Evidence-Based Complementary and Alternative Medicine

Volume 2014 (2014), Article ID 601064, 21 pages

http://dx.doi.org/10.1155/2014/601064

Research Article

Treatment of Acute Lymphoblastic Leukemia from Traditional Chinese Medicine

Ya-Li Hsiao,¹ Pei-Chun Chang,¹ Hung-Jin Huang,² Chia-Chen Kuo,¹ and Calvin Yu-Chian Chen1,2

¹Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan

²School of Medicine, College of Medicine, China Medical University, Taichung 40402, Taiwan

Received 3 January 2014; Accepted 7 January 2014; Published 22 May 2014

Academic Editor: Fuu-Jen Tsai

Copyright © 2014 Ya-Li Hsiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Acute lymphoblastic leukemia (ALL) is a cancer that immature white blood cells continuously overproduce in the bone marrow. These cells crowd out normal cells in the bone marrow bringing damage and death. Methotrexate (MTX) is a drug used in the treatment of various cancer and autoimmune diseases. In particular, for the treatment of childhood acute lymphoblastic leukemia, it had significant effect. MTX competitively inhibits dihydrofolate reductase (DHFR), an enzyme that participates in the tetrahydrofolate synthesis so as to inhibit purine synthesis. In addition, its downstream metabolite methotrexate polyglutamates (MTX-PGs) inhibit the thymidylate synthase (TS). Therefore, MTX can inhibit the synthesis of DNA. However, MTX has cytotoxicity and neurotoxin may cause multiple organ injury and is potentially lethal. Thus, the lower toxicity drugs are necessary to be developed. Recently, diseases treatments with Traditional Chinese Medicine (TCM) as complements are getting more and more attention. In this study, we attempted to discover the compounds with drug-like potential for ALL treatment from the components in TCM. We applied virtual screen and QSAR models based on structure-based and ligand-based studies to identify the potential TCM component compounds. Our results show that the TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose could have potential to improve the side effects of MTX for ALL treatment.

1. Introduction

Dihydrofolate reductase (DHFR) is essential in cellular metabolism and cell growth. It catalyzes the conversion of dihydrofolate into tetrahydrofolate which is a carrier for the methyl group. The methyl group carried by tetrahydrofolate is required for de novo synthesis of varieties of essential metabolites including amino acids, lipids, pyrimidines, and purines. Methotrexate (MTX), a folate antagonist, arrests cell growth by competitively binding to DHFR, thereby, blocking de novo synthesis of nucleotide precursors and inhibiting DNA synthesis [1]. MTX has been found to be useful as an antineoplastic and immunosuppressive agent because it inhibits the proliferation of rapidly dividing malignant [2].

MTX tightly binding on DHFR is one of the most widely used drugs in cancer treatment and is especially effective in the treatment of acute lymphocytic leukemia [3]. In addition, its folate analogue is widely used in the treatment of acute lymphoblastic leukemia (ALL) [4], ovarian cancer [5], osteosarcoma [6], rheumatoid arthritis [7], psoriasis [8], and inflammatory bowel disease [9] and for prevention of graft-versus-host disease after transplantation [10].

In the cells, MTX acts by inhibiting two enzymes. First, as an analog of folate, MTX is a powerful competitive inhibitor with 1000-fold more potent than the natural substrate of DHFR. DHFR is responsible for converting dihydrofolate (FH2) to their active form tetrahydrofolate (FH4), which is a substrate of thymidylate synthase (TS). Second, MTX is converted to active methotrexate polyglutamates (MTX-PGs) by folylpolyglutamate synthase [11, 12]. The polyglutamated forms of MTX inhibit TS directly. Due to these inhibitions, the cells will not be capable of de novo synthesis of purines and thymidylate, and thus DNA synthesis will be inhibited [13].

The primary action of MTX is inhibition of the enzyme DHFR, which converts dihydrofolate (FH2) to tetrahydrofolate (FH4) [11, 14]. MTX-PGs exert a stronger inhibition of DHFR and TS [15–17]. Thus, through direct inhibition by MTX and due to lack of FH4 and accumulation of FH2, deoxythymidine monophosphate synthesis and purine de novo synthesis is blocked, which eventually lead to leukemic cell death, bone marrow suppression, gastrointestinal mucositis, liver toxicity, and, rarely, alopecia [14, 15, 18, 19]. In fact, both MTX and natural folates undergo polyglutamylation catalyzed by the enzyme folylpolyglutamyl synthase. The MTX-PGs ensure intracellular retention and, furthermore, increase the affinity for the MTX-sensitive enzymes [16, 18, 20] (Figure 1).

