Identification of Novel Aurora Kinase A (AURKA) Inhibitors via

Subscriber access provided by FLORIDA STATE UNIV

Article

Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening Yue Kong, Andreas Bender, and Aixia Yan J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.7b00300 • Publication Date (Web): 04 Dec 2017 Downloaded from http://pubs.acs.org on December 4, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Chemical Information and Modeling is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Chemical Information and Modeling

Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening

Yue Kong,†,‡ Andreas Bender,‡ Aixia Yan*, †

†

State Key Laboratory of Chemical Resource Engineering, Department of

Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. China. ‡

Centre for Molecular Informatics, Department of Chemistry, University of

Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom

*

Corresponding author, Email: [email protected] 1

ACS Paragon Plus Environment

Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT Aurora kinases are essential for cell mitosis, amplified and overexpressed in various human malignancies. Therefore, Aurora kinases have been promising targets for anticancer therapies, which has prompted an intensive search for their small-molecule inhibitors. In this work, we performed a hierarchical and time-efficient virtual screening cascade for scaffold hopping, aiming to obtain structurally novel and highly potent hit compounds targeting Aurora kinases. The cascade consisted of a shape- and an electrostatic-based protocol, combined with a QSAR-based selection protocol. This virtual screening cascade was used to screen two databases, one commercial database named J&K database containing about 5.2 million diverse molecules and Drugbank database. Experimental validations led to the identification of one structurally novel and highly potent hit compound (hit 1, found to possess an IC50 of 8.1 and 19 nM for Aurora kinases A and B, respectively), which can be a promising starting point for further exploration. Additionally, Aurora kinases were identified as off-targets for hits 2-6 (Crizotinib, CI-1033, Dasatinib, Bosutinib, MLN-518), which are approved or investigational drugs as listed in Drugbank, plausibly suggesting targeting Aurora kinases may even contribute to their mechanism of action.

2


Page 2 of 38

Page 3 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION Aurora kinases belong to the serine/threonine subclass of kinases and are involved in the regulation of mitosis.1 Since their discovery in 1995 and the first observation of expression in human cancer tissue in 1998, these kinases soon became the focus of much attention because they are aberrantly expressed in a variety of solid and liquid tumors including lung, liver, breast and pancreatic tumors2, 3 and display oncogenic activity.4-6 Three isoforms (A, B, and C) of the Aurora kinase family are presently known, which differ in their amino acid length and sequence at the N-terminal domain but have a conserved ATP binding site.7 Aurora kinase A (AURKA) is involved in centrosome maturation and separation, bipolar spindle assembly, and mitotic entry,8 while Aurora kinase B (AURKB) is essential for accurate chromosome segregation and cytokinesis.9 Aurora kinase C (AURKC) complements the function of AURKB, and it has been characterized less well until this stage and there is a paucity of AURKC-related data,10 so here, which led us to focus solely on AURKA and AURKB in this study. Given their involvement in regulating cell cycle, Aurora kinases have been proposed to be promising targets for anticancer therapies. Several ATP-competitive inhibitors targeting AURKA are currently in clinical development11 (see Table 1); however, none of them has been approved at this stage.12 Under these circumstances, a fast and efficient protocol to identify AURKA inhibitors remains an important objective.13 Virtual screening (VS) is a chemical library searching approach that aims to find novel compounds with a required biological activity as alternatives to existing ligands.14 VS has already been widely used to identify novel inhibitors for biological targets, including AURKA.15, 16 VS can be divided into two broad categories, namely structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS).17 SBVS utilizes the structural information of the biological target and typically is based on molecular docking, which is computationally expensive when applied to a large database.18 LBVS, on the other hand, can perform a fast screen of a database of millions of compounds, based on the similarity to existing bioactive structures.19 However, most commonly used methods for LBVS, such as fingerprint-based VS and pharmacophore-based VS, are considered to be less likely to discover structurally diverse compounds, since these methods are based on a fundamental assumption that molecules with similar structures tend to have similar properties.20-22 Therefore, how to discover bioactive compounds with novel scaffolds within a fast LBVS protocol has become an important consideration in our work when we were applying the LBVS method to identify novel AURKA inhibitors. In the context of VS, “scaffold hopping” focuses on discovering bioactive compounds with novel backbones that have potentially improved properties, and 3



many scaffold hopping methodologies are currently being applied in this context.23 Generally, shape-based scaffold hopping within VS has been reported to lead to a high degree of novelty and has been effectively applied in many prospective applications in the drug discovery context.24,25 Furthermore, the shape-based approach is often combined with electrostatic-based ranking method, given that shape and electrostatic complementarity are generally thought to be major determinants of bioactivity against a protein, and the combination of these two methods has successfully identified some bioactive molecules that structurally depart from known active reference molecules.26, 27 Quantitative structure-activity relationships (QSAR) enable us to use a wide variety of machine learning methods to build quantitative models linking molecular descriptors to bioactivity properties.28 Traditional QSAR focuses on achieving statistically significant models; however, in the more practical sense QSAR integrated in VS aims to generate models to prioritize molecules with desired properties for subsequent (and hopefully successful) biological evaluation. Our group has previously published QSAR research on predicting the bioactivity of AURKA inhibitors;29 however, QSAR-based VS against AURKA is rarely seen generally in the literatures. In this work, we aimed to accomplish scaffold hopping for AURKA inhibitors in the context of an LBVS protocol (see the whole VS cascade in Figure 1). In order to do this, we performed a hierarchical virtual screening cascade consisting of a shapeand electrostatic-based protocol and a QSAR-based protocol, aiming to design a fast overall VS cascade with the aim to obtain structurally novel and highly potent inhibitors targeting AURKA. The whole VS cascade was used to screen two databases, followed by experimental validations. As a result, we found the hierarchical virtual screening cascade was efficient to identify AURKA inhibitors and identified four hits active against AURKA and five hits active against AURKB.

4


Page 4 of 38

Page 5 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


METHODS Data Assembly and Preparation In this work, three datasets of Aurora Kinase A (AURKA) inhibitors were collected for different purposes (Table 2). The structure information of these three datasets is shown in Appendixes I (dataset 1), II (dataset 2), and III (dataset 3) in the Supporting Information. The details of these three datasets are outlined in the following section. Dataset 1 consisted of 72 ligands extracted from AURKA X-ray crystal complex structures from PDB database.30 The experimental conformations of the ligands were used to build queries for the shape-based virtual screening protocol. The reason why we used dataset 1 instead of the dataset of known AURKA inhibitors named dataset 2 (see the following paragraph for details) is that dataset 1 provides the experimental conformations of AURKA ligands, but on the other hand the conformations of molecules in dataset 2 need to be computationally predicted, which will add experimental error. Using experimentally validated conformations will add our confidence when we choose the query for the shape-based screening. Dataset 2, which covered a larger chemical space than the dataset in our previous study29 on the prediction of AURKA inhibitors, was used to build classification models. We collected 1,463 AURKA inhibitors with the experimental IC50 values ranging from 0.1 nM to 100,000 nM from the BindingDB database.31 IC50s of 400 nM and 600 nM were used as the thresholds to classify the compounds, and compounds with IC50 < 400 nM were classified as highly active inhibitors, while compounds with IC50 > 600 nM were classified as weakly active inhibitors, hence resulting in 832 highly active inhibitors and 631 weakly active inhibitors. Dataset 3, including 356 AURKA inhibitors with pIC50 (-logIC50) ranging from 1 to 9.82, extracted from dataset 2, was used to build regression models. Only compounds whose IC50s were detected by the same testing methods (i.e., radiolabelling based filtration binding assay) were included in dataset 3 in order to have comparable data. Selection of Shape Query The shape query was generated and selected using ROCS.32 Within ROCS, the query building wizard was used to construct shape queries with molecules in dataset 1 as input. It employed up to three of the molecules to find the most representative of the active AURKA ligands.

