1. Introduction
The blood–brain barrier (BBB) term is used to describe the unique properties of the microvasculature of the central nervous system (CNS) [
1]. The endothelial cells of brain microvasculature are the anatomical components of the BBB. They form tight junctions, which, along with pericytes, astrocytes, oligodendrocytes, microglia, and neurons, construct the neurovascular unit [
2]. Pericytes, being embedded in the basement membrane of brain capillaries, play a key role in the development of cerebral microcirculation [
3,
4]. Some previous studies have shown that, for instance, pericyte deficiency leads to brain vascular damage and can also be responsible for BBB breakdown [
2,
3,
4].
The blood–brain barrier forms a dynamic interface between blood and the brain and is a diffusion barrier that is essential for the normal functioning of the central nervous system [
5]. The endothelial cells of the BBB regulate CNS homeostasis and protect the CNS from toxins, pathogens, inflammation, injuries, and diseases [
1]. Despite the protective nature of the blood–brain barrier on the CNS, the inability of substances to cross the BBB is a key factor that should be taken into account in the early stages of drug discovery processes [
6]. The BBB is present along the vasculature of the brain except for the circumventricular organs where the blood vessels of the brain have fenestrations that permit the diffusion of blood-borne molecules across vessel walls [
5,
7]. The BBB is formed by endothelial cells that line cerebral microvessels [
7].
The passive passage of molecules across the endothelium cells of the BBB can occur through the paracellular (between adjacent cells) or transcellular (through the cells) pathways [
8]. The paracellular pathway can only allow small, usually hydrophilic, solutes. These molecules and/or ions simply diffuse between adjacent cells down their concentration gradient [
9,
10]. The transcellular pathway involves different mechanisms including the passive diffusion of lipophilic compounds, receptor-mediated shuttling, and transcytosis [
7,
11]. Some molecules such as oxygen, CO
2, alcohol, and steroid hormones penetrate transcellularly by dissolving in their lipid plasma membrane [
12,
13]. They can pass the BBB freely by diffusion [
14]. For almost all other substances, including essential materials such as glucose and amino acids, transport proteins (carriers), which are specific receptor–mediated or vesicular mechanisms (adsorptive transcytosis), are required to pass the BBB [
12]. This indicates that hydrophilic molecules may enter the brain through specific transport mechanisms [
15].
The experimental determination of BBB permeability is usually time consuming and expensive and requires complex techniques. Therefore, it seems to be very difficult [
16,
17,
18]. However, modern methods do exist, including computational and non-cell-based in vitro approaches. Among them Parallel Artificial Membrane Permeability Assays (PAMPAs) and quantitative structure–activity relationship (QSAR) analysis both provide fast analysis of BBB permeation, which is of particular importance in the early stages of drug discovery processes [
19,
20,
21]. In the area of the CNS, there are widely used QSAR predictive models [
22] based on various combinations of physicochemical parameters. In
Table 1, some of the previously reported QSAR models are compared with the newly established models.
Most of the recognized QSAR models prove that there is a relationship between molecule transport across the blood–brain barrier and the steric, lipophilic, and electronic characteristics of a molecule, which is obviously consistent with the Hansch approach [
35,
36,
37,
38,
39].
We established new models here to estimate the BBB penetration of astragalosides which are triterpenoid saponins, composed of triterpene aglycones joined with various sugar moieties. They are generally predominant in cultivated crops, mainly in legumes such as soybeans, beans, peas,
Lucerne, etc. as well as in alliums, tea, spinach, sugar beet, quinoa, liquorice, sunflower, horse chestnut, and ginseng [
40].
Astragalosides commonly exist in the roots of different types of milkvetch, including
Astragalus membranaceus (Fisch.) Bunge and
Astragalus mongholicus Bunge (Fabaceae).
Astragali radix being dried roots of the above-mentioned plants is the name of an herbal drug frequently used in traditional Chinese medicine, however, also recognized practically all over the world [
41]. It owes its vast applicability due to a wide spectrum of action on living organisms.
