Artificial intelligence based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

Abstract

BackgroundIdentification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs.ResultsIn this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimer’s disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib)DiscussionsOur computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.

Neurodegeneration and astrogliosis in the entorhinal cortex in Alzheimer's disease: Stereological layer‐specific assessment and proteomic analysis

“…SYN1, DLG4, and RAB3A, which have been described to be essential for synaptic function, 50–52 were expressed at lower levels in AD. Our data agree with other studies showing altered synapse function 53 .…”

“…We report here a reduction in SYN1 levels in the human EC in AD and spatial colocalization with astrocytes (Figure 5I and S8). Low presynaptic SYN1 levels may also exert a negative effect on the postsynaptic level, where DLG4 was downregulated 50,51 . The literature is controversial regarding how DLG4 protein levels are affected in the AD brain 55 .…”

“…GSK3B is associated with cognitive function and has been used as a cerebrospinal fluid biomarker, because GSK3B inhibitors reduce AD pathology. 48,49 SYN1, DLG4, and RAB3A, which have been described to be essential for synaptic function, [50][51][52] were expressed at lower levels in AD.…”

“…Low presynaptic SYN1 levels may also exert a negative effect on the postsynaptic level, where DLG4 was downregulated. 50,51 The literature is controversial regarding how DLG4 protein levels are affected in the AD brain. 55 Our proteomic study revealed a marked reduction in this protein.…”

Safety, target engagement, and biomarker effects of bosutinib in dementia with Lewy bodies

“…9 In animal models Abl/Src inhibition is an optimal strategy to reduce misfolded proteins and protect dopaminergic neurons. 7,13 Dementia with Lewy bodies (DLB) is characterized by loss of dopaminergic neurons and accumulation of misfolded alpha-synuclein in Lewy bodies (LBs). Alpha-synuclein, hyper-phosphorylated tau (p-tau), and Aβ aggregates are present in neurodegenerative diseases.…”