Biophysical characterization of drug‐resistant mutants of fibroblast growth factor receptor 1

Yoza, Kaito; Himeno, Rika; Amano, Shoichi; Kobashigawa, Yoshihiro; Amemiya, Shun; Fukuda, Natsuki; Kumeta, Hiroyuki; Morioka, Hiroshi; Inagaki, Fuyuhiko

doi:10.1111/gtc.12405

Cited by 21 publications

(14 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other mutations-induced TKI resistances are relatively infrequent compared to gatekeeper mutations in FGFR, but they are still important ( Table 2). For example, the FGFR1 N546K mutation confers resistance by increasing affinity for ATP [22]. The FGFR2 N550H mutation is regarded as an auto-inhibitory molecule brake that restricts the kinase to be an uncontrolled active state [23,24].…”

Section: Mutations In Kinase Especially At Gatekeeper Residues Confmentioning

confidence: 99%

See 1 more Smart Citation

FGFR-TKI resistance in cancer: current status and perspectives

Yue

Chen

et al. 2021

J Hematol Oncol

View full text Add to dashboard Cite

Fibroblast growth factor receptors (FGFRs) play key roles in promoting the proliferation, differentiation, and migration of cancer cell. Inactivation of FGFRs by tyrosine kinase inhibitors (TKI) has achieved great success in tumor-targeted therapy. However, resistance to FGFR-TKI has become a concern. Here, we review the mechanisms of FGFR-TKI resistance in cancer, including gatekeeper mutations, alternative signaling pathway activation, lysosome-mediated TKI sequestration, and gene fusion. In addition, we summarize strategies to overcome resistance, including developing covalent inhibitors, developing dual-target inhibitors, adopting combination therapy, and targeting lysosomes, which will facilitate the transition to precision medicine and individualized treatment.

show abstract

Section: Mutations In Kinase Especially At Gatekeeper Residues Confmentioning

confidence: 99%

“…The molecular structural explanation is that the FGFR3 V555M mutation generates steric clashes with the phenyl ring of PD173074 by structural modeling, resulting in enhanced resistance [32,34]. An additional factor is steric clashes caused by conformational changes in the P-loop region [22].…”

Section: Fgfr3 Gatekeeper Mutationmentioning

confidence: 99%

FGFR-TKI resistance in cancer: current status and perspectives

Yue

Chen

et al. 2021

J Hematol Oncol

View full text Add to dashboard Cite

show abstract

“…Most protein kinases harbor a threonine that plays a major role in the interaction with the inhibitor by forming a critical hydrogen bond via its side chain hydroxyl oxygen. The mutation of this residue to a bulky hydrophobic amino acid, either Met or Ile, breaks the hydrogen interaction and introduces steric hindrance for inhibitor binding [76,106].…”

Section: Fgfr Gatekeeper Mutation and Drug Resistancementioning

confidence: 99%

Fibroblast Growth Factor Receptors (FGFRs): Structures and Small Molecule Inhibitors

Dai

Zhou

Chen

et al. 2019

Cells

207

149

View full text Add to dashboard Cite

Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases expressed on the cell membrane that play crucial roles in both developmental and adult cells. Dysregulation of FGFRs has been implicated in a wide variety of cancers, such as urothelial carcinoma, hepatocellular carcinoma, ovarian cancer and lung adenocarcinoma. Due to their functional importance, FGFRs have been considered as promising drug targets for the therapy of various cancers. Multiple small molecule inhibitors targeting this family of kinases have been developed, and some of them are in clinical trials. Furthermore, the pan-FGFR inhibitor erdafitinib (JNJ-42756493) has recently been approved by the U.S. Food and Drug Administration (FDA) for the treatment of metastatic or unresectable urothelial carcinoma (mUC). This review summarizes the structure of FGFR, especially its kinase domain, and the development of small molecule FGFR inhibitors.

show abstract

“…AZD4547) inhibits FGFR1 in its DFG-in conformation via interaction with Asp641 thus interrupting the coordination of ATP phosphate group [35,36]; Type II (e.g. Ponatinib) inhibits FGFR1 in its DFG-out conformation and forms interactions with the conserved Glu531 of the αC helix region [36,37].…”

Section: Discussionmentioning

confidence: 99%

“…Acquired resistance is a challenge when considering long term efficacy of FGFR1 inhibitors, this resistance occurs via mutation of the gateway residue with a bulky amino acid e.g. methionine or isoleucine (resulting in the "gates been closed") [36,37,39]. Simulating this mutation(Val561 to Met561) we predict that the compounds might still be effective regardless of such mutations since the compounds still interacted with Met561: compound 2912 formed π-sulfur interaction, compound 3488, and 5227 from π-alkyl interaction suggesting effectiveness despite this acquired resistance.…”

Section: Discussionmentioning

confidence: 99%

Descriptor-Free QSAR: Effectiveness and Screening for Putative Inhibitors of FGFR1

Sulaimon

Joel

Adigun

et al. 2021

Preprint

View full text Add to dashboard Cite

The effectiveness of descriptors-utilizing quantitative structure-activity relationship models in drug design remains limited by the quality of descriptors used in training, this then raises the question: can QSAR models be directly trained on compound SMILES? Long short-term memory (LSTM) algorithm has been employed to answer this question however, direct application remain scarce. The effectiveness of a descriptor-free QSAR (LSTM-SM) in modeling FGFR1 inhibitors dataset while comparing with two conventional QSAR using descriptors (126bits Morgan fingerprint and 2D descriptors respectively) was investigated in this study. The validated descriptor-free QSAR model was thereafter used to screen for active FGFR1 inhibitors in ChemDiv database and subjected to molecular docking, induced-fit docking, and QM-MM optimization to filter for compounds with high binding affinity and suggest putative mechanism of inhibition and specificity. The LSTM-SM model, when compared with the conventional QSAR models, performed better having accuracy, specificity, and sensitivity of 0.92, model loss of 0.025 and AUC of 0.95.Fifteen thousand compounds were predicted as actives from the ChemDiv database and four compounds finally selected. Of the four, three showed putatively effective binding interactions with key active site residues and were also effective against acquired resistance due to gateway residue mutations. The advent of self-feature extracting machine learning algorithms, therefore, has provided the possibility of better predictive model quality that is not necessarily limited by compound descriptors thus we apply this approach in discovering putatively active FGFR1 inhibitors and elucidated putative mechanism of inhibition and specificity for the obtained compounds.

show abstract

Biophysical characterization of drug‐resistant mutants of fibroblast growth factor receptor 1

Cited by 21 publications

References 36 publications

FGFR-TKI resistance in cancer: current status and perspectives

FGFR-TKI resistance in cancer: current status and perspectives

Fibroblast Growth Factor Receptors (FGFRs): Structures and Small Molecule Inhibitors

Descriptor-Free QSAR: Effectiveness and Screening for Putative Inhibitors of FGFR1

Contact Info

Product

Resources

About