Figure 1: Inhibition mechanism of MTX in DNA synthesis pathway. MTX: methotrexate; FPGS: folylpolyglutamate synthetase; MTX-PGs: methotrexate polyglutamates; DHFR: dihydrofolate reductase; TS: thymidylate synthase; FH4: tetrahydrofolate; FH2: dihydrofolate; Methylene-THF: 5,10-methylenetetrahydrofolate; Methyl-THF: 5-methyltetrahydrofolate; dUMP: deoxyurindine-5′-monophosphate; dTMP: deoxythymidine-5′-monophosphate; MTRR: methionine synthase reductase; SHMT: serine hydroxymethyltransferase.

However, MTX may lead to acute renal cytotoxicity [21] which is serious and potentially fatal in the spinal canal and may occur after the administration of neurotoxicity [22–25] and hematological toxicity [26] caused by animal somatic cells and human bone marrow chromosomal lesions [27] which led to the hematopoietic system abnormalities [28], gastrointestinal toxicity [29] made multiorgan dysfunction [30], nephrotoxicity [31] made renal failure [31, 32], and hepatotoxicity made liver fibrosis [33]. Higher concentrations of long-chain MTX-PGs have been in the risk of gastrointestinal and hepatic toxicity [12, 34, 35]. Thus, the lower toxicity drugs are necessary to be developed. Recently, the increasing numbers of mechanisms of different diseases have been clarified to detect the helpful target protein for diseases treatment [36–49], and diseases treatments with traditional Chinese medicine (TCM) as complements are getting more and more attention. The compounds extracted from traditional Chinese medicine have displayed their potential as lead compounds against tumors [50–54], stroke [55–58], viral infection [59–63], metabolic syndrome [64–66], diabetes [67], inflammation [62], and other diseases [68, 69]. For this trend, we attempted to discover the compounds with drug-like potential and lower toxicity for ALL treatment from the components in traditional Chinese medicine.

2. Materials and Methods

2.1. Virtual Screening

The receptors, human dihydrofolate reductase (DHFR) and human thymidylate synthase (TS) proteins were downloaded from Protein Data Bank of 1U72 (PDB ID: 1U72) [70] and 1HVY (PDB ID: 1HVY) [71]. We adopted the traditional Chinese medicine formulas that treat acute lymphoblastic leukemia from database “Shanghai Innovative Research Center of Traditional Chinese Medicine” (http://www.sirc-tcm.sh.cn/en/index.html) [72]. The component compounds of these formulas were integrated with the herbs data from the TCM Database@Taiwan [73] and became the ALL disease-specific compound library. Virtual screening of candidates from the compound library was conducted using the LigandFit Module of DS 2.5 under the Chemistry at HARvard Macromolecular Mechanics (CHARMm) force field. DockScore was selected as output values. Candidates were ranked according to DockScore and pharmacokinetic characteristics including absorption, solubility, blood brain barrier (BBB), and plasma protein binding (PPB) were predicted by ADMET protocols for each candidate.

2.2. 2D-Quantitative Structure Activity Relationship (2D-QSAR) Models

In this study, 45 candidates (Figure 2) with known experimental pIC50 values [74] that have inhibitory activities toward DHFR were used in the QSAR studies (Table 1). The 45 known inhibitors were randomly divided into a training set of 36 candidates and a test set of 9 candidates. The chemical structures of these candidates were drawn by ChemDraw Ultra 10.0 (CambridgeSoft Inc., USA) and transformed to 3D molecule models by Chem3D Ultra 10.0 (CambridgeSoft Inc., USA). Molecular descriptors for each candidate were calculated using the DS 2.5 Calculate Molecular Property Module. Genetic function approximation (GFA) model was used to select representative descriptors that correlated to bioactivity (pIC50) which were used to construct 2D-QSAR models. The training set was used to construct multiple linear regression (MLR), support vector machine (SVM), and Bayesian network (BN) models. The test set was used to test the accuracy of these models.

Table 1: Experimental pIC50 values for DHFR inhibitors [40].

