamber lined with filter paper and previously saturated with the mobile phase vapor for 20 min. The mobile phase consisted of acetonitrile - pH 7.4 phosphate buffered saline 70:30 (v/v). The development distance was 95 mm from the plate bottom edge. After development, the plates were dried at room temperature and examined under UV light (254 nm) and with the Desaga CD60 densitometer (Multiwavelength Scan, 200-300 nm at 20 nm intervals). All chromatograms were repeated in duplicate, and the mean Rf values were used in further investigations.

2.3 Calculated molecular descriptors

The molecular descriptors for compounds investigated during this study were calculated with HyperChem 7.0 [37], utilizing PM3 semi-empirical method with Polak-Ribiere’a algorithm [38] (total dipole moment – DM [D], logarithm of the octanol/water partition coefficient – log P, van der Waals molar volume – V (V/100) [Å3], grid surface area – Sa (as Sa/100) [Å2], molecular weight – Mw (as Mw/100) [g/mol], energy of the highest occupied molecular orbital – eH [eV], energy of the lowest unoccupied molecular orbital – eL (eLx10) [eV]). The distribution coefficient – logD, polar surface area – %PSA, and PSA [Å2], the number of H-bond donors – HD and the number and of H-bond acceptors – HA were calculated using ACD/Labs 8.0 software [39]. The theoretical values describing BBB permeability were calculated B1 (log BB = 0.139 + 0.152 log P – 0.0148 PSA) [31] and B2 (log BB = 0.547 – 0.016 PSA) [32]

The experimental BBB permeability (BB vivo) values and CNS+/CNS- binary BBB bioavailability scores were taken from the literature sources [40,41]. The chromatographic data and molecular descriptors for compounds 1-154 are presented in Tables 1a-e (Supplementary Materials).

2.4 Statistical analysis

154 compounds analyzed during these investigations were divided into two subsets: the training set (compounds with the known experimental BBB permeability BB vivo, values 1-46) and the training set (compounds without the known experimental BBB permeability BB vivo, values 47-154).

2.4.1 Stepwise Multiple Regression Analysis

The physicochemical parameters related to the compounds’ BBB permeability were determined by the use of MR analysis. The stepwise multiple regression analysis and the correlation analysis were carried out using STATISTICA 10.0 [42]. The values of the BBB permeability (BB vivo), determined for 46 cases, and the calculated values of log BB (B1 and B2) for 154 analyzed compounds were used as dependent variables and other physicochemical molecular descriptors and chromatographic data as independent variables. The statistical significance (p-level) of a result as an estimated measure of the degree to which it represents the population was determined as p ≤0.05. The correlation matrix was used to correlate the biological activities with the various variables. If two independent variables showed a correlation greater than R2<0.4 one of them was removed.

Validation of the correlation models was carried out by the general internal cross-validation procedures: “leave-one-out” (LOO) and “leave-many-out” (LMO). In the LOO approach, one element is removed from the whole data set and used to verify the model generated with the remaining n–1 elements; the procedure is then repeated with another element. In the LMO method the data set is repeatedly divided into two subsets used for model generation and its verification, respectively. The predictive power of the developed models was evaluated using the following indicators: cross-validated squared correlation coefficient (Q2LO(M)O), predicted residual sum of squares (PRESS), standard deviation based on PRESS (SPRESS), and standard deviation of error of prediction (SDEP). The LMO cross-validation was applied by deleting 25% of the compounds in four cycles and predicting the BBB permeability of compounds deleted in each cycle from the corresponding equations derived from the reduced data set. Some criteria for the reliability prediction and robustness of the models are suggested in Ref. [43, 44, 45, 46]: R2 < 0.6 and Q2LO(M)O < 0.5; R2 ≥ Q2LO(M)O and Q2LOO ≈ Q2LMO.

2.4.2 Discriminant Function Analysis (DFA)

Investigations of the CNS activity of the drugs analyzed throughout this study were based on the discriminant function analysis (DFA) using the physicochemical and chromatographic data connected with the BBB permeability and selected by MLR analysis. All results were compared with the models obtained and tested in the previous investigations [33,34]. In this DFA 111 structurally different compounds were assigned to the CNS+ or CNS- group of activity (defined according to Ref. [40], the remaining 43 compounds are not defined as CNS+/-). The classification functions were determined and validated for the compounds with the experimentally obtained BBB permeability (44 cases) and for compounds with calculated BBB permeability (B2) (111 cases).

Discriminant function analysis is a multivariate technique that has two purposes: to separate cases from distinct populations; and to allocate new cases into previously defined populations [47]. The DFA was performed by STATISTICA 10.0 [41] software. In all subsequently performed analyses the stepwise method was applied. The model was formed by introducing subsequent variables that mostly contributed to group discrimination. After introducing sufficient grouping variables to the model (i.e. after obtaining the maximum probability of a priori classification) discriminant functions (roots) discriminating the activity groups were calculated. The maximum number of functions will be equal to the number of groups minus one, or the number of variables in the analysis, whichever is less. The quality of the discriminant function was evaluated by Wilks’ lambda parameter, which is a multivariate analysis of variance statistics that tests the quality of group means for the variable(s) in the discriminant function [47]. The Wilks’ lambda can assume values in the range of 0 (perfect discrimination) to 1 (no discrimination) and the statistical significance of roots (discriminant functions) used for interpretation was established on the basis of χ2 tests of subsequent roots. Using statistically significant discriminant functions as the basis, canonical values were determined for the particular grouping variables. The scatter diagrams of the canonical values of the subsequent cases for the first two roots determined in the course of the analysis cannot be drawn to evaluate the discriminant power of the obtained models, because there are only two discriminated groups. The final phase of the qualitative analysis of the compounds was to determine the classification functions for each activity group. After calculation of the classification scores for a case, it is easy to decide how to classify it: in general we assign a case to a group for which it has the highest classification score. The tool used to determine how well the classification functions predict the group membership of cases is a classification matrix. The classification matrix shows the number of cases that were correctly classified (on the diagonal of the matrix) and those that were incorrectly classified.

The obtained discriminant models were evaluated by classification of 67 cases not included in the model (test set 47-113) with the known CNS+/- activity [40]. The values of the more important variables obtained with the DFA methodology were calculated for the test set. Than these values were introduced into the discriminant functions (12) and (13) obtained in validated DFA. We classify the case as belonging to the group for which it has the highest classification score. The new compounds were assigned to the CNS+ or CNS- group of activity.

Ethical approval: The conducted research is not related to either human or animal use.

