Synthesis and biological evaluation of tricyclic anilinopyrimidines as IKKβ inhibitors

Crombie, Aimee L.; Sum, Fuk‐Wah; Powell, Dennis; Hopper, Darrin W.; Torres, Nancy; Berger, Dan M.; Zhang, Yixian; Gavriil, Maria; Sadler, Tammy; Arndt, Kim

doi:10.1016/j.bmcl.2010.04.022

Cited by 16 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of small molecules have been reported through intensive studies and drug discovery programs for the development of potent and selective IKKβ inhibitors. − In addition to various ATP competitive inhibitors, the allosteric inhibitors have also been shown to selectively impair the activity of IKKβ . Although most IKKβ inhibitors were identified via the high-throughput screening of a chemical library and the structural modifications of known inhibitors, some rational drug design approaches have also been actively pursued based on a variety of computational methods. − However, the lack of 3D structure of IKKβ has limited the applicability of the computational methods due to the considerable uncertainty in estimating the biochemical potencies of putative inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Computational Design and Discovery of Nanomolar Inhibitors of IκB Kinase β

et al. 2015

View full text Add to dashboard Cite

IκB kinase β (IKKβ) is a useful target for the discovery of new medicines for cancer and inflammatory diseases. In this study, we aimed to identify new classes of potent IKKβ inhibitors based on structure-based virtual screening, de novo design, and chemical synthesis. To increase the probability of finding actual inhibitors, we improved the scoring function for the estimation of the IKKβ-inhibitor binding affinity by introducing proper solvation free energy and conformational destabilization energy terms for putative inhibitors. Using this modified scoring function, we have been able to identify 15 submicromolar-level IKKβ inhibitors that possess the phenyl-(4-phenyl-pyrimidin-2-yl)-amine moiety as the molecular core. Decomposition analysis of the calculated binding free energies showed that a high biochemical potency could be achieved by lowering the desolvation cost and the conformational destabilization for the inhibitor required for binding to IKKβ as well as by strengthening the interactions in the ATP-binding site. The formation of two hydrogen bonds with backbone amide groups of Cys99 in the hinge region was found to be necessary for tight binding of the inhibitors in the ATP-binding site. From molecular dynamics simulations of IKKβ-inhibitor complexes, we also found that complete dynamic stability of the bidentate hydrogen bond with Cys99 was required for low nanomolar-level inhibitory activity. This implies that the scoring function for virtual screening and de novo design would be further optimized by introducing an additional energy term to measure the dynamic stability of the key interactions in enzyme-inhibitor complexes.

show abstract

Section: Introductionmentioning

confidence: 99%

Computational Design and Discovery of Nanomolar Inhibitors of IκB Kinase β

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Our goal is to therefore to identify EGFR inhibitors with greater efficiency and specificity for CCA treatment 4-aryl-N-phenylpyrimidin-2-amine derivatives have been confirmed as EGFR inhibitors using a variety of methods discussed herein. Related compounds have also previously reported that target protein kinase (Pelletier et al, 2009;Crombie et al, 2010;Kamenecka et al, 2010), including at some downstream targets of EGFR. A new synthetic 13f or 3-[(4-phenylpyrimidin-2yl)amino] benzene-1-sulfonamide is modification to the 2-position and the 4-position of the pyrimidine scaffold with 3-sulfonamido aniline at the R2, phenyl at the R1 (13f) (Toviwek et al, 2017).…”

Section: Discussionmentioning

confidence: 93%

The Effect of the EGFR - Targeting Compound 3-[(4-Phenylpyrimidin-2-yl) Amino] Benzene-1-Sulfonamide (13f) against Cholangiocarcinoma Cell Lines

Samatiwat

Tabtimmai

Suphakun

et al. 2021

Asian Pac J Cancer Prev

View full text Add to dashboard Cite

“…The somewhat less favorable Pearson r of 0.56 for IKKA indicates this model to be less predictive than that for IKKB. Since the inhibitor data sets were obtained from 28 references (2002–2011 − ) listed in the BindingDB and the compounds from the same report always share high structural similarity, we considered the possibility that the FB-QSAR models are biased by the excessively focused structural frameworks. Thus, the two data sets were ordered by Canvas’s hierarchical clustering methodology.…”

Section: Resultsmentioning

confidence: 99%

Models for Predicting IKKA and IKKB Blockade

Snyder

2012

J. Chem. Inf. Model.

View full text Add to dashboard Cite

We describe the application of different methods in the development of QSAR models for IKKA and IKKB inhibition. The results show that the best QSAR models provide highly accurate predictions for existing IkB-kinase (IKK) inhibitors. The exceptions, corresponding to 5% of the known collection of inhibitors, are five classes of compounds incorporating the nitrile or sulfonamide moieties, small compounds with molecular weights of less than 300, and two classes of blockers considered to be type II kinase inhibitors. Comparison of our novel IKKB homology model and the recently reported IKKB crystal structure implies that a predictive protein-antagonist complex structure is more likely to exist as an inactive form in the crystalline state as observed in the recent protein X-ray structure.

show abstract

Synthesis and biological evaluation of tricyclic anilinopyrimidines as IKKβ inhibitors

Cited by 16 publications

References 25 publications

Computational Design and Discovery of Nanomolar Inhibitors of IκB Kinase β

Computational Design and Discovery of Nanomolar Inhibitors of IκB Kinase β

The Effect of the EGFR - Targeting Compound 3-[(4-Phenylpyrimidin-2-yl) Amino] Benzene-1-Sulfonamide (13f) against Cholangiocarcinoma Cell Lines

Models for Predicting IKKA and IKKB Blockade

Contact Info

Product

Resources

About