Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

Modulatory effect of metformin and its transporters on immune infiltration in tumor microenvironment: a bioinformatic study with experimental validation

Metformin is a traditional antidiabetic drug for type 2 diabetes mellitus. However, it showed antitumor activity in many types of tumors, and it also has an influence on tumor metastasis in several types of tumors. It is transported through organic cationic transporters (OCTs), OCT1, OCT2, and OCT3, into the cells or into tumor microenvironment (TME). The complex interaction of metformin and its transporters on immune infiltration in TME of different types of tumors of The Cancer Genomic Atlas (TCGA) is not yet studied. The objective of this study is to identify the most suitable therapeutic target of tumors and immune infiltrates for metformin and its transporters in the TME. TIMER2.0, a bioinformatic tool, and other computational analysis were used to investigate this complex interaction; moreover, the identification of metformin target protein in TME is also investigated. The results revealed that the most suitable therapeutic target for metformin and OCTs among 32 types of TCGA data tumor types is Breast Invasive carcinoma (BRCA), and the most relevant immune infiltrate among 14 types of immune infiltrates that yields better prognosis and better therapeutical effect in TME is Macrophage M1. Furthermore, metformin showed a cytotoxic effect and an inhibitory effect on Urokinase Plasminogen Activator (uPA) gene expression in a concentration dependent fashion in MDA-MB-231 breast cancer cell line. This may suggest that metformin is a promising antitumor drug, stimulant for natural antitumor immune infiltrates, and inhibitor for metastasis in breast cancer.

Metformin-Induced Invertase Unfolding: Enzyme Kinetics and Activity Regulation

The effects of metformin on invertase activity and its inhibition on sucrose digestion were studied. The rapid unfolding kinetics of invertases, followed a two-state model with an inactive intermediate formation. The dynamic interaction between metformin and invertase caused the secondary structure of the enzyme to become less β-sheet, more α-helix, and random coiling oriented, which weakened the binding force between enzyme and its substrate. Metformin acted as a chaotrope and disrupted the hydrogen bonds of water, which facilitated the unfolding of invertase. However, some sugar alcohols, which promoted the H-bond formation of water, could repair the secondary structure of metformin-denatured invertase and therefore regulate the enzyme activity. This research enriches our understanding of the mechanism of enzyme unfolding induced by guanidine compounds. Moreover, because metformin and sugar substitutes are of concern to diabetes, this research also provides useful information for understanding the activity of the digestive enzyme that coexists with metformin and sugar alcohols.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Effective holistic characterization of small molecule effects using heterogeneous biological networks

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities

BACKGROUND

The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial.

AIMS AND METHODS

To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment.

RESULTS AND CONCLUSIONS

We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.

Metabolic and Metabolomic Effects of Metformin in Murine Model of Pulmonary Adenoma Formation

Epidemiologic studies of diabetic patients treated with metformin identified significantly lower incidences of cancer. From this, there is growing interest in the use of metformin to treat and prevent cancer. Studies have investigated chemopreventive mechanisms including alterations in calorie intake, cancer metabolism, and cell signaling. Repurposing the drug is challenging due to its metabolic effects and non-uniform effects on different types of cancer. In our previously published studies, we observed that benzo[a]pyrene treated mice receiving metformin significantly reduced lung adenomas; however, mice had reduced weight gain. In this study, we compared chemoprevention diets with and without metformin to evaluate the effects of diet vs. effects of metformin. We also performed tandem mass spectrometry on mouse serum to assess metabolomic alterations associated with metformin treatment. In metformin cohorts, the rate of weight gain was reduced, but weights did not vary between diets. There was no weight difference between diets without metformin. Interestingly, caloric intake was increased in metformin treated mice. Metabolomic analysis revealed metabolite alterations consistent with metformin treatment. Based on these results, we conclude that previous reductions in lung adenomas may have been occurred from anticancer effects of metformin rather than a potentially toxic effect such as calorie restriction.

