3D-QSAR Studies on 4-([1,2,4]Triazolo[1,5-α]pyridin-6-yl)-5(3)-(6-methylpyridin-2-yl)imidazole Analogues as Potent Inhibitors of Transforming Growth Factor-β Type I Receptor Kinase

SUN Li-Qin MENG Li-Qing YAN Cho-Qun CUI Dong-Xio MIAO Jun-Qiu CHEN Jing-Run LIANG Ti-Gng, LI Qing-Shn,

a (College of Pharmacy, Shanxi Medical University, Taiyuan 030001, China)

b (Shanxi University of Chinese Medicine, Taiyuan 030024, China)

1 INTRODUCTION

The transforming growth factor-β (TGF-β) is a critical member of TGF-β superfamily, which consists of TGF-β1, TGF-β2, TGF-β3, activins, inhibins and bone morphogenetic proteins (BMPs).These family members induce various effects and have been reported to control differentiation, proliferation, migration and apoptosis of many different cell types[1].They trigger signals bind to the complex of TGF-β receptors that are composed of two type I receptors and two type II receptors.Moreover, both of these receptors are serine/threonine kinase receptors[2].After the ligand binds to the constitutively active type II receptor, the type I receptor, also called activin receptor-like kinase 5 (ALK5), is phosphorylated, which further phosphorylates Smad2/Smad3 proteins. In the nucleus, phosphorylated Smad2/Smad3 proteins form a heteromeric complex with Smad4 binding other DNA-binding transcripttion factors as partners for TGF-β target genes recognition and transcriptional regulation[3].TGF-β plays an essential role in the initiation and developpment of fibrosis in kidney[4], heart[5], lung[6], and liver[7].Slight changes of TGF-β signaling have been also concerned with various diseases including cancer[8], pancreatic diseases[9]and hematological malignancies[10].Thus selecting ALK5 inhibitors might have preclinical and clinical potential for the treatment of related diseases.

Nowadays, quantitative structure-activity relationship (QSAR) has been applied extensively in correlating molecular structural features with biological activities.In addition, QSAR models are ideal alternatives to replace or reduce experiments because of their higher efficiency and lower cost in many fields like toxicology[11], environmental science[12]and other fields[13,14].For instance, Qu et al.[12]revealed the main molecular descriptors controlling the degradation rate of different PFCAs species through theory-based calculations, which will provide useful information for future researches.

Presently, three-dimensional quantitative structureactivity relationship (3D-QSAR) method has been applied broadly in correlating molecular structure features with biological activities and then could be helpful to more new chemical compounds’ synthesis and design[15].It can reflect spatial information between medicine and receptor and reveal the interaction mechanism more deeply.Generally speaking, the most common 3D-QSAR methods are comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA).CoMFA calculates steric and electrostatic properties according to Lennard-Jones and Coulomb potentials,while CoMSIA model includes five field descriptors such as hydrophobic, hydrogen bond donor, hydrogen-bond acceptor and two above-mentioned fields[16].CoMFA and CoMSIA have been widely used: Wu et al.[17]and Liu et al.[18]predicted the activities of compounds with CoMFA and CoMSIA methods and both of these models had certain predictive ability.

In this study, a series of 4-([1,2,4]Triazolo[1,5-α]pyridin-6-yl)-5(3)-(6-methylpyridin-2-yl)imidazole and -thiazole derivatives has been synthesized and evaluated for their ALK5 inhibitory activity.Therefore, CoMFA and CoMSIA models were used to foresee activities of new compounds.The obtained models could be meaningful to identify the key structural features affecting the ALK5 inhibitory activities and consequently all the results would be useful in the design of novel ALK5 inhibitors.

2 MATERIALS AND METHODS

2.1 Dataset

In the current work, 123 compounds used in 3D-QSAR studies were obtained from the literatures[1,3,19,20].The biological activities were expressed in IC50values and converted into pIC50values by using the formula pIC50= –logIC50.The structures of the compounds and their biological data are given in Table 1.Then, 95 compounds were randomly selected as the training set.External validation was performed with a test set of 28 compounds.

Table 1. Structures and Biological Activities of the Training and Test Sets of Molecules

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2.2 Molecular modeling and alignment

All molecular modeling and calculations for CoMFA and CoMSIA were using SYBYL package(SYBYL-X2.0, Tripos Inc., St.Louis, MO, USA) on windows operating system.The structures of all compounds were revealed in SYBYL and the energy minimization was performed using Tripos force field with a distance-dependent dielectric function and Powell conjugate gradient algorithm with a convergence criterion of 0.05 kcal/mol ? using 1000 iterations.Partial atomic charges were calculated using the Gastieger-Huckel method[21].

