A Large-Scale Gene Expression Intensity-Based Similarity Metric for Drug Repositioning

MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature

Understanding the mechanisms of actions (MOAs) of compounds is crucial in drug discovery. A common step in drug MOAs annotation is to query the dysregulated gene signatures induced by drugs in a reference library of pre-defined signatures. However, traditional similarity-based computational strategies face challenges when dealing with high-dimensional and noisy transcriptional signature data. To address this issue, we introduce MOASL (MOAs prediction via Similarity Learning), a novel approach that contrastive to learn similarity embeddings among signatures with shared MOAs automatically. We evaluated the accuracy of signature matching on various transcriptional activity score (TAS) datasets and individual cell lines by using MOASL. The results show MOASL achieved higher performance over several statistical and machine learning methods. Furthermore, we provided the rationale of our model by visualizing the signature annotation procedure. Using MOASL, the MOAs label of query signature could be conveniently defined by calculating the similarity between the query embedding and the reference embeddings. Finally, we applied MOASL to repurpose thousands of compounds as glucocorticoid receptor (GR) agonists, accurately identifying 8 out of the top 10 compounds. MOASL is conveniently accessible on GitHub at https://github.com/jianglikun/MOASL, empowering researchers and practitioners in the field of drug discovery to predict the MOAs of drug.

Deep learning in drug discovery: an integrative review and future challenges

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

BI-2536 Promotes Neuroblastoma Cell Death via Minichromosome Maintenance Complex Components 2 and 10

DNA replication is initiated with the recognition of the starting point of multiple replication forks by the origin recognition complex and activation of the minichromosome maintenance complex 10 (MCM10). Subsequently, DNA helicase, consisting of the MCM protein subunits MCM2-7, unwinds double-stranded DNA and DNA synthesis begins. In previous studies, replication factors have been used as clinical targets in cancer therapy. The results showed that MCM2 could be a proliferation marker for numerous types of malignant cancer. We analyzed samples obtained from patients with neuroblastoma, revealing that higher levels of MCM2 and MCM10 mRNA were associated with poor survival rate. Furthermore, we combined the results of the perturbation-induced reversal effects on the expression levels of MCM2 and MCM10 and the sensitivity correlation between perturbations and MCM2 and MCM10 from the Cancer Therapeutics Response Portal database. Small molecule BI-2536, a polo-like kinase 1 (PLK-1) inhibitor, is a candidate for the inhibition of MCM2 and MCM10 expression. To test this hypothesis, we treated neuroblastoma cells with BI-2536. The results showed that the drug decreased cell viability and reduced the expression levels of MCM2 and MCM10. Functional analysis further revealed enrichments of gene sets involved in mitochondria, cell cycle, and DNA replication for BI-2536-perturbed transcriptome. We used cellular assays to demonstrate that BI-2536 promoted mitochondria fusion, G2/M arrest, and apoptosis. In summary, our findings provide a new strategy for neuroblastoma therapy with BI-2536.

Deciphering the Role of Pyrvinium Pamoate in the Generation of Integrated Stress Response and Modulation of Mitochondrial Function in Myeloid Leukemia Cells through Transcriptome Analysis

Pyrvinium pamoate, a widely-used anthelmintic agent, reportedly exhibits significant anti-tumor effects in several cancers. However, the efficacy and mechanisms of pyrvinium against myeloid leukemia remain unclear. The growth inhibitory effects of pyrvinium were tested in human AML cell lines. Transcriptome analysis of Molm13 myeloid leukemia cells suggested that pyrvinium pamoate could trigger an unfolded protein response (UPR)-like pathway, including responses to extracellular stimulus [p-value = 2.78 × 10^-6] and to endoplasmic reticulum stress [p-value = 8.67 × 10^-7], as well as elicit metabolic reprogramming, including sulfur compound catabolic processes [p-value = 2.58 × 10^-8], and responses to a redox state [p-value = 5.80 × 10^-5]; on the other hand, it could elicit a pyrvinium blunted protein folding function, including protein folding [p-value = 2.10 × 10^-8] and an ATP metabolic process [p-value = 3.95 × 10^-4]. Subsequently, pyrvinium was verified to induce an integrated stress response (ISR), demonstrated by activation of the eIF2α-ATF4 pathway and inhibition of mTORC1 signaling, in a dose- and time-dependent manner. Additionally, pyrvinium could co-localize with mitochondria and then decrease the mitochondrial basal oxidative consumption rate, ultimately dysregulating the mitochondrial function. Similar effects were observed in cabozantinib-resistant Molm13-XR cell lines. Furthermore, pyrvinium treatment retarded Molm13 and Molm13-XR xenograft tumor growth. Thus, we concluded that pyrvinium exerts anti-tumor activity, at least, via the modulation of the mitochondrial function and by triggering ISR.

A Quick Guide to Small-Molecule Inhibitors of Eukaryotic Protein Synthesis

Eukaryotic ribosome and cap-dependent translation are attractive targets in the antitumor, antiviral, anti-inflammatory, and antiparasitic therapies. Currently, a broad array of small-molecule drugs is known that specifically inhibit protein synthesis in eukaryotic cells. Many of them are well-studied ribosome-targeting antibiotics that block translocation, the peptidyl transferase center or the polypeptide exit tunnel, modulate the binding of translation machinery components to the ribosome, and induce miscoding, premature termination or stop codon readthrough. Such inhibitors are widely used as anticancer, anthelmintic and antifungal agents in medicine, as well as fungicides in agriculture. Chemicals that affect the accuracy of stop codon recognition are promising drugs for the nonsense suppression therapy of hereditary diseases and restoration of tumor suppressor function in cancer cells. Other compounds inhibit aminoacyl-tRNA synthetases, translation factors, and components of translation-associated signaling pathways, including mTOR kinase. Some of them have antidepressant, immunosuppressive and geroprotective properties. Translation inhibitors are also used in research for gene expression analysis by ribosome profiling, as well as in cell culture techniques. In this article, we review well-studied and less known inhibitors of eukaryotic protein synthesis (with the exception of mitochondrial and plastid translation) classified by their targets and briefly describe the action mechanisms of these compounds. We also present a continuously updated database (http://eupsic.belozersky.msu.ru/) that currently contains information on 370 inhibitors of eukaryotic protein synthesis.