The tested saponins possess various biological activities, among which the antioxidant, antifungal, molluscicidal, anti-inflammatory, antitumor, and antiviral properties are most widely described [
42]. Astragalosides (especially astragaloside IV) show immunostimulant, anti-perspirant, antidiarrheal, anti-diabetic, and tonic properties, among others [
43]. Moreover, they are characterized by their anti-cancer effect on lung, gastric, breast, and colorectal cancer (in vitro tests) [
44,
45,
46,
47]; anti-photoaging effects [
48]; and influence on the cardiovascular and nervous systems, the metabolism of collagen, and the immune system [
49].
The aim of this study was to evaluate the ability of triterpenoid saponins occurring in
Astragali radix to cross the BBB based on a QSAR methodology combined with postmortem studies on the brain tissues of mice. QSAR was investigated here using linear free energy relationships (LFER) descriptors [
35,
36,
37,
38,
39], as well as steric, lipophilic, and electronic parameters. In this investigation, the relationships between the logBB values and various partition indices were examined to compare their possible effectiveness in describing BBB passage, e.g., in Equation (2), we combine the logBB values with the hydrogen bond ΔlogP value, the lipophilic logP
ow value, and the excess molar refraction E. Moreover, we experimentally determined the logBB value for the most active saponin (AIV) in mice. This is the first time such an experiment has been performed. All planned studies were carried out to show the path from in silico modeling to the postmortem determination of both the logBB value and the concentration of the most neuroactive components of
Astragalus roots in the brain tissues of mice.
2. Results
2.1. Division of the Dataset for the Computational Studies
The dataset used here includes 47 chemically diverse compounds (most of them with the corresponding experimentally determined logBB values), which were taken from the literature [
50], including the tested triterpenoid saponins. The chemical structures of the investigated saponins are presented in
Table 2. The dataset was separately divided into training and test sets in a random manner for each of the newly constructed models. In total, a random division of the whole dataset was made several times. The lowest value of the mean square error of the leave-ten-out cross-validation (i.e., the adjusted mean square error of leave-ten-out cross-validation (adjusted MSECV)) process decided between inclusion in either the training or test set. Among the tested substances, 10 were chosen as the test set, whereas 30 compounds were selected as a training set and then were used to establish new QSAR models. The tested saponins were external for the models, meaning that they were not used to develop any of the models. Moreover, the self-contained test set, comprised of seven substances, was used for the external validation. The predictive potency of newly constructed QSAR models was estimated by the leave-ten-out (LTO) cross-validation procedure. The coefficient of determination (R
2), root-mean-square error (RMSE), root-mean-square error of leave-ten-out cross-validation (RMSECV), and predicted residual sum of squares (PRESS) statistical parameters were obtained. The QSAR models were based on the multiple linear regression (MLR) methodology with the backward elimination of variables in order to limit the differences between the actual and the estimated BBB values. Many attempts were made to obtain the best relationships between the logBB values and various physicochemical descriptors. The best models were selected based on the analysis of variance using the adjusted sum of squares (adjusted SS), adjusted mean square errors (adjusted MSE), standard errors (SE), variance inflation factors (VIF), R
2 values,
p-values, T-values, and the Fisher criterion (F-values) parameters. Then, to evaluate the reliability of the QSAR models, the applicability domain (AD) was applied.
2.2. BBB Descriptors Calculated in Silico
At the stage of the in silico computational studies, the most important pharmacokinetic descriptors of the brain for the tested saponins were calculated, i.e., the blood–brain barrier penetration descriptor (logBB), the permeability–surface area product (PS) usually given as a logPS value, the brain/plasma equilibration rate (log(PS
Fubrain)), the fraction unbound in plasma (F
u), and the fraction unbound in the brain (F
b) (
Table 3).
