In Silico and In Vitro Exploration of Poziotinib and Olmutinib Synergy in Lung Cancer: Role of hsa-miR-7-5p in Regulating Apoptotic Pathway Marker Genes

Abstract

Background and objectives: Non-small cell lung cancer (NSCLC) is often caused by EGFR mutations, leading to overactive cell growth pathways. Drug resistance is a significant challenge in lung cancer treatment, affecting therapy effectiveness and patient survival. However, combining drugs in research shows promise in addressing or delaying resistance, offering a more effective approach to cancer treatment. In this study, we investigated the potential alterations in the apoptotic pathway in A549 cells induced by a combined targeted therapy using tyrosine kinase inhibitors (TKIs) olmutinib and poziotinib, focusing on cell proliferation, differential gene expression, and in silico analysis of apoptotic markers. Methods: A combined targeted therapy involving olmutinib and poziotinib was investigated for its impact on the apoptotic pathway in A549 cells. Cell proliferation, quantitative differential gene expression, and in silico analysis of apoptotic markers were examined. A549 cells were treated with varying concentrations (1, 2.5, and 5 μM) of poziotinib, olmutinib, and their combination. Results: Treatment with poziotinib, olmutinib, and their combination significantly reduced cell proliferation, with the most pronounced effect at 2.5 μM (p < 0.005). A synergistic antiproliferative effect was observed with the combination of poziotinib and olmutinib (p < 0.0005). Quantitative differential gene expression showed synergistic action of the drug combination, impacting key apoptotic genes including STK-11, Bcl-2, Bax, and the Bax/Bcl-2 ratio. In silico analysis revealed direct interactions between EGFR and ERBB2 genes, accounting for 77.64% of their interactions, and 8% co-expression with downstream apoptotic genes. Molecular docking indicated strong binding of poziotinib and olmutinib to extrinsic and intrinsic apoptotic pathway markers, with binding energies of -9.4 kcal/mol and -8.5 kcal/mol, respectively, on interacting with STK-11. Conclusions: Combining poziotinib and olmutinib therapies may significantly improve drug tolerance and conquer drug resistance more effectively than using them individually in lung cancer patients, as suggested by this study's mechanisms.

Keywords: Bax; Bcl-2; STK-11; lung cancer; olmutinib; poziotinib; tyrosine kinase inhibitor.

Figure 1

Poziotinib and olmutinib structures were adapted from PubChem and SMILES were used for BOILED-Egg demonstration in Swiss ADME’s prediction tool. Poziotinib and olmutinib are absorbed passively by gastrointestinal absorption. Both TKIs do not cross the blood–brain barrier. The olmutinib in egg white (blue dot), demonstrates it as Pgp substrate and hence could be pumped out in secretions or feces while poziotinib may retain its concentrations for longer as it is a Pgp non-substrate.

Figure 2

A network of epidermal growth factor receptor (Egfr) and tyrosine kinase receptor (Erbb2) interactions. EGFR and ERBB2 show maximum physical interactions (77.6%) and co-expression (8%) with bcl-2, bax, and stk-11 proteins with implicated pathways as apoptotic mitochondrial changes, intrinsic and extrinsic pathways.

Figure 3

The Cell proliferation assay data in A549. Treatment of cells at concentrations of 1, 2.5, and 5 μM of poziotinib, olmutinib, and their Combination for 24 h, 48 h, and 72 h. Absorbance results represented as mean and standard error measured at 405 nm are expressed in the graph. The combination of poziotinib and olmutinib at 48 h is seen to have a maximum antiproliferative effect on A549 cells compared to all other treatments. This indicates that there was a significant reduction in cell proliferation potential with the combination (p < 0.05).

Figure 4

Relative gene expression levels of apoptotic markers. (A) Bcl; (B) Bax; (C) stk-11; and (D) bax/bcl-2. The expression levels of apoptotic gene markers were assessed using qPCR in cells treated with poziotinib and olmutinib alone and in combination. All assessed genes showed synergism in drug combination and better potential. Values are represented as mean ± SD of three replicates *** p < 0.005 and **** p < 0.0005, ns: not significant.

Figure 5

Target interaction network of miRNAs targeted by Bcl-2, bax, and stk-11 genes using MIENTURNET. (A) miRNA and gene Networks where each bar represents the degree of involvement of three genes in chemotherapeutic mechanisms. Three interactions were observed for miRNA-5-7p. (B) Significant network layout of involvement of miRNA-5-7p. The size of the nodes corresponds to the gene ratio, yellow nodes indicate genes and blue-colored nodes indicate miRNAs. Hsa-miRNA-5-7p showed significant interactions with all three apoptotic markers.

Figure 6

Reactome and WikiPathways Enrichment Analysis of the pathways targeted by miRNA-5-7p. (A) The key pathways involved in the therapeutic synergistic action of poziotinib and olmutinib are apoptosis, platinum drug resistance pathway, p53-dependent apoptotic pathway, EGFR tyrosine kinase resistance pathway, and colorectal cancer pathway. (B) The mechanisms of regulation were related to the integrated breast cancer pathway at a p-value of 0.0003. The enrichment of the network’s miRNAs is shown as dot plots with the annotation on the Y-axis and the miRNAs on the X-axis, the number (3) in the bracket after the miRNA name is the number of identified targets. The size of each dot corresponds to the gene ratio with FDR.

Figure 7

Molecular docking conformations of (a) poziotinib and STK-11; (b) olmutinib and STK-11; (c) poziotinib and Bcl-2; (d) olmutinib Bcl-2; (e) poziotinib and Bax; (f) olmutinib and Bax.