bioRxiv. 2023 Jun 3:2023.03.13.532291. doi: 10.1101/2023.03.13.532291. Preprint.

ABSTRACT

We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived a network of interacting ligands and biosynthetic enzymes of organic ligands to infer extracellular activation processes, which we combined with cognate GPCRs and downstream effectors to predict GPCR signaling pathway activation. We found multiple GPCRs that are differentially regulated together with their ligands across cancers and revealed a widespread perturbation of these signaling axes in specific cancer molecular subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulated pathway activity signatures from metabolomics datasets, therefore providing valuable surrogate information for GPCRs responding to organic ligand systems. The expression of several GPCRs signaling components was significantly associated with patient survival in a cancer subtype specific fashion. In particular, the expression of both receptor-ligand, as well as receptor-biosynthetic enzymes interaction partners improved the stratification of patients based on survival, suggesting a potential synergistic role for the activation of specific GPCR networks in modulating cancer phenotypes. Remarkably, we identified many receptor-ligand or enzyme pairs to be significantly associated with patient survival across several cancer molecular subtypes. Furthermore, we found that GPCRs from these actionable axes are the targets of multiple drugs displaying anti-growth effects in large, drug repurposing screens in cancer cells. This study provides a comprehensive map of GPCR signaling axes that can be exploited as actionable targets for personalized cancer treatments. We have made the results generated in this study freely available for further exploration to the community through a webapp (gpcrcanceraxes.bioinfolab.sns.it).

PMID:37398064 | PMC:PMC10312480 | DOI:10.1101/2023.03.13.532291

bioRxiv. 2023 Jun 1:2023.05.30.542911. doi: 10.1101/2023.05.30.542911. Preprint.

ABSTRACT

BACKGROUND: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues.

RESULTS: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches.

CONCLUSIONS: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

PMID:37397987 | PMC:PMC10312590 | DOI:10.1101/2023.05.30.542911

Genes Dis. 2023 Jan 2;10(4):1687-1701. doi: 10.1016/j.gendis.2022.12.005. eCollection 2023 Jul.

ABSTRACT

Ovarian cancer (OC) is one of the most lethal malignancies of the female reproductive system. OC patients are usually diagnosed at advanced stages due to the lack of early diagnosis. The standard treatment for OC includes a combination of debulking surgery and platinum-taxane chemotherapy, while several targeted therapies have recently been approved for maintenance treatment. The vast majority of OC patients relapse with chemoresistant tumors after an initial response. Thus, there is an unmet clinical need to develop new therapeutic agents to overcome the chemoresistance of OC. The anti-parasite agent niclosamide (NA) has been repurposed as an anti-cancer agent and exerts potent anti-cancer activities in human cancers including OC. Here, we investigated whether NA could be repurposed as a therapeutic agent to overcome cisplatin-resistant (CR) in human OC cells. To this end, we first established two CR lines SKOV3CR and OVCAR8CR that exhibit the essential biological characteristics of cisplatin resistance in human cancer. We showed that NA inhibited cell proliferation, suppressed cell migration, and induced cell apoptosis in both CR lines at a low micromole range. Mechanistically, NA inhibited multiple cancer-related pathways including AP1, ELK/SRF, HIF1, and TCF/LEF, in SKOV3CR and OVCAR8CR cells. NA was further shown to effectively inhibit xenograft tumor growth of SKOV3CR cells. Collectively, our findings strongly suggest that NA may be repurposed as an efficacious agent to combat cisplatin resistance in chemoresistant human OC, and further clinical trials are highly warranted.

PMID:37397523 | PMC:PMC10311098 | DOI:10.1016/j.gendis.2022.12.005

Front Cardiovasc Med. 2023 Jun 16;10:1156456. doi: 10.3389/fcvm.2023.1156456. eCollection 2023.