It was observed that a decrease in the BBB permeability–surface area product (PS) or the fraction unbound in the brain (F
b) prolonged the time to reach equilibrium in the brain [
51]. However, this time value did not change when PS decreased and F
b increased or, inversely, when PS
Fubrain was kept constant. Therefore, compounds having similar PS
Fubrain values should exhibit a similar time to reach equilibrium, although they may have a much different PS value.
In our experiment, the logBB values, predicted for substances I–IV, were greater than 0, with average logPS values equal to −4.5. These values reveal that compounds I–IV can cross the BBB and that they may have neuroactive potential. In contrast, astragalosides V–VII have a logBB value <−2. At the same time, substances V–VII have a logPS value <−5. This indicates that those compounds are not permeable through the BBB, even though the in silico estimated values of the fraction unbound in plasma are relatively high. Accordingly, astragalosides V–VII were neglected in further procedures, i.e., in both the in vitro and postmortem studies.
2.3. QSAR Studies for the BBB Permeation
To predict the ability of the seven tested saponins which naturally occur in the roots of
Astragalus mongholicus to cross the blood–brain barrier, new QSAR models were generated using experimentally obtained logBB values for 40 other molecules that have been reported in the literature [
50] (
Table S1). Therefore, the studied compound group consisted of seven substances (
Table 2), while 40 compounds were selected to establish the QSAR models (
Table S1). In the QSAR methodology, many physicochemical descriptors are used to predict various biological activities. According to the Hansch approach, the most important parameters governing transport and drug–receptor interaction are the steric, electronic, and lipophilic characteristics of molecules [
35].
Another commonly used approach is the linear free energy relationship (LFER), suggested by Abraham, which is based on parameters such as hydrogen-bond acidity (A) and basicity (B), polarizability (S), molar refraction (E), and the McGowan volume (V) of a solute. In our research, we used both of the above-mentioned theories. Therefore, the most important physicochemical descriptors, as well as the LFER parameters, were calculated and are presented here in
Table 4 (ACD/Percepta software). On the basis of the LFER parameters, the following model (Equation (1)) was generated:
In the above equation, n = 40, R2CV = 80.80%, R2pred= 76.80%, and S = 0.432, where n is the number of compounds, R2CV is the cross-validated coefficient of determination, R2pred is the predicted R2 value, and S is the standard deviation.
In the next model (Equation (2)), we decided to correlate BBB penetration with the lipophilic properties (logP
ow), excess molar refraction (E), and hydrogen-bonding potential, expressed as the difference between the
n-octanol/water and cyclohexane/water logP values (ΔlogP) [
52,
53,
54]. Thus, we built the following model:
In the above equation, n = 40, R2CV = 78.25%, R2pred = 74.02%, and S = 0.436.
On the basis of Equation (2), the logBB values were calculated (logBB
pred) and then they were correlated and compared with the experimental logBB values (logBB
exp), taking into account the training and the test sets (
Figure 1).
According to
Figure 1, it can be stated that both tools used, i.e., the ACD/Percepta software and the newly established QSAR (Equation (2)), can be recognized as good predictive models for assessing the ability of the tested compounds to penetrate through the blood–brain barrier.
2.4. Applicability Domain
There are regulations related to the use of alternative methods to in vivo ones, which are used especially in the initial phases of research on various compounds. The main aim of the European Centre for the Validation of Alternative Methods (ECVAM) is to promote alternative methods which
reduce, refine, and
replace the use of laboratory animals. According to the Organisation for Economic Co-operation and Development (OECD) and the European Commission guidance, general principles exist for QSAR validation [
55]. One of them states that “a (Q)SAR should be associated with a defined domain of applicability”. The applicability domain (AD) should be understood as the response and chemical structure space in which the model makes predictions with a given reliability [
56]. There is a need to establish the scope and limitations of a model based on the physicochemical and response information in the model training set. In our case, the response space concerns the BBB permeation of compounds. The AD was evaluated to confirm the reliability and predictability of the model expressed in Equation (2). Different approaches have been developed for statistically-based QSARs. In our experiment, we used a distance-based method, which can be used to separate regions of varying density by imposing cut-off values. The distance from a query data point to a dataset was calculated via this approach. If there is a criterion for the distance to be below a defined threshold, the decision as to whether a data point is close to the dataset can be made [
56].