5



A validation run with ROCS allows us to select a set of active molecules and a set of decoy molecules against which to run the query, which is particularly important when selecting a complex query because it suggests confidence levels for this query in future ROCS runs against compounds with unknown activity. Given the importance of building the ligand set and the decoy set, we adopted Xia’s method33 to build an unbiased ligand set and an unbiased decoy set, which can greatly reduce the “artificial enrichment” and “analogue bias” to validate the performance of each query, thus to achieve a suitable query for virtual screening. Within the validation ROCS protocol, a total of 62,268 queries were generated based on dataset 1 and we compared these queries by ROC (receiver operating characteristic, with the fraction decoys found as the X-axis and the fraction actives found as the Y-axis) curve together with its AUC (area under the curve). The comparison of the best queries overlaid by one molecule, two molecules and three molecules is shown in Figure S1 in Supporting Information. Shape- and Electrostatic-Based VS Protocol A pipeline consisting of the shape-based ROCS32 and electrostatic-based EON34 in sequence was performed for scaffold hopping (see the whole VS cascade in Figure 1). The shape-based search in ROCS was performed with default settings. When analyzing the results of the ROCS run, we used the TanimotoCombo score to rank the molecules, which means we utilized an alignment algorithm accounting shape and physicochemical properties for the query shape. The electrostatic-based EON was performed with the software EON34 to search the compounds which have similar electrostatic property with a query compound with default settings.

Preparations for QSAR Models Descriptor generation. Descriptors were generated for the molecules in both dataset 2 and dataset 3 by CORINA35 after energy minimization in the same software. A total of 1273 descriptors were generated, including 21 global molecular descriptors, eight size and shape descriptors, 88 2D autocorrelation descriptors, 96 3D autocorrelation descriptors, 36 surface property autocorrelation descriptors and 1024 property-weighted radial distribution functions (RDF) descriptors. Dataset splitting. The 1,463 AURKA inhibitors of dataset 2 were randomly divided into the training set and the test set (see Table S1 for details in Supporting Information). Given the small size of the dataset 3 (356 AURKA inhibitors) the Kohonen’s self-organizing maps (SOMs)36 as well as the random splitting method were used to split the dataset in order to cover a larger chemical space (see Table S1). The SOMs model was performed in SONNIA37 with 166 bits of MACCS fingerprints 6


Page 6 of 38

Page 7 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(generated by MOE)38 as inputs. The assignment of compounds to training and test sets was based on the rules that if two or three compounds are clustered together, one is put into the test set and the others are put into the training set; if more than three compounds are clustered together, about one third of the compounds are put into the test set and the others are put into the training set. The initial learning rate was set to 0.7 and the rate factor was set to 0.95, with training performed for a period of 1,600 epochs in an unsupervised manner. Descriptor selection. Correlation analysis and stepwise linear regression variable selection method were used to select the descriptors in sequence. Cutoffs for correlation coefficient between each descriptor and the activity value and for pairwise correlation coefficient between each two descriptors were set to more than 0.1 and less than 0.8, respectively. Then with the stepwise linear regression variable selection method 29 and 21 descriptors were chosen for the classification models and the regression models, respectively. See Table S2 in Supporting Information for the selected descriptors in details. Modeling Methods In this work, Kohonen’s Self-organizing Maps (SOMs) as one representative of unsupervised machine learning methods, and Support Vector Machines (SVMs) as one representative of supervised machine learning methods were used to build QSAR models, together with Multilinear Regression (MLR) analysis as a baseline algorithm. Kohonen’s Self-organizing Maps (SOMs). Kohonen’s self-organizing maps (SOMs) is a neural network model introduced by Kohonen for the construction of a non-linear projection of objects from high-dimensional space into a lower dimensional space.39 As a standard data analysis method, the SOMs has been widely used in unsupervised learning. Herein, the SOMs was used in constructing unsupervised classification models as well as splitting dataset 3. With the same settings as in splitting dataset 3 (described in the section of dataset splitting), SOMs classification models used the selected CORINA descriptors as inputs. Support Vector Machines (SVMs). Based on the Vapnik Chervonenkis dimension and Vapnik’s Structural Risk Minimization principle, SVMs can non-linearly map vectors from the input space into a high-dimensional feature space.40 In this work, SVMs was used in building both the classification models and regression models. The package Libsvm41 was adopted for SVMs analysis. The Radial Basis Function (RBF) was chosen as the kernel. Two parameters c and g, which are the key points to achieve high training accuracy, were searched by grid-search based on five-fold cross-validation. Multilinear Regression (MLR) analysis. MLR attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observe data. Herein, we used MLR to build regression models.

7



Page 8 of 38

Performance Evaluation of Classification Models In order to evaluate the performance of the classification models, several values were calculated for the models, including accuracy, sensitivity (SE), specificity (SP), and the Matthews Correlation Coefficient (MCC).42 These evaluation standards were calculated by the following equations (Equations 1). Among them, TP stands for true positives, TN stands for true negatives, FP represents false positives and FN represents false negatives. Accuracy =

TP + TN × 100% TP + TN + FP + FN

TP × 100% TP + FN TN SP = × 100% FP + TN TP × TN − FP × FN MCC = (TP + FP )(TP + FN )(TN + FN ) (TN + FP) SE =

（Equations 1）

From Equations 1, the model with higher accuracy, SE, SP and MCC values performs better. Screening Library Assembly and Preparation This hierarchical VS cascade was applied to two databases, namely the J&K database (https://www.jk-scientific.com) and Drugbank43 database. The former database contains about 5.2 million diverse and commercially available compounds (data used in Jan. 2015). The latter database, used in order to discover off-targets for existing drugs, contained 6,183 compounds (data used in Jan. 2015) including FDA-approved small-molecule drugs, FDA-approved biotech drugs, nutraceuticals and experimental drugs. For the J&K database, molecules with undesirable properties like toxic functionalities and a low probability of oral bioavailability were removed by FILTER44 with default settings. See the detailed filter rules in Appendix IV in Supporting Information. Next, a maximum of 500 conformations for each molecule in the J&K database and the Drugbank database were generated by OMEGA45 producing a conformational database prepared as the inputs for the virtual screening cascade.

8


Page 9 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Experimental Assay and purchase of compounds The final selected compounds were assessed in dose-response biochemical assay against AURKA and AURKB (due to frequently encountered off-target effects)12 using mobility-shifted assay technology.46 Each compound was tested in duplicate and tested from 10µM or 100µM and diluted for ten concentrations. Additionally, control assays were performed to the compounds with IC50 < 500 nM to avoid potential aggregation by using detergent at different concentration. IC50 of 10µM was used as the threshold to define the actives and the inactives. See the Supporting Information for the detailed kinase assay method. All the final selected compounds were purchased from the vendor Topscience (http://tsbiochem.biogo.net). The purity was declared > 95% (see details of the compound purity in Supporting Information).