A guide for bioinformaticians: 'omics-based drug discovery for precision oncology

Bioinformatics-centric drug development is inevitable in the era of precision medicine. Clinical 'omics information, including genomics, epigenomics, transcriptomics, and proteomics, provides the most comprehensive molecular landscape in which each patient's pathological history is delineated. Hence, the capability of bioinformaticians to manage integrative 'omics data is crucial to current drug development. Bioinformatics can accelerate drug development from initial time-consuming discoveries to the clinical stage by providing information-guided solutions. However, many bioinformaticians do not have opportunities to participate in drug discovery programs. As a starting point for bioinformaticians with no prior drug development experience, here we discuss bioinformatics applications during drug development with a focus on working-level omics-based methodologies.

Multiomics Reveals Ectopic ATP Synthase Blockade Induces Cancer Cell Death via a lncRNA-mediated Phospho-signaling Network

The EGFR tyrosine kinase inhibitor gefitinib is commonly used for lung cancer patients. However, some patients eventually become resistant to gefitinib and develop progressive disease. Here, we indicate that ecto-ATP synthase, which ectopically translocated from mitochondrial inner membrane to plasma membrane, is considered as a potential therapeutic target for drug-resistant cells. Quantitative multi-omics profiling reveals that ecto-ATP synthase inhibitor mediates CK2-dependent phosphorylation of DNA topoisomerase IIα (topo IIα) at serine 1106 and subsequently increases the expression of long noncoding RNA, GAS5. Additionally, we also determine that downstream of GAS5, p53 pathway, is activated by ecto-ATP synthase inhibitor for regulation of programed cell death. Interestingly, GAS5-proteins interactomic profiling elucidates that GAS5 associates with topo IIα and subsequently enhancing the phosphorylation level of topo IIα. Taken together, our findings suggest that ecto-ATP synthase blockade is an effective therapeutic strategy via regulation of CK2/phospho-topo IIα/GAS5 network in gefitinib-resistant lung cancer cells.

A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond

Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug-drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug-drug similarity network. The resulting database, 'DrugSimDB' currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http://vafaeelab.com/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information.

Drivers of topoisomerase II poisoning mimic and complement cytotoxicity in AML cells

Recently approved cancer drugs remain out-of-reach to most patients due to prohibitive costs and only few produce clinically meaningful benefits. An untapped alternative is to enhance the efficacy and safety of existing cancer drugs. We hypothesized that the response to topoisomerase II poisons, a very successful group of cancer drugs, can be improved by considering treatment-associated transcript levels. To this end, we analyzed transcriptomes from Acute Myeloid Leukemia (AML) cell lines treated with the topoisomerase II poison etoposide. Using complementary criteria of co-regulation within networks and of essentiality for cell survival, we identified and functionally confirmed 11 druggable drivers of etoposide cytotoxicity. Drivers with pre-treatment expression predicting etoposide response (e.g., PARP9) generally synergized with etoposide. Drivers repressed by etoposide (e.g., PLK1) displayed standalone cytotoxicity. Drivers, whose modulation evoked etoposide-like gene expression changes (e.g., mTOR), were cytotoxic both alone and in combination with etoposide. In summary, both pre-treatment gene expression and treatment-driven changes contribute to the cell killing effect of etoposide. Such targets can be tweaked to enhance the efficacy of etoposide. This strategy can be used to identify combination partners or even replacements for other classical anticancer drugs, especially those interfering with DNA integrity and transcription.

Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

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Compound signatures targeting non-oncogene addiction for combinatorial drug discovery

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These signatures are reproducible and linked to cancer hallmarks and drug sensitivity

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Two synergistic drug combinations are experimentally confirmed in vitro

Therapeutic Targeting of Non-oncogene Dependencies in High-risk Neuroblastoma

PURPOSE

Neuroblastoma is a pediatric malignancy of the sympathetic nervous system with diverse clinical behaviors. Genomic amplification of MYCN oncogene has been shown to drive neuroblastoma pathogenesis and correlate with aggressive disease, but the survival rates for those high-risk tumors carrying no MYCN amplification remain equally dismal. The paucity of mutations and molecular heterogeneity has hindered the development of targeted therapies for most advanced neuroblastomas. We use an alternative method to identify potential drugs that target nononcogene dependencies in high-risk neuroblastoma.

EXPERIMENTAL DESIGN

By using a gene expression-based integrative approach, we identified prognostic signatures and potentially effective single agents and drug combinations for high-risk neuroblastoma.

RESULTS

Among these predictions, we validated in vitro efficacies of some investigational and marketed drugs, of which niclosamide, an anthelmintic drug approved by the FDA, was further investigated in vivo. We also quantified the proteomic changes during niclosamide treatment to pinpoint nucleoside diphosphate kinase 3 (NME3) downregulation as a potential mechanism for its antitumor activity.

CONCLUSIONS

Our results establish a gene expression-based strategy to interrogate cancer biology and inform drug discovery and repositioning for high-risk neuroblastoma.