Molecular alignments of the compounds is a vital step in 3D-QSAR studies[22].In the present work, the most potent compound 28 was used as the template and the remanent molecules in the training set were aligned to it by using the common substructure.Fig.1 describes the common substructure for the alignment which is marked in red and the aligned compounds are displayed in Fig.2.

Fig.1. Chemical structure of compound 28 used as template molecule in 3D-QSAR modeling.The common substructure used for molecular alignments is represented in red

Fi g.2.Alignment of 95 compounds of the training set for 3D-QSAR stu dies

2.3 3D-QSAR mode ls studies

In order tostatistically evaluate the3D-QSAR models,partialleast-squares(PLS)approachwas used.The CoMFA, CoMSIA descriptors wereused asindependent variablesand biologicalactivity(pIC50) asdependent variables in PLS analysis.The cross-validation analysiswasimplemented by the leave-one-out (LOO)method toobtain the highest cross-validated (q2)and the optimal number of components(N)which would beused todothe compute again to get Standard Error of Estimate, in addition to F and r squared values.

3 RESULTS AND DI SCUSSION

3.1 3D-QSAR statistical results

Theresultsof PLSanalysiscorrelated with CoMFA and CoMSIA modelsare demonstrated in Table 2.The statistical parameters of CoMFA including a cross-validated correlation (q2)were0.652 with 6components.Thenon-cross-validated PLS analysisgenerated a highnon-cross-validated correlation coefficient (r2) of 0.876 with the F value of 103.363,anda standarderror estimate (SEE)of 0.106.These statisticalargumentsimplied that the CoMFA modelhas a good interior predictability.

Tabl e 2.Statistic al Parameters of CoMFA an d CoMSIA Mo dels by PLS Analysis

CoMSIA models were performed by five different fields: steric, electrostatic, hydrophobic, hydrogenbond donor and acceptor in multiple combinations.After clarifying every single CoMSIA field, the model with single electrostatic field was low (＜ 0.5),conversely both steric, hydrophobic, hydrogen-bond donor and acceptor fields are fundamental on CoMSIA study.The model with steric, electrostatic,hydrophobic, hydrogen-bond acceptor was selected based on an overall consideration, which gave a q2value of 0.648 using 6 components with an r2value of 0.884, a SEE value of 0.102, and an F value of 111.392.Both models with AHS (A = hydrogen-bond acceptor, H = hydrophobic, S = steric) and HSE (H =hydrophobic, S = steric, E = electrostatic) gave similar q2value of 0.549 among diverse field combinations.

3.2 Validation of models

In the present work, the external test set of 28 molecules excluded in model generation was used to assess the predictive ability of both models.The actual activities, predicted activities and residuals of all set compounds are shown in Table 4.In both CoMFA and CoMSIA models, the predicted values fell close to the actual values, deviating by not more than 1.0 logarithmic unit.Fig.3 shows the plots of experimental versus predicted activities for both training and test sets of the two constructed QSAR models.

Table 3. Results of CoMSIA Models Based on Different Field Combinations (Final CoMSIA Model in Bold)

Table 4. Experimental Activities, Predicted Activities and Residual Values of 123 ALK5 Inhibitors Shown in CoMFA and CoMSIA Models

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Fig.3. Plots of the experimental pIC50 versus predicted pIC50 for CoMFA (A) and CoMSIA (B)

3.3 CoMFA contour maps

In the CoMFA model, the proportion of steric field contribution occupies 84.8%, while the contribution of electrostatic field only accounts for 15.2% in the whole variance, which suggested the steric field was vital in explaining the variations of these compounds.

Fig.4(A) shows the steric contour map for the CoMFA model with the most active compound 28 as the reference.The green contours in CoMFA steric map indicate areas where bulky groups would increase the potency, while yellow contours indicate areas where bulky groups would be unfavorable to the activity.There is a yellow contour located near the 3- and 4-positions of E ring.It can explain well why compounds 40～42 which possessed a relative bulkier group on this region showed significantly decreased activities compared with compound 38.For instance, compound 41 bearing a -OCF3group at the 4-position of E ring indicated decreased potential activity than compound 38 with a -Me group.The same phenomenon was observed that the comparison among compounds 99, 100 and 101 turns out that 99(pIC50= 8.058) ＞ 100 (pIC50= 7.614) ＞ 101 (pIC50=7.215) which contain groups -Me, -OMe and -OCF3,respectively.On the contrary, there is a large green contour around the E ring, which means a bulkier group is highly favorable to the biological activity at this area.After checking up all molecules by these groups, it was found that compounds 68, 70 and 71 have an activity order of 71 (pIC50= 7.658) ＞ 70(pIC50= 7.444) ＞ 68 (pIC50= 7.319).It can also explain why compounds 64 and 72 showed lower activities: 64 (pIC50= 7.244), 72 (pIC50= 7.108).