3 Results and discussion

3.1 Stepwise multiple regression analysis

A number of computed or measured chromatographically (RP-18 TLC) BBB permeation descriptors have been presented in our previous papers [33,34,48]. The relationships between these descriptors and the observed (in vivo) BBB bioavailability were studied by MR and DF analysis for a group of compounds of diverse structures and successfully validated. The chromatographic data of the proposed analytical model (RP-18 TLC with the acetonitrile – pH 7.4 phosphate buffered saline 70:30 (v/v) mobile phase) - Rf and Rf/PSA were correlated with the parameters describing the ability of the compounds to penetrate the brain. The correlations are visible for the experimental BBB permeation as well as for the computed parameters B1, B2 and they are completed with the physicochemical parameters that are traditionally linked to the BBB permeation – log D, HD, HA, DM, log P, PSA, Sa, Mw [49]. The other chromatographic data (RM, RM/PSA, Rf/Mw, RM/Mw) were tested as the parameters describing the ability of the compounds to penetrate the brain, but only the Rf and Rf/PSA descriptors are significant and have a predictive capability. The theoretical BBB permeation parameters are given by the already mentioned equations: B1 (log BB = 0.139 + 0.152 log P – 0.0148 PSA) [31] and B2 (log BB = 0.547 – 0.016 PSA) [32]. It was demonstrated that the theoretical values B1, B2 describing the BBB permeability can be used to predict the BBB bioavailability of compounds even if they are not physically available [33]. A useful model of the blood-brain distribution was developed and validated. Models for discrimination between the CNS+ and CNS- compounds were built on the basis of the chromatographic and physicochemical parameters (HD, HA, Rf, Rf/PSA, DM and B2) [33] and used successfully for a different group of compounds [34]. All compounds examined throughout these study were successfully assigned to one of two clusters: CNS+ or CNS-.

Such favorable results of analysis require confirmation. Our current research is aimed at proving the value of the previously proposed [33] analytical and mathematical models with the experiment involving a large (154 cases) and structurally diverse group of compounds.

The correlations of the BBB permeability factors (for the compounds with the experimentally obtained BBB permeability) with the chromatographic parameters (Rf and Rf/PSA) have been investigated (Equations (1)-(3)). The strongest relationship (R = 0.67, n = 46) can be found for B2 (Equation (3)), which is in line with the observation that simpler models are usually better [48]. All models contain the chromatographic parameters Rf and Rf/PSA.

(1)B1=0.44(±0.48)−1.91(±0.72)Rf+16.39(±7.21)Rf/PSA
(2)R=0.44; R2=0.19; F=5.1542; p< 0.00985; n=46; s=0.81335BB vivo=0.48(±0.47)+16.97(±7.03)Rf/PSA-1.15(±0.69)Rf
(3)R=0.38; R2=0.15; F=3.71; p< 0.03265; n=46; s=0.7824B2=0.41(±0.24)+16.63(±4.75)Rf/PSA−1.59(±0.36) Rf
R=0.67; R2=0.44; F=17.050; p< 0.0000; n=46; s=0.40861

The experimental BBB permeability values for 46 compounds from the training set (1-46) were compared with the calculated B1 and B2 parameters and then with all the corresponding physicochemical parameters and chromatographic data. The resulting correlations differed significantly with R = 0.44 for BB vivo/B1 and R = 0.55 for BB vivo/B2, respectively; as B2 was in better agreement with the experimental values than B1, the former was used in further investigations.

At the next stage of these investigations the stepwise analysis was carried out using all the physicochemical parameters collected for 46 compounds from the training set. After the analysis the following relationship (4) was obtained, containing statistically significant descriptors:

(4)BB vivo=3.96(±1.39)+0.14(±0.048)log D+0.60(±0.18)B2+0.42(±0.15)eH -  0.07(±0.04)DM
R=0.77; R2=0.59 F=14.594; p < 0.00000; s=0.55729; n=46

On the basis of the stepwise analysis the parameter HA is introduced into the model. However, this parameter is too strongly correlated with another independent variable – B2 so it had to be removed from the equation. One of the descriptors used in the model was again B2; additionally, the ability of the compound to cross the BBB depends on the distribution coefficient (log D), the total dipole moment (DM) and the energy of the highest occupied molecular orbital (eH). The positive influence of log D (the parameter that takes into account the molecule’s ionization level) on the compound’s partition between the phases is clearly visible. The model explains 59% of the total variance.

The possibility of replacing the difficult-to-obtain BB vivo parameter with the B2 value as the BBB penetration index was considered. Initially, the variables of the equation (4) given above were introduced, including HA and performing the regression analysis by the standard method:

(5)B2=− 0.28(± 0.58)−0.20(± 0.01) HA−0.03(± 0.02) DM+                     0.04(± 0.01)log D−0.09(± 0.06)eH
R=0.91; R2=0.83; F(4,41)=49.484; p < 0.00000; s=                                         0.23211 n=46
Q2LOO=0.77, SDEP=0.074572, PRESS=3.43294, SPRESS=                                         0.273078, Q2LMO=0.71

The result of the MR analysis for the B2 descriptor performed exclusively for the compounds with the known biological activity is very promising and reveals the importance of the same physicochemical parameters. The model explains 83% of the total variance. Next, the stepwise analysis by the forward selection method was performed. The result was very similar, although its statistical significance and the correlation coefficient R are higher. The resulting model explains 92% of the total variance in the studied group (46 compounds).

(6)B2=0.55(± 0.05)−0.14(± 0.01) HA−0.03(± 0.02) DM−                                         0.16(± 0.02) HD
R=0.96; R2=0.92; F=163.84; p < 0.00000;s=0.15533 n=46Q2LOO=0.90, SDEP=0.028056, PRESS=1.290571,SPRESS=0.167499, Q2LMO=0.88

The B2 parameter may be successfully used as a measure of the BBB permeability. It is determined by the same molecular descriptors so it may replace the difficult-to-measure BB in vivo parameter which makes it possible to expand the group of studied compounds by including the molecules without the BBB permeability determined in vivo (in this case to the total of 154 cases). The MR analysis performed with the physicochemical data confirms this conclusion. The equation (7) explains 88% of the total variance despite the threefold increase in the number of cases (n).

(7)B2=1.13(± 0,26)−0.14(± 0.01) HA−0.19(± 0,01) HD+                        0.02(± 0.00) eL+0.07(± 0.02) eH
R=0.94; R2=0.88; F 281.81; p < 0.0000; s=0.23578;n=154 Q2LOO=0.87, SDEP=0.057613, PRESS=9.448573,            SPRESS=0.240028, Q2LMO=0.86

Our further investigations concentrated on the possibility of using reversed-phase thin layer chromatography to partially mimic the physiological conditions of crossing the BBB. It was assumed that the results of our biochromatographic experiments should improve the predictive capabilities of the purely computational models. The chromatographic data alone did not a give statistically significant correlation with BB vivo (R = 0.40). Molecular descriptors closely related to the compounds’ bioavailability should, however, contribute to the predictive value of the model. The results of the stepwise MR analysis obtained after all the physicochemical parameters and chromatographic data had been introduced confirmed this assumption.