ABSTRACT

In today's era of individualized precision medicine drug repurposing represents a promising approach to offer patients fast access to novel treatments. Apart from drug repurposing in cancer treatments, cardiovascular pharmacology is another attractive field for this approach. Patients with angina pectoris without obstructive coronary artery disease (ANOCA) report refractory angina despite standard medications in up to 40% of cases. Drug repurposing also appears to be an auspicious option for this indication. From a pathophysiological point of view ANOCA patients frequently suffer from vasomotor disorders such as coronary spasm and/or impaired microvascular vasodilatation. Consequently, we carefully screened the literature and identified two potential therapeutic targets: the blockade of the endothelin-1 (ET-1) receptor and the stimulation of soluble guanylate cyclase (sGC). Genetically increased endothelin expression results in elevated levels of ET-1, justifying ET-1 receptor blockers as drug candidates to treat coronary spasm. sGC stimulators may be beneficial as they stimulate the NO-sGC-cGMP pathway leading to GMP-mediated vasodilatation.

PMID:37396593 | PMC:PMC10313125 | DOI:10.3389/fcvm.2023.1156456

Alzheimers Dement. 2023 Jul 3. doi: 10.1002/alz.13349. Online ahead of print.

ABSTRACT

INTRODUCTION: Metformin has been suggested as a therapeutic agent for dementia, but the relevant evidence has been partial and inconsistent.

METHODS: We established a national cohort of 210,237 type 2 diabetes patients in the UK Clinical Practice Research Datalink. Risks of incident dementia were compared between metformin initiators and those who were not prescribed any anti-diabetes medication during follow-up.

RESULTS: Compared with metformin initiators (n = 114,628), patients who received no anti-diabetes medication (n = 95,609) had lower HbA1c and better cardiovascular health at baseline. Both Cox regression and propensity score weighting analysis showed metformin initiators had lower risk of dementia compared to those non-users (adjusted hazard ratio = 0.88 [95% confidence interval: 0.84-0.92] and 0.90 [0.84-0.96]). Patients on long-term metformin treatment had an even lower risk of dementia.

DISCUSSION: Metformin may act beyond its glycemic effect and reduce dementia risk to an even lower level than that of patients with milder diabetes and better health profiles.

HIGHLIGHTS: Metformin initiators had a significantly lower risk of dementia compared with patients not receiving anti-diabetes medication. Compared with metformin initiators, diabetes patients not receiving pharmacological treatment had better glycemic profiles at baseline and during follow-up. Patients on long-term metformin treatment had an even lower risk of subsequent dementia incidence. Metformin may act beyond its effect on hyperglycemia and has the potential of being repurposed for dementia prevention.

PMID:37395154 | DOI:10.1002/alz.13349

Med Oncol. 2023 Jul 2;40(8):218. doi: 10.1007/s12032-023-02094-7.

ABSTRACT

Cervical cancer is a significant cause of morbidity and mortality in women worldwide. Despite the availability of effective therapies, the development of drug resistance and adverse side effects remain significant challenges in cervical cancer treatment. Thus, repurposing existing drugs as multitargeted therapies for cervical cancer is an attractive approach. In this study, we extensively screened the complete prepared FDA-approved drugs and identified the repurposing potential of taxifolin, a flavonoid with known antioxidant and anti-inflammatory properties, as a multitargeted therapy for cervical cancer. We performed a computational analysis using molecular docking with various sampling algorithms, namely HTVS, SP, and XP algorithms, for robust sampling pose and filtered with MM/GBSA analysis to determine the binding affinity of taxifolin with potential targets involved in cervical cancer, such as Symmetric Mad2 Dimer, replication initiation factor MCM10-ID, TPX2, DNA polymerase epsilon B-subunit, human TBK1, and alpha-v beta-8. We then conducted MD simulations to investigate the stability and conformational changes of the complex formed between taxifolin and the mentioned proteins. Our results suggest that taxifolin has a high binding affinity ranging from - 6.094 to - 9.558 kcal/mol, indicating its potential as a multitargeted therapy for cervical cancer. Furthermore, interaction fingerprints, pharmacokinetics and MD simulations revealed that the Taxifolin-target complexes remained stable over the simulation period, indicating that taxifolin may bind to the targets for an extended period. Our study suggests that taxifolin has the potential as a multitargeted therapy for cervical cancer, and further experimental studies are necessary to validate our findings.