Docking Method Docking study was performed to explore the mode of interactions between the experimentally identified bioactive compounds and the active site of AURKA. Hybrid47 is a docking program which uses not only the protein structure but also the elements of the bound ligand, thus enhancing performance. Hybrid was used to dock 500 conformations of each hit into the AURKA protein (PDB 3R21).48 Within the docking process, the resolution was set to“high” during the exhaustive search and the optimization, with other parameters set to default.

RESULTS AND DISCUSSIONS Selected Queries for Shape- and Electrostatic-based VS Protocol The query overlaid by two molecules (PDB ligands L0C and VX6) gave the best performance discriminating active ligands and decoys with an AUC of 0.769. The query shape overlaid by L0C and VX6 is shown in Figure 2. This query shape was used for subsequent shape-based ROCS virtual screening protocol. Given only one molecule could be used as query in EON, PHA-739358 (shown in Table 1) was chosen as the query for the electrostatic-based virtual screening protocol due to its high potency of AURKA inhibition with an IC50 of 13 nM and its good performance in clinical trial phase II for the treatment of many kinds of cancers (i.e. hormone refractory prostate cancer) as a pan-Aurora kinase inhibitor.49

9



Classification Models Two classification models (SOMs and SVMs models) were built with the 29 selected descriptors (see Table S2 in Supporting Information) as input based on the random training set splits of dataset 2. Model C1: Classification Model using Kohonen’s Self-organizing Maps (SOMs). With the parameters described in the methods section, a rectangular map with 31×20 neurons was built based on the training set (of dataset 2). The resulting map on the basis of most frequent occupation for the training set is shown in Figure S2, where we can see that the highly active and weakly active inhibitors are mostly projected into different areas, that is, the highly and weakly active inhibitors can be well separated according to the map. Afterwards, with all the compounds of dataset 2, a rectangular map with 38×25 neurons was built with the same parameters as those used in the training set. The resulting map on the basis of most frequent occupation is shown in Figure 3. According to this resulting map, the accuracies of class assignment for the training set and the test set are 94.4% and 91.6 %, respectively. For the training set, a sensitivity (SE) of 96.2%, a specificity (SP) of 92.0%, and a Matthews Correlation Coefficient (MCC) of 0.89 were obtained. For the test set, an SE of 92.3%, an SP of 90.6%, and an MCC of 0.83 were obtained. The results of model C1 are shown in Table 3. Model C2: Classification model using Support Vector Machines (SVMs). The best combination of c = 6.2 and g = 16 obtained from the grid-search were used to build the SVMs model. The accuracies for the training and the test set are 94.1% and 86.1%, respectively, as shown in Table 3. For the training set, an SE of 95.5%, an SP of 92.4%, and an MCC of 0.88 were obtained. For the test set, an SE of 86.5%, an SP of 85.6%, and an MCC of 0.72 were obtained. Comparative Analysis of Model C1 and Model C2. In terms of the model performance, both the two classification models lead to a powerful predictability, though overall model C1 performed slightly better than model C2. Since the SOMs is an unsupervised learning method, it constructs a non-linear projection of objects from a high dimensional space into a low dimensional space without paying attention to class labels, while the SVMs is a supervised learning and projects samples from a low dimensional space into a high dimensional space. The good model performance obtained by two different modeling algorithms boosted our confidence when we used the models to classify the AURKA inhibitors and both the model C1 and the model C2 were used in the following virtual screening process to identify AURKA inhibitors from the screening library.

10


Page 10 of 38

Page 11 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Regression Models Four regression models (models R1-R4) were built with the 21 selected descriptors (see Table S2 in Supporting Information), which were based on the training set split of dataset 3 with both random and SOMs training/test set splits. Model R1 and Model R2: Regression models using Multilinear Regression (MLR). Model R1 and model R2 were built with MLR method. For Model R1, r (the correlation coefficient) = 0.82, s (the standard deviation) = 0.87 and MAE (the mean value of the absolute errors) = 0.14 were achieved for the training set, and r = 0.79, s = 1.00 and MAE = 0.16 were achieved for the test set. For Model R2, r = 0.81, s = 0.87 and MAE = 0.27 were achieved for the training set; r = 0.80, s = 1.03 and MAE = 0.05 were achieved for the test set. The results are shown in Table 4 and Figure 4. Model R3 and Model R4: Regression models using Support Vector Machines (SVMs). With the SVMs method, we built two regression models (model R3 and model R4) based on dataset 3. The training sets used to build model R3 and model R4 were obtained from dataset 3 by random and SOMs splitting, respectively. The key combination of parameters c, g and p were searched by grid-search method with 5-fold cross-validation. As a result, the combination of c = 64, g = 0.0625, p=0.5 and the combination of c = 8, g = 1, p = 0.25 were obtained for the best models and used for model R3 and model R4, respectively. In terms of the model performance, r = 0.89, s = 0.67, MAE = 0.12 for the training set and r = 0.81, s = 0.94, MAE = 0.50 for the test set were achieved for the model R3; r = 0.87, s = 0.73, MAE = 0.10 for the training set and r = 0.83, s = 0.98, MAE = 0.16 for the test set were achieved for the model R4. The results are shown in Table 4 and Figure 4. Comparative Analysis of Models R1-R4. From Figure 4, we can see that the models R1-R4 have good predictive performance for the compounds with pIC50 values in the range of 4 to 9. In terms of the r for the test set, the models based on the SOMs splitting method (model R3 and model R4) performed slightly better than the models based on random splitting method (model R1 and model R2), which is as expected because the chemical space of the test set split by the SOMs method was covered better by the training set, thus the test set can be well predicted by the model built based on these (more similar) training sets. The average of the regression values predicted by the models R1-R4 was used to give potential AURKA bioactivity in the following virtual screening process.

QSAR Models Interpretation Analysis of the Classification and Regression Models with the Selected Descriptors. In this work, 50 descriptors in total were used in the models C1-C2 and the models R1-R4. These descriptors can be roughly grouped into six classes shown in Table S2. They are hydrogen bonding-related descriptor (class I), polarizabilities (class 11