The analysis of the training set makes it possible to introduce to the computational model 8 independent variables (46 cases). The further stepwise analysis carried out with all the parameters, in 7 steps, results in the introduction of the additional variables, strongly connected with bioavailability. The chromatographic parameters Rf and Rf/PSA confirm the reasonable expectations connected with biochromatography. The descriptors equally often linked to the compound’s ability to cross the

Figure 2 Plot of observed vs. predicted values. Dependent variable B2.
Figure 2

Plot of observed vs. predicted values. Dependent variable B2.

Figure 3 Plot of observed vs. predicted values (for model 7). Dependent variable B2.
Figure 3

Plot of observed vs. predicted values (for model 7). Dependent variable B2.

BBB: log D, HA, eH, eL and V (combined here with the Rf and Rf/PSA parameters) are present in the obtained model (8). The correlation coefficient is, however, not very high and the model explains 63% of the total variance.

(8)BB vivo=1.78(± 1.60)+0.24(± 0.06)log D−0.17(± 0.06)HA+0.20(± 0.16)eH+0.63(± 0.62)Rf−0.05(± 0.02)eL+                       5.94(± 5.70)Rf/PSA−0.11(± 0.11) V
R=0.80; R2=0.63; F 8.9987; p < 0.00000;                        s=0.53719; n=46

The computed parameter B2 is well defined by the chromatographic data which confirms the applicability of chromatography to predict the CNS bioavailability. The model (3) explains 44% of the total variance.

Table 2

Correlation matrix for model (9).

HDHAeHRf/PSA Rf
HD1.000000.58546-0.07994-0.265110.54010
HA1.00000-0.08071-0.338890.32215
eH1.000000.062360.08418
Rf/PSA 1.000000.17133
Rf1.00000

The relevance of the chromatographic parameters to the BBB permeability studies of 46 compounds of the known experimental BBB permeability (BB vivo) suggests the possibility of using these data to model the variance of B2. After the chromatographic data have been introduced, the regression model (9) is generated in 4 steps:

(9)B2=− 0.25(± 0.29)−0.15(± 0.01) HA−0.15(± 0.01) HD−                   0.07(± 0.03) eH+3.84(± 1.27)Rf/PSA
R=0.97;  R2=0.94;  F=154.82;  p < 0.00000  s=0.13519;n=46  Q2LOO=0.86,  SDEP=0.040670,  PRESS=1.870843,                 SPRESS=0.201669, Q2LMO=0.85

This model explains the total variance in 94% and its predictive quality is validated. The influence of the Rf data on the prediction of the B2 variance has been studied as well. The regression model containing the variables: HA, HD, eH and Rf explains 92% and the model based on HA, HD, eH, Rf/PSA and Rf 93% of the total variance, respectively. The independent variables are not intercorrelated. This confirms the predictive capability of both chromatographic descriptors and the selection of the equation (9) as the B2 variability model.

The correlation between B2 and the chromatographic data generated on the basis of all 154 cases (including the test set 1-46) gives an equation that explains 31% of B2 variance (R = 0.56; n=154). The molecular descriptors and the chromatographic data as described above were used to generate the model given by Equation (10) for the whole group of 154 compounds including the test set. The outcome of this analysis is very good – the model explains over 90% of the total variance.

(10)B2=0.93(± 0.27)−0.15(± 0.01) HA−0.19(± 0.27) HD+ 0.06(± 0.02) eH+0.02(± 0.00) eL+0.16(± 0.10) Rf+                                  1.78(± 1.09) Rf/PSA
Figure 4 Plot of observed vs. predicted values for model (9). Dependent variable B2.
Figure 4

Plot of observed vs. predicted values for model (9). Dependent variable B2.

Figure 5 Plot of observed vs. predicted values for model (10). Dependent variable B2.
Figure 5

Plot of observed vs. predicted values for model (10). Dependent variable B2.

R=0.94;  R2=0.90;  F=196.52;  p < 0.00000  s=0.23038;  n=154     Q2LOO=0.87, SDEP=0.059787, PRESS=9.207211,  SPRESS=                                              0.244514,  Q2LMO=0.86.
Table 3

Correlation matrix for models (10) and (11).

RfRf/PSAHDHAeHeL
Rf1.000000.170630.422180.377100.06021-0.09090
Rf/PSA1.00000-0.21772-0.312670.134790.20615
HD1.000000.55937-0.02446-0.30065
HA1.00000-0.13247-0,44623
eH1.000000.03204
eL1.00000

The MR analysis based on the independent variables given by the B2 variability model presented earlier [33] has been repeated. The model obtained in this manner explained 95% of the variance for the group of 34 cases and involved the variables HA, HD, Rf/PSA [33]. The resulting equation (11) has very good parameters and confirms the value of this model describing the properties of any group of cases. The model is resistant to a significant increase in the number of cases.

(11)B2=0.46(± 0.05)−0.16(± 0.01)HA−0.19(± 0.01)HD+                                    2.94(± 1.09)Rf/PSA
R=0.93;  R2=0.90;  F=342.74;  p < 0.00000  s=0.24540;  n=154  Q2LOO=0.85,  SDEP=0.068583,  PRESS=10.561839,  SPRESS=                                            0.261884,  Q2LMO=0.85.

There are many parameters that enable the simplified prediction of the ability of drugs to enter the CNS. The molecular descriptors, describing the physicochemical properties of solutes and gathered in the „Rule of 5” have been selected after the analysis of thousands of compounds.

Complex patterns of drug - BBB interactions in vivo lead to discrepancies of the results of this rule application. Numerous sorptions and desorptions of a drug over its distribution path intensify these differences. Studying the structure and physicochemical properties arising from the compound structure is crucial but usually insufficient for the quantitative description of the distribution effect.

All the parameters proposed in the „Rule of 5” have the limited ranges of effectiveness for the BBB permeation (CNS+). These ranges are usually quite narrow and their extension or shifting is usually connected with other properties of a compound. There are very few mathematical models of biological activity based on a single independent value. Hence, the prediction of a drug brain absorption exclusively on the basis of its

Figure 6 Predicted vs. observed values for model (11). Dependent variable B2.
Figure 6

Predicted vs. observed values for model (11). Dependent variable B2.

physicochemical properties (e.g. “Rule of 5”) has certain limitations. Descriptors derived from these parameters are used. Linking two single parameters has given rise to the descriptors such as: log P – N+O, %PSA (PSA and SA). Frequently used logarithmic derivatives (e.g. log Mw) facilitate a better fit to the complex behavior phenomena (with the tendency to reach a state of equilibrium) or to other logarithmic measures of activity (pA2, pD2, log BB etc.).

If compounds under investigation are available, their activity may be studied in specially designed analytical models, mimicking the conditions in vivo. The subtle differences in behavior must be multiplied to become traceable and expressed quantitatively. The application of a chromatographic method gives such possibilities both in studying the CNS bioavailability [29,33,34] and in other areas of medicinal chemistry [50]. The analytical models are used to study and compare the solutes of a great diversity of physicochemical properties. In the case of the chromatographic models the phase systems are required that would make it possible to study all interesting cases without changing the conditions. Every chromatographic model imitates the complex conditions of in vivo interactions and must, in a simplified way, represent a specific phenomenon, not the universal features of the basic chromatographic separation. This is the reason why it is essential to introduce special features related to the studied biological activity. The phase affinity in the adsorption retention mechanism is of a hydro-lipophilic nature and the hydrophobic interactions are also important. The RP-18 phase exhibits the features of a pseudo-adsorptive support.