Name
R1
R2
X
R3
pIC50

1
CH₃
CH₃
CH₂
H
4.71

2
CH₃
CH₃
CH₂
4′-CH₃
4.6091

3*
CH₃
CH₃
CH₂
4′-OCH₃
4.2306

4
CH₃
CH₃
CH₂
4′-F
4.6615

5*
CH₃
CH₃
CH₂
4′-Cl
4.5243

6
CH₃
CH₃
CH₂
3′,4′-diCl
4.8928

7*
CH₃
CH₃
–O-CH₂–
H
7.1612

8
CH₃
C₂H₅
–O-CH₂–
H
6.8097

9*
H
c-Pr
–O-CH₂–
H
6.2612

10
–(CH₂)₃–

–O-CH₂–
H
6.8729

11
– (CH₂)₄–

–O-CH₂–
H
6.762

12
– (CH₂)₅–

–O-CH₂–
H
5.7471

13
– (CH₂)₆–

–O-CH₂–
H
5.2733

14
– (CH₂)₄–

–O-CH₂CH₂–
H
7.5086

15
– (CH₂)₅–

–O-CH₂CH₂–
H
8.0458

16
– (CH₂)₄–

–O-(CH₂)₃-O–
H
7.699

17
– (CH₂)₅–

–O-(CH₂)₃-O–
H
7.4949

18
CH₃
CH₃
–O-(CH₂)₃-O–
H
8.2218

19
CH₃
CH₃
–O-(CH₂)₄-O–
H
7.5686

20
CH₃
C₂H₅
–O-(CH₂)₃-O–
H
8.0969

21
H
c-Pr
–O-(CH₂)₃-O–
H
8.1549

22
– (CH₂)₄–

–O-(CH₂)₃-O–
H
8.699

23*
– (CH₂)₄–

–O-(CH₂)₄-O–
H
7.3768

24
– (CH₂)₅–

–O-(CH₂)₃-O–
H
8.1549

25
– (CH₂)₅–

–O-(CH₂)₄-O–
H
6.8069

26
–(CH₂)₆–

–O-(CH₂)₃-O–
H
7.9586

27
–(CH₂)₅–

–O-(CH₂)₃-O–
F
7.8239

28
–(CH₂)₅–

–O-(CH₂)₃-O–
Cl
7.8539

29
–(CH₂)₅–

–O-(CH₂)₃-O–
NO₂
7.8239

30
–(CH₂)₅–

–O-(CH₂)₃-O–
Me
7.7447

31
–(CH₂)₅–

–O-(CH₂)₃-O–
t-Bu
7.6576

32
–(CH₂)₅–

–O-(CH₂)₃-O–
OMe
8.2218

33*
–(CH₂)₅–

–O-(CH₂)₃-O–
CN
8

34
–(CH₂)₅–

–O-(CH₂)₃-O–
COCH₃
7.8861

35
–(CH₂)₅–

–O-(CH₂)₃-O–
SO₂NH₂
8.2218

36*
–(CH₂)₄–

–O-(CH₂)₃-O–
F
8

37
–(CH₂)₄–

–O-(CH₂)₃-O–
Cl
8.1549

38
–(CH₂)₄–

–O-(CH₂)₃-O–
NO₂
8.0969

39*
–(CH₂)₄–

–O-(CH₂)₃-O–
Me
8

40
–(CH₂)₄–

–O-(CH₂)₃-O–
t-Bu
7.7696

41
–(CH₂)₄–

–O-(CH₂)₃-O–
OMe
7.9586

42
–(CH₂)₄–

–O-(CH₂)₃-O–
CN
8.0969

43
–(CH₂)₄–

–O-(CH₂)₃-O–
COCH₃
8.0458

44*
–(CH₂)₄–

–O-(CH₂)₃-N(Me)–
H
7.3872

45
–(CH₂)₄–

–O-(CH₂)₃–
H
7.4949

MTX

8.5229

test set.

Figure 2: Chemical structure of DHFR inhibitors [40].

2.2.1. Multiple Linear Regression (MLR) Model

Multiple linear regression [75] attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. The model was built in the form of equation as follows:

where represents the th molecular descriptor and is its fitting coefficient. The generated MLR model was validated with test dataset. The square correlation coefficients between predicted and actual pIC50 of the training set was used to verify accuracy of the model. This building model was applied to predict the pIC50 values of the TCM candidates.

2.2.2. Support Vector Machine (SVM) Model

SVM implement classification or regression analysis with linear or nonlinear algorithms [76]. The algorithm identifies a maximum-margin hyper-plane to discriminate two class training samples. Samples on the margin are called the support vectors. Lagrange multipliers and kernels were introduced to form the final pattern separating regression model. In this study, LibSVM [77–79] package was selected to build our regression SVM model. The selected kernel was the Gaussian radial basis function kernel equation:

Cross-validation of the SVM model was also conducted following the default settings in LibSVM [80]. The generated regression SVM model was validated with test dataset. The square correlation coefficients between predicted and actual pIC50 of the training set was used to verify accuracy of the model. This building model was applied to predict the pIC50 values of the TCM candidates.

2.2.3. Bayesian Network Model

We used the Bayes Net Toolbox (BNT) in Matlab (https://code.google.com/p/bnt) to create Bayesian network model [81] by the training data set. After data discretization, we applied linear regression analysis for each pIC50 category in the training dataset. For the th pIC50 category with candidates, let and represent the pIC50 value and the th descriptor value in the th ligand, respectively. The regression model of the data sets is formulated as

where

and and are the regression coefficients and error term in the th pIC50 category. We used ordinary least squares to estimate the unknown regression coefficient :

The Banjo (Bayesian network inference with Java objects) is software for structure learning of static Bayesian networks (BN) [82]. It is implemented in Java. We used training dataset to discover the relationships in the BN structure among the descriptors and the pIC50 by the Banjo package. After that, we used test data to assess the accuracy of our algorithm. For the test data , the pIC50 category is predicted by the following formula:

where represented the th category of pIC50 and represented the total number of the pIC50 categories. The marginal probability can be calculated by BNT module. Finally, the pIC50 value is calculated as follows:

The square correlation coefficients () between predicted and actual pIC50 of the training set were used to verify accuracy of the model. This building model was applied to predict the pIC50 values of the TCM candidates.