Drug repurposing: re-inventing therapies for cancer without re-entering the development pipeline—a review

While majority of the current treatment approaches for cancer remain expensive and are associated with several side effects, development of new treatment modalities takes a significant period of research, time, and expenditure. An alternative novel approach is drug repurposing that focuses on finding new applications for the previously clinically approved drugs. The process of drug repurposing has also been facilitated by current advances in the field of proteomics, genomics, and information computational biology. This approach not only provides cheaper, effective, and potentially safer drugs with less side effects but also increases the processing pace of drug development. In this review, we wish to highlight some recent developments in the area of drug repurposing in cancer with a specific focus on the repurposing potential of anti-psychotic, anti-inflammatory and anti-viral drugs, anti-diabetic, antibacterial, and anti-fungal drugs.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

The Antidiabetic Drug Metformin Regulates Voltage-Gated Sodium Channel NaV1.7 via the Ubiquitin-Ligase NEDD4-2

The antidiabetic drug metformin has been shown to reduce pain hypersensitivity in preclinical models of chronic pain and in neuropathic pain in humans. Multiple intracellular pathways have been described as metformin targets. Among them, metformin is an activator of the adenosine 5′-monophosphate protein kinase that can in turn modulate the activity of the E3 ubiquitin ligase NEDD4-2 and thus post-translational expression of voltage-gated sodium channels (Na_Vs). In this study, we found that the bulk of the effect of metformin on Na1.7 is dependent on NEDD4-2. In HEK cells, the expression of Na_V1.7 at the membrane fraction, obtained by a biotinylation approach, is only reduced by metformin when cotransfected with NEDD4-2. Similarly, in voltage-clamp recordings, metformin significantly reduced Na_V1.7 current density when cotransfected with NEDD4-2. In mouse dorsal root ganglion (DRG) neurons, without changing the biophysical properties of Na_V1.7, metformin significantly decreased Na_V1.7 current densities, but not in Nedd4L knock-out mice (SNS-Nedd4L^−/−). In addition, metformin induced a significant reduction in NEDD4-2 phosphorylation at the serine-328 residue in DRG neurons, an inhibitory phosphorylation site of NEDD4-2. In current-clamp recordings, metformin reduced the number of action potentials elicited by DRG neurons from Nedd4L^fl/fl, with a partial decrease also present in SNS-Nedd4L^−/− mice, suggesting that metformin can also change neuronal excitability in an NEDD4-2-independent manner. We suggest that NEDD4-2 is a critical player for the effect of metformin on the excitability of nociceptive neurons; this action may contribute to the relief of neuropathic pain.

Metformin Downregulates the Expression of Epidermal Growth Factor Receptor Independent of Lowering Blood Glucose in Oral Squamous Cell Carcinoma

PURPOSE

Type 2 diabetes mellitus (T2DM) is among the risk factors for the occurrence and development of cancer. Metformin is a potential anticancer drug. Epidermal growth factor receptor (EGFR) plays an important role in the progression of oral squamous cell carcinoma(OSCC), but the relationship between metformin and EGFR expression in OSCC remains unclear.

METHODS

This study involved the immunohistochemical detection of EGFR expression in cancer tissues of patients with T2DM and OSCC. The patients were divided into groups according to whether they were taking metformin for the treatment of T2DM, and the expression of EGFR in different groups was compared. Correlation analysis between the expression of EGFR and the fluctuation value of fasting blood glucose (FBG) was carried out. Immunohistochemistry was used to detect the expression of EGFR in cancer tissues of patients with recurrent OSCC. These patients had normal blood glucose and took metformin for a long time after the first operation.

RESULTS

EGFR expression in T2DM patients with OSCC taking metformin was significantly lower than that in the non-metformin group. FBG fluctuations were positively correlated with the expression of EGFR in the OSCC tissues of the non-metformin group of T2DM patients. In patients with recurrent OSCC with normal blood glucose, metformin remarkably reduced the expression of EGFR in recurrent OSCC tissues.

CONCLUSION

Metformin may regulate the expression of EGFR in a way that does not rely on lowering blood glucose. These results may provide further evidence for metformin in the treatment of OSCC.