PMID:37394519 | DOI:10.1007/s12032-023-02094-7

Appl Biochem Biotechnol. 2023 Jul 1. doi: 10.1007/s12010-023-04608-5. Online ahead of print.

ABSTRACT

Cervical cancer is one of the main causes of cancer death in women globally, and its epidemiology is similar to that of a low-infectious venereal illness. Many sexual partners and early age at first intercourse have been demonstrated to have a significant influence on risk. TGF-β1 is a multifunctional cytokine that is required for cervical carcinoma metastasis, tumor development, progression, and invasion. The TGF-β1 signaling system plays a paradoxical function in cancer formation, suppressing early-stage tumor growth while increasing tumor progression and metastasis. Importantly, TGF-β1 and TGF-β receptor 1 (TGF-βR1), two components of the TGF-β signaling system, are substantially expressed in a range of cancers, including breast cancer, colon cancer, gastric cancer, and hepatocellular carcinoma. The current study aims to investigate possible inhibitors targeting TGF-β1 using molecular docking and dynamic simulations. To target TGF-β1, we used anti-cancer drugs and small molecules. MVD was utilized for virtual screening, and the highest scoring compound was then subjected to MD simulations using Schrodinger software package v2017-1 (Maestro v11.1) to identify the most favorable lead interactions against TGF-β1. The Nilotinib compound has shown the least XP Gscore of -2.581 kcal/mol, 30ns MD simulations revealing that the Nilotinib- TGF-β1 complex possesses the lowest energy of -77784.917 kcal/mol. Multiple parameters, including Root Mean Square Deviation, Root Mean Square Fluctuation, and Intermolecular Interactions, were used to analyze the simulation trajectory. Based on the results; we conclude that the ligand nilotinib appears to be a promising prospective TGF-β1inhibitor for reducing TGF-β1 expression ad halting cervical cancer progression.

PMID:37392324 | DOI:10.1007/s12010-023-04608-5

BMC Bioinformatics. 2023 Jun 30;24(1):272. doi: 10.1186/s12859-023-05373-2.

ABSTRACT

This paper applies different link prediction methods on a knowledge graph generated from biomedical literature, with the aim to compare their ability to identify unknown drug-gene interactions and explain their predictions. Identifying novel drug-target interactions is a crucial step in drug discovery and repurposing. One approach to this problem is to predict missing links between drug and gene nodes, in a graph that contains relevant biomedical knowledge. Such a knowledge graph can be extracted from biomedical literature, using text mining tools. In this work, we compare state-of-the-art graph embedding approaches and contextual path analysis on the interaction prediction task. The comparison reveals a trade-off between predictive accuracy and explainability of predictions. Focusing on explainability, we train a decision tree on model predictions and show how it can aid the understanding of the prediction process. We further test the methods on a drug repurposing task and validate the predicted interactions against external databases, with very encouraging results.

PMID:37391722 | DOI:10.1186/s12859-023-05373-2

Mol Divers. 2023 Jun 30. doi: 10.1007/s11030-023-10673-z. Online ahead of print.

ABSTRACT

Tyrosine Kinase beta (TRKβ), is a type I membrane receptor which plays a major role in various signalling pathways. TRKβ was found to be upregulated in various cancers and contrastingly downregulated in various neurodegenerative disorders. Hitherto, contemporary drug research is oriented towards discovery of TRKβ inhibitors, thus neglecting the development of TRKβ agonists. This research is aimed at identifying FDA approved drugs exhibiting repurposable potential as TRKβ agonists by mapping them with fingerprints of the BDNF/TRKβ interaction interface. Initially, crucial interacting residues were retrieved and a receptor grid was generated around it. TRKβ agonists were retrieved from literature search and a drug library was created for each agonist based on its structural and side effect similarities. Subsequently, molecular docking and dynamics were performed for each library to identify the drugs possessing affinity towards the binding pocket of TRKβ. The study revealed molecular interactions of Perospirone, Droperidol, Urapidil, and Clobenzorex with the crucial amino acids lining the active binding pocket of TRKβ. Subsequent network pharmacological analysis of the above drugs revealed their interactions with key proteins involved in neurotransmitter signalling pathways. Clobenzorex displayed high stability in dynamics simulation and therefore this drug is recommended for further experimental evaluations to attain better mechanistic insights and predict its implications in correcting neuropathological aberrations. This study's focus on the interaction interface between TRKβ and BDNF, combined with the utilization of fingerprint analysis for drug repurposing, contributes to our understanding of neurotrophic signalling and holds potential for identifying new therapeutic options for neurological disorders.

