Pharmacophore modeling and virtual screening studies of checkpoint kinase 1 inhibitors

Design, synthesis, docking, and anticancer evaluations of phthalazines as VEGFR-2 inhibitors

Twenty new N-substituted-4-phenylphthalazin-1-amine derivatives were designed, synthesized, and evaluated for their anticancer activities against HepG2, HCT-116, and MCF-7 cells as VEGFR-2 inhibitors. HCT-116 was the most sensitive cell line to the influence of the new derivatives. In particular, compound 7f was found to be the most potent derivative among all the tested compounds against the three cancer cell lines, with 50% inhibition concentration, IC₅₀  = 3.97, 4.83, and 4.58 µM, respectively, which is more potent than both sorafenib (IC₅₀  = 9.18, 5.47, and 7.26 µM, respectively) and doxorubicin (IC₅₀  = 7.94, 8.07, and 6.75 µM, respectively). Fifteen of the synthesized derivatives were selected to evaluate their inhibitory activities against VEGFR-2. Compound 7f was found to be the most potent derivative that inhibited VEGFR-2 at an IC₅₀ value of 0.08 µM, which is more potent than sorafenib (IC₅₀  = 0.10 µM). Compound 8c inhibited VEGFR-2 at an IC₅₀ value of 0.10 µM, which is equipotent to sorafenib. Moreover, compound 7a showed very good activity with IC₅₀ values of 0.11 µM, which is nearly equipotent to sorafenib. In addition, compounds 7d, 7c, and 7g possessed very good VEGFR-2-inhibitory activity, with IC₅₀ values of 0.14, 0.17, and 0.23 µM, respectively.

Pharmacophore approaches in protein kinase inhibitors design

Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized nature of protein kinases makes them excellent targets for drug development. Pharmacophore approaches have become one of the major tools in the area of drug discovery. Application of pharmacophore modeling approaches allows reducing of expensive overall cost associated with drug development project. Pharmacophore models are important functional groups of atoms in the proper spatial position for interaction with target protein. Various ligand-based and structure-based methods have been developed for pharmacophore model generation. Despite the successes in pharmacophore models generation these approaches have not reached their full capacity in application for drug discovery. In the following review, we summarize the published data on pharmacophore models for inhibitors of tyrosine protein kinases (EGFR, HER2, VEGFR, JAK2, JAK3, Syk, ZAP-70, Tie2) and inhibitors of serine/threonine kinases (Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3, mTOR, p38 MAPK, PKB). Here, we have described the achievements of pharmacophore modeling for protein kinase inhibitors, which provide key points for further application of generated pharmacophore hypotheses in virtual screening, de novo design and lead optimization.

Discovery and Bioevaluation of Novel Pyrazolopyrimidine Analogs as Competitive Hsp90 Inhibitors Through Shape-Based Similarity Screening

Hsp90 as a promising therapeutic target for the treatment of cancer has received great attention. Many Hsp90 inhibitors such as BIIB021 and CUDC-305 have been in clinical. In this paper shape-based similarity screening through ROCS overlays on the basis of CUDC-305, BIIB021, PU-H71 and PU-3 were performed to discover HSP90 inhibitors. A set of 19 novel pyrazolopyrimidine analogues was identified and evaluated on enzyme level and cell-based level as Hsp90 inhibitors. The compound HDI4-04 with IC50 0.35 µM in the Hsp90 ATP hydrolysis assay exhibited potent cytotoxicity against five human cancer cell lines. Western blot analysis and Hsp70 luciferase reporter assay further confirmed that HDI4-04 targeted the Hsp90 protein folding machinery. And according to the biological assay, the SAR was discussed and summarized, which will guide us for further optimization of these compounds.

Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification

Protein kinase CK2 (CK2), a ubiquitous serine/threonine protein kinase for hundreds of endogenous substrates, serves as an attractive anticancer target. One of its most potent inhibitors, CX-4945, has entered a phase I clinical trial. Herein we present an integrated workflow combining shape-based virtual screening for the identification of novel CK2 inhibitors. A shape-based model derived from CX-4945 was built, and the subsequent virtual screening led to the identification of several novel scaffolds with high shape similarity to that of CX-4945. Among them two tricyclic scaffolds named [1,2,4]triazolo[4,3-c]quinazolin and [1,2,4]triazolo[4,3-a]quinoxalin attracted us the most. Combining strictly chemical similarity analysis, a second-round shape-based screening was performed based on the two tricyclic scaffolds, leading to 28 derivatives. These compounds not only targeted CK2 with potent and dose-dependent activities but also showed acceptable antiproliferative effects against a series of cancer cell lines. Our workflow supplies a high efficient strategy in the identification of novel CK2 inhibitors. Compounds reported here can serve as ideal leads for further modifications.

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

3D-QSAR based pharmacophore modeling and virtual screening for identification of novel pteridine reductase inhibitors

Pteridine reductase is a promising target for development of novel therapeutic agents against Trypanosomatid parasites. A 3D-QSAR pharmacophore hypothesis has been generated for a series of L. major pteridine reductase inhibitors using Catalyst/HypoGen algorithm for identification of the chemical features that are responsible for the inhibitory activity. Four pharmacophore features, namely: two H-bond donors (D), one Hydrophobic aromatic (H) and one Ring aromatic (R) have been identified as key features involved in inhibitor-PTR1 interaction. These features are able to predict the activity of external test set of pteridine reductase inhibitors with a correlation coefficient (r) of 0.80. Based on the analysis of the best hypotheses, some potent Pteridine reductase inhibitors were screened out and predicted with anti-PTR1 activity. It turned out that the newly identified inhibitory molecules are at least 300 fold more potent than the current crop of existing inhibitors. Overall the current SAR study is an effort for elucidating quantitative structure-activity relationship for the PTR1 inhibitors. The results from the combined 3D-QSAR modeling and molecular docking approach have led to the prediction of new potent inhibitory scaffolds.