Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach

Abstract

Aim/hypothesis: The complexity and heterogeneity of multiple pathological features make Alzheimer's disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on "omics" data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD.

Results: We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein-protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug-target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors.

Conclusion: Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.

Figure 1

Flow chart of drug repurposing by omics data mining: We retrieved information on AD risk genes from GWAS, transcriptomics, and metabolomics approaches. We found 2914 AD risk genes from which 58 genes were extracted from GWAS, 229 genes were extracted from GEO transcriptomics data, and 2627 genes were related to 128 metabolites from the HMDB database. After functional enrichment analysis, we filtered out 49 AD-associated targets. The PPI network analysis resulted in 641 PPI interactions. We performed drug target mapping to find candidate drugs from DrugBank and TTD databases. Out of 641, 25 PPI interactions were found to be associated with 36 approved anticancer drugs. We excluded the information related to investigational and experimental drugs. We analyzed gene–gene and gene–drug interactions and selected the top 10 PPI interactions that correspond to 30 anticancer compounds. These 30 drugs were then analyzed by the CoDReS web tool that proposes 10 candidate drugs for AD. These drugs were then compared with the available Alzheimer’s therapeutics for structural and functional similarities, where six drugs have shown to be hierarchically clustered. ADMET analysis, pathway analysis, and functional similarity with miRNAs resulted in potential repurposing anticancer drugs against AD.

Figure 2

(A) Network is showing PPI interactions for AD-related genes. (B) STRING network of experimentally significant interactions. Glycogen synthase kinase 3 beta (GSK3B), vascular endothelial growth factor receptor 2 (KDR), APP, vascular endothelial growth factor receptor 1 (FLT1), and epidermal growth factor receptor (EGFR) were identified as the hub nodes. (C) STITCH network of drug-gene interactions. Nintedanib, sunitinib, vandetanib, dasatinib, erlotinib, imatinib, ponatinib, and bosutinib were reported as hub nodes as drugs. The size of individual nodes and the thickness of edges correspond to the significance and strength of interactions, respectively.

Figure 3

Summary of AD risk genes, genes in direct PPI, and targeted anticancer drugs. Drugs shown in yellow boxes were known in clinical studies as AD therapeutics, and drugs in green boxes were considered as potential repurposing candidates. Some drugs such as afatinib, axitinib, lenvatinib, nintedanib, pazopanib, sorafenib, and ponatinib interact with more than one target. NRG1: neuregulin 1; ERBB4: Erb-B2 receptor tyrosine kinase 4; LRP1: LDL receptor-related protein 1; EGFR: epidermal growth factor receptor; HSPG2: heparan sulfate proteoglycan 2; FLT1: Fms-related receptor tyrosine kinase 1; KDR: kinase insert domain receptor; SNCA: synuclein alpha; ABL1: ABL proto-oncogene 1, nonreceptor tyrosine kinase, NSCLC: nonsmall cell lung cancer, PC: pancreatic cancer, HBC: HER-positive breast cancer, RCC: renal cell carcinoma, STS: soft-tissue sarcoma, HC: hepatocellular carcinoma, GIST: gastrointestinal tumors, MTC: medullary thyroid cancer, AML: acute myelogenous leukemia, and CML: chronic myelogenous leukemia.

Figure 4

(A) Functional scores of different candidate repurposing drugs as calculated using the CoDReS tool. (B) Structural scores of different candidate repurposing drugs as calculated using the CoDReS tool. (C) CoDReS scores of candidate repurposing drugs. Erlotinib is shown as the most promising repurposing drug with good structural and functional scores. The structural scores of the drugs are more or less similar, while the functional scores have shown great variations. (D) Clustered heat map of candidate repurposing drugs with known Alzheimer’s drugs donepezil, rivastigmine, galantamine, and memantine. The heat map is generated using a distance matrix as the input generated by subtracting the similarity coefficient from 1. The colors from blue to red represent the correlation intensities of drugs where blue represents complete correlation and red represents no correlation.

Figure 5

(A) Network is showing the interrelationship of miRNAs associated with AD and those associated with repurposed anticancer drugs erlotinib, gefitinib, and vandetanib. The network shows that vandetanib shares many common targets such as EGFR, PTK6, RET, TEK, and VEGFA with AD-related miRNAs, while both erlotinib and gefitinib share functional similarity through the EGFR gene. (B–D) Association of erlotinib, gefitinb, and vandetanib with miRNAs, respectively, where miRNAs shown in green are neuroprotective, while miRNAs shown in purple are neurodegenerative as identified through literature analysis. miRNA-200a is the only one that shows association with all three repurposed drugs.

Figure 6

(A) Figure showing the functional categories of AD-related genes/PPI genes. The relative area of each segment corresponds to the relative fraction of a particular target class. As shown, protein kinases represent the major functional target protein class. (B) Functional classification of candidate repurposing anticancer drugs for AD. As expected, kinase inhibitors are the most prevalent drugs having neuroprotective functions.

Figure 7

Schematic representation of the proposed mechanism of neuroprotective functions of EGFR inhibitors in AD. The binding of a ligand to the EGFR causes conformational changes in the receptor and activates various signaling cascades. Activation of the PI3K/Akt axis activates mTOR that is a major inhibitor of the autophagic process. The inhibition of autophagy leads to neuronal death. Activated mTOR is responsible for tau phosphorylation and Aβ production, the two major pathological hallmarks of AD. Activated Akt further induces endothelial nitric oxide synthase (eNOS) that generates nitric oxide (NO), a neurotoxin. The activated Akt instigates inflammatory cytokine production by inducing NF-κB production. The activated EGFR induces Ca²⁺ release from the endoplasmic reticulum by inducing phospholipase C gamma (PLC-γ) production. Excessive release of Ca²⁺ causes synaptic dysfunction and Aβ production from APP. All the events trigger neuroinflammation and neurodegeneration. Pharmacological inhibition of the EGFR by inhibitors, erlotinib, gefitinib, and vandetanib, may reverse the downstream signaling cascades of the EGFR and provide neuroprotection, a reduction in synaptic dysfunction, reduced tau phosphorylation, inhibition of neuronal death, and inhibition of neuroinflammatory processes. Dotted arrows represent the proposed neuroprotective functions of the repurposed drugs.