PMID:37389778 | DOI:10.1007/s11030-023-10673-z

Bioinformatics. 2023 Jun 30:btad418. doi: 10.1093/bioinformatics/btad418. Online ahead of print.

ABSTRACT

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking.

RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

AVAILABILITY AND IMPLEMENTATION: https://kghub.org.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37389415 | DOI:10.1093/bioinformatics/btad418

Sci Rep. 2023 Jun 29;13(1):10583. doi: 10.1038/s41598-023-36342-7.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.

PMID:37386052 | DOI:10.1038/s41598-023-36342-7

Trends Parasitol. 2023 Jun 27:S1471-4922(23)00132-0. doi: 10.1016/j.pt.2023.06.001. Online ahead of print.

ABSTRACT

Highly druggable and essential to almost all aspects of cellular life, the protein and phosphoinositide kinase gene families offer a wealth of potential targets for pharmacological modulation for both noncommunicable and infectious diseases. Despite the success of kinase inhibitors in oncology and other disease indications, targeting kinases comes with significant challenges. Key hurdles for kinase drug discovery include selectivity and acquired resistance. The phosphatidylinositol 4-kinase beta inhibitor MMV390048 showed good efficacy in Phase 2a clinical trials, demonstrating the potential of kinase inhibitors for malaria treatment. Here we argue that the potential benefits of Plasmodium kinase inhibitors outweigh the risks, and we highlight the opportunity for designed polypharmacology to reduce the risk of resistance.

PMID:37385921 | DOI:10.1016/j.pt.2023.06.001

Front Oncol. 2023 Jun 13;13:1192208. doi: 10.3389/fonc.2023.1192208. eCollection 2023.

ABSTRACT

INTRODUCTION: Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes.

METHODS: In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival.

RESULTS: We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2- receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

CONCLUSION: We applied a computational drug repurposing approach to identify potential agents to sensitize drug resistant breast cancers in the I-SPY 2 TRIAL. We identified fulvestrant as a potential drug hit and showed that it increased response in a paclitaxel-resistant triple negative breast cancer cell line, HCC-1937, when treated in combination with paclitaxel.

PMID:37384294 | PMC:PMC10294228 | DOI:10.3389/fonc.2023.1192208

Front Pharmacol. 2023 Jun 13;14:1223200. doi: 10.3389/fphar.2023.1223200. eCollection 2023.

NO ABSTRACT

PMID:37383724 | PMC:PMC10295121 | DOI:10.3389/fphar.2023.1223200

Antimicrob Agents Chemother. 2023 Jun 29:e0050323. doi: 10.1128/aac.00503-23. Online ahead of print.

ABSTRACT

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure toward resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 μM. Multiple compounds showed a phenyl sulfone chemotype, prompting further analysis. Of these phenyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

PMID:37382550 | DOI:10.1128/aac.00503-23

Parasitol Int. 2023 Jun 26:102774. doi: 10.1016/j.parint.2023.102774. Online ahead of print.