Introducing a measure of the analyte polar surface area refines the effect of the chromatographic separation (Rf) with the properties strongly connected with the modeled phenomenon of the BBB permeation. It seems that taking into account the polarity of the molecules surface extends the possibility of studying the solutes of different structures. The variation of Rf/PSA is larger than that of Rf for the studied group of compounds.

In the experiment for 111 cases presented above (21 cases with the defined CNS- parameter and 90 cases from the CNS+ group), the values of the Rf/PSA parameter were ≥ 0.009 for CNS+ (14 errors with values 0.004-0.008) and > 0.009 for CNS- (3 errors with values 0.0092-0.0099); the values of Rf for the same group were 0.10-0.90 for CNS+ and 0.50-0.90 for CNS-. These results clearly indicate the greater stability of the Rf/PSA parameter towards the predicted BBB permeability.

The parameter B2 is also very stable in this experiment. In the group of 111 cases with the defined CNS+/- bioavailability the values of B2 for 90 CNS+ cases were ≥ – 0.9 (6 errors were detected with the values – 0.97 to – 1.38). The values of the B2 parameter in the group of 21 CNS- cases were > – 0.9 (3 errors were detected with the values 0.41 to – 0.81). The analytes with the known permeability descriptor BBB vivo were within the same limits. Out of 6 CNS- cases 2 solutes were misclassified and in the group of 39 CNS+ compounds the number of incorrectly classified compounds was also 2.

The values of the BBB vivo parameter were < – 0.9 for CNS+ (3 errors in the group of 30 cases) and > – 0.9 for CNS- (6 cases without errors).

3.2 Discriminant Function Analysis

Drugs whose ability to cross the BBB has been measured experimentally are often described in the literature and databases [40,48] as CNS positive (CNS+) or CNS negative (CNS-). The qualitative parameter CNS+/CNS- may be used as a grouping variable in DFA to establish classification functions to predict the bioavailability of new drugs. 111 compounds from the training set of 154 cases were found in the database [40] and separated into two groups: CNS-(21 cases, code 0) and CNS+ (90 cases, code 1).

Table 4

Classification matrix for model with discriminating variables (B2, eL and Rf/PSA) (12, 13).

observedPercentage of cases correctly classifiedCNS-CNS+
CNS-83.333351
CNS+100.0000038
Total97.7273539

The stepwise DFA was carried out for 44 cases: 1-9, 11-31 and 33-46 with the measured BBB permeability BB vivo (for two solutes – acyclovir and tibolon no CNS+/-classification has been found in Ref. [40]) on the basis of standardized data, using the chromatographic and physicochemical parameters selected in the course of the multiple regression analysis (8): log D, HA, eH, Rf, B2, eL, Rf/PSA. Almost full separation of two groups: CNS- and CNS+ was achieved in three steps, with B2, eL and Rf/PSA as discriminating variables, which additionally confirms the value of the chromatographic parameter Rf/PSA in describing the compounds’ ability to cross the BBB. The subsequent results in the form of classification matrices and functions are presented below.

The classification functions for each group of activity CNS- and CNS+ were calculated:

(12)CNS -=-6.47816−5.58576  B2+1.24573  eL+                              0.80487 Rf/PSA
(13)CNS +=- 0.292523+1.020848 B2−   0.322966  eL−0.106046Rf/PSA

The outcome of the last DFA was verified demonstrating the high classification power of the model (Wilks’ lambda parameter = 0.394087, χ2 = 37.71291, p-level = 0.000000).

The reliability of the model derived from DFA was determined by a cross-validation test based on the leave-one-out methodology. 44 cases (1-9, 11-31, 33-46) with the measured BBB permeability (BB vivo) were examined. The procedure is described above. The results obtained with the methodology are summarized in a cross-validation matrix (Table 5). Using the results obtained via the cross-validation methodology we can confirm the reliability of the DFA model (the cross-validation error is 0%). The examination of the cross-validation matrix suggested that the classification probability was the same as the classification probability obtained a posteriori (97.73%) (Table 5). The reliability of the analyses was proved. The models presented above not only described precisely the investigated groups of cases but have diagnostics value for new cases.

Table 5

Cross-validation matrix.

observedPercentage of cases correctly classified
CNS-83.3333
CNS+100.0000
Total97.7273

In order to validate the methods and confirm the discriminating value of the descriptors selected in the course of this study a group of 67 cases without the measured BBB vivo but with the BBB permeability known from the database [40] (47-113) was introduced. The predictive values of the determined classification functions (equations: (12) and (13)) proposed for the classification of compounds as CNS+/- were assessed. For this purpose, the variables calculated for the other compounds (47-113) with the known BBB permeability: CNS- (15 cases) and CNS+ (52 cases) were added to the raw variable file for the compounds 1-9, 11-31, 33-46 and then standardized together.

The classification functions: (12) and (13) were subsequently applied to calculate the appropriate qualification values for the compounds 47-52, 62-70 – 15 cases CNS- and 53-61, 71-113 – 52 cases CNS+ (Table 6, Supplementary Materials).

The effectiveness of the classification functions calculated in the previous paper (Equation 9 and Equation 10 in Ref. [33]) and used successfully in Ref. [34] has been investigated for the cases 47-113. The results are presented in Table 6.

CNS−=−10.8254 B2+3.2154Rf/PSA−6.3785                      (Equation (9) in Ref.  [33])CNS+=5.32916 B2−1.76240Rf/PSA−1.00757                      (Equation (10) in Ref.  [33])

On the basis of the Equation (12) 6 out of 15 CNS- cases were misclassified vs. 2 out of 52 CNS+ compounds misclassified according to the Equation (13). The total number of correctly classified cases reached 88.06%.

At the same time, the application of the classification functions established in the previous DFA (Equation 9 and Equation 10 in Ref. [33]) and presented below furnished 1 (out of 15) misclassified CNS- case and 7 (out of 52) misclassified CNS+ cases. The total number of correctly classified cases reached 88.06%.

The investigation of cases with the known BB vivo parameter revealed 6 CNS- compounds (2 classification errors) and 38 CNS+ cases with 5 classification errors, respectively. The results confirm the value of the chromatographic parameters Rf/PSA and B2 in simple defining the ability of compounds to cross the BBB. The total number of the correctly classified cases was 84.09%.

Just like in the case of the training set (1-9, 11-31, 33-46) the stepwise DFA was carried out for the set of all compounds with defined CNS+/- (111 cases: 1-9, 11-31 and 33-113) involving the independent variables used in the previous analyses (Equations (14) and (15)). The classification matrix for the total of 111 cases is given in Table 7.