2.3. 3D-Quantitative Structure Activity Relationship (3D-QSAR) Models

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by Sybyl-X 1.1.1 (Tripos Inc., St. Louis, MO, USA) for DHFR inhibitors. Lennard-Jones potential and Coulomb potential were employed to calculate steric and electrostatic interaction energies. The two 3D-QSAR models were further evaluated by cross-validated correlation coefficient and non-cross-validated correlation coefficient (). The correlation between the force field and biological activities was calculated by partial least squares (PLSs) method.

The flowchart for the entire experimental procedure for TCM candidates screening is illustrated in Figure 3.

Figure 3: The experimental flowchart.

3. Results and Discussion

3.1. Virtual Screening

The virtual screening was performed by the LigandFit Module of DS 2.5 in force field of CHARMm. The receptor binding sites were defined by the binding position of MTX on DHFR protein and by the binding position of MTX-PGs on TS protein. The compounds from our library were docked into the two receptors. In this protocol, the receptors were fixed, and the ligands that complement the binding sites were flexible in energy minimization process. The control compound used in this study was MTX which contains aromatic and heterocyclic rings (Figure 4).

(a)

(b)

Figure 4: (a)-(b) The chemical scaffolds of MTX, MTX-PGs, and TCM candidates for acute lymphoblastic leukemia treatment.

The top eighteen results from DHFR docking score are tabulated in Table 2. The TS docking score for the eighteen candidates are also tabulated in Table 2. All the eighteen TCM candidates had higher Dock Scores than the control methotrexate (MTX) and MTX-PGs. Chemical scaffolds of MTX, MTX-PGs, and the eighteen TCM candidates are shown in Figure 4. Adsorption, solubility, hepatotoxicity, and plasma protein binding were assessed to evaluate pharmacokinetic properties of the selected candidates (Table 3). Considering the factor of hepatotoxicity, we selected the TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose for advanced study. MTX and TCM candidates had very poor absorption for human intestine. Binding strength of the ligands to carrier proteins in the blood stream is indicated by the plasma protein binding (PPB) value [21]. MTX has more than 90% for PPB but adenosine triphosphate, manninotriose, raffinose, and stachyose were less than 90% for PPB.

Table 2: DHFR and TS docking score of TCM candidates.

Index
TCM candidate
DHFR docking score
TS docking score

1
Adenosine triphosphate
226.6790
186.2170

2
Methyl 6-O-digalloyl-beta-D-glucopyranoside (II)
162.6260
154.1730

3
Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside
153.7500
148.2880

4
Methyl 6-O-digalloyl-beta-D-glucopyranoside
151.7650
158.0350

5
Manninotriose
129.7870
114.6030

6
Forsythiaside
129.6030
27.9940

7
Isoacteoside
124.5900
30.6190

8
Rehmannioside B
119.9930
79.2920

9
Rehmannioside A
116.4330
71.3970

10
Raffinose
115.4940
134.2120

11
Cistanoside C
112.4270
—

12
Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside
109.9470
20.7830

13
Stachyose
107.0940
8.5760

14
Chlorogenic acid
103.8080
—

15
Jionoside D
103.5050
39.3430

16
Isochlorogenic acid
102.9470
—

17
Jionoside C
102.3940
—

18
Rutin
101.1310
78.816

*
MTX
97.0960
—

**
MTX-PGs
—
69.671

control.

Methotrexate polyglutamate.

Table 3: Predicted pharmacokinetic properties of TCM candidates and MTX.

Index
TCM candidate
Pharmacokinetic properties

Absorption
Solubility
Hepatotoxicity
PPB

1
Adenosine triphosphate
3
2
1
0

2
Chlorogenic acid
3
4
1
0

3
Cistanoside C
3
2
1
2

4
Forsythiaside
3
2
1
0

5
Isoacteoside
3
2
1
0

6
Isochlorogenic acid
3
4
1
0

7
Jionoside C
3
3
1
2

8
Jionoside D
3
2
1
2

9
Manninotriose
3
3
0
0

10
Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside
3
2
1
0

11
Methyl 6-O-digalloyl-beta-D-glucopyranoside
3
2
1
0

12
Methyl 6-O-digalloyl-beta-D-glucopyranoside (II)
3
2
1
0

13
Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside
3
0
1
0

14
Raffinose
3
3
0
0

15
Rehmannioside A
3
4
1
0

16
Rehmannioside B
3
4
1
0

17
Rutin
3
1
1
2

18
Stachyose
3
1
0
0

Control
MTX
3
3
1
1

Absorption (Human intestinal absorption), there are four prediction levels: 0 (good absorption), 1 (moderate absorption), 2 (poor absorption), 3 (very poor absorption).