Metabolic reprogramming by driver mutation-tumor microenvironment interplay in pancreatic cancer: new therapeutic targets

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers globally with a mortality rate exceeding 95% and very limited therapeutic options. A hallmark of PDAC is its acidic tumor microenvironment, further characterized by excessive fibrosis and depletion of oxygen and nutrients due to poor vascularity. The combination of PDAC driver mutations and adaptation to this hostile environment drives extensive metabolic reprogramming of the cancer cells toward non-canonical metabolic pathways and increases reliance on scavenging mechanisms such as autophagy and macropinocytosis. In addition, the cancer cells benefit from metabolic crosstalk with nonmalignant cells within the tumor microenvironment, including pancreatic stellate cells, fibroblasts, and endothelial and immune cells. Increasing evidence shows that this metabolic rewiring is closely related to chemo- and radioresistance and immunosuppression, causing extensive treatment failure. Indeed, stratification of human PDAC tumors into subtypes based on their metabolic profiles was shown to predict disease outcome. Accordingly, an increasing number of clinical trials target pro-tumorigenic metabolic pathways, either as stand-alone treatment or in conjunction with chemotherapy. In this review, we highlight key findings and potential future directions of pancreatic cancer metabolism research, specifically focusing on novel therapeutic opportunities.

Cisplatin's potential for type 2 diabetes repositioning by inhibiting CDKN1A, FAS, and SESN1

Cisplatin is a DNA-damaging chemotherapeutic agent used for treating cancer. Based on cDNA dataset analysis, we investigated how cisplatin modified gene expression and observed cisplatin-induced dysregulation and system-level variations relating to insulin resistance and type 2 diabetes mellitus (T2DM). T2DM is a multifactorial disease affecting 462 million people in the world, and drug-induced T2DM is a serious issue. To understand this etiology, we designed an integrative, system-level study to identify associations between cisplatin-induced differentially expressed genes (DEGs) and T2DM. From a list of differential expressed genes, cisplatin downregulated the cyclin-dependent kinase inhibitor 1 (CDKN1A), tumor necrosis factor (FAS), and sestrin-1 (SESN1) genes responsible for modifying signaling pathways, including the p53, JAK-STAT, FOXO, MAPK, mTOR, P13-AKT, Toll-like receptor (TLR), adipocytokine, and insulin signaling pathways. These enriched pathways were expressively associated with the disease. We observed significant gene signatures, including SMAD3, IRS, PDK1, PRKAA1, AKT, SOS, RAS, GRB2, MEK1/2, and ERK, interacting with source genes. This study revealed the value of system genetics for identifying the cisplatin-induced genetic variants responsible for the progression of T2DM. Also, by cross-validating gene expression data for T2DM islets, we found that downregulating IRS and PRK families is critical in insulin and T2DM signaling pathways. Cisplatin, by inhibiting CDKN1A, FAS, and SESN1, promotes IRS and PRK activity in a similar way to rosiglitazone (a popular drug used for T2DM treatment). Our integrative, network-based approach can help in understanding the drug-induced pathophysiological mechanisms of diabetes.

Immunotherapy in endometrial cancer: rationale, practice and perspectives

Tumor immunotherapy has attracted more and more attention nowadays, and multiple clinical trials have confirmed its effect in a variety of solid tumors. Immune checkpoint inhibitors (ICIs), cancer vaccines, adoptive cell transfer (ACT), and lymphocyte-promoting cytokines are the main immunotherapy methods. Endometrial cancer (EC) is one of the most frequent tumors in women and the prognosis of recurrent or metastatic EC is poor. Since molecular classification has been applied to EC, immunotherapy for different EC subtypes (especially POLE and MSI-H) has gradually attracted attention. In this review, we focus on the expression and molecular basis of the main biomarkers in the immunotherapy of EC firstly, as well as their clinical application significance and limitations. Blocking tumor immune checkpoints is one of the most effective strategies for cancer treatment in recent years, and has now become the focus in the field of tumor research and treatment. We summarized clinical date of planned and ongoing clinical trials and introduced other common immunotherapy methods in EC, such as cancer vaccine and ACT. Hormone aberrations, metabolic syndrome (MetS) and p53 mutant and that affect the immunotherapy of endometrial cancer will also be discussed in this review.

