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Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

ABSTRACT: Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-? in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

SUBMITTER: Khalid S

PROVIDER: S-EPMC5963753 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

Khalid Samra S Hanif Rumeza R Jabeen Ishrat I Mansoor Qaisar Q Ismail Muhammad M

PloS one 20180522 5

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, M ...[more]

PMID: 29787591

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Project description:Ewing Sarcoma is caused by a pathognomonic genomic translocation that places an N-terminal EWSR1 gene in approximation with one of several ETS genes (typically FLI1). This aberration, in turn, alters the transcriptional regulation of more than five hundred genes and perturbs a number of critical pathways that promote oncogenesis, cell growth, invasion, and metastasis. Among them, translocation-mediated up-regulation of the insulin-like growth factor receptor 1 (IGF-1R) and mammalian target of rapamycin (mTOR) are of particular importance since they work in concert to facilitate IGF-1R expression and ligand-induced activation, respectively, of proven importance in ES transformation. When used as a single agent in Ewing sarcoma therapy, IGF-1R or mTOR inhibition leads to rapid counter-regulatory effects that blunt the intended therapeutic purpose. Therefore, identify new mechanisms of resistance that are used by Ewing sarcoma to evade cell death to single-agent IGF-1R inhibition might suggest a number of therapeutic combinations that could improve its clinical activity. TC32 and TC71 ES clones with acquired resistance to OSI-906 or NVP-ADW-742 were generated by maintaining the corresponding parental cell lines with increasing concentrations of the agents (up to 2.3 Î¼M for OSI-906, 1.5 Î¼M for NVP-ADW-742) for 7 months. All parental and acquired drug resistant cell lines were tested twice per year for mycoplasma contamination using the MycoAlert Detection Kit (Lonza Group Ltd.) according to the manufacturerâs protocol and validated using short-tandem repeat fingerprinting with an AmpFLSTR Identifier kit as previously described. Herein, we determine subtle differences in acquired mechanism of resistance by two promising small molecule inhibitors of IGF-1R/IR-Î±. OSI-906, which inhibits IGF-1R and IR, and NVP-ADW-742, which inhibits only IGF-1R, were evaluated using in vitro assays to decipher the mechanism(s) by which IGF-1R inhibition induces drug resistance in Ewing sarcoma cells. The preparation of extracted proteins from sensitive and acquired resistant Ewing sarcoma cells to OSI-906 and NVP-ADW-742 for reverse-phase protein lysate array (RPPA) analysis were prepared using the same array. Lysates were processed, spotted onto nitrocellulose-coated FAST slides, probed with 115 validated primary antibodies, and detected using a DakoCytomation-catalyzed system with secondary antibodies. MicroVigene software program (VigeneTech) was used for automated spot identification, background correction, and individual spot-intensity determination. Expression data was normalized for possible unequal protein loading, taking into account the signal intensity for each sample for all antibodies tested. Log2 values were media-centered by protein to account for variability in signal intensity by time and were calculated using the formula log2 signal â log2 median. Principal component analysis was used to check for a batch effect and feature-by-feature two-sample t-tests were used to assess differences between sensitive and resistant cell lines to drug treatments. We also used feature-by-feature one-way analysis of variance (ANOVA) followed by the Tukey test to perform pair comparisons for all groups. Beta-uniform mixture models were used to fit the resulting p value distributions to adjust for multiple comparisons. The cutoff p values and number of significant proteins were computed for several different false discovery rates (FDRs). Biostatistical analyses comparing two groups were performed using an unpaired t-test with Gaussian distribution followed by the Welch correction. To distinguish between treatment groups, we used one-way ANOVA with the Geisser-Greenhouse correction. Differences with p values <0.05 were considered significant. Within clustered image maps (CIM), unsupervised double hierarchical clustering used the Pearson correlation distance and Wardâs linkage method as the clustering algorithm to link entities (proteins) and samples.

Dataset Information

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

Publications

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

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