Table 7

Classification matrix for model with discriminating variables (B2, eL, HA, log D and Rf/PSA) (14, 15).

observedPercentage of cases correctly classifiedCNS-CNS+
CNS-66.66666147
CNS+97.77778288
Total91.891891695
(14)CNS -=−3.72522+0.68546 ΗΑ+0.54721 eL−0.64253 log                            D−2.31095  B2+0.45783 Rf/PSA
(15)CNS +=− 0.281386+0.008041 HA−0.5091631 eL+     0.040695  log D+0.504695 B2+0.46771 Rf/PSA

The quality of DFA leading to the model (14)/(15) was verified by Wilks’ lambda parameter 0.531980; χ2 =67.21731; p-level = 0.000000 confirmed the model’s high discriminating potential. Only 9 cases in the group of 111 compounds were incorrectly classified. Examination of the cross-validation matrix proved that the classification probability was higher than the classification probability obtained a posteriori (Table 8).

Table 8

Cross-validation matrix.

observedPercentage of cases correctly classified
CNS-67.83
CNS+97.74
Total92.04

4 Conclusions

The results of our studies confirmed the values of the previously proposed [33] analytical and mathematical models for the experiment with a large and structurally diverse group of compounds. The presented results show that the proposed analytical and mathematical models are universal and can be applied to any chemical compound.

The chromatographic data (Rf and Rf/PSA ) of the proposed analytical model (RP-18 TLC) are correlated with the parameters describing the ability of the compounds to penetrate the brain. The chromatographic parameters (Rf and especially Rf/PSA) obtained from the previous studies have a universal predictive capacity and can be therefore considered as one of the descriptors supplementing the “Rule of 5” parameter group [49].

The correlations are visible for the computed parameter B2 and they are completed with the physicochemical parameters that are traditionally linked to the BBB permeation – log D, HD, HA, DM („Rule of 5”, Ref. [49]). The results of the simple, inexpensive and very rapid chromatographic analysis proposed in this study may be applied to predict the BBB permeability of compounds isolated or synthetized on a very small scale.

The parameter B2, given by the equation B2 = log BB = 0.547 – 0.016 PSA [32] has a predictive and discriminating value corroborated by the equations (12)-(15). The confirmed value of the computed BBB bioavailability indicator can be used to predict the BBB bioavailability of compounds without the experimental BBB permeability data [48]. The proposed chromatographic model of the BBB permeation based on RP-18 TLC and the computed B2 parameter can be used in future investigations of compounds of different chemical structures. The results of this and earlier [33,34] investigations confirm not only the assumed ability of compounds to cross the BBB but also the universal character and efficiency of all generated mathematical and analytical models to predict the BBB permeability of compounds available on a very small scale (sufficient for thin-layer chromatographic analysis) or even physically unavailable.

Taking into consideration the great importance of the calculated log BB descriptors - B1 and B2 revealed in the earlier analyses [33,34,48], a new element theoretically connected with the BBB permeation has been introduced that describes the molecular structure and is present in the B1 and B2 algorithms [31,32] (PSA value). A new descriptor Rf/PSA has been created.

Introducing a measure of the analyte polar surface area refines the effect the chromatographic separation (Rf) with the properties strongly connected with the modeled phenomenon of the BBB permeation. It seems that taking into account the polarity of the molecules surface extends the possibility of studying the solutes of different structures. The variation of Rf/PSA is larger than that of Rf for the studied group of compounds. All the experiments performed so far [33,34] confirm the importance of the Rf/PSA parameter as the value closely related to the CNS+/-descriptor.

It was therefore concluded that the reversed-phase thin layer chromatographic data obtained on the RP-18 F254s support and with acetonitrile-pH 7.4 phosphate buffered saline 70:30 (v/v) mobile phase may be used as the BBB permeability descriptors. The range of the Rf/PSA parameter indicating the likely CNS bioavailability was, for the group of 34 cases studied earlier [33], ≥ 0.009 for CNS+ and > 0.009 for CNS- (without the errors). In an identical experiment for 111 cases presented above, the values of the Rf/PSA parameter were ≥ 0.009 for CNS+ and > 0.009 for CNS-. These results clearly indicate the greater stability of the Rf/PSA parameter towards the predicted BBB permeability.

The parameter B2 is also very stable in this experiment. In the group of 111 cases with the defined CNS+/- bioavailability the values of B2 for 90 CNS+ cases were ≥– 0.9. The values of the B2 parameter in the group of 21 CNS- cases were > – 0.9.

The values of the BBB vivo parameter were < – 0.9 for CNS+ and > – 0.9 for CNS-.

Acknowledgements

This research was supported by an internal grant of the Medical University of Łódź no. 503/3-016-03/503-31-001. The authors are very grateful to the companies: CIBA, Polfa Pabianice, Symrise, BASF and Merck for the samples of compounds used throughout this study.

  1. Conflict of interest: Authors declare no conflict of interest.

  2. Supplemental Material: The online version of this article offers supplementary material (https://doi.org/10.1515/chem-2019-0005)

List of Abbreviations

BBB

Blood-Brain Barrier

BB

Blood/Brain Partition Coefficient

CNS

Central Nervous System

D

Distribution Coefficient

DFA

Discriminant Function Analysis

DM

Total Dipole Moment

eH

Energy of the Highest Occupied Molecular

eL

Energy of the Lowest Occupied Molecular

HA

Hydrogen Bond Acceptor

HD

Hydrogen Bond Donor

LMO

Leave-Many-Out

LOO

Leave-One-Out

MRA

Multiple Regression Analysis

MW

Molecular Weight

NPTLC

Normal-Phase Thin Layer Chromatography

P

Partition Coefficient

PRESS

Predicted Residual Sum of Squares

PSA

Polar Surface Area

Rf

Retardation Factor

RPTLC

Reversed-Phase Thin Layer Chromatography

Sa

Grid Surface Area

SOTLC

Salting-Out Thin Layer Chromatography

SDEP

Standard Deviation of Error of Prediction

TPSA

Topological Polar Surface Area

V

van der Waals molar volume

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Received: 2018-07-13
Accepted: 2018-11-02
Published Online: 2019-02-02

© 2019 Anna W. Sobańska, Karolina Wanat, Elżbieta Brzezińska, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Articles in the same Issue