2Solubility, there are gour prediction levels: 0 (extremely low), 1 (very low, but possible), 2 (low), 3 (good), 4 (optimal), 5 (too soluble), 6 (warning).

3Hepatotoxicity, there are four prediction levels: 0 (nontoxic), 1 (toxic).

4PPB (Plasma protein binding), there are there prediction levels: 0 (binding is <90%), 1 (binding is >90%), 2 (binding >95%).

Ligand-receptor interactions during docking are shown in Figures 5 and 6. MTX docked on DHFR (Figure 5(a)) through four hydrogen bondings of Glu30, Gln35, Lys68, and Arg70. Adenosne triphosphate formed three H-bonds with Glu30, Gln35, and Arg70 (Figure 5(b)). Manninotriose formed H-bond with Arg28 (Figure 5(c)). Raffinose formed H-bonds with Asn64 and NDP (Figure 5(d)). Stachyose formed H-bonds with Lys63, Asn64, and Lys68 (Figure 5(e)). MTX-PGs docked on TS (Figure 5(f)) by single H-bond with Arg50. Adenosne triphosphate, manninotriose, and stachyose docked on TS (Figures 5(g), 5(h), and 5(j)) by single H-bond with Arg50. Raffinose docked on TS by single H-bond with Met309 (Figure 5(i)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 5: Docking pose of MTX and TCM candidates with DHFR for (a), (b), (c), (d), and (e). Docking pose of MTX-PGs with TS for (f), (g), (h), (i), and (j). TCM candidates are shown in cyan. The cofactors are shown in purple. In H-bond interactions, nitrogen atoms are shown in blue, hydrogen atoms are shown in gray, oxygen atoms are shown in magenta, hydrogen bonds are shown in red dotted line, pi bonds are shown in orange solid line. (a) MTX, (b) and (g) adenosine triphosphate, (c) and (h) manninotriose, (d) and (i) raffinose, (e) and (j) stachyose, and (f) MTX-PGs.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 6: The Ligplot analysis of hydrophobic interactions between DHFR and TCM candidates and between TS and TCM candidates. (a) MTX with DHFR, (b) and (g) adenosine triphosphate with DHFR and TS, (c) and (h) manninotriose with DHFR and TS, (d) and (i) raffinose with DHFR and TS, (e) and (j) stachyose with DHFR and TS, and (f) MTX-PGs with DHFR and TS. Bonds: ligand bonds, nonligand bonds, hydrogen bonds, and hydrophobic are shown in purple, orange, olive green, and brick red, respectively. Atoms: nitrogen, oxygen, carbon, and sulfur are shown in blue, red, black, and yellow, respectively.

Analysis of hydrophobic interactions showed that MTX docking on DHFR was more stable than the TCM candidates. Comparing with chemical structures of the TCM candidates, it could be attributed to the larger size for MTX docking on DHFR (Figures 6(a), 6(b), 6(c), 6(d), and 6(e)). However, the TCM candidates docking on TS were more stable than MTX-PGs due to hydrophobic interactions (Figures 6(f), 6(g), 6(h), 6(i), and 6(j)).

3.2. Bioactivity Prediction Using QSAR Models

QSAR models were constructed using known DHFR inhibitors [40] and applied for predicting molecular properties of the TCM ligands. Molecular descriptors associated with bioactivity including BD_Count, Num_RotatableBonds, CHI_V_1, IAC_Mean, JX, JY, SC_3_C, Jurs_FNSA_1, Jurs_RPCS, Jurs_SASA, and Shadow_Xlength were used to construct MLR model, SVM model, and Bayesian network model.

Our MLR model was as follows.

GFATempModel_1 = 31.623 + 2.5173    HBD_Count − 0.47471    Num_RotatableBonds − 1.7664    CHI_V_1 − 12.997    IAC_Mean − 45.669    JX + 36.62    JY + 0.11612    SC_3_C + 18.941    Jurs_FNSA_1 − 4.8012    Jurs_RPCS + 0.029451    Jurs_SASA − 0.084377    Shadow_Xlength.

In CoMFA model, the steric fields were the primary contributing factor. In CoMSIA, various factors were considered and modeled. The optimum CoMSIA models were “EHA model” and “EHDA model” based on high , high , and low SEE values (Table 4). The “EHA model” was consisting of electrostatic field and hydrophobic and hydrogen bond acceptor. The “EHDA model” was consisting of electrostatic field and hydrophobic and hydrogen bond donor, and hydrogen bond acceptor. The CoMFA model and CoMSIA model of EHDA and of EHA were with ONC of 7, 11, and 12, respectively.

Table 4: Partial Least Square (PLS) analysis for CoMFA and CoMSIA models.

