Discovery of Novel Allosteric EGFR L858R Inhibitors for the Treatment of Non-Small-Cell Lung Cancer as a Single Agent or in Combination with Osimertinib

Obst‐Sander, Ulrike; Ricci, Antônio; Kuhn, Bernd; Friess, Thomas; Koldewey, Philipp; Kuglstatter, A.; Hewings, David S.; Goergler, Annick; Steiner, Sandra; Rueher, Daniel; Imhoff, Marie‐Paule; Raschetti, Noemi; Marty, Hans-Peter; Dietzig, Aline; Rynn, Caroline; Ehler, A.; Burger, Dominique; Kornacker, Martin; Schaffland, Jeannine Petrig; Herting, Frank; Pao, William; Bischoff, James R.; Martoglio, Bruno; Nagel, Yvonne A.; Jaeschke, Georg

doi:10.1021/acs.jmedchem.2c00893

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…Molecular docking studies were performed using in silico procedure to define the binding modes of compound 4i (active compound) in the active regions of enzymes X‐ray crystal structures of EGFR (PDB ID:8A2B) (Obst‐Sander et al, 2022) were retrieved from Protein Data Bank server (http://www.pdb.org, accessed October 24, 2022). Schrödinger Maestro interface ( Schrödinger release 2020–3, 2020a) was used for molecular docking study and the enzymes crystals were processed using the Protein Preparation Wizard protocol of the Schrödinger Suite 2020.…”

Section: Methodsmentioning

confidence: 99%

Design, synthesis, and molecular docking studies of novel quinoxaline derivatives as anticancer agents

et al. 2023

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As lung cancer was placed foremost part among other types of cancer in terms of mortality. Recent researches are widely focused on developing multi‐targeted and site‐specific targeted drug designs. In the present study, we designed and developed a series of quinoxaline pharmacophore derivatives as active EGFR inhibitors for the treatment of non‐small cell lung cancer. The compounds were synthesized through a condensation reaction between hexane‐3,4‐dione and methyl 3,4‐diaminobenzoate as a first step. Their structures were confirmed by 1H‐NMR, 13C‐NMR, and HRMS spectroscopic methods. Cytotoxicity (MTT) were applied to determine anticancer activity of the compounds against breast (MCF7), fibroblast (NIH3 T3), and lung (A549) cell lines as EGFR inhibitors. Doxorubicin was used as a reference agent, compound 4i exhibited a significant effect among other derivatives with IC50 = 3.902 ± 0.098 μM value against A549 cell line. The docking study showed that the best position on EGFR receptor could be observed with 4i. From the obtained evaluations of the designed series, compound 4i was a promising agent as EGFR inhibitor for further investigation and evaluation studies in the future.

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Section: Methodsmentioning

confidence: 99%

Design, synthesis, and molecular docking studies of novel quinoxaline derivatives as anticancer agents

et al. 2023

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“…Neratinib shares this unique structural motif (pyridine versus fluorobenzyl) and function in common with the reversible inhibitor lapatinib, and it is often classified as a type 1.5 TKI due to the requirement for this inhibitor to reinforce the αC-helix-out inactive conformation . It is noteworthy that the presence of this phenyl ring in the allosteric pocket is a feature shared with recently reported mutant-selective allosteric inhibitors − and ATP-allosteric chimeric molecules. − …”

Section: Egfr Inhibitorsmentioning

confidence: 99%

Pitfalls and Considerations in Determining the Potency and Mutant Selectivity of Covalent Epidermal Growth Factor Receptor Inhibitors

Hoyt,

Urul,

Ogboo

et al. 2023

J. Med. Chem.

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Enzyme inhibitors that form covalent bonds with their targets are being increasingly pursued in drug development. Assessing their biochemical activity relies on time-dependent assays, which are distinct and more complex compared with methods commonly employed for reversible-binding inhibitors. To provide general guidance to the covalent inhibitor development community, we explored methods and reported kinetic values and experimental factors in determining the biochemical activity of various covalent epidermal growth factor receptor (EGFR) inhibitors. We showcase how liquid handling and assay reagents impact kinetic parameters and potency interpretations, which are critical for structure−kinetic relationships and covalent drug design. Additionally, we include benchmark kinetic values with reference inhibitors, which are imperative, as covalent EGFR inhibitor kinetic values are infrequently consistent in the literature. This overview seeks to inform best practices for developing new covalent inhibitors and highlight appropriate steps to address gaps in knowledge presently limiting assay reliability and reproducibility. ■ SIGNIFICANCE• The potency and selectivity of covalent enzyme inhibitors can only be determined in time-dependent activity assays (IC 50 values are inappropriate). • Kinetic values for covalent EGFR inhibitors reported in the literature are not consistent, and investigators must include benchmark compounds as important controls. • Inhibitor molecular properties, assay reagent concentrations, and dilution protocols can significantly impact potency values.

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“…We conducted a case study to demonstrate the application of our proposed TEFDTA model in the field of drug design and, in particular, to investigate the ability of our method to discriminate molecules’ activity cliffs. For this purpose, a set of novel heterodimeric inhibitors of epidermal growth factor receptor (EGFR)L858R, which were not included in either our training or test set, was selected them to perform the prediction ( Obst-Sander et al 2022 ).…”

Section: Case Studymentioning

confidence: 99%

TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug–target affinities

Li,

Ren,

Yang

et al. 2023

Bioinformatics

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Motivation The prediction of binding affinity between drug and target is crucial in drug discovery. However, the accuracy of current methods still needs to be improved. On the other hand, most deep learning methods focus only on the prediction of non-covalent (non-bonded) binding molecular systems, but neglect the cases of covalent binding, which has gained increasing attention in the field of drug development. Results In this work, a new attention-based model, TEFDTA (A Transformer Encoder and Fingerprint combined Prediction method for Drug-Target Affinity) is proposed to predict the binding affinity for bonded and non-bonded drug-target interactions. To deal with such complicated problems, we used different representations for protein and drug molecules, respectively. In detail, an initial framework was built by training our model using the datasets of non-bonded protein-ligand interactions. For the widely used dataset Davis, an additional contribution of this study is that we provide a manually corrected Davis database. The model was subsequently fine-tuned on a smaller dataset of covalent interactions from the CovalentInDB database to optimize performance. The results demonstrate a significant improvement over existing approaches, with an average improvement of 7.6% in predicting non-covalent binding affinity and a remarkable average improvement of 62.9% in predicting covalent binding affinity compared to using BindingDB data alone. At the end, the potential ability of our model to identify activity cliffs was investigated through a case study. The prediction results indicate that our model is sensitive to discriminate the difference of binding affinities arising from small variances in the structures of compounds. Availability and implementation The codes and datasets of TEFDTA are available at https://github.com/lizongquan01/TEFDTA

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Discovery of Novel Allosteric EGFR L858R Inhibitors for the Treatment of Non-Small-Cell Lung Cancer as a Single Agent or in Combination with Osimertinib

Cited by 13 publications

References 33 publications

Design, synthesis, and molecular docking studies of novel quinoxaline derivatives as anticancer agents

Design, synthesis, and molecular docking studies of novel quinoxaline derivatives as anticancer agents

Pitfalls and Considerations in Determining the Potency and Mutant Selectivity of Covalent Epidermal Growth Factor Receptor Inhibitors

TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug–target affinities

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