II), electronegativities (class III), atom charges (class IV), surface potential descriptors (V), and atom identity and structural descriptors (class VI). Class I Descriptor. H-bond interactions between AURKA and its ATP competitive inhibitors are essential for its inhibition.50,51 It has been found that five direct H-bonds with the hinge region of Aurora A at the ATP-binding site, namely, residues Ala213, Glu211, and Asp274, are critical interactions for AURKA inhibition.52 This finding is consistent with our docking simulation carried out based on the complex crystal structure of AURKA (PDB code: 4J8M)53 with bound ligand PDB CD532 at 1.85Å resolution by using MOE,38 shown in Figure 5. In our study, one H-bond-related descriptor, HAcc_N, which means the number of hydrogen bonding acceptors derived from nitrogen atoms in the molecule, was selected and used in the QSAR models. The descriptor HAcc_N verifies the importance of the H-bond interactions between the nitrogen atoms of inhibitors and ATP-site of AURKA. Class II Descriptor. Polarizability is also important for inhibition of AURKA at the ATP site. Some residues, like Lys 162 and Asp 274, could form potential polar interactions with the inhibitors.54 Two polarizability-related descriptors of class II were selected by the model, which is in line with previous observations that polarizability plays an important role in inhibitor binding to AURKA.55 Class III and IV Descriptors. Electronegativity-related descriptors (class III), including σ atom electronegativities, π atom electronegativites, and lone-pair electronegativities, were selected by the model. A number of charge-related descriptors (class IV), including total charges, σ charges and π charges were selected, too. Most of the descriptors in these two classes are RDF descriptors and we take the descriptor RDF_PiEN_27 and RDF_PiChg_26 as examples, which have high correlation coefficient values with activity > 0.25. They describe the π atom electronegativities and π atom charges, respectively, where the distance of atom pairs are in the range of 2.5– 2.7 Å. This indirectly indicates AURKA inhibitors tend to contain fused aromatic rings, conjugated system. Besides, these are common substructural features in the AURKA inhibitors. The descriptors in class III and IV may not be readily interpretable; however, they tell us the atom electronegativities and charges of a molecule play important roles in the inhibition of AURKA, and they appear to increase the prediction power of models for AURKA inhibitors when being included in the models. Class V and VI Descriptors. The descriptors of class V denote that the binding strength of a protein-drug complex depends on the shape of the substrate surface and on the distribution of certain properties on this surface. The class VI descriptors are related to atom identity and structural features; however, these descriptors somehow are difficult to explain. It guides us to take these factors into considerations when we handle with the inhibition between AURKA and its descriptors. ECFP_4 Fingerprint Analysis. In order to better understand the structural features of AURKA inhibitors in the classification models, 2,048 bits of the ECFP_4 fingerprints56 were calculated for the 1,463 compounds of dataset 2. The frequency for all the ECFP_4 substructures appearing in highly active and weakly active inhibitors of 12


Page 12 of 38

Page 13 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


AURKA was calculated, respectively. Several representative substructures of active inhibitors were selected and they are displayed in Table 5. It has been found that there are some substructures that only appears in highly active inhibitors but not in weakly active inhibitors, such as substructures containing a sulfur atom (#1 and #2 in Table 5), imidazole derivative (#3 in Table 5), pyrimidine derivative (#4 and #5 in Table 5) and trifluoromethy pyridine (#6 in Table 5). These substructural features are closely associated with the highly active AURKA inhibitors, giving us directions and insights for designing and synthesizing AURKA inhibitors.

Prospective Virtual Screening Cascade The whole prospective VS cascade for scaffold hopping of AURKA inhibitors is shown in Figure 1, involving a hierarchical virtual screening process consisting of a shape- and electrostatic-based screening protocol, as well as a QSAR-based screening protocol. Finally, after the step of scaffold analysis, the selected molecules were obtained for the next round of experimental validations. As we can see from the two side columns in the Figure 1, the whole VS cascade was applied to two databases (the J&K database and the Drugbank database) at the same time but with different numbers of compounds selected in every step. For the J&K database, 5.2 million compounds experienced a sharp decrease to 2,000 compounds after the shape-based screening. Next, the electrostatic-based EON screening retained 1,000 compounds out of the 2,000 compounds. Then the QSAR-based screening protocol picked the top 200 compounds. The overview of the whole QSAR-based screening protocol is shown in Figure S3, which shows how the classification models and the regression models were combined to select compounds: the models C1 and C2 were both used and the compounds predicted as highly active AURKA inhibitors by both of the two models were subsequently predicted by the models R1-R4; then according to the average value of the four regression values, the top 200 were selected for the next step. Afterwards, the final 14 compounds were selected by the Bemis-Murcko scaffold analysis for the following experimental evaluations. On the other hand, 200 compounds out of 6,183 compounds in Drugbank were retained by the shape-based screening and 100 compounds by the electrostatic-based screening. Then the QSAR-based protocol picked 50 compounds, among which ten compounds were picked by the Bemis-Murcko scaffold analysis to the next round. As a result, 24 compounds selected by our virtual screening cascade, together with four known AURKA inhibitors as reference compounds (28 compounds in total) were picked for subsequent experimental evaluation. Among the 24 selected compounds, 14 compounds were picked from J&K database and the other ten compounds were picked from Drugbank database.

13



Experimental assay results All the structure information and experimental assay results of the 28 tested compounds can be found in the Table 6 and the structures of the six hit compounds are shown in Figure 7. Among the tested compounds, four compounds (hits 1-4) were found to possess bioactivity towards AURKA with IC50 values of 8.1 nM, 50 nM, 1,877 nM and 2,386 nM, and five compounds (hits 1-3 and 5-6) towards AURKB with IC50 values of 19 nM, 22 nM, 904 nM, 6,789 nM and 4,197 nM. Taken together, six compounds were identified to have inhibition activity below 10 µM towards both or either of AURKA and AURKB. Among them, three compounds inhibit both the AURKA and AURKB; one compound only inhibits AURKA and while two compounds only inhibit AURKB. Identification of a Potent AURKA and AURKB Inhibitor from the J&K Database. The most potent hit - hit1 as shown in Figure 7 – has been obtained from the J&K database and inhibited an IC50 of 8.1 nM against AURKA and an IC50 of 19 nM against AURKB. This is as potent as current drugs in clinical trials, whose IC50 values against AURKA are usually around 20 nM, as the examples shown in Table 1. The hit1 with a novel scaffold and high potency to inhibit both AURKA and AURKB can be a promising starting point for derivatives analysis and further biological evaluation, which was further studied by docking as described in the subsequent section. Identifying Targets for Drugbank Compounds. Through the VS of the Drugbank database, Aurora kinases were identified or confirmed as off-targets for five approved or investigational drugs (Crizotinib, CI-1033, Dasatinib, Bosutinib, and MLN-518; see structures 2-6 in Figure 7). Crizotinib (hit2, an IC50 of 50 nM against AURKA and 22 nM against AURKB) and CI-1033 (hit3, an IC50 of 1,877 nM against AURKA and 904 nM against AURKB) inhibit both AURKA and AURKB. Dasatinib (hit4, an IC50 of 2,386 nM against AURKA), Bosutinib (hit5, an IC50 of 6,789 nM against AURKB) and MLN-518 (hit6, an IC50 of 4,197 nM against AURKB) are AURKA or AURKB-specific inhibitors within the resolution of the assays employed.

Structural Diversity Analysis and Docking Study for Hit 1 Next, a similarity analysis and docking study was performed on hit1 as the most active compound identified in order to explore structural novelty and the putative mode of interaction with the enzyme. As the Bemis-Murcko scaffold analysis that was performed in the hierarchical VS cascade only selected compounds with unique scaffolds, the hit1 was already known to be structurally distinct from known AURKA inhibitors (see Figure S4 in Supporting Information for the scaffolds of hits). MACCS fingerprints similarity analysis was now performed between hit1 and each the highly active ARUKA inhibitor in dataset 2. The results showed the MACCS similarity Tanimoto coefficient between hit1 and hit1’s nearest neighbor in dataset 2 was 0.739. 14


Page 14 of 38

Page 15 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


This indicates hit1 was structurally distinct from any known highly active ARUKA inhibitor as 0.75 was often used as the cutoff of high similarity. Additionally, the shape Tanimoto coefficient between hit1 and the query (overlaid by molecules VX6 and L0C) was 0.769; however, the MACCS similarity Tanimoto coefficients between hit1 and these two molecules were relatively low, 0.521 and 0.505, respectively. Hence, we would possibly fail to identify the (very potent) hit1 by using 2D structural similarity searching alone. So this result indicates that 3D shape-based similarity searching, rather than just 2D structural similarity, can efficiently identify structurally diverse molecules different from the query molecules to perform scaffold hopping in the context of virtual screening. Hit1 was successfully docked into 3R21 (PDB code) AURKA protein, which can also help to explore how hit1 interacts with the protein (see Figure 6). An arene-H bond can be identified between the arene of the inhibitor and Leu139 at the ATP-binding site in the hinge region of AURKA. This interaction pattern is different to that between the bound ligand D36 (PDB code) and 3R21 AURKA protein where formed an H-bond between the nitrogen atom of the ligand and the NH group of Ala213 and an arene-H bond between the arene of the ligand and Leu263 (see Figure S5). This indicates that trying to form an H-bond with Ala213 can be a good option for further optimization of hit1.

