Identification of inhibitors for vascular endothelial growth factor receptor by using dynamic combinatorial chemistry

Zhao, Yang; Fang, Zheng; He, Wei; Wang, Zhixiang; Gan, Haifeng; Tian, Qitao; Guo, Kai

doi:10.1016/j.bmcl.2016.02.063

Cited by 8 publications

(12 citation statements)

References 20 publications

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“…By using an excess of amine, side reactions with the N terminus of the protein or lysine residues can be avoided. The generated imines are labile—in fact there are only two reports on direct analysis of imine libraries—and usually require reduction with sodium cyanoborohydride– or tetrabutylammonium cyanoborohydride to form stable amines. It should be noted that these reductive agents could potentially hamper the activity of the target .…”

Section: Implementation Of Tddccmentioning

confidence: 95%

“…Apart from HPLC, MS analysis of tdDCC experiments has been widely used (Table ) ,,,. Especially in non‐comparative setups, non‐denaturing MS experiments, which detect the native ligand–target complex, have been successfully applied ,.…”

Section: Methods Of Analysis and Data Processionmentioning

confidence: 99%

“…In the non‐comparative approach, high‐affinity ligands are identified without comparison to a blank library, for example, when products are generated exclusively in the presence of a target, thereby omitting the need for a blank experiment ,. More commonly, hit identification in non‐comparative setups is achieved by either analyzing the native ligand–target complex– or, after isolation of the complex and its disruption, the released ligands ,,. Non‐comparative setups frequently employ either mass spectrometry,, especially non‐denaturing MS used in the measurement of native ligand–target complexes,, and ligand‐observed nuclear magnetic resonance techniques,– whereas analysis by HPLC is less frequent .…”

Section: Implementation Of Tddccmentioning

confidence: 99%

“…Since the first example of tdDCC by Huc and Lehn in 1997 (Figure ), reversible imine formation from amine and aldehyde building blocks (and in an isolated case from a ketone), has been widely explored. By using an excess of amine, side reactions with the N terminus of the protein or lysine residues can be avoided.…”

Section: Implementation Of Tddccmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Combinatorial Chemistry: A New Methodology Comes of Age

Frei

Hevey

Ernst

2018

Chemistry A European J

105

101

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Dynamic combinatorial chemistry (DCC) has repeatedly proven to be an effective approach to generate directed ligand libraries for macromolecular targets. In the absence of an external stimulus, a dynamic library forms from reversibly reacting building blocks and reaches a stable thermodynamic equilibrium. However, upon addition of a macromolecular host which can bind and stabilize certain components of the library, the equilibrium composition changes and induces an evolution-like selection and enrichment of high-affinity ligands. A valuable application of this so-called target-directed DCC (tdDCC) is the identification of potent ligands for pharmacologically relevant targets. Over time, the term tdDCC has been applied to describe a number of different experimental set-ups, leading to some ambiguity concerning its definition. This article systematically classifies known procedures for tdDCC and related approaches, with a special focus on the methods used for analysis and evaluation of experiments.

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Section: Implementation Of Tddccmentioning

confidence: 95%

Section: Methods Of Analysis and Data Processionmentioning

confidence: 99%

Section: Implementation Of Tddccmentioning

confidence: 99%

Section: Implementation Of Tddccmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Combinatorial Chemistry: A New Methodology Comes of Age

Frei

Hevey

Ernst

2018

Chemistry A European J

105

101

View full text Add to dashboard Cite

show abstract

“…The main advantage of DCC is that several potential ligands for a protein can be screened simultaneously, avoiding the individual synthesis of every compound. This emerging technology has been used for bacterial targets [180] and cancer targets such as VEGFR [181]. Another strategy is the use of drug repositioning, which is a process of discovering a new therapeutic use for existing drugs, allowing the prediction of novel targets and therapeutic indications [182].…”

Section: Future Trendsmentioning

confidence: 99%

Current Screening Methodologies in Drug Discovery for Selected Human Diseases

et al. 2018

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The increase of many deadly diseases like infections by multidrug-resistant bacteria implies re-inventing the wheel on drug discovery. A better comprehension of the metabolisms and regulation of diseases, the increase in knowledge based on the study of disease-born microorganisms’ genomes, the development of more representative disease models and improvement of techniques, technologies, and computation applied to biology are advances that will foster drug discovery in upcoming years. In this paper, several aspects of current methodologies for drug discovery of antibacterial and antifungals, anti-tropical diseases, antibiofilm and antiquorum sensing, anticancer and neuroprotectors are considered. For drug discovery, two different complementary approaches can be applied: classical pharmacology, also known as phenotypic drug discovery, which is the historical basis of drug discovery, and reverse pharmacology, also designated target-based drug discovery. Screening methods based on phenotypic drug discovery have been used to discover new natural products mainly from terrestrial origin. Examples of the discovery of marine natural products are provided. A section on future trends provides a comprehensive overview on recent advances that will foster the pharmaceutical industry.

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Proteomic Tools for the Quantitative Analysis of Artificial Peptide Libraries: Detection and Characterization of Target‐Amplified PD‐1 Inhibitors

et al. 2022

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We report a quantitative proteomics data analysis pipeline, which coupled to protein‐directed dynamic combinatorial chemistry (DDC) experiments, enables the rapid discovery and direct characterization of protein‐protein interaction (PPI) modulators. A low‐affinity PD‐1 binder was incubated with a library of >100 D‐peptides under thiol‐exchange favoring conditions, in the presence of the target protein PD‐1, and we determined the S‐linked dimeric species that resulted, amplified in the protein samples versus the controls. We chemically synthesized the target dimer candidates and validated them by thermophoresis binding and protein‐protein interaction assays. The results provide a proof‐of‐concept for using this strategy in the high‐throughput search of improved drug‐like peptide binders that block therapeutically relevant protein‐protein interactions.

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Identification of inhibitors for vascular endothelial growth factor receptor by using dynamic combinatorial chemistry

Cited by 8 publications

References 20 publications

Dynamic Combinatorial Chemistry: A New Methodology Comes of Age

Dynamic Combinatorial Chemistry: A New Methodology Comes of Age

Current Screening Methodologies in Drug Discovery for Selected Human Diseases

Proteomic Tools for the Quantitative Analysis of Artificial Peptide Libraries: Detection and Characterization of Target‐Amplified PD‐1 Inhibitors

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