Cross Validation
Non-cross Validtion
Fraction

ONC

SEE

S
E
H
D

CoMFA

7
0.5250
0.9630
0.2590
136.2760
0.7970
0.2030
—
—
—

CoMSIA

S
36
0.6350
0.9890
0.3040
19.6900
1.0000
0.0000
0.0000
0.0000
0.0000

E
—
—
—
—
—
—
—
—
—
—

H
2
0.6130
0.7760
0.5940
72.7070
0.0000
0.0000
1.0000
0.0000
0.0000

D
7
0.4180
0.7160
0.7130
13.3480
0.0000
0.0000
0.0000
1.0000
0.0000

A
1
0.0810
0.1600
1.1380
8.1640
0.0000
0.0000
0.0000
0.0000
1.0000

SE
37
0.6050
0.9890
0.3250
16.7260
0.9980
0.0200
0.0000
0.0000
0.0000

SH
2
0.5970
0.7790
0.5910
73.9120
0.3880
0.0000
0.6120
0.0000
0.0000

SD
36
0.6670
0.9890
0.3020
19.9580
0.6350
0.0000
0.0000
0.3650
0.0000

SA
30
0.7020
0.9890
0.2340
39.7820
0.7480
0.0000
0.0000
0.0000
0.2520

EH
7
0.6270
0.9540
0.2860
110.4680
0.0000
0.0500
0.9500
0.0000
0.0000

ED
7
0.4130
0.7090
0.7210
12.9020
0.0000
0.0180
0.0000
0.9820
0.0000

EA
2
0.0760
0.1830
1.1350
4.6860
0.0000
0.2000
0.0000
0.0000
0.8000

HD
2
0.5780
0.7940
0.5690
81.1680
0.0000
0.0000
0.7220
0.2780
0.0000

HA
2
0.5890
0.7910
0.5740
79.5410
0.0000
0.0000
0.7450
0.0000
0.2550

DA
9
0.4300
0.7290
0.7160
10.4430
0.0000
0.0000
0.0000
0.7800
0.2200

SHE
8
0.5850
0.9690
0.2400
139.5820
0.3570
0.0440
0.6000
0.0000
0.0000

SED
38
0.6500
0.9890
0.3490
14.1810
0.6340
0.0010
0.0000
0.3650
0.0000

SEA
31
0.7030
0.9880
0.2430
35.7980
0.7420
0.0110
0.0000
0.0000
0.2470

SHD
22
0.5780
0.9890
0.1830
89.3410
0.3070
0.0000
0.4490
0.2430
0.0000

SHA
2
0.5800
0.7950
0.5680
81.4850
0.3130
0.0000
0.4980
0.0000
0.1900

SDA
30
0.7170
0.9890
0.2320
40.3890
0.5640
0.0000
0.0000
0.2910
0.1450

EDA
11
0.4240
0.7380
0.7250
8.4650
0.0000
0.0200
0.0000
0.7640
0.2150

EHA *
11
0.5770
0.9800
0.1990
148.9890
0.0000
0.0630
0.6910
0.0000
0.2460

HAD
2
0.5550
0.8020
0.5580
85.2150
0.0000
0.0000
0.6150
0.2080
0.1770

SEHD
23
0.5970
0.9890
0.1870
81.1730
0.2940
0.0230
0.4520
0.2310
0.0000

SEHA
23
0.5970
0.9800
0.1880
80.4080
0.3000
0.0420
0.4620
0.0000
0.1960

SEDA
31
0.7110
0.9890
0.2420
36.1870
0.5640
0.0050
0.0000
0.2840
0.1470

SHDA
5
0.5630
0.9290
0.3470
102.3970
0.2600
0.0000
0.3980
0.1920
0.1510

EHDA *
12
0.6070
0.9820
0.1940
143.5670
0.0000
0.0500
0.5880
0.2040
0.1580

SEHDA
23
0.6120
0.9890
0.1880
80.3300
0.2690
0.0340
0.4020
0.1630
0.1330

OCN: Optimal number of components.

SEE: Standard error of estimate.

F: F-test value.

Prediction model.

S: Steric.

H: Hydrophobic.

D: Hydrogen bond donor.

A: Hydrogen bone acceptor.

E: Electrostatic.

Experimental and predicted pIC50 values of 45 DHFR inhibitors using CoMFA and CoMSIA models are shown in Table 5. Residuals calculated from the differences between observed and predicted pIC50 values ranged between −0.3655 and 0.4311 for the CoMFA, between −0.411 and 0.589 for the CoMSIA with “EHA model,” and between −0.431 and 0.569 for CoMSIA with “EHDA model.”

Table 5: Experimental and predicted pIC50 values of 45 DHFR inhibitors using the constructed CoMFA and CoMSIA models.