CONCLUSION In this work, a hierarchical ligand-based virtual screening cascade consisting of shape- and electrostatic-based screening protocol with a QSAR-based selection protocol was successfully applied to screen two databases (about 5.2 million compounds in total) for compounds exhibiting AURKA inhibitory activity. This resulted in the identification of six structurally novel and potent Aurora kinase inhibitors, four for AURKA and five for AURKB, corresponding to the hit rate of 25%. Hits 1-3 exhibited high potencies to inhibit both AURKA and AURKB with IC50 values of 8.1 and 19 nM, 50 and 22 nM, 1,877 and 904 nM, respectively. Hit 4 was identified as an AURKA inhibitor with an IC50 of 2,386 nM, while hits 5-6 were identified as AURKB inhibitors with IC50 values of 6,789 and 4,197 nM. Among these hits, hit 1, with an IC50 of 8.1 and 19 nM for AURKA and AURKB respectively (and hence similar potency to inhibitors currently in clinical trials) is the most promising Aurora kinase inhibitor which can be a starting point for further optimization. Hits 2-6 (Crizotinib, CI-1033, Dasatinib, Bosutinib, MLN-518) are approved or investigational drugs as listed in Drugbank for which Aurora kinases were identified as off-targets, suggesting targeting Aurora kinases may potentially even contribute to their efficacy. The results overall confirm the validity of the hierarchical ligand-based VS cascade employed here, and suggest that scaffold hopping can be

15



accomplished in the context of virtual screening by combining different types of compound selection steps.

16


Page 16 of 38

Page 17 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ASSOCIATED CONTENT Supporting Information Supplementary tables (Tables S1- S5), supplementary figures (Figures S1- S5), Kinase assay method and Characterization data for 28 purchased compounds, the structure information for datasets 1-3 and the Filter rules are shown in the SI.docx.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] *Tel: +86-10-64455320 Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (21375007 and 21675010) and “Chemical Grid Project” of Beijing University of Chemical Technology (3003001000). We thank the Molecular Networks GmbH, Erlangen, Germany for providing the programs CORINA Symphony and SONNIA for our scientific work.

17



CAPTIONS OF FIGURES Figure 1. Overview of the hierarchical ligand-based virtual screening cascade employed in this work, involving shape- based and electrostatic-based, as well as QSAR-based compound selection steps. This cascade was used to screen two databases, namely the J&K database as well as Drugbank. The numbers of compounds retained in each selection step are shown. Figure 2. Shape query resulting from an overlay of PDB L0C and VX6 shown as a transparent surface. Figure 3. Rectangular Kohonen’s self-organizing map (size 38×25) on the basis of the most frequent occupation for dataset2. Circles represent highly active AURKA inhibitors and triangles represent weakly active AURKA inhibitors, respectively. Figure 4. Calculated and experimental pIC50 values for regression models R1-R4. Models R1 and R2 were built with Multilinear Regression (MLR), while models R3 and R4 were built with SVMs algorithm. Training sets for models R1 and R3, and models R2 and R4, were obtained via random splitting and SOMs splitting, respectively. Figure 5. Complex crystal structure of CD532 bound to AURKA (PDB 4J8M). This graph shows the interactions between CD532 and AURKA. Five important H-bonds can be identified between the nitrogen atom of the inhibitor and the NH group of Ala213, Glu211 and Asp274 at the ATP-binding site in the hinge region of AURKA. Figure 6. Ligand-protein interactions of hit1 docked to 3R21 (PDB code) AURKA protein. An arene-H bond can be identified between the arene of the inhibitor and Leu139 at the ATP-binding site in the hinge region of AURKA. Figure 7. Structures of the six hits (hits1-6) in this study. .

18


Page 18 of 38

Page 19 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1

19



Figure 2

20


Page 20 of 38

Page 21 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3

21



Figure 4

22


Page 22 of 38

Page 23 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5

23



Figure 6

24


Page 24 of 38

Page 25 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 7

25



Table 1. Structures and reported bioactivity of several AURKA inhibitors under clinical study. Name Structure Clinical Trial In vitro potency l C

O

N

MLN8237

e M O

N

HN

F

Phase II

IC50=1 nM

Phase I

IC50=5.0 nM

Phase I

IC50=9 nM

Phase II

IC50=13 nM

Phase I

IC50=3 nM

Terminated due to severe toxicities

Ki=0.7 nM

N O

e M O

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 38

CF3

O N N H N

PF-03814735

N H

N

N H

O

N

SNS-314

N

H N

H N

Cl

S

S NH N

O

H N N

O

O

N N H

PHA-739358

O N

N

O

N H HN

AT9283

H N O N

NH N

N

N

HN

H N HN O N

VX-680 S N N

N

26


Page 27 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. Number of compounds, sources and utilazation of ligand datasets used in this work. Dataset

Number of compounds

1

72

2

1,463

3

356

Source

Utilization in current study

PDB30

Building queries

BindingDB31

Building classification models

BindingDB

Building regression models

27



Page 28 of 38

Table 3. Performance of classification models C1 and C2. Model C1 was built with Kohonen’s Self-organizing Maps (SOMs) algorithm and Model C2 was built with Support Vector Machines (SVMs) algorithm. Training set (988/561/427) a Model

Accuracyb

SEc (%)

SPd (%)

Test set (475/271/204) MCCe

Accuracy SE(%)

SP(%)

MCC

(%)

(%) Model C1

94.4

96.2

92.0

0.89

91.6

92.3

90.6

0.83

Model C2

94.1

95.5

92.4

0.88

86.1

86.5

85.6

0.72

a

Numbers of compounds (total/highly active/weakly active). bPrediction accuracy for overall

compounds. cPrediction accuracy of the highly active AURKA inhibitors. dPrediction accuracy of the weakly active AURKA inhibitors.

e

Matthews Correlation Coefficient (MCC).

28


Page 29 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 4. Performance of regression models R1-R4. Models R1 and R2 were built with Multilinear Regression (MLR) algorithm and models R3 and R4 were built with SVMs algorithm. Training sets for models R1 and R3 and models R2 and R4 were obtained by random splitting method and SOMs splitting method, respectively. Training set Model

Test set

n

ra

sb

MAEc

n

r

s

MAE

Model R1

259

0.82

0.87

0.14

97

0.79

1.00

0.16

Model R2

263

0.81

0.87

0.27

93

0.80

1.03

0.05

Model R3

259

0.89

0.67

0.12

97

0.81

0.94

0.50

Model R4

263

0.87

0.73

0.10

93

0.83

0.98

0.16

a

Correlation coefﬁcient. bStandard deviation. cMean absolute error.

