Selection of lansoprazole from an FDA-approved drug library to inhibit the Alzheimer's disease seed-dependent formation of tau aggregates

The efficacy of current treatments is still insufficient for Alzheimer's disease (AD), the most common cause of Dementia. Out of the two pathological hallmarks of AD amyloid-β plaques and neurofibrillary tangles, comprising of tau protein, tau pathology strongly correlates with the symptoms of AD. Previously, screening for inhibitors of tau aggregation that target recombinant tau aggregates have been attempted. Since a recent cryo-EM analysis revealed distinct differences in the folding patterns of heparin-induced recombinant tau filaments and AD tau filaments, this study focused on AD seed-dependent tau aggregation in drug repositioning for AD. We screened 763 compounds from an FDA-approved drug library using an AD seed-induced tau aggregation in SH-SY5Y cell-based assay. In the first screening, 180 compounds were selected, 72 of which were excluded based on the results of lactate dehydrogenase assay. In the third screening with evaluations of soluble and insoluble tau, 38 compounds were selected. In the fourth screening with 3 different AD seeds, 4 compounds, lansoprazole, calcipotriene, desogestrel, and pentamidine isethionate, were selected. After AD seed-induced real-time quaking-induced conversion, lansoprazole was selected as the most suitable drug for repositioning. The intranasal administration of lansoprazole for 4 months to AD seed-injected mice improved locomotor activity and reduced both the amount of insoluble tau and the extent of phosphorylated tau-positive areas. Alanine replacement of the predicted binding site to an AD filament indicated the involvement of Q351, H362, and K369 in lansoprazole and C-shaped tau filaments. These results suggest the potential of lansoprazole as a candidate for drug repositioning to an inhibitor of tau aggregate formation in AD.

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.

A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer

There is an unmet clinical need to identify patients with early-stage non-small cell lung cancer (NSCLC) who are likely to develop recurrence and to predict their therapeutic responses. Our previous study developed a qRT-PCR-based seven-gene microfluidic assay to predict the recurrence risk and the clinical benefits of chemotherapy. This study showed it was feasible to apply this seven-gene panel in RNA sequencing profiles of The Cancer Genome Atlas (TCGA) NSCLC patients (n = 923) in randomly partitioned feasibility-training and validation sets (p < 0.05, Kaplan-Meier analysis). Using Boolean implication networks, DNA copy number variation-mediated transcriptional regulatory network of the seven-gene signature was identified in multiple NSCLC cohorts (n = 371). The multi-omics network genes, including PD-L1, were significantly correlated with immune infiltration and drug response to 10 commonly used drugs for treating NSCLC. ZNF71 protein expression was positively correlated with epithelial markers and was negatively correlated with mesenchymal markers in NSCLC cell lines in Western blots. PI3K was identified as a relevant pathway of proliferation networks involving ZNF71 and its isoforms formulated with CRISPR-Cas9 and RNA interference (RNAi) profiles. Based on the gene expression of the multi-omics network, repositioning drugs were identified for NSCLC treatment.

Gossypol, a novel modulator of VCP, induces autophagic degradation of mutant huntingtin by promoting the formation of VCP/p97-LC3-mHTT complex

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by toxic aggregates of mutant huntingtin protein (mHTT) in the brain. Decreasing mHTT is a potential strategy for therapeutic purpose of HD. Valosin-containing protein (VCP/p97) is a crucial regulator of proteostasis, which regulates the degradation of damaged protein through proteasome and autophagy pathway. Since VCP has been implicated in pathogenesis of HD as well as other neurodegenerative diseases, small molecules that specifically regulate the activity of VCP may be of therapeutic benefits for HD patients. In this study we established a high-throughput screening biochemical assay for VCP ATPase activity measurement and identified gossypol, a clinical approved drug in China, as a novel modulator of VCP. Gossypol acetate dose-dependently inhibited the enzymatic activity of VCP in vitro with IC₅₀ of 6.53±0.6 μM. We further demonstrated that gossypol directly bound to the interface between the N and D1 domains of VCP. Gossypol acetate treatment not only lowered mHTT levels and rescued HD-relevant phenotypes in HD patient iPS-derived Q47 striatal neurons and HD knock-in mouse striatal cells, but also improved motor function deficits in both Drosophila and mouse HD models. Taken together, gossypol acetate acted through a gain-of-function way to induce the formation of VCP-LC3-mHTT ternary complex, triggering autophagic degradation of mHTT. This study reveals a new strategy for treatment of HD and raises the possibility that an existing drug can be repurposed as a new treatment of neurodegenerative diseases.

LINCS gene expression signature analysis revealed bosutinib as a radiosensitizer of breast cancer cells by targeting eIF4G1