DHFR inhibitors no.
Experimental pIC50
CoMFA
CoMSIA_EHDA
CoMSIA_EHA

Predicted
Residual
Predicted
Residual
Predicted
Residual

1
4.710
4.652
0.0580
4.481
0.229
4.532
0.178

2
4.609
4.606
0.0031
4.635
−0.026
4.662
−0.053

3*
4.231
4.576
−0.3454
4.333
−0.102
4.407
−0.176

4
4.662
5.027
−0.3655
4.698
−0.037
4.701
−0.039

5*
4.524
4.571
−0.0467
4.807
−0.283
4.797
−0.273

6
4.893
4.476
0.4168
4.723
0.170
4.651
0.242

7*
7.161
6.810
0.3512
7.287
−0.126
7.359
−0.198

8
6.810
6.529
0.2807
6.722
0.088
6.723
0.087

9*
6.261
6.495
−0.2338
6.270
−0.009
6.240
0.021

10
6.873
6.648
0.2249
6.808
0.065
6.832
0.041

11
6.762
6.793
−0.0310
6.686
0.076
6.645
0.117

12
5.747
5.749
−0.0019
5.767
−0.020
5.705
0.042

13
5.273
5.346
−0.0727
5.245
0.028
5.279
−0.006

14
7.509
7.454
0.0546
7.494
0.015
7.522
−0.013

15
8.046
8.322
−0.2762
8.056
−0.010
8.052
−0.006

16
7.699
8.127
−0.4280
8.130
−0.431
8.110
−0.411

17
7.495
7.670
−0.1751
7.871
−0.376
7.820
−0.325

18
8.222
8.079
0.1428
8.130
0.092
8.105
0.117

19
7.569
7.561
0.0076
7.581
−0.012
7.609
−0.040

20
8.097
8.207
−0.1101
8.105
−0.008
8.240
−0.143

21
8.155
8.007
0.1479
8.242
−0.087
8.215
−0.060

22
8.699
8.127
0.5720
8.130
0.569
8.110
0.589

23*
7.377
7.636
−0.2592
7.325
0.052
7.318
0.059

24
8.155
7.670
0.4849
7.871
0.284
7.820
0.335

25
6.807
7.113
−0.3061
6.902
−0.095
6.824
−0.017

26
7.959
7.987
−0.0284
7.887
0.072
7.975
−0.016

27
7.824
7.763
0.0609
7.981
−0.157
7.955
−0.131

28
7.854
7.839
0.0149
7.906
−0.052
7.850
0.004

29
7.824
7.843
−0.0191
7.824
0.000
7.827
−0.003

30
7.745
7.914
−0.1693
7.736
0.009
7.733
0.012

31
7.658
8.069
−0.4114
7.665
−0.007
7.654
0.004

32
8.222
8.005
0.2168
7.848
0.374
7.814
0.408

33*
8.000
8.100
−0.1000
7.978
0.022
8.010
−0.010

34
7.886
7.455
0.4311
7.947
−0.061
7.811
0.075

35
8.222
7.981
0.2408
8.208
0.014
8.237
−0.015

36*
8.000
8.173
−0.1730
8.130
−0.130
8.139
−0.139

37
8.155
8.180
−0.0251
8.170
−0.015
8.187
−0.032

38
8.097
8.122
−0.0251
8.097
0.000
8.097
0.000

39*
8.000
7.990
0.0100
8.007
−0.007
8.054
−0.054

40
7.770
7.683
0.0866
7.832
−0.062
7.697
0.073

41
7.959
8.223
−0.2644
7.883
0.076
7.907
0.052

42
8.097
7.974
0.1229
8.040
0.057
8.150
−0.053

43
8.046
7.996
0.0498
8.052
−0.006
8.061
−0.015

44*
7.387
7.542
−0.1548
7.567
−0.180
7.590
−0.203

45
7.495
7.449
0.0459
7.484
0.011
7.516
−0.021

test set.

The correlations between the predicted and actual bioactivity for DHFR inhibitors are shown in Figure 7. The values are 0.936 for MLR, 0.734 for Bayesian network, 0.884 for SVM, 0.957 for CoMFA, 0.977 for CoMSIA with EHA model, and 0.978 for CoMSIA with EHDA model implicate high correlation. High correlation coefficients validated the reliability of the constructed CoMFA and CoMSIA models. The predicted bioactivity values of TCM candidates by 2D-QSAR and 3D-QSAR models are listed in Table 6.

Table 6: Predicted bioactivity (pIC50) of MTX and TCM candidates using MLR, Bayesian, SVM, CoMFA and CoMSIA models.