Metformin and Bone Metabolism in Endogenous Glucocorticoid Excess: An Exploratory Study

CONTEXT

Glucocorticoid excess exhibits multiple detrimental effects by its catabolic properties. Metformin was recently suggested to protect from adverse metabolic side-effects of glucocorticoid treatment. Whether metformin is beneficial in patients with endogenous glucocorticoid excess has not been clarified.

OBJECTIVE

To evaluate the phenotype in patients with endogenous Cushing's syndrome (CS) treated with metformin at the time of diagnosis.

PATIENTS AND METHODS

As part of the German Cushing's Registry we selected from our prospective cohort of 96 patients all 10 patients who had been on pre-existing metformin treatment at time of diagnosis (CS-MET). These 10 patients were matched for age, sex and BMI with 16 patients without metformin treatment (CS-NOMET). All patients had florid CS at time of diagnosis. We analyzed body composition, metabolic parameters, bone mineral density and bone remodeling markers, muscle function and quality of life.

RESULTS

As expected, diabetes was more prevalent in the CS-MET group, and HbA1c was higher. In terms of comorbidities and the degree of hypercortisolism, the two groups were comparable. We did not observe differences in terms of muscle function or body composition. In contrast, bone mineral density in metformin-treated patients was superior to the CS-NOMET group at time of diagnosis (median T-Score -0.8 versus -1.4, p = 0.030). CS-MET patients showed decreased β-CTX levels at baseline (p = 0.041), suggesting reduced bone resorption under metformin treatment during glucocorticoid excess.

CONCLUSION

This retrospective cohort study supports potential protective effects of metformin in patients with endogenous glucocorticoid excess, in particular on bone metabolism.

Receptor tyrosine kinase targeting in glioblastoma: performance, limitations and future approaches

From all central nervous system tumors, gliomas are the most common. Nowadays, researchers are looking for more efficient treatments for these tumors, as well as ways for early diagnosis. Receptor tyrosine kinases (RTKs) are major targets for oncology and the development of small-molecule RTK inhibitors has been proven successful in cancer treatment. Mutations or aberrant activation of the RTKs and their intracellular signaling pathways are linked to several malignant diseases, including glioblastoma. The progress in the understanding of malignant glioma evolution has led to RTK targeted therapies with high capacity to improve the therapeutic response while reducing toxicity. In this review, we present the most important RTKs (i.e. EGFR, IGFR, PDGFR and VEGFR) currently used for developing cancer therapeutics together with the potential of RTK-related drugs in glioblastoma treatment. Also, we focus on some therapeutic agents that are currently at different stages of research or even in clinical phases and proved to be suitable as re-purposing candidates for glioblastoma treatment.

Systems biology and network pharmacology of frailty reveal novel epigenetic targets and mechanisms

Frailty is an age-associated condition, characterized by an inappropriate response to stress that results in a higher frequency of adverse outcomes (e.g., mortality, institutionalization and disability). Some light has been shed over its genetic background, but this is still a matter of debate. In the present study, we used network biology to analyze the interactome of frailty-related genes at different levels to relate them with pathways, clinical deficits and drugs with potential therapeutic implications. Significant pathways involved in frailty: apoptosis, proteolysis, muscle proliferation, and inflammation; genes as FN1, APP, CREBBP, EGFR playing a role as hubs and bottlenecks in the interactome network and epigenetic factors as HIST1H3 cluster and miR200 family were also involved. When connecting clinical deficits and genes, we identified five clusters that give insights into the biology of frailty: cancer, glucocorticoid receptor, TNF-α, myostatin, angiotensin converter enzyme, ApoE, interleukine-12 and −18. Finally, when performing network pharmacology analysis of the target nodes, some compounds were identified as potentially therapeutic (e.g., epigallocatechin gallate and antirheumatic agents); while some other substances appeared to be toxicants that may be involved in the development of this condition.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Drug Repositioning Inferred from E2F1-Coregulator Interactions Studies for the Prevention and Treatment of Metastatic Cancers

Metastasis management remains a long-standing challenge. High abundance of E2F1 triggers tumor progression by developing protein-protein interactions (PPI) with coregulators that enhance its potential to activate a network of prometastatic transcriptional targets.