Radioresistance is the predominant cause for radiotherapy failure and disease progression, resulting in increased breast cancer‑associated mortality. Using gene expression signature analysis of the Library of Integrated Network‑Based Cellular Signatures (LINCS) and Gene Expression Omnibus (GEO), the aim of the present study was to systematically identify potential candidate radiosensitizers from known drugs. The similarity of integrated gene expression signatures between irradiated eukaryotic translation initiation factor 4 γ 1 (eIF4G1)‑silenced breast cancer cells and known drugs was measured using enrichment scores (ES). Drugs with positive ES were selected as potential radiosensitizers. The radiosensitizing effects of the candidate drugs were analyzed in breast cancer cell lines (MCF‑7, MX‑1 and MDA‑MB‑231) using CCK‑8 and colony formation assays following exposure to ionizing radiation. Cell apoptosis was measured using flow cytometry. The expression levels of eIF4G1 and DNA damage response (DDR) proteins were analyzed by western blotting. Bosutinib was identified as a promising radiosensitizer, as its administration markedly reduced the dosage required both for the drug and for ionizing radiation, which may be associated with fewer treatment‑associated adverse reactions. Moreover, combined treatment of ionizing radiation and bosutinib significantly increased cell killing in all three cell lines, compared with ionizing radiation or bosutinib alone. Among the three cell lines, MX‑1 cells were identified as the most sensitive to both ionizing radiation and bosutinib. Bosutinib markedly downregulated the expression of eIF4G1 in a dose‑dependent manner and also reduced the expression of DDR proteins (including ATM, XRCC4, ATRIP, and GADD45A). Moreover, eIF4G1 was identified as a key target of bosutinib that may regulate DNA damage induced by ionizing radiation. Thus, bosutinib may serve as a potential candidate radiosensitizer for breast cancer therapy.

Computational drug repositioning based on the relationships between substructure-indication

At present, computational methods for drug repositioning are mainly based on the whole structures of drugs, which limits the discovery of new functions due to the similarities between local structures of drugs. In this article, we, for the first time, integrated the features of chemical-genomics (substructure-domain) and pharmaco-genomics (domain-indication) based on the assumption that drug-target interactions are mediated by the substructures of drugs and the domains of proteins to identify the relationships between substructure-indication and establish a drug-substructure-indication network for predicting all therapeutic effects of tested drugs through only information on the substructures of drugs. In total, 83 205 drug-indication relationships with different correlation scores were obtained. We used three different verification methods to indicate the accuracy of the method and the reliability of the scoring system. We predicted all indications of olaparib using our method, including the known antitumor effect and unknown antiviral effect verified by literature, and we also discovered the inhibitory mechanism of olaparib toward DNA repair through its specific sub494 (o = C-C: C), as it participates in the low synthesis of the poly subfunction of the apoptosis pathway (hsa04210) by inhibiting the Inositol 1,4,5-trisphosphate receptor(s) (ITPRs) and hydrolyzing poly (ADP ribose) polymerases. ElectroCardioGrams of four drugs (quinidine, amiodarone, milrinone and fosinopril) demonstrated the effect of anti-arrhythmia. Unlike previous studies focusing on the overall structures of drugs, our research has great potential in the search for more therapeutic effects of drugs and in predicting all potential effects and mechanisms of a drug from the local structural similarity.

Discovering proteasomal deubiquitinating enzyme inhibitors for cancer therapy: lessons from rational design, nature and old drug reposition

The ubiquitin proteasome system has been validated as a target of cancer therapies evident by the US FDA approval of anticancer 20S proteasome inhibitors. Deubiquitinating enzymes (DUBs), an essential component of the ubiquitin proteasome system, regulate cellular processes through the removal of ubiquitin from ubiquitinated-tagged proteins. The deubiquitination process has been linked with cancer and other pathologies. As such, the study of proteasomal DUBs and their inhibitors has garnered interest as a novel strategy to improve current cancer therapies, especially for cancers resistant to 20S proteasome inhibitors. This article reviews proteasomal DUB inhibitors in the context of: discovery through rational design approach, discovery from searching natural products and discovery from repurposing old drugs, and offers a future perspective.

Repositioning antipsychotic chlorpromazine for treating colorectal cancer by inhibiting sirtuin 1

Investigating existing drugs for repositioning can enable overcoming bottlenecks in the drug development process. Here, we investigated the effect and molecular mechanism of the antipsychotic drug chlorpromazine (CPZ) and identified its potential for treating colorectal cancer (CRC). Human CRC cell lines harboring different p53 statuses were used to investigate the inhibitory mechanism of CPZ. CPZ effectively inhibited tumor growth and induced apoptosis in CRC cells in a p53-dependent manner. Activation of c-jun N-terminal kinase (JNK) was crucial for CPZ-induced p53 expression and the subsequent induction of tumor apoptosis. Induction of p53 acetylation at lysine382 was involved in CPZ-mediated tumor apoptosis, and this induction was attenuated by sirtuin 1 (SIRT1), a class III histone deacetylase. By contrast, knocking down SIRT1 sensitized tumor cells to CPZ treatment. Moreover, CPZ induced the degradation of SIRT1 protein participating downstream of JNK, and JNK suppression abrogated CPZ-mediated SIRT1 downregulation. Clinical analysis revealed a significant association between high SIRT1 expression and poor outcome in CRC patients. These data suggest that SIRT1 is an attractive therapeutic target for CRC and that CPZ is a potential repositioned drug for treating CRC.

A subpathway-based method of drug reposition for polycystic ovary syndrome

The need for development of new therapeutic agents for polycystic ovary syndrome (PCOS) is urgent due to general lack of efficient and specialized drugs currently available. We aimed to explore the metabolic mechanism of PCOS and inferred drug reposition for PCOS by a subpathway-based method. Using the GSE34526 microarray data from the Gene Expression Omnibus database, we first identified the differentially expressed genes (DEGs) between PCOS and normal samples. Then, we identified 13 significantly enriched metabolic subpathways that may be involved in the development of PCOS. Finally, by an integrated analysis of PCOS-involved subpathways and drug-affected subpathways, we identified 54 novel small molecular drugs capable to target the PCOS-involved subpathways. We also mapped the DEGs of PCOS and a potential novel drug (alprostadil) into purine metabolism pathway to illustrate the potentially active mechanism of alprostadil on PCOS. Candidate agents identified by our approach may provide insights into a novel therapy approach for PCOS.