Name
MLR
Bayesian
SVM
CoMFA
CoMSIA_EHDA*
CoMSIA_EHA**

Adenosine triphosphate
6.4559
5.8145
8.7175
7.9640
7.8600
7.8350

Methyl 6-O-digalloyl-beta-D-glucopyranoside (II)
27.5044
5.1810
8.0157
6.9800
6.6030
5.5170

Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside
27.7317
5.4868
8.4131
7.5490
6.5980
5.9300

Methyl 6-O-digalloyl-beta-D-glucopyranoside
26.7188
5.2477
7.8936
6.8980
6.6620
5.6840

Manninotriose
29.1034
5.1934
5.9247
7.6470
6.2450
5.3700

Forsythiaside
29.9821
5.3595
8.5713
7.7140
8.0830
7.8950

Isoacteoside
27.6319
6.3265
8.1255
7.6550
7.7990
7.5430

Rehmannioside B
26.7291
4.3032
7.3293
6.9990
6.8000
5.8300

Rehmannioside A
30.3632
4.4182
9.3324
6.7480
5.8070
4.6750

Raffinose
32.8592
5.1647
8.4766
6.9350
5.9620
4.2830

Cistanoside C
26.1802
5.7174
8.2029
7.6060
8.0200
7.9640

Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside
30.7405
6.0369
8.3193
6.7670
6.3240
6.6300

Stachyose
40.5491
5.9779
8.5055
7.4300
5.6830
4.4510

Chlorogenic acid
17.3951
4.2335
7.8897
7.8080
7.9640
7.7680

Jionoside D
26.0421
5.5238
8.2089
7.5080
7.4900
7.2820

Isochlorogenic acid
16.1484
4.4196
7.4839
7.1990
6.3590
6.4480

Jionoside C
23.7203
5.6640
8.2741
7.7600
7.0800
6.9110

Rutin
30.3096
5.6910
8.2465
6.5720
8.0190
7.6830

The pIC50 experimental values ​​of MTX was 8.5229.

EHDA model of CoMSIA.

EHA model of CoMSIA.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 7: Correlation of observed and predicted activity (pIC50) using 2D-QSAR models and 3D-QSAR models. MLR, Bayesian network, and SVM were 2D-QSAR model. CoMFA, CoMSIA_EHDA, and CoMSIA_EHA were 3D-QSAR model.

3.3. The Contour Maps of CoMFA and CoMSIA Models

Ligand activities of MTX and the TCM candidates can be predicted based on the 3D-QSAR contour map, including features in steric field, hydrophobic field, and H-bond donor/acceptor characteristics. MTX and the TCM candidates contoured well to the steric features of the CoMFA in Figure 8. CoMSIA map provides more information with regard to bioactivity differences for “EHA model” and “EHDA model” in Figures 9 and 10, respectively. From the consistent results observed among the 3D-QSAR models validations, we inferred that adenosine triphosphate, manninotriose, raffinose, and stachyose of TCM candidates might have good biological activity for DHFR.

(a)

(b)

(c)

(d)

(e)

Figure 8: The CoMFA contour maps for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Green and yellow contours denote regions favoring and disfavoring steric fields, respectively. Blue and red contours denote regions favoring and disfavoring electrostatic fields, respectively.

(a)

(b)

(c)

(d)

(e)

Figure 9: The CoMSIA contour maps of EHA model for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Blue and orange contours denote regions favoring and disfavoring electrostatic fields, respectively. Yellow and white contours denote regions favoring and disfavoring hydrophobic fields, respectively. Green and red contours denote regions favoring and disfavoring H-bond acceptor fields, respectively.

(a)

(b)

(c)

(d)

(e)

Figure 10: The CoMSIA contour maps of EHDA model for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Blue and orange contours denote regions favoring and disfavoring electrostatic fields, respectively. Yellow and white contours denote regions favoring and disfavoring hydrophobic fields, respectively. Green and red contours denote regions favoring and disfavoring H-bond acceptor fields, respectively. Cyan and purple contours denote regions favoring and disfavoring H-bond donor fields, respectively.

Contour to steric favoring and hydrophobic favoring regions was observed for adenosine triphosphate, manninotriose, raffinose, and stachyose. Consistent with the docking pose contour (Figures 8, 9, and 10), we propose that the four TCM candidates may maintain bioactivity for DHFR under dynamic conditions in physiological environments.

4. Conclusion

DHFR and TS proteins are key regulators in de novo synthesis of purines and thymidylate. Inhibiton of these proteins has the potential for treating acute lymphoblastic leukemia. In this study, we applied virtual screen and QSAR models based on structure-based and ligand-based methods in order to identify the potential TCM compounds. The TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose could bind on DHFR and TS specifically and had low hepatotoxicity. These TCM compounds had potential to improve the side effects of MTX for ALL treatment.

Conflict of Interests

The authors declare that they have no conflict of interests regarding the publication of this paper.

Acknowledgments

The research was supported by Grants from the National Science Council of Taiwan (NSC102-2632-E-468-001-MY3, NSC102-2325-B039-001, and NSC102-2221-E-468-027-), Asia University (101-ASIA-24, 101-ASIA-59, ASIA100-CMU-2, and ASIA101-CMU-2), and China Medical University Hospital (DMR-103-001, DMR-103-058, and DMR-103-096). This study is also supported in part by Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004) and Taiwan Department of Health Cancer Research Center of Excellence (MOHW103-TD-B-111-03), and CMU under the Aim for Top University Plan of the Ministry of Education, Taiwan.

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