The CoMFA contour map of electrostatic is shown in Fig.4(B).Similarly, in the electrostatic field, the blue contours indicate areas where the addition of electropositive substituent increases the activity; red contours indicate areas where the addition of electronegative substituent increases the activity.In CoMFA electrostatic contour, onelarge red contour surrounded E ring showed that electron-rich in this area will increase the inhibitory activity.It may be for the reason that compounds 85 (pIC50= 8.035), 87(pIC50= 8.115) and 88 (pIC50= 7.967) have higher biological activities with electron-withdrawing groupssuch as-F,-CNand -CONH2.Meanwhile,this is in agreement with the fact that compounds 5(pIC50= 8.046), 6 (pIC50= 8.155), 28 (pIC50= 8.301)and 29 (pIC50=8.046)showed more potency.It is also a possiblereason why compounds 39, 40 and 50 which containe lectropo sitive groups-i–Pr,-OMe and -NHCOMe on the E ring have decreased activity than compound 36which containselectroneg ative group -Cl at this area.

Fig.4.CoMFA stdev*coeff contour plots for steric (A) and electrostatic (B) fields.Compound 28 was displayed as reference.Sterically favored/disfavored areas are shown in green/yellow, while the blue/red polyhedra depict the favorable site for positively/negatively chargedgroups.Favored and disfavored levels of these displayedinteraction fields were fixedat 80% and 20%, respectively

3.4 CoMSIA contour maps

Compared to standard CoMFA,four contributors obtained by CoMSIAstudies including steric,electrostatic,hydrophobicand hydrogen-bond acceptor fields arepresented as3D contourplots in Fig.5.In CoMSIAstudy, the contributionsfrom steric,electrostatic,hydrophobic and hydrogen-bond acceptor fields for the present models are 28.7%, 5.9%, 31.8%and 33.6%,respectively.Fig.5(A)describesthe steric andelectrost aticcontour maps of the CoMSIA models.These conclusions are similar to the CoMFA ones.

The hydrophobiccontour mapof theCoMSIA model in the presence of compound 28 is displayed in Fig.5(B).The whiteand yellow contour maps highlightareas where hydrophilicand hydrophobic properties are preferred.One moderate yellowcontour was observed around the E ring of the C-3 and C-4 positions, which means that hydrophobic groups are necessary to improve biological activity.It is also supportedby the factthat compounds 35～37with hydrophobic substituents (-F, -Cl, -Br)at the paraposition of theE ring exhibit potent activity, whereas compound 45with ahydrophilicsubstituent(-CONH2) of the C-4 position displayslow activity.Meanwhile,compounds13(pIC50=7.854)and 27(pIC50=7.921)have better potent activity with hydrophobic group (-CH=CH2) than compound 15(pIC50= 7.523) with -CONH2except for compound 29(pIC50= 8.046).On theother hand,thereare several white contours located below the E ring, thus displacement of benzenewith the pyridine ring maybe increases the potency.

The hydrogen-bond acceptor contour map of the CoMSIA model with compound 28isdepicted in Fig.5(C).The magenta contours identified favorable positions in the hydrogen-bond acceptor field, while the red ones identified the unfavorable positions.A red contour near the E ring of the C-4 position indicatesthat hydrogen-bond acceptorgroupsare unfavorable there.This finding can account for the fact that compounds 45 (pIC50= 7.260), 48 (pIC50=7.108) and 49 (pIC50=7.409) showed less activity by the introduction of hydrogen-bond acceptor groups-CONH2,-CO2Me and -NHCOMe.Several magenta contours werefound above the D ring, which can explain that most compounds with pyridinyl substituent could exhibit high ALK5 inhibitory activities.

4 CON CLUSION

In this work, CoMFA and CoMSIA model swere developed for a seriesof 4-([1,2,4]triazolo[1,5-α]pyridin-6-yl)-5(3)-(6-methylpyridin-2-yl)imidazole analogues as ALK5 inhibitors.The cross-validated q2and non-cross-validated r2of CoMFA and CoMSIA are 0.652and 0.648, 0.876 and 0.884, respectively.The predictive ability of the models was manifested in trivial residues between actual pIC50and predicted pIC50values of the test compounds.The contour mapsderived from CoMFA and CoMSIA models indicated that activity sitesof these compounds were 3-and 4-positions of E ring.Overall, the CoMSIA model described better herein than CoMFA, which implied different contributions of steric,electrostatic,hydrophobic and H-bond acceptor fields around the molecules.In addition, the3D-QSAR contour maps provided enough information tounderstand the structure-activity relationshipof thesecompounds,and further guided the design and chemical synthesis ofnovel ALK5inhibitors.

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