  1. Regular Articles
  2. Research on correlation of compositions with oestrogenic activity of Cistanche based on LC/Q-TOF-MS/MS technology
  3. Efficacy of Pyrus elaeagnifolia subsp. elaeagnifolia in acetic acid–induced colitis model
  4. Anti-inflammatory and antinociceptive features of Bryonia alba L.: As a possible alternative in treating rheumatism
  5. High efficiency liposome fusion induced by reducing undesired membrane peptides interaction
  6. Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography
  7. Phytic Acid Extracted from Rice Bran as a Growth Promoter for Euglena gracilis
  8. Development of a validated spectrofluorimetric method for assay of sotalol hydrochloride in tablets and human plasma: application for stability-indicating studies
  9. Topological Indices of Hyaluronic Acid-Paclitaxel Conjugates’ Molecular Structure in Cancer Treatment
  10. Thermodynamic properties of the bubble growth process in a pool boiling of water-ethanol mixture two-component system
  11. Critical Roles of the PI3K-Akt-mTOR Signaling Pathway in Apoptosis and Autophagy of Astrocytes Induced by Methamphetamine
  12. Characteristics of Stable Hydrogen and Oxygen Isotopes of Soil Moisture under Different Land Use in Dry Hot Valley of Yuanmou
  13. Specific, highly sensitive and simple spectrofluorimetric method for quantification of daclatasvir in HCV human plasma patients and in tablets dosage form
  14. Chromium-modified cobalt molybdenum nitrides as catalysts for ammonia synthesis
  15. Langerhans cell-like dendritic cells treated with ginsenoside Rh2 regulate the differentiation of Th1 and Th2 cells in vivo
  16. Identification of Powdery Mildew Blumeria graminis f. sp. tritici Resistance Genes in Selected Wheat Varieties and Development of Multiplex PCR
  17. Computational Analysis of new Degree-based descriptors of oxide networks
  18. The Use Of Chemical Composition And Additives To Classify Petrol And Diesel Using Gas Chromatography–Mass Spectrometry And Chemometric Analysis: A Uk Study
  19. Minimal Energy Tree with 4 Branched Vertices
  20. Jatropha seed oil derived poly(esteramide-urethane)/ fumed silica nanocomposite coatings for corrosion protection
  21. Calculating topological indices of certain OTIS interconnection networks
  22. Energy storage analysis of R125 in UIO-66 and MOF-5 nanoparticles: A molecular simulation study
  23. Velvet Antler compounds targeting major cell signaling pathways in osteosarcoma - a new insight into mediating the process of invasion and metastasis in OS
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  25. Aqueous Micro-hydration of Na+(H2O)n=1-7 Clusters: DFT Study
  26. A proposed image-based detection of methamidophos pesticide using peroxyoxalate chemiluminescence system
  27. Phytochemical screening and estrogenic activity of total glycosides of Cistanche deserticola
  28. Biological evaluation of a series of benzothiazole derivatives as mosquitocidal agents
  29. Chemical pretreatments of Trapa bispinosa's peel (TBP) biosorbent to enhance adsorption capacity for Pb(ll)
  30. Dynamic Changes in MMP1 and TIMP1 in the Antifibrotic Process of Dahuang Zhechong Pill in Rats with Liver Fibrosis
  31. The Optimization and Production of Ginkgolide B Lipid Microemulsion
  32. Photodynamic Therapy Enhanced the Antitumor Effects of Berberine on HeLa Cells
  33. Chiral and Achiral Enantiomeric Separation of (±)-Alprenolol
  34. Correlation of Water Fluoride with Body Fluids, Dental Fluorosis and FT4, FT3 –TSH Disruption among Children in an Endemic Fluorosis area in Pakistan
  35. A one-step incubation ELISA kit for rapid determination of dibutyl phthalate in water, beverage and liquor
  36. Free Radical Scavenging Activity of Essential Oil of Eugenia caryophylata from Amboina Island and Derivatives of Eugenol
  37. Effects of Blue and Red Light On Growth And Nitrate Metabolism In Pakchoi
  38. miRNA-199a-5p functions as a tumor suppressor in prolactinomas
  39. Solar photodegradation of carbamazepine from aqueous solutions using a compound parabolic concentrator equipped with a sun tracking system
  40. Influence of sub-inhibitory concentration of selected plant essential oils on the physical and biochemical properties of Pseudomonas orientalis
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  44. Analysis and Biological Evaluation of Arisaema Amuremse Maxim Essential Oil
  45. A preliminary assessment of potential ecological risk and soil contamination by heavy metals around a cement factory, western Saudi Arabia
  46. Anti- inflammatory effect of Prunus tomentosa Thunb total flavones in LPS-induced RAW264.7 cells
  47. Collaborative Influence of Elevated CO2 Concentration and High Temperature on Potato Biomass Accumulation and Characteristics
  48. Methods of extraction, physicochemical properties of alginates and their applications in biomedical field – a review
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https://doi.org/10.1515/chem-2019-0005
Keywords for this article
RP-18 TLC; QSAR; Blood-Brain Barrier; chemometric techniques
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Research on correlation of compositions with oestrogenic activity of Cistanche based on LC/Q-TOF-MS/MS technology
  3. Efficacy of Pyrus elaeagnifolia subsp. elaeagnifolia in acetic acid–induced colitis model
  4. Anti-inflammatory and antinociceptive features of Bryonia alba L.: As a possible alternative in treating rheumatism
  5. High efficiency liposome fusion induced by reducing undesired membrane peptides interaction
  6. Prediction of the Blood-Brain Barrier Permeability Using RP-18 Thin Layer Chromatography
  7. Phytic Acid Extracted from Rice Bran as a Growth Promoter for Euglena gracilis
  8. Development of a validated spectrofluorimetric method for assay of sotalol hydrochloride in tablets and human plasma: application for stability-indicating studies
  9. Topological Indices of Hyaluronic Acid-Paclitaxel Conjugates’ Molecular Structure in Cancer Treatment
  10. Thermodynamic properties of the bubble growth process in a pool boiling of water-ethanol mixture two-component system
  11. Critical Roles of the PI3K-Akt-mTOR Signaling Pathway in Apoptosis and Autophagy of Astrocytes Induced by Methamphetamine
  12. Characteristics of Stable Hydrogen and Oxygen Isotopes of Soil Moisture under Different Land Use in Dry Hot Valley of Yuanmou
  13. Specific, highly sensitive and simple spectrofluorimetric method for quantification of daclatasvir in HCV human plasma patients and in tablets dosage form
  14. Chromium-modified cobalt molybdenum nitrides as catalysts for ammonia synthesis
  15. Langerhans cell-like dendritic cells treated with ginsenoside Rh2 regulate the differentiation of Th1 and Th2 cells in vivo
  16. Identification of Powdery Mildew Blumeria graminis f. sp. tritici Resistance Genes in Selected Wheat Varieties and Development of Multiplex PCR
  17. Computational Analysis of new Degree-based descriptors of oxide networks