The model space can be represented by the descriptor matrix (X), which is the two-dimensional matrix comprised of
n chemicals (rows) and
k variables (columns). A measure of the distance of the chemical from the centroid of X can define the leverage of a chemical (h
i). The leverages of all chemicals in the dataset were generated here by manipulating X according to the following equation [
56] (Equation (3)):
where
i = 1 to
n, x
i is the row vector of the descriptors, T is the matrix/vector transposed, and i and X are the variable matrices deduced from the training set variable values.
The critical leverage value (h*) can be generally obtained using the following equation (Equation (4)):
where
k is the number of predictor variables and
n is the number of training compounds [
56].
To identify both the chemicals that are outside the AD and the leverage points which could destabilize the model, the Williams plot can be used. The plot is presented in
Figure 2. The standardized residual equals the value of a residual divided by an estimate of its standard deviation.
The h* value calculated for our QSAR model (Equation (2)) was equal to 0.128. It is assumed that a compound where h
i > h* has a great impact on regression occurrence; therefore, it may be excluded from the AD. It is assumed that substances with a standardized residual greater than 3SD (standard deviation) units may recognized as outliers, while chemicals with a leverage value higher than h* are recognized as influential or as high-leverage chemicals [
57]. In the process of developing the applicability domain, substances that were not previously used to build the QSAR model were used. In other words, the applicability of model was valued here, indicating how well it is able to predict the end-point values of the compounds that did not develop the correlation. In our case, such substances were triterpenoid saponins. As shown in
Figure 2, all substances, including triterpenoid saponins, were within the AD. Moreover, the correlation between the fitted and cross-validated responses versus the actual responses is presented in
Figure 3. It was found that there were no considerable differences between the fitted and the cross-validates values. Based on the results in
Figure 2 and
Figure 3, it can be proved that our QSAR model is reliable within the applicability domain in which was developed.
2.5. Acetylcholinesterase-Inhibition Activity Test
The inhibition of acetylcholinesterase (AChE) is considered to be a promising strategy for the treatment of neurodegenerative diseases. The main role of AChE is the termination of nerve impulse transmission at cholinergic synapses via the rapid hydrolysis of acetylcholine (ACh). An increase in AChE levels can be observed in elderly patients, leading to the excessive degradation of acetylcholine before it binds to the receptors in the postsynaptic membrane. A very quick degradation of acetylcholine, especially when levels are already significantly reduced at an elderly age, causes neuronal signal transmission to be disturbed. In view of the above, AChE inhibitors are seen as drugs that can effectively improve the cognitive functions of the brain. Compounds from this group are commonly seen as primary drugs for treating dementia. However, due to various adverse effects of synthetic drugs used in the therapy of memory disorders and their pharmacokinetics, new AChE inhibitors are constantly sought, mainly from natural sources [
58].
To confirm the effectiveness of the tested compounds on the CNS, an acetylcholinesterase inhibition activity test can be performed using in vitro or in vivo (postmortem) tests. The former approach has commonly been used by numerous researchers, and the shape of the applied methodology varies, depending on the substrate or color developer [
59,
60,
61]. Thin layer chromatography (TLC)-bioautography helps to clearly indicate the potential inhibitory action against AChE. In the applied methodology, the developed TLC plates were treated with an enzyme itself (AChE), and, by using this technique, active AChE inhibitors appeared as bright spots on a purple background. The intensity of the color and size of the spots in direct comparison (on the same TLC plate) with a reference compound allows the determination of the strength of the tested compound [
59]. Analyzing the in silico obtained parameters (
Table 3), three saponins which can cross the BBB most easily and therefore require the shortest time to achieve brain equilibrium (astragalosides I, III and IV) were further examined. As mentioned above, the in silico estimated BBB data show that astragalosides V–VII are not permeable across the BBB, and, therefore, they were excluded from the further research presented in the manuscript, whereas among the tested compounds that can cross the BBB (i.e., astragalosides I–IV), astragaloside II demonstrates the highest values of unbound fractions in both the brain and plasma and the lowest logBB value. As this compound requires the longest time to reach brain equilibrium, it was excluded from the studies on AChE inhibition activity. In
Figure 4, the chromatogram of the AChE inhibition properties is presented. The white areas on the TLC chromatogram indicate the inhibitory activity of the tested astragalosides against acetylcholinesterase.