29



Page 30 of 38

Table 5. Some important ECFP_4 substructures for AURKA inhibition and their presence in highly active and weakly active AURKA inhibitors. Frequency in highly active inhibitors

Frequency in weakly active inhibitors

#1

73

0

#2

68

0

#3

60

0

#4

111

0

#5

116

0

#6

112

0

No.

Substructures

30


Page 31 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 6. Structure information and experimental assay results of all the 28 tested compounds against AURKA and AURKB. Names of compounds which are from Drugbank are shown in the table. The last four compounds are used as reference molecules. Compound

SMILES

Name

AURKA (nM)

AURKB (nM)

#1 (hit1)

Clc1cnc(nc1Nc1ccccc1S(=O)(=O)C(C)C)Nc1ccc(N2CCC(N3CCN(CC3)C)CC2)cc1OC

-

8.1

19

#2 (hit2)

Clc1c([C@H](Oc2cc(cnc2N)-c2cn(nc2)C2CCNHCC2)C)c(Cl)ccc1F

Crizotinib

50

22

#3 (hit3)

Clc1cc(Nc2ncnc3c2cc(NC(=O)C=C)c(OCCCN2CCOCC2)c3)ccc1F

CI-1033

1,877

904

#4 (hit4)

Clc1cccc(C)c1NC(=O)c1sc(nc1)Nc1nc(nc(N2CCN (CC2)CCO)c1)C

Dasatinib

2,386

>10,000

#5 (hit5)

Clc1cc(Cl)c(OC)cc1Nc1c2cc(OC)c(OCCCN3CCN(CC3)C)cc2ncc1C#N

Bosutinib

>10,000

6,789

#6 (hit6)

O(CCCN1CCCCC1)c1cc2ncnc(N3CCN(CC3)C(=O)Nc3ccc(OC(C)C)cc3)c2cc1OC

MLN-518

>10,000

4,197

#7

O1CCN(CC1)c1nc(nc(Oc2nnc(OCCOc3ccccc3C)cc2)n1)Nc1ccc(cc1)C

-

>10,000

>10,000

#8

s1c2c(nc1NC(=O)C1=CN(c3c(cc(F)c(N4CCN(CC4)C(=O)c4occc4)c3)C1=O)CC)cccc2

-

>10,000

>10,000

#9

Clc1cc(Nc2nc(nc(n2)Nc2cc(Cl)ccc2C)N2CCN(CC2)C)c(cc1)C

-

>10,000

>10,000

#10

O=C(Nc1cc(Nc2nc(ccn2)-c2cccnc2)c(cc1)C)c1ccc(cc1)CN1CCN(CC1)C

Imatinib

>10,000

>10,000

#11

O1[C@](NC(=O)[C@@H]2C=C3[C@H](N(C2)C)Cc2c4c3cccc4[nH]c2)(C)C(=O)N2[C@@H](Cc3ccccc3)C(=O)N3[C@@H](CCC3)[C@]12O

Ergotamine

>10,000

>10,000

#12

FC(F)(F)c1cc(NC(=O)c2cc(Nc3nc(ccn3)-c3cccnc3)c(cc2)C)cc(-n2cc(nc2)C)c1

Nilotinib

>10,000

>10,000

#13

O(C(=O)CCN(C(=O)c1cc2nc(n(c2cc1)C)CNc1ccc(cc1)/C(=N/C(OCCCCCC)=O)/N)c1ncccc1)CC

Dabigatran

>10,000

>10,000

#14

Clc1cc(Nc2ncnc3c2cc(NC(=O)\C=C\C[NH+](C)C)c(O[C@H]2CCOC2)c3)ccc1F

Afatinib

>10,000

>10,000

#15

S(CC(=O)Nc1ccc(OCC)cc1)c1nc(nc2Oc3c(cc(cc3)C)Cc12)-c1ccc(cc1)C

-

>10,000

>10,000

#16

S(=O)(=O)(N1CCOCC1)c1cc(NC(=O)c2ccc(nc2)-n2ccnc2)ccc1OC

-

>10,000

>10,000

#17

O1CCN(CC1)C1(CCCCC1)CNC(=O)Nc1cc(OC)c(NC(=O)c2ccccc2)cc1OC

-

>10,000

>10,000

#18

S(CC(=O)NCC(N1CCOCC1)(C)C)c1n-2c(nn1)C=C(c1c-2cc(OC)cc1)C

-

>10,000

>10,000

#19

O1CCN(CC1)CCNc1nc(nc(n1)Nc1ccc(OC)cc1)Nc1ccc(OC)cc1

-

>10,000

>10,000

#20

S(CC=1NC(=NC(=O)C=1)Nc1nc(c2c(n1)c(cc(c2)C)C)C)c1[nH]c2c(n1)cccc2

-

>10,000

>10,000

#21

N1(CCN(CC1)c1nc(nc(n1)Nc1c2c(ccc1)cccc2)Nc1c2c(ccc1)cccc2)C

-

>10,000

>10,000

#22

s1c2-c3n(C=Nc2c2c4c(CCC4)c(nc12)-c1ccccc1)c(SCC(=O)Nc1ccc(OC)cc1)nn3

-

>10,000

>10,000

#23

Clc1cc(Nc2nc(nc(n2)N)CN2CCN(CC2)Cc2cc3OCOc3cc2)ccc1C

-

>10,000

>10,000

#24

Fc1cc2c(N(C=C(C(=O)NCCc3ccc(O)cc3)C2=O)C2CC2)cc1N1CCN(CC1)CC

-

>10,000

>10,000

#25(ref)

FC(F)(F)c1cnc(nc1NC1CCC1)Nc1cc2C3N(C(CC3)c2cc1)C(=O)CNC(=O)C

PF-03814735

1.4

5.8

#26(ref)

O1CCN(CC1)Cc1cc2[nH]c(nc2cc1)-c1n[nH]cc1NC(=O)NC1CC1

AT-9283

3

4.7

#27(ref)

S(c1ccc(NC(=O)C2CC2)cc1)c1nc(N2CCN(CC2)C)cc(n1)Nc1n[nH]c(c1)C

VX-680

1.3

13

#28(ref)

CO[C@@H](C(=O)N1Cc2[nH]nc(NC(=O)c3ccc(cc3)N4CCN(C)CC4)c2C1)c5ccccc5

PHA-739358

2.7

2.7

31



REFERENCES (1) Carmena, M.; Earnshaw, W. C. The Cellular Geography of Aurora Kinases. Nat. Rev. Mol. Cell Biol. 2003, 4, 842-84.