  18. The Use Of Chemical Composition And Additives To Classify Petrol And Diesel Using Gas Chromatography–Mass Spectrometry And Chemometric Analysis: A Uk Study
  19. Minimal Energy Tree with 4 Branched Vertices
  20. Jatropha seed oil derived poly(esteramide-urethane)/ fumed silica nanocomposite coatings for corrosion protection
  21. Calculating topological indices of certain OTIS interconnection networks
  22. Energy storage analysis of R125 in UIO-66 and MOF-5 nanoparticles: A molecular simulation study
  23. Velvet Antler compounds targeting major cell signaling pathways in osteosarcoma - a new insight into mediating the process of invasion and metastasis in OS
  24. Effects of Azadirachta Indica Leaf Extract, Capping Agents, on the Synthesis of Pure And Cu Doped ZnO-Nanoparticles: A Green Approach and Microbial Activity
  25. Aqueous Micro-hydration of Na+(H2O)n=1-7 Clusters: DFT Study
  26. A proposed image-based detection of methamidophos pesticide using peroxyoxalate chemiluminescence system
  27. Phytochemical screening and estrogenic activity of total glycosides of Cistanche deserticola
  28. Biological evaluation of a series of benzothiazole derivatives as mosquitocidal agents
  29. Chemical pretreatments of Trapa bispinosa's peel (TBP) biosorbent to enhance adsorption capacity for Pb(ll)
  30. Dynamic Changes in MMP1 and TIMP1 in the Antifibrotic Process of Dahuang Zhechong Pill in Rats with Liver Fibrosis
  31. The Optimization and Production of Ginkgolide B Lipid Microemulsion
  32. Photodynamic Therapy Enhanced the Antitumor Effects of Berberine on HeLa Cells
  33. Chiral and Achiral Enantiomeric Separation of (±)-Alprenolol
  34. Correlation of Water Fluoride with Body Fluids, Dental Fluorosis and FT4, FT3 –TSH Disruption among Children in an Endemic Fluorosis area in Pakistan
  35. A one-step incubation ELISA kit for rapid determination of dibutyl phthalate in water, beverage and liquor
  36. Free Radical Scavenging Activity of Essential Oil of Eugenia caryophylata from Amboina Island and Derivatives of Eugenol
  37. Effects of Blue and Red Light On Growth And Nitrate Metabolism In Pakchoi
  38. miRNA-199a-5p functions as a tumor suppressor in prolactinomas
  39. Solar photodegradation of carbamazepine from aqueous solutions using a compound parabolic concentrator equipped with a sun tracking system
  40. Influence of sub-inhibitory concentration of selected plant essential oils on the physical and biochemical properties of Pseudomonas orientalis
  41. Preparation and spectroscopic studies of Fe(II), Ru(II), Pd(II) and Zn(II) complexes of Schiff base containing terephthalaldehyde and their transfer hydrogenation and Suzuki-Miyaura coupling reaction
  42. Complex formation in a liquid-liquid extraction-chromogenic system for vanadium(IV)
  43. Synthesis, characterization (IR, 1H, 13C & 31P NMR), fungicidal, herbicidal and molecular docking evaluation of steroid phosphorus compounds
  44. Analysis and Biological Evaluation of Arisaema Amuremse Maxim Essential Oil
  45. A preliminary assessment of potential ecological risk and soil contamination by heavy metals around a cement factory, western Saudi Arabia
  46. Anti- inflammatory effect of Prunus tomentosa Thunb total flavones in LPS-induced RAW264.7 cells
  47. Collaborative Influence of Elevated CO2 Concentration and High Temperature on Potato Biomass Accumulation and Characteristics
  48. Methods of extraction, physicochemical properties of alginates and their applications in biomedical field – a review
  49. Characteristics of liposomes derived from egg yolk
  50. Preparation of ternary ZnO/Ag/cellulose and its enhanced photocatalytic degradation property on phenol and benzene in VOCs
  51. Influence of Human Serum Albumin Glycation on the Binding Affinities for Natural Flavonoids
  52. Synthesis and antioxidant activity of 2-methylthio-pyrido[3,2-e][1,2,4] triazolo[1,5-a]pyrimidines
  53. Comparative study on the antioxidant activities of ten common flower teas from China
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  56. Phytochemical analysis, Antioxidant and Antiprotoscolices potential of ethanol extracts of selected plants species against Echinococcus granulosus: In-vitro study
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  76. Exploring the optoelectronic properties of a chromene-appended pyrimidone derivative for photovoltaic applications
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  79. Greener synthesis of dimethyl carbonate from carbon dioxide and methanol using a tunable ionic liquid catalyst
  80. Nonisothermal Cold Crystallization Kinetics of Poly(lactic acid)/Bacterial Poly(hydroxyoctanoate) (PHO)/Talc
  81. Enhanced adsorption of sulfonamide antibiotics in water by modified biochar derived from bagasse
  82. Study on the Mechanism of Shugan Xiaozhi Fang on Cells with Non-alcoholic Fatty Liver Disease
  83. Comparative Effects of Salt and Alkali Stress on Antioxidant System in Cotton (Gossypium Hirsutum L.) Leaves
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  86. Study on the removal of high contents of ammonium from piggery wastewater by clinoptilolite and the corresponding mechanisms
  87. Phytochemistry and toxicological assessment of Bryonia dioica roots used in north-African alternative medicine
  88. The essential oil composition of selected Hemerocallis cultivars and their biological activity
  89. Mechanical Properties of Carbon Fiber Reinforced Nanocrystalline Nickel Composite Electroforming Deposit
  90. Anti-c-myc efficacy block EGFL7 induced prolactinoma tumorigenesis
  91. Topical Issue on Applications of Mathematics in Chemistry
  92. Zagreb Connection Number Index of Nanotubes and Regular Hexagonal Lattice
  93. The Sanskruti index of trees and unicyclic graphs
  94. Valency-based molecular descriptors of Bakelite network BNmn
  95. Computing Topological Indices for Para-Line Graphs of Anthracene
  96. Zagreb Polynomials and redefined Zagreb indices of Dendrimers and Polyomino Chains
  97. Topological Descriptor of 2-Dimensional Silicon Carbons and Their Applications
  98. Topological invariants for the line graphs of some classes of graphs
  99. Words for maximal Subgroups of Fi24‘
  100. Generators of Maximal Subgroups of Harada-Norton and some Linear Groups
  101. Special Issue on POKOCHA 2018
  102. Influence of Production Parameters on the Content of Polyphenolic Compounds in Extruded Porridge Enriched with Chokeberry Fruit (Aronia melanocarpa (Michx.) Elliott)
  103. Effects of Supercritical Carbon Dioxide Extraction (SC-CO2) on the content of tiliroside in the extracts from Tilia L. flowers
  104. Impact of xanthan gum addition on phenolic acids composition and selected properties of new gluten-free maize-field bean pasta
  105. Impact of storage temperature and time on Moldavian dragonhead oil – spectroscopic and chemometric analysis
  106. The effect of selected substances on the stability of standard solutions in voltammetric analysis of ascorbic acid in fruit juices