The chromatogram shows that AIV delivers the highest peak area, found using the ImageJ program (peak area of 7451 u), followed by AI (peak area of 4113 u), and AIII (peak area of 6556 u), and, therefore, it can be recognized as the most neuroactive compound among the tested compounds (
Figure 4). The results of in silico and in vitro studies allow astragaloside IV to be selected as the most promising compound with potential procognitive properties. Therefore, the authors have planned its isolation from aqueous methanolic extract of
Astragalus mongholicus roots.
2.6. Isolation of Astragaloside IV
The isolation of astragaloside IV from aqueous methanolic extract of the roots was performed by semi-preparative HPLC chromatography. For this purpose, 52 fractions were collected in a single-cycle separation process. The compositions of the obtained fractions were analyzed using high performance liquid chromatography, coupled with mass spectrometry (HPLC-MS) to provide high-resolution qualitative data. The analysis confirmed the presence of high purity astragaloside IV in the tested fractions (
Figure 5 and
Figure 6) and those rich in the saponin of interest were collected together, evaporated to dryness under reduced pressure at 40 °C, and then used in the in vivo studies.
2.7. Postmortem Studies
Astragaloside IV was traced in the brain tissues and plasma of mice at two doses, namely, 12.5 and 25 mg/kg b.w. i.p. Before the LC-MS analysis, a thorough investigation of optimal chromatographic and spectrometric conditions was performed to select the settings that provide the highest response to the tested astragalosides, as described
Section 4.6. The calibration curve of astragaloside IV obtained for the standard solutions was as follows: y = 1,052,449x + 28,518,981. The linearity of the graph was noted as R² = 0.9866 and was found to be precise enough to carry out quantitative studies. This slight decrease in the R² value was influenced by the wide range of concentrations analyzed in the study and was strongly influenced by the lowest concentrations of AIV. The limit of detection (LOD) values for this compound in the prepared method were calculated to be 5 µg/mL.
The presence of AIV was noted in the brain samples for the two groups of mice tested. The results show a regular dependence of the final concentration in the analyzed brain samples of animals treated with 12.5 and 25 mg/kg b.w. In these two groups, the concentration of astragaloside IV was calculated to be an average of 0.00012299 and 0.0002306 mg, per brain, respectively.
2.8. LogBB Determination
The blood–brain (BB) distribution is a measure which is defined as the brain/blood concentration. BBB penetration is frequently shown as logBB, which is the logarithm of the ratio between the brain and the blood concentration of tested substances [
22,
23]. The blood–brain distribution shows the suitability of a molecule for being a potential drug for the central nervous system [
24], and, therefore, the ability of a molecule to penetrate the BBB is one of the biopharmaceutical properties that is essential in drug design. To be used as therapeutic agents, potential neuroactive compounds, which have to interact with their molecular targets in the CNS, must cross the BBB. To avoid side effects, peripherally acting agents should not cross the BBB simultaneously. In both cases, the BBB permeability of the molecules must be known [
62].
The logBB value was calculated here based on the experimentally obtained concentrations of astragaloside IV in the brains and plasma of mice after the analyses of the two groups of animals that received 12.5 and 25 mg/kg b.w. of astragaloside IV. The obtained collective value of the logBB is equal to 0.49 ± 0.03 here.