Methods: To identify E2F1-coregulators, we integrated high-throughput Co-immunoprecipitation (IP)/mass spectometry, GST-pull-down assays, and structure modeling. Potential inhibitors of PPI discovered were found by bioinformatics-based pharmacophore modeling, and transcriptome profiling was conducted to screen for coregulated downstream targets. Expression and target gene regulation was validated using qRT-PCR, immunoblotting, chromatin IP, and luciferase assays. Finally, the impact of the E2F1-coregulator complex and its inhibiting drug on metastasis was investigated in vitro in different cancer entities and two mouse metastasis models.

Results: We unveiled that E2F1 forms coactivator complexes with metastasis-associated protein 1 (MTA1) which, in turn, is directly upregulated by E2F1. The E2F1:MTA1 complex potentiates hyaluronan synthase 2 (HAS2) expression, increases hyaluronan production and promotes cell motility. Disruption of this prometastatic E2F1:MTA1 interaction reduces hyaluronan synthesis and infiltration of tumor-associated macrophages in the tumor microenvironment, thereby suppressing metastasis. We further demonstrate that E2F1:MTA1 assembly is abrogated by small-molecule, FDA-approved drugs. Treatment of E2F1/MTA1-positive, highly aggressive, circulating melanoma cells and orthotopic pancreatic tumors with argatroban prevents metastasis and cancer relapses in vivo through perturbation of the E2F1:MTA1/HAS2 axis.

Conclusion: Our results propose argatroban as an innovative, E2F-coregulator-based, antimetastatic drug. Cancer patients with the infaust E2F1/MTA1/HAS2 signature will likely benefit from drug repositioning.

The RANK-RANKL axis: an opportunity for drug repurposing in cancer?

Drug repurposing offers advantages over traditional drug development in terms of cost, speed and improved patient outcomes. The receptor activator of nuclear factor kappa B (RANK) ligand (RANKL) inhibitor denosumab is approved for the prevention of skeletal-related events in patients with advanced malignancies involving bone, including solid tumours and multiple myeloma. Following improved understanding of the role of RANK/RANKL in cancer biology, denosumab has already been repurposed as a treatment for giant cell tumour of bone. Here, we review the role of RANK/RANKL in tumourigenesis, including effects on tumour initiation, progression and metastasis and consider the impact of RANK/RANKL on tumour immunology and immune evasion. Finally, we look briefly at ongoing trials and future opportunities for therapeutic synergy when combining denosumab with anti-cancer agents such as immune checkpoint inhibitors.

Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.

Combination therapy Eve and Pac to induce apoptosis in cervical cancer cells by targeting PI3K/AKT/mTOR pathways

This study aimed to investigate the anti-cervical cancer effects of everolimus (Eve) and paclitaxel (Pac) when used alone or in combination. Human cervical cancer cells HeLa and SiHa were divided into four group: Blank control group (control), everolimus group (Eve), paclitaxel group (Pac) and combined therapy group (Eve + Pac). The cell viability was detected by CCK-8 assay and the cell cloning ability was detected by clonegenic assay. Flow cytometry was used to detect cell apoptosis. Meanwhile, the expression of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), mammalian target of rapamycin (mTOR) and their phosphorylated proteins were studied by western blot. The HeLa and SiHa cells proliferation and cloning ability were significantly inhibited in drug treatment groups compared with control group (p < .05), and the Eve + Pac combinatorial therapy showed the better results than single treatment with Eve or Pac. Combination of Eve and Pac has synergistic effect on the induction of apoptosis in cervical cancer cells. In addition, the protein ratios in HeLa and SiHa cell treated with the Eve + Pac combination were significantly lower than that of cervical cancer cells treated with either Eve or Pac cell alone. Our study suggested that Eve + Pac provide a novel therapeutic strategy for cervical cancer.