(2) Katsha, A.; Belkhiri, A.; Goff, L.; El-Rifai, W. Aurora Kinase A in Gastrointestinal Cancers: Time to Target. Mol. Cancer 2015, 14, 106. (3) Iankov, I. D.; Kurokawa, C. B.; D'Assoro, A. B.; Ingle, J. N.; Domingo-Musibay, E.; Allen, C.; Crosby, C. M.; Nair, A. A.; Liu, M. C.; Aderca, I.; Federspiel, M. J.; Galanis, E. Inhibition of the Aurora A Kinase Augments the Anti-Tumor Efficacy of Oncolytic Measles Virotherapy. Cancer Gene Ther. 2015, 22, 438-444. (4) Shiao, H. Y.; Coumar, M. S.; Chang, C. W.; Ke, Y. Y.; Chi, Y. H.; Chu, C. Y.; Sun, H. Y.; Chen, C. H.; Lin, W. H.; Fung, K. S.; Kuo, P. C.; Huang, C. T.; Chang, K. Y.; Lu, C. T.; Hsu, J. T.; Chen, C. T.; Jiaang, W. T.; Chao, Y. S.; Hsieh, H. P. Optimization of Ligand and Lipophilic Efficiency to Identify an in Vivo Active Furano-Pyrimidine Aurora Kinase Inhibitor. J. Med. Chem. 2013, 56, 5247-5260. (5) Hrabakova, R.; Kollareddy, M.; Tyleckova, J.; Halada, P.; Hajduch, M.; Gadher, S. J.; Kovarova, H. Cancer Cell Resistance to Aurora Kinase Inhibitors: Identification of Novel Targets for Cancer Therapy. J. Proteome. Res. 2013, 12, 455-469. (6) Keen, N.; Taylor, S. Aurora-Kinase Inhibitors as Anticancer Agents. Nat. Rev. Cancer 2004, 4, 927-936.

(7) Kollareddy, M.; Dzubak, P.; Zheleva, D.; Hajduch, M. Aurora Kinases: Structure, Functions and their Association with Cancer. Biomed. Pap. Med. Fac. Univ. Palacky. Olomouc. Czech. Repub. 2008, 152, 27-33.

(8) Stenoien, D. L.; Sen, S.; Mancini, M. A.; Brinkley, B. R. Dynamic Association of a Tumor Amplified Kinase, Aurora-A, with the Centrosome and Mitotic Spindle. Cell Motil. Cytoskeleton 2003, 55, 134-146.

(9) Gohard, F. H.; St-Cyr, D. J.; Tyers, M.; Earnshaw, W. C. Targeting the INCENP IN-Box-Aurora B Interaction to Inhibit CPC Activity in Vivo. Open Biol. 2014, 4, 140-163.

32


Page 32 of 38

Page 33 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(10) Fu, J.; Bian, M.; Jiang, Q.; Zhang, C. Roles of Aurora Kinases in Mitosis and Tumorigenesis. Mol. Cancer Res. 2007, 5, 1-10. (11) Kollareddy, M.; Zheleva, D.; Dzubak, P.; Brahmkshatriya, P. S.; Lepsik, M.; Hajduch, M. Aurora Kinase Inhibitors: Progress towards the Clinic. Invest. New Drugs 2012, 30, 2411-2432. (12) Bavetsias, V.; Linardopoulos, S. Aurora Kinase Inhibitors: Current Status and Outlook. Front Oncol. 2015, 5, 278. (13) Dar, A. A.; Goff, L. W.; Majid, S.; Berlin, J.; El-Rifai, W. Aurora Kinase Inhibitors--Rising Stars in Cancer Therapeutics? Mol. Cancer Ther. 2010, 9, 268-278. (14) Rester, U. From Virtuality to Reality - Virtual Screening in Lead Discovery and Lead Optimization: a Medicinal Chemistry Perspective. Curr. Opin. Drug Discov. Devel. 2008, 11, 559-568.

(15) Kilchmann, F.; Marcaida, M. J.; Kotak, S.; Schick, T.; Boss, S. D.; Awale, M.; Gonczy, P.; Reymond, J. L. Discovery of a Selective Aurora A Kinase Inhibitor by Virtual Screening. J. Med. Chem. 2016, 59, 7188–7211. (16) Raghu, R.; Devaraji, V.; Leena, K.; Riyaz, S. D.; Rani, P. B.; Kumar, B. S.; Naik, P. K.; Dubey, P. K.; Velmurugan, D.; Vijayalakshmi, M. Virtual Screening and Discovery of Novel Aurora Kinase Inhibitors. Curr. Top Med. Chem. 2014, 14, 2006-2019. (17) Lavecchia, A.; Di Giovanni, C. Virtual Screening Strategies in Drug Discovery: a Critical Review. Curr. Med. Chem. 2013, 20, 2839-2860. (18) Lionta, E.; Spyrou, G.; Vassilatis, D. K.; Cournia, Z. Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Curr. Top Med. Chem. 2014, 14, 1923-1938.

(19) Ripphausen, P.; Nisius, B.; Bajorath, J. State-of-the-Art in Ligand-Based Virtual Screening. Drug Discov. Today 2011, 16, 372-376. (20)

Scior,

T.;

Bender,

A.;

Tresadern,

G.;

Medina-Franco,

J.

L.;

Martinez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D. K. Recognizing Pitfalls in Virtual Screening: a Critical Review. J. Chem. Inf. Model. 2012, 52, 867-881.

(21)

Willett, P. Searching Techniques for Databases of Two- and

Three-Dimensional Chemical Structures. J. Med. Chem. 2005, 48, 4183-4199.

33



(22) Scior, T.; Bernard, P.; Medina-Franco, J. L.; Maggiora, G. M. Large Compound Databases for Structure-Activity Relationships Studies in Drug Discovery. Mini Rev. Med. Chem. 2007, 7, 851-860.

(23) Lloyd, D. G. Approaches to Scaffold Hopping. Drug Discov. Today Technol. 2013, 10, 451-452. (24) Sun, H. P.; Jia, J. M.; Jiang, F.; Xu, X. L.; Liu, F.; Guo, X. K.; Cherfaoui, B.; Huang, H. Z.; Pan, Y.; You, Q. D. Identification and Optimization of Novel Hsp90 Inhibitors with Tetrahydropyrido[4,3-D]Pyrimidines Core through Shape-Based Screening. Eur. J. Med. Chem. 2014, 79, 399-412. (25) Sun, W.; Su, Q.; Cao, X.; Shang, B.; Chen, A.; Yin, H.; Liu, B. High Expression Of Polo-Like Kinase 1 Is Associated With Early Development Of Hepatocellular Carcinoma. Int. J. Genomics 2014, Article ID:312130. (26) Hevener, K. E.; Mehboob, S.; Su, P. C.; Truong, K.; Boci, T.; Deng, J.; Ghassemi, M.; Cook, J. L.; Johnson, M. E. Discovery of a Novel and Potent Class of F. Tularensis Enoyl-Reductase (Fabi) Inhibitors by Molecular Shape And Electrostatic Matching. J. Med. Chem. 2012, 55, 268-279. (27) Naylor, E.; Arredouani, A.; Vasudevan, S. R.; Lewis, A. M.; Parkesh, R.; Mizote, A.; Rosen, D.; Thomas, J. M.; Izumi, M.; Ganesan, A.; Galione, A.; Churchill, G. C. Identification of a Chemical Probe for NAADP by Virtual Screening. Nat. Chem. Biol. 2009, 5, 220-226.

(28) Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin, II; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.; Todeschini, R.; Consonni, V.; Kuz'min, V. E.; Cramer, R.; Benigni, R.; Yang, C.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR Modeling: Where have you been? Where are You going to? J. Med. Chem. 2014, 57, 4977-5010. (29) Wang, L.; Wang, Z.; Yan, A.; Yuan, Q. Classification of Aurora-A Kinase Inhibitors Using Self-Organizing Map (SOM) and Support Vector Machine (SVM). Mol. Inform. 2011, 30, 35-44.