Moreover, a comparison of logBB values for AIV predicted by our model with the values experimentally obtained and those determined using ACD/Percepta software has been made (
Figure 7). The closest logBB value to the value obtained experimentally for the mouse brain tissues is that obtained from the Equation (2) (the absolute error is equal to 0.056). Therefore, it can be seen that the second model corresponds to the value obtained in vivo. Based on the analysis of variance (
Table 5), Equation (1) was not used for further consideration.
4. Materials and Methods
4.1. Computer Programs
Within in silico studies, ACD/Percepta software (version 2012, Advanced Chemistry Development, Inc., Toronto, ON, Canada) was used. Statistical analysis of the obtained results was made using the Minitab 18 Statistical Software (Minitab Inc., State College, PA, USA). For studying the TLC-bioautography chromatogram, ImageJ software (1.48v, Wayne Rasband, National Institutes of Health, Maryland, USA) was used.
4.2. Extraction of Plant Material
The roots of Astragalus mongholicus were obtained from Ulaanbaatar (Bayangol district) in July 2017. They were authenticated by Dr. Otgonbataar Urjin from the Mongolian National University of Medical Sciences. The extraction process was performed on dried and ground roots. In the process, 500 g of powdered material was extracted by a mixture of water/methanol (50:50 v/v) as a result of overnight maceration. The solid/liquid ratio was equal to 1:10.
4.3. Determination of the Acetylcholinesterase Inhibitory Activity in a TLC-Bioautography Based Assay
To evaluate the blood–brain barrier permeability of the extract constituents, an acetylcholinesterase (AChE) inhibitory activity assay was performed for the major constituents of the tested extract. For this purpose, the solutions of analytical standards of astragalosides I, III, and IV (Sigma Aldrich, St. Louis, MO, USA) were prepared in methanol (Merck, Darmstadt, Germany, p.a.) at a concentration of 1 mg/mL. Next, 4 μL of all standards was introduced onto the TLC plates (silica gel, F
254s, 10 × 10 mm, Merck, Darmstadt, Germany) with the CAMAG Linomat 5 instrument (Camag, Muttenz, Switzerland) as 8-mm bands. The TLC plates were further developed in horizontal TLC sandwich chambers (Chromdes, Lublin, Poland) at a humidity rate of 75% after the initial saturation of the chamber with the developing phase. The mobile phase was composed of water/methanol/ethyl acetate (1.7:2.1:16.2
v/
v/
v; Merck, p.a) with the addition of 30 mg of naphthyl acetate (Sigma Aldrich, Saint Louis, MO, USA; p.a) per 20 mL of the mobile phase and 2% ammonia (POCH, Gliwice, Poland, p.a.). The further stages of the study were conducted in accordance with the procedure described by Kukula-Koch and Mroczek [
59]. Briefly, the developed TLC plates were first dried in the air, sprayed with the solution of the acetylcholinesterase enzyme in the bovine serum, and incubated for 10 min at 37 °C in an increased humidity environment. The chromatograms were left under the fume hood and after 10 min they were derivatized with a solution of Fast Blue B salt. The following reagents were used in these steps (Sigma Aldrich): acetylcholinesterase (type VI-S), bovine serum albumin (> 96%), Fast Blue B (95%), and 2-amino-2-(hydroxymethyl)-1,3-propanediol (> 99.9%). The detection and visualization of the obtained chromatograms was done under UV light at a wavelength of 254 nm and was then archived using a Reprostar 3 video camera with the WinCats software (version 1.4.4, Camag, Muttenz, Switzerland). The chromatogram was carefully studied with the help of an image processing program (ImageJ software; 1.48v, Wayne Rasband, National Institutes of Health, Maryland, USA) and the peak areas were recorded for the studied astragalosides. The spots were comparable as each compound was applied at a concentration of 1 mg/mL (4 μL) at a band of 8 mm.
