Computational Drug Repositioning and Experimental Validation of Ivermectin in Treatment of Gastric Cancer

Abstract

Objective: The aim of the present study was repositioning of ivermectin in treatment of gastric cancer (GC) by computational prediction based on gene expression profiles of human and mouse model of GC and validations with in silico, in vitro and in vivo approaches.Methods: Computational drug repositioning was performed using connectivity map (cMap) and data/pathway mining with the Ingenuity Knowledge Base. Tissue samples of GC were collected from 16 patients and 57 mice for gene expression profiling. Additional seven independent datasets of gene expression of human GC from the TCGA database were used for validation. In silico testing was performed by constructing interaction networks of ivermectin and the downstream effects in targeted signaling pathways. In vitro testing was carried out in human GC cell lines (MKN74 and KATO-III). In vivo testing was performed in a transgenic mouse model of GC (INS-GAS mice).Results: GC gene expression “signature” and data/pathway mining but not cMAP revealed nine molecular targets of ivermectin in both human and mouse GC associated with WNT/β-catenin signaling as well as cell proliferation pathways. In silico inhibition of the targets of ivermectin and concomitant activation of ivermectin led to the inhibition of WNT/β-catenin signaling pathway in “dose-depended” manner. In vitro, ivermectin inhibited cell proliferation in time- and concentration-depended manners, and cells were arrested in the G1 phase at IC50 and shifted to S phase arrest at >IC50. In vivo, ivermectin reduced the tumor size which was associated with inactivation of WNT/β-catenin signaling and cell proliferation pathways and activation of cell death signaling pathways.Conclusion: Ivermectin could be recognized as a repositioning candidate in treatment of gastric cancer.

Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model)

“…Machine learning methods have been widely used in many fields of research, such as text classification [23], sentiment analysis [24], breast cancer diagnosis [25], web page classification [26], and image generation [27]. Many researchers have used computational and modeling methods to identify potential combinatorial drugs [17,[28][29][30], and the use of machine learning to predict drug combinations has notably increased in recent years [31][32][33][34]. However, nearly all of this research has focused of combinations of Western drugs.…”

Current trends and future prospects of drug repositioning in gastrointestinal oncology

“…Nitazoxanide also yielded favorable outcomes, as it exhibited activity across GC cell lines tested ( Ribeiro et al, 2023 ). In addition, Rabben et al (2021a) computationally predicted the repositioning of ivermectin for the treatment of GC based on gene expression profiles of both human and mouse models of GC. They further validated their in silico prediction used human GC cell lines MKN74 and KATO-III in vitro.…”

“…They further validated their in silico prediction used human GC cell lines MKN74 and KATO-III in vitro. Transgenic insulin–gastrin (INS-GAS) mice were employed for experimental validation of ivermectin in GC treatment ( Rabben et al, 2021a ). Furthermore, in vivo and in vitro anti-tumor and growth suppression effects of ivermectin were demonstrated on GC, showing that ivermectin suppressed MKN1 cells growth through yes-associated protein 1 (YAP1) downregulation ( Nambara et al, 2017 ).…”

Drug repurposing based on the similarity gene expression signatures to explore for potential indications of quercetin: a case study of multiple sclerosis

“…Repurposed drugs can reveal new targets and pathways that can be further exploited (Pushpakom et al, 2019;Loscalzo, 2023). The advantage of drug repurposing is that it takes full advantage of the physical and chemical properties of known approved or experimental drugs and well-defined pharmacological or toxicological mechanisms to shorten the time and cost of new drug development and reduce the potential risk of toxic side effects (Rabben et al, 2021). It is worth noting that most preclinical and clinical trial data are currently available through databases such as DrugBank (https://go.drugbank.com/) and ClinicalTrials (https://beta.clinicaltrials.gov/) (Antoszczak et al, 2020).…”