Metformin is associated with reduced cell proliferation in human endometrial cancer by inbibiting PI3K/AKT/mTOR signaling

Metformin recently gained traction as potential anti-endometrial cancer agent for its new applications. However, the underlying mechanisms of the anti-cancer effect of metformin in the endometrial cancer have not yet been fully elucidated. Sixty-five patients diagnosed as endometrial carcinoma were grouped into (n = 33) and non-treatment mixed (n = 32) for analysis. Thirty healthy donors were recruited as controls. We attempt to investigate the effect of metformin on Ki-67, PI3K, p-AKT, p-S6K1, and p-4EBP1 staining in human endometrial cancer by immunohistochemical staining. We found that increased Ki-67 expression in women with endometrial cancer, which were reversed by conventional anti-diabetic doses of metformin in present work. In parallel, the reduced PI3K, p-AKT, p-S6K1, and p-4EBP1 staining induced by metformin appeared to play an important role for the anti-proliferative effects of metformin in endometrial cancer patients. Metformin significantly decreased proliferation in human endometrial cancer may by inhibiting PI3K/AKT/mTOR signaling. Our present results add to the growing body of evidence supporting metformin as a potential anti-cancer agent in endometrial cancer.

Metformin-induced anticancer activities: recent insights

Metformin is a widely used antidiabetic drug, and there is evidence among diabetic patients that metformin is a chemopreventive agent against multiple cancers. There is also evidence in human studies that metformin is a cancer chemotherapeutic agent, and several clinical trials that use metformin alone or in combination with other drugs are ongoing. In vivo and in vitro cancer cell culture studies demonstrate that metformin induces both AMPK-dependent and AMPK-independent genes/pathways that result in inhibition of cancer cell growth and migration and induction of apoptosis. The effects of metformin in cancer cells resemble the patterns observed after treatment with drugs that downregulate specificity protein 1 (Sp1), Sp3 and Sp4 or by knockdown of Sp1, Sp3 and Sp4 by RNA interference. Studies in pancreatic cancer cells clearly demonstrate that metformin decreases expression of Sp1, Sp3, Sp4 and pro-oncogenic Sp-regulated genes, demonstrating that one of the underlying mechanisms of action of metformin as an anticancer agent involves targeting of Sp transcription factors. These observations are consistent with metformin-mediated effects on genes/pathways in many other tumor types.

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease

It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.

Drug repurposing for the treatment of glioblastoma multiforme

Background

Glioblastoma Multiforme is the deadliest type of brain tumor and is characterized by very poor prognosis with a limited overall survival. Current optimal therapeutic approach has essentially remained unchanged for more than a decade, consisting in maximal surgical resection followed by radiotherapy plus temozolomide.

Main body

Such a dismal patient outcome represents a compelling need for innovative and effective therapeutic approaches. Given the development of new drugs is a process presently characterized by an immense increase in costs and development time, drug repositioning, finding new uses for existing approved drugs or drug repurposing, re-use of old drugs when novel molecular findings make them attractive again, are gaining significance in clinical pharmacology, since it allows faster and less expensive delivery of potentially useful drugs from the bench to the bedside. This is quite evident in glioblastoma, where a number of old drugs is now considered for clinical use, often in association with the first-line therapeutic intervention. Interestingly, most of these medications are, or have been, widely employed for decades in non-neoplastic pathologies without relevant side effects. Now, the refinement of their molecular mechanism(s) of action through up-to-date technologies is paving the way for their use in the therapeutic approach of glioblastoma as well as other cancer types.

Short conclusion

The spiraling costs of new antineoplastic drugs and the long time required for them to reach the market demands a profoundly different approach to keep lifesaving therapies affordable for cancer patients. In this context, repurposing can represent a relatively inexpensive, safe and fast approach to glioblastoma treatment. To this end, pros and cons must be accurately considered.

MeSHDD: Literature-based drug-drug similarity for drug repositioning

OBJECTIVE

Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.

METHODS

We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.

RESULTS

We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.

CONCLUSION

Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).