4.4. Isolation of the Selected Active Natural Product
Semi-preparative HPLC was used to isolate and purify the most active saponin in the extract. For this purpose, the Vp liquid chromatographic system was used equipped with an LC 10AT pump, an SPD 10A UV-VIS detector, an SCL 10A system controller, a CTO-10 AS chromatographic oven, and a Rheodyne injector valve with 20 μL loop (Shimadzu, Kyoto, Japan). This system utilizes the Class-Vp computer program to control the hardware and acquire and store data, as well as determine retention times. The analysis was carried out at a wavelength of 254 nm and at a temperature of 20 °C.
The isolation of the individual components was carried out with the Kromasil 100-10-C-18 column (250 mm × 4.6 mm, 10 μm; Akzo Nobel, Bohus, Sweden) and a mobile phase composed of acetonitrile and water. The elution was performed using a 0.5 mL/min mobile phase gradient programmed from 90% water (A) to 60% acetonitrile (B) as follows (A:B): 90:10 (t = 5 min), 80:20 (t = 10 min), 75:25 (t = 20 min), 67:33 (t = 30 min), 65:35 (t = 40 min), 40:60 (t = 55min). Overall, 52 fractions were collected in one cycle of the separation process. All experiments were reproduced 10 times. The compositions of the obtained fractions were evaluated using liquid chromatography mass spectrometry, specifically, HPLC/DAD/ESI-Q-TOF-MS (Agilent Technologies, Santa Clara, CA, USA).
The performed optimization of the mass spectrometer settings led to the selection of the following parameters as the most advantageous ones, namely a capillary voltage of 3500 V, a fragmentor voltage of 110 V, and a gas temperature of 325 °C. The other parameters (i.e., nozzle voltage or skimmer voltage) did not have a significant influence on the instrument response in the analysis of astragaloside IV. Here, 10 V for the CID fragmentation energy was found to be preferable to compare the MS/MS spectra of the standard and the biological samples; however, the fragmentation of the investigated saponin was very strong.
4.5. Postmortem Studies
The performed tests were first approved by the Ethics Committee at the University of Life Sciences in Lublin, Poland (26 Mar 2018; agreement number: 45/2018).
Mice (Swiss Webster, male, 36 subjects) were treated with astragaloside IV (12.5 and 25 mg/kg b.w.), isolated from the tested aqueous methanolic extract of Astragalus mongholicus roots. The relevant fractions of the extract rich in AIV were collected together and evaporated to dryness under reduced pressure at 40 °C. The isolated AIV was redissolved in saline and then used in the in vivo studies Astragaloside IV was given by a single intraperitoneal administration (i.p.) for each of the tested animals. Out of the studied animals, the authors analyzed the content of astragaloside IV in 6 brains from each of the two groups that were dosed at 12.5 and 25 mg/kg.
The doses of astragaloside were chosen based on the literature data [
89], as well as preliminary studies. The blood samples collected from the animals delivered plasma that was immediately deep frozen and kept for quantitative analysis. Then, the mice were decapitated 1 h after the last injection and the brains were collected and homogenized in a plastic Eppendorff vial kept on ice. The homogenates of the brain and plasma samples were further vortexed for 10 min with 200 μL of 50% ethanol. The Eppendorf vials were further centrifuged at 3500 RPM for 5 min and the supernatant was filtered through a nylon syringe filter with a diameter of 0.22 μm to the autosampler vials with glass inserts.
The analytical standard of astragaloside IV was dissolved in 50% ethanol and dissolved into 10 different concentrations within the range of 0.002–0.2 mg/mL to obtain the calibration curve. Due to the low sizes and masses of brains, astragaloside IV was quantified in these organs after the addition of the reference compound to the samples. For this purpose, 100 µL of 0.5 mg/mL astragaloside IV was introduced to each brain sample. The peak areas of astragaloside IV in the plasma and brain samples were compared to the blank solution of a standard compound.
