• Journal of Integrative Natural Science
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• v.10 no.2
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• pp.85-94
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• 2017

Fibroblast growth factor receptor (FGFR) belongs to the family of receptor tyrosine kinase. They play important roles in cell proliferation, differentiation, development, migration, survival, wound healing, haematopoiesis and tumorigenesis. FGFRs are reported to cause several types of cancers in humans which make it an important drug target. In the current study, HQSAR analysis was performed on a series of recently reported 1H-Pyrazolo [3,4-b]pyridine derivatives as FGFR antagonists. The model was developed with Atom (A) and bond (B) connection (C), chirality (Ch), hydrogen (H) and donor/acceptor (DA) parameters and with different set of atom counts to improve the model. A reasonable HQSAR model ($q^2=0.701$, SDEP=0.654, NOC=5, $r^2=0.926$, SEE=0.325, BHL=71) was generated which showed good predictive ability. The contribution map depicted the atom contribution in inhibitory effect. A contribution map for the most active compound (compound 24) indicated that hydrogen and nitrogen atoms in the side chains of ring B as well as hydrogen atoms in the side chain of ring C and the nitrogen atom in the ring D contributed positively to the activity in inhibitory effect whereas, the lowest active compound (compound 04) showed negative contribution to inhibitory effect. Thus results of our study can provide insights in the designing potent and selective FGFR kinase inhibitors.

• Bulletin of the Korean Chemical Society
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• v.25 no.12
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• pp.1801-1806
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• 2004

Ligand-based quantitative structure-activity relationship (QSAR) studies were performed on indolinones derivatives as a potential inhibitor of the protein tyrosine kinase of fibroblast growth factor receptor (FGFR) by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) implemented in the SYBYL packages. The initial X-ray structure of docked ligand (Su5402) to FGFR was used to minimize the 27 training set molecules using TRIPOS force field. Seven models were generated using CoMFA and CoMSIA with grid spacing 2 ${\AA}$. After the PLS analysis the best predicted CoMSIA model with hydrophobicity, hydrogen bond donor and acceptor property showed that a leave-one out(LOO) cross validated value $({r^2}_{cv})^$ and non-cross validated conventional value $({r^2}_{ncv})^$ are 0.543 and 0.938, respectively.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.186.3-186.3
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• 2003

The three-dimensional quantitative structure-activity relationship (3D-QSAR) study using the comparative molecular field analysis (CoMFA) was performed on indolinones derivatives as an inhibitor of the protein tyrosine kinase of fibroblast growth factor receptor (FGFR). In the training set, twenty-four indolinone derivatives were aligned based on the indole fragment and the steric and electrostatic fields were included in the analysis. The best predicted model showed the cross-validated coefficient (r$^2$$\sub$cv/) of 0.804 and bib-cross validated coefficient (r$^2$) of 0.942. The CoMFA study can be used to predict several new inhibitors of the FGFR.

• Journal of Gastric Cancer
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• v.20 no.1
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• pp.29-40
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• 2020

Purpose: Gastrointestinal stromal tumors (GISTs) frequently harbor activating gene mutations in either KIT or platelet-derived growth factor receptor A (PDGFRA) and are highly responsive to several selective tyrosine kinase inhibitors. In this study, a targeted next-generation sequencing (NGS) assay with an Oncomine Focus Assay (OFA) panel was used for the genetic characterization of molecular targets in 30 Korean patients with GIST. Materials and Methods: Using the OFA that enables rapid and simultaneous detection of hotspots, single nucleotide variants (SNVs), insertion and deletions (Indels), copy number variants (CNVs), and gene fusions across 52 genes relevant to solid tumors, targeted NGS was performed using genomic DNA extracted from formalin-fixed and paraffin-embedded samples of 30 GISTs. Results: Forty-three hotspot/other likely pathogenic variants (33 SNVs, 8 Indels, and 2 amplifications) in 16 genes were identified in 26 of the 30 GISTs. KIT variants were most frequent (44%, 19/43), followed by 6 variants in PIK3CA, 3 in PDGFRA, 2 each in JAK1 and EGFR, and 1 each in AKT1, ALK, CCND1, CTNNB1, FGFR3, FGFR4, GNA11, GNAQ, JAK3, MET, and SMO. Based on the mutation types, majority of the variants carried missense mutations (60%, 26/43), followed by 8 frameshifts, 6 nonsense, 1 stop-loss, and 2 amplifications. Conclusions: Our study confirmed the advantage of using targeted NGS with a cancer gene panel to efficiently identify mutations associated with GISTs. These findings may provide a molecular genetic basis for developing new drugs targeting these gene mutations for GIST therapy.