(30) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-342.

34


Page 34 of 38

Page 35 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(31) Gilson, M. K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. Bindingdb in 2015: a Public Database for Medicinal Chemistry, Computational Chemistry and Systems Pharmacology. Nucleic Acids Res. 2016, 44, 1045-1053. (32) ROCS, version 3.1.2; OpenEye Scientific Software, Inc.: Santa Fe, USA, 2016; http://www.eyesopen.com (accessed June 2016). (33) Xia, J.; Jin, H.; Liu, Z.; Zhang, L.; Wang, X. S. An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application to GPCRs. J. Chem. Inf. Model. 2014, 54, 1433-1450. (34) EON, version 2.1.0; OpenEye Scientific Software, Inc.: Santa Fe, USA, 2016; http://www.eyesopen.com (accessed June 2016). (35) CORINA Symphony, version 1.0; Molecular Networks GmbH: Germany and Altamira, LLC, USA, 2016; https://www.mn-am.com/products/corinasymphony (accessed June 2016). (36) Kohonen, T. Self-Organized Formation of Topologically Correct Feature Maps. Biol. Cybern. 1982, 43, 59-69. (37) SONNIA, version 4.2; Molecular Networks GmbH: Germany and Altamira, LLC, USA,2016; https://www.mn-am.com/products/sonnia (accessed June 2016). (38) MOE, version 2010; Molecular Operating Environment, Chemical Computing Group Inc, Montreal, Canada; http://www.chemcomp.com (accessed June 2016). (39) Kohonen, T. Self-Organized Formation of Topologically Correct Feature Maps, Neurocomputing: Foundations of Research; MIT Press: Cambridge, MA, 1988.

(40) Vapnik, V.; Chapelle, O. Bounds on Error Expectation for Support Vector Machines. Neural Comput. 2000, 12, 2013-2036. (41) Chang, C.-C.; Lin, C.-J. LIBSVM: a Library for Support Vector Machines. ACM T. Intel. Syst. Tec. 2011, 2, 27.

(42) Matthews, B. W. Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme. Biochim. Biophys. Acta 1975, 405, 442-451. (43) Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A. C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z. T.; Han, B.; Zhou, Y.; Wishart, D. S. Drugbank 4.0: Shedding New Light on Drug Metabolism. Nucleic Acids Res. 2014, 42, D1091-1097.

35



(44) Filter, version 2.1.1; OpenEye Scientific Software, Inc.: Santa Fe, USA, 2016; http://www.eyesopen.com (accessed June 2016). (45) OMEGA, version 2.4.6; OpenEye Scientific Software, Inc.: Santa Fe, USA, 2016; http://www.eyesopen.com (accessed June 2016). (46) Smith, A. J. P.; Humphries, S. E., Characterization of DNA-binding proteins using multiplexed competitor EMSA. J. Mol. Biol. 2009, 385, 714-717. (47) Hybrid, version 3.0.1; OpenEye Scientific Software, Inc.: Santa Fe, USA, 2016; http://www.eyesopen.com (accessed June 2016). (48) Zhang, L.; Fan, J.; Chong, J. H.; Cesena, A.; Tam, B. Y.; Gilson, C.; Boykin, C.; Wang, D.; Aivazian, D.; Marcotte, D.; Xiao, G.; Le Brazidec, J. Y.; Piao, J.; Lundgren, K.; Hong, K.; Vu, K.; Nguyen, K.; Gan, L. S.; Silvian, L.; Ling, L.; Teng, M.; Reff, M.; Takeda, N.; Timple, N.; Wang, Q.; Morena, R.; Khan, S.; Zhao, S.; Li, T.; Lee, W. C.; Taveras, A. G.; Chao, J. Design, Synthesis, and Biological Evaluation of Pyrazolopyrimidine-Sulfonamides as Potent Multiple-Mitotic Kinase (MMK) Inhibitors (Part I). Bioorg. Med. Chem. Lett. 2011, 21, 5633-5637. (49) Meulenbeld, H. J.; Mathijssen, R. H.; Verweij, J.; de Wit, R.; de Jonge, M. J. Danusertib, an Aurora Kinase Inhibitor. Expert Opin. Investig. Drugs 2012, 21, 383-393. (50) Carry, J. C.; Clerc, F.; Minoux, H.; Schio, L.; Mauger, J.; Nair, A.; Parmantier, E.; Le Moigne, R.; Delorme, C.; Nicolas, J. P.; Krick, A.; Abecassis, P. Y.; Crocq-Stuerga, V.; Pouzieux, S.; Delarbre, L.; Maignan, S.; Bertrand, T.; Bjergarde, K.; Ma, N.; Lachaud, S.; Guizani, H.; Lebel, R.; Doerflinger, G.; Monget, S.; Perron, S.; Gasse, F.; Angouillant-Boniface, O.; Filoche-Romme, B.; Murer, M.; Gontier, S.; Prevost, C.; Monteiro, M. L.; Combeau, C. Sar156497, an Exquisitely Selective Inhibitor of Aurora Kinases. J. Med. Chem. 2015, 58, 362-375. (51) Bavetsias, V.; Large, J. M.; Sun, C.; Bouloc, N.; Kosmopoulou, M.; Matteucci, M.; Wilsher, N. E.; Martins, V.; Reynisson, J. h.; Atrash, B. Imidazo [4, 5-b] pyridine derivatives as inhibitors of Aurora kinases: lead optimization studies toward the identification of an orally bioavailable preclinical development candidate. J. Med. Chem. 2010, 53, 5213-5228.

(52) Gustafson, W. C.; Meyerowitz, J. G.; Nekritz, E. A.; Chen, J.; Benes, C.; Charron, E.; Simonds, E. F.; Seeger, R.; Matthay, K. K.; Hertz, N. T.; Eilers, M.;

36


Page 36 of 38

Page 37 of 38 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Shokat, K. M.; Weiss, W. A. Drugging MYCN through an Allosteric Transition in Aurora Kinase A. Cancer Cell 2014, 26, 414-427. (53) Seeger, M. A.; Zbinden, R.; Flutsch, A.; Gutte, P. G.; Engeler, S.; Roschitzki-Voser, H.; Grutter, M. G. Design, Construction, and Characterization of a Second-Generation DARP in Library with Reduced Hydrophobicity. Protein Sci. 2013, 22, 1239-1257.

(54) Wang, S.; Midgley, C. A.; Scaërou, F.; Grabarek, J. B.; Griffiths, G.; Jackson, W.; Kontopidis, G.; McClue, S. J.; McInnes, C.; Meades, C. Discovery of N-Phenyl-4-(Thiazol-5-Yl) Pyrimidin-2-Amine Aurora Kinase Inhibitors. J. Med. Chem. 2010, 53, 4367-4378.

(55) Yan, A.; Chong, Y.; Wang, L.; Hu, X.; Wang, K. Prediction of Biological Activity of Aurora-A Kinase Inhibitors by Multilinear Regression Analysis and Support Vector Machine. Bioorg. Med. Chem. Lett. 2011, 21, 2238-2243. (56) Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50, 742-754.

37



TOC graphic

38


Page 38 of 38

Identification of Novel Aurora Kinase A (AURKA) Inhibitors via

Recommend Documents