4.6. HPLC-MS Based Qualitative and Quantitative Analyses of Biological Samples
The qualitative and quantitative analyses of the plant extract, obtained fractions, purified compounds, biological samples, and the analytical standards were performed using an Agilent Technologies (Santa Clara, CA, USA) mass spectrometer (ESI-Q-TOF-MS, 6500 Series) with a high performance liquid chromatograph (HPLC), also by Agilent Technologies (1200 Series, Santa Clara, CA, USA), equipped with a binary pump, solvent degasser, thermostated column chamber, autosampler, and a PDA detector. The analyses were performed using spectroscopic purity solvents (acetonitrile, water, and formic acid; J.T. Baker).
The exact mass spectrometer was carefully adjusted on the astragaloside IV standard sample to induce the highest possible response from the detector. For this purpose, a series of astragaloside IV injections was made with a capillary voltage from 2000 to 4000 V (2000, 2500, 3000, 3500, and 4000 V), a fragmentor voltage from 90 to 130 V (90, 100, 110, 120, and 130 V), and a gas temperature from 225 to 325 °C (225, 250, 275, 300, and 325 °C). The skimmer voltage was set at 65 V, the nebulizer at 30 PSI, the gas flow at 12 L/h, and the operation mode to the positive ionization mode. The spectra were collected between 100 and 1200 m/z, and the injection volume was set at 20 μL. The two values of the CID energy (10 and 20 V) were applied to obtain the MS/MS spectra. The column temperature was 20 °C and the following gradient of acetonitrile with the addition of 0.2% formic acid (solvent B) in 0.2% formic acid (solvent A) was applied: 0 min 2% B in A; 2 min 10% B in A; 6 min 40% B in A; 35 min 95% B in A, held for 1 min; 37 min 2% of B in A. The analysis lasted for 40 min, the post run was set at 10 min, and the applied flow rate was equal to 0.2 mL/min. The Zorbax RP18 column by Agilent Technologies (Santa Clara, CA, USA) was selected for the analysis (150 mm × 2.1. mm, d = 3.5 μm).
5. Conclusions
In this study, triterpenoid saponins from the root of
Astragalus mongholicus were tested. This root is commonly used in the traditional medicine of China and currently is among the 10 most used plant medicines in the world, especially in the treatment of cancer [
90]. Even if frequently administered,
Astragalus is mainly supplemented in the form of the total extract. Several previous studies have already mentioned the high potential of
Astragalus in the elevation of memory and cognition; however, not much is known about the activity of single metabolites of the plant. No experimental values of logBB have yet been found in the literature.
The aim of this study was to evaluate the ability to cross the BBB by triterpenoid saponins occurring in the Astragali radix based on a QSAR methodology combined with postmortem studies on the brain tissues of mice. The paper shows the results of the analytical studies, which include in silico BBB tests with QSAR modeling, an in vitro acetylcholinesterase (AChE) inhibitory assay, and postmortem studies of the determination of the concentration of the most neuroactive compound in the brain tissues and blood of mice. Based on the obtained values, the logBB value was determined experimentally.
The presented computational studies confirm the ability of saponins I–IV to cross the blood–brain barrier. Statistical analyses of the newly constructed model based on E, ΔlogP, and logPow has proven both the applicability and predictability in estimating the ability of triterpenoid saponins to cross the BBB. In the in vitro tests, the most neuroactive compound of the tested compounds was indicated, although all saponins inhibited AChE. Astragaloside IV was then successfully isolated using semi-preparative HPLC.
Consequently, it was appropriate to investigate whether the most neuroactive compound of the root of Astragalus could be located in the brain. The results of postmortem studies show a regular dependence of the final concentration of AIV in the analyzed brain samples of animals treated with 12.5 and 25 mg/kg b.w. (0.00012299 and 0.0002306 mg, respectively, per one brain). The accumulation of the active substance in the brain tissues confirms that it can cross the blood–brain barrier. The experimentally determined logBB value was equal to 0.49 ± 0.03. The logBB value calculated on the basis of Equation (2) is similar to that determined experimentally with the mice brain tissues (the approximation error is equal to 0.056 here). This means that the newly established model corresponds to the value obtained in vivo.