  107. Determination of the content of Pb, Cd, Cu, Zn in dairy products from various regions of Poland
  108. Special Issue on IC3PE 2018 Conference
  109. The Photocatalytic Activity of Zns-TiO2 on a Carbon Fiber Prepared by Chemical Bath Deposition
  110. N-octyl chitosan derivatives as amphiphilic carrier agents for herbicide formulations
  111. Kinetics and Mechanistic Study of Hydrolysis of Adenosine Monophosphate Disodium Salt (AMPNa2) in Acidic and Alkaline Media
  112. Antimalarial Activity of Andrographis Paniculata Ness‘s N-hexane Extract and Its Major Compounds
  113. Special Issue on ABB2018 Conference
  114. Special Issue on ICCESEN 2017
  115. Theoretical Diagnostics of Second and Third-order Hyperpolarizabilities of Several Acid Derivatives
  116. Determination of Gamma Rays Efficiency Against Rhizoctonia solani in Potatoes
  117. Studies On Compatibilization Of Recycled Polyethylene/Thermoplastic Starch Blends By Using Different Compatibilizer
  118. Liquid−Liquid Extraction of Linalool from Methyl Eugenol with 1-Ethyl-3-methylimidazolium Hydrogen Sulfate [EMIM][HSO4] Ionic Liquid
  119. Synthesis of Graphene Oxide Through Ultrasonic Assisted Electrochemical Exfoliation
  120. Special Issue on ISCMP 2018
  121. Synthesis and antiproliferative evaluation of some 1,4-naphthoquinone derivatives against human cervical cancer cells
  122. The influence of the grafted aryl groups on the solvation properties of the graphyne and graphdiyne - a MD study
  123. Electrochemical modification of platinum and glassy carbon surfaces with pyridine layers and their use as complexing agents for copper (II) ions
  124. Effect of Electrospinning Process on Total Antioxidant Activity of Electrospun Nanofibers Containing Grape Seed Extract
  125. Effect Of Thermal Treatment Of Trepel At Temperature Range 800-1200˚C
  126. Topical Issue on Agriculture
  127. The effect of Cladophora glomerata exudates on the amino acid composition of Cladophora fracta and Rhizoclonium sp.
  128. Influence of the Static Magnetic Field and Algal Extract on the Germination of Soybean Seeds
  129. The use of UV-induced fluorescence for the assessment of homogeneity of granular mixtures
  130. The use of microorganisms as bio-fertilizers in the cultivation of white lupine
  131. Lyophilized apples on flax oil and ethyl esters of flax oil - stability and antioxidant evaluation
  132. Production of phosphorus biofertilizer based on the renewable materials in large laboratory scale
  133. Human health risk assessment of potential toxic elements in paddy soil and rice (Oryza sativa) from Ugbawka fields, Enugu, Nigeria
  134. Recovery of phosphates(V) from wastewaters of different chemical composition
  135. Special Issue on the 4th Green Chemistry 2018
  136. Dead zone for hydrogenation of propylene reaction carried out on commercial catalyst pellets
  137. Improved thermally stable oligoetherols from 6-aminouracil, ethylene carbonate and boric acid
  138. The role of a chemical loop in removal of hazardous contaminants from coke oven wastewater during its treatment
  139. Combating paraben pollution in surface waters with a variety of photocatalyzed systems: Looking for the most efficient technology
  140. Special Issue on Chemistry Today for Tomorrow 2019
  141. Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins
  142. Chemometric Expertise Of Clinical Monitoring Data Of Prolactinoma Patients
  143. Chemomertic Risk Assessment of Soil Pollution
  144. New composite sorbent for speciation analysis of soluble chromium in textiles
  145. Photocatalytic activity of NiFe2O4 and Zn0.5Ni0.5Fe2O4 modified by Eu(III) and Tb(III) for decomposition of Malachite Green
  146. Photophysical and antibacterial activity of light-activated quaternary eosin Y
  147. Spectral properties and biological activity of La(III) and Nd(III) Monensinates
  148. Special Issue on Monitoring, Risk Assessment and Sustainable Management for the Exposure to Environmental Toxins
  149. Soil organic carbon mineralization in relation to microbial dynamics in subtropical red soils dominated by differently sized aggregates
  150. A potential reusable fluorescent aptasensor based on magnetic nanoparticles for ochratoxin A analysis
  151. Special Issue on 13th JCC 2018
  152. Fluorescence study of 5-nitroisatin Schiff base immobilized on SBA-15 for sensing Fe3+
  153. Thermal and Morphology Properties of Cellulose Nanofiber from TEMPO-oxidized Lower part of Empty Fruit Bunches (LEFB)
  154. Encapsulation of Vitamin C in Sesame Liposomes: Computational and Experimental Studies
  155. A comparative study of the utilization of synthetic foaming agent and aluminum powder as pore-forming agents in lightweight geopolymer synthesis
  156. Synthesis of high surface area mesoporous silica SBA-15 by adjusting hydrothermal treatment time and the amount of polyvinyl alcohol
  157. Review of large-pore mesostructured cellular foam (MCF) silica and its applications
  158. Ion Exchange of Benzoate in Ni-Al-Benzoate Layered Double Hydroxide by Amoxicillin
  159. Synthesis And Characterization Of CoMo/Mordenite Catalyst For Hydrotreatment Of Lignin Compound Models
  160. Production of Biodiesel from Nyamplung (Calophyllum inophyllum L.) using Microwave with CaO Catalyst from Eggshell Waste: Optimization of Transesterification Process Parameters
  161. The Study of the Optical Properties of C60 Fullerene in Different Organic Solvents
  162. Composite Material Consisting of HKUST-1 and Indonesian Activated Natural Zeolite and its Application in CO2 Capture
  163. Topical Issue on Environmental Chemistry
  164. Ionic liquids modified cobalt/ZSM-5 as a highly efficient catalyst for enhancing the selectivity towards KA oil in the aerobic oxidation of cyclohexane
  165. Application of Thermal Resistant Gemini Surfactants in Highly Thixotropic Water-in-oil Drilling Fluid System
  166. Screening Study on Rheological Behavior and Phase Transition Point of Polymer-containing Fluids produced under the Oil Freezing Point Temperature
  167. The Chemical Softening Effect and Mechanism of Low Rank Coal Soaked in Alkaline Solution
  168. The Influence Of NO/O2 On The NOx Storage Properties Over A Pt-Ba-Ce/γ-Al2O3 Catalyst
  169. Special Issue on the International conference CosCI 2018
  170. Design of SiO2/TiO2 that Synergistically Increases The Hydrophobicity of Methyltrimethoxysilane Coated Glass
  171. Antidiabetes and Antioxidant agents from Clausena excavata root as medicinal plant of Myanmar
  172. Development of a Gold Immunochromatographic Assay Method Using Candida Biofilm Antigen as a Bioreceptor for Candidiasis in Rats
  173. Special Issue on Applied Biochemistry and Biotechnology 2019
  174. Adsorption of copper ions on Magnolia officinalis residues after solid-phase fermentation with Phanerochaete chrysosporium
  175. Erratum
  176. Erratum to: Sand Dune Characterization For Preparing Metallurgical Grade Silicon
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