The SGK1 inhibitor SI113 induces autophagy, apoptosis, and endoplasmic reticulum stress in endometrial cancer cells

Endometrial cancer is often characterized by PI3K/AKT pathway deregulation. Recently it has been suggested that SGK1, a serine/threonine protein kinase that shares structural and functional similarities with the AKT family, might play a role in cancer, since its expression and/or activity has been found to be deregulated in different human tumors. However, the role of SGK1 in endometrial cancer has been poorly investigated. Here, we show that SGK1 expression is increased in tissue specimens from neoplastic endometrium. The SGK1 inhibitor SI113 induced a significant reduction of endometrial cancer cells viability, measured by the (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. This effect was associated to the increase of autophagy, as revealed by the increase of the markers LC3B-II and beclin I, detected by both immunofluorescence and western blot analysis. SI113 treatment caused also apoptosis of endometrial cancer cells, evidenced by the cleavage of the apoptotic markers PARP and Caspase-9. Intriguingly, these effects were associated to the induction of endoplasmic reticulum stress markers GRP78 and CHOP evaluated by both Real-Time RT-PCR and Western Blot analysis. Increased expression of SGK1 in endometrial cancer tissues suggest a role for SGK1 in this type of cancer, as reported for other malignancies. Moreover, the efficacy of SI113 in affecting endometrial cancer cells viability, possibly via endoplasmic reticulum stress activation, identifies SGK1 as an attractive molecular target for new tailored therapeutic intervention for the treatment of endometrial cancer.

Combining metformin and nelfinavir exhibits synergistic effects against the growth of human cervical cancer cells and xenograft in nude mice

Human cervical cancer is the fourth most common carcinoma in women worldwide. However, the emergence of drug resistance calls for continuously developing new anticancer drugs and combination chemotherapy regimens. The present study aimed to investigate the anti-cervical cancer effects of metformin, a first-line therapeutic drug for type 2 diabetes mellitus, and nelfinavir, an HIV protease inhibitor, when used alone or in combination. We found that both metformin and nelfinavir, when used alone, were moderately effective in inhibiting proliferation, inducing apoptosis and suppressing migration and invasion of human cervical cell lines HeLa, SiHa and CaSki. When used in combination, these two drugs acted synergistically to inhibit the growth of human cervical cancer cells in vitro and cervical cancer cell xenograft in vivo in nude mice, and suppress cervical cancer cell migration and invasion. The protein expression of phosphoinositide 3-kinase catalytic subunit PI3K(p110α), which can promote tumor growth, was remarkably downregulated, while the tumor suppressor proteins p53 and p21 were substantially upregulated following the combinational treatment in vitro and in vivo. These results suggest that clinical use of metformin and nelfinavir in combination is expected to have synergistic antitumor efficacy and significant potential for the treatment of human cervical cancer.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Molecular mechanisms involved in the side effects of fatty acid amide hydrolase inhibitors: a structural phenomics approach to proteome-wide cellular off-target deconvolution and disease association

Fatty acid amide hydrolase (FAAH) is a promising therapeutic target for the treatment of pain and CNS disorders. However, the development of potent and safe FAAH inhibitors is hindered by their off-target mediated side effect that leads to brain cell death. Its physiological off-targets and their associations with phenotypes may not be characterized using existing experimental and computational techniques as these methods fail to have sufficient proteome coverage and/or ignore native biological assemblies (BAs; i.e., protein quaternary structures). To understand the mechanisms of the side effects from FAAH inhibitors and other drugs, we develop a novel structural phenomics approach to identifying the physiological off-targets binding profile in the cellular context and on a structural proteome scale, and investigate the roles of these off-targets in impacting human physiology and pathology using text mining-based phenomics analysis. Using this integrative approach, we discover that FAAH inhibitors may bind to the dimerization interface of NMDA receptor (NMDAR) and several other BAs, and thus disrupt their cellular functions. Specifically, the malfunction of the NMDAR is associated with a wide spectrum of brain disorders that are directly related to the observed side effects of FAAH inhibitors. This finding is consistent with the existing literature, and provides testable hypotheses for investigating the molecular origin of the side effects of FAAH inhibitors. Thus, the in silico method proposed here, which can for the first time predict proteome-wide drug interactions with cellular BAs and link BA–ligand interaction with clinical outcomes, can be valuable in off-target screening. The development and application of such methods will accelerate the development of more safe and effective therapeutics.

Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case

Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.