ABSTRACT

Babesia gibsoni is mainly transmitted by hard ticks of the genus Rhipicephalus (R. sanguineus) and Haemaphysalis (H. longicornis), and causes canine babesiosis. Clinical manifestations of B. gibsoni infection include fever, hemoglobinemia, hemoglobinuria, and progressive anemia. Traditional antibabesial therapy, such as imidocarb dipropionate or diminazene aceturate, can only alleviate severe clinical manifestations and cannot eliminate parasites in the host. Food and Drug Administration (FDA)-approved drugs are a solid starting point for researching novel therapy strategies for canine babesiosis. In this work, we screened 640 FDA-approved drugs against the growth of B. gibsoni in vitro. Among them, 13 compounds (at 10 μM) exhibited high growth inhibition (>60%), and two compounds, namely idarubicin hydrochloride (idamycin) and vorinostat, were chosen for further investigation. The half-maximal inhibitory concentration (IC50) values of idamycin and vorinostat were determined to be 0.044 ± 0.008 μM and 0.591 ± 0.107 μM, respectively. Viability results indicated that a concentration of 4 × IC50 of vorinostat prevented the regrowth of treated B. gibsoni, whereas parasites treated with 4 × IC50 concentration of idamycin remained viable. The B. gibsoni parasites treated with vorinostat exhibited degeneration within erythrocytes and merozoites, in contrast to the oval or signet-ring shape of normal B. gibsoni parasites. In conclusion, FDA-approved drugs offer a valuable platform for drug repositioning in antibabesiosis research. Particularly, vorinostat demonstrated promising inhibitory effects against B. gibsoni in vitro, and further studies on vorinostat are necessary to elucidate its mechanism as a novel treatment in infected animal models.

PMID:37380124 | DOI:10.1016/j.parint.2023.102774

Int J Biol Macromol. 2023 Jun 26:125587. doi: 10.1016/j.ijbiomac.2023.125587. Online ahead of print.

ABSTRACT

Tyrosinase, a rate-limiting enzyme for melanin production, has been the most efficient target for the development of depigmenting agents. Although hydroquinone, kojic acid, and arbutin are the most well-known tyrosinase inhibitors, their adverse effects are inevitable. In the present study, an in silico drug repositioning combined with experimental validation was performed to search for novel potent tyrosinase inhibitors. Docking-based virtual screening results revealed that, among the 3210 FDA-approved drugs available in the ZINC database, amphotericin B, an antifungal drug exhibited the highest binding efficiency against human tyrosinase. Results from tyrosinase inhibition assay demonstrated that amphotericin B could inhibit the activity of mushroom and cellular tyrosinases, especially from MNT-1 human melanoma cells. Molecular modeling results revealed that amphotericin B/human tyrosinase complex exhibited high stability in an aqueous environment. Melanin assay results demonstrated that amphotericin B significantly suppressed melanin production in α-MSH-induced B16F10 murine melanoma and MNT-1 human melanoma cell lines better than the known inhibitor, kojic acid. Mechanistically, amphotericin B treatment significantly activated ERK and Akt signaling pathways, resulting in the decreased expression of MITF and tyrosinase. The obtained results may pursue pre-clinical and clinical studies to examine the possibility of using amphotericin B as an alternative treatment for hyperpigmentation disorders.

PMID:37379954 | DOI:10.1016/j.ijbiomac.2023.125587

Drug Discov Today. 2023 Jun 26:103684. doi: 10.1016/j.drudis.2023.103684. Online ahead of print.

ABSTRACT

Hurdles in the identification of new drugs for cancer treatment have made drug repurposing an increasingly appealing alternative. The approach involves the use of old drugs for new therapeutic purposes. It is cost-effective and facilitates rapid clinical translation. Given that cancer is also considered a metabolic disease, drugs for metabolic disorders are being actively repurposed for cancer therapeutics. In this review, we discuss the repurposing of such drugs approved for two major metabolic diseases, diabetes and cardiovascular disease (CVD), which have shown potential as anti-cancer treatment. We also highlight the current understanding of the cancer signaling pathways that these drugs target.

PMID:37379903 | DOI:10.1016/j.drudis.2023.103684

BMC Bioinformatics. 2023 Jun 28;24(1):266. doi: 10.1186/s12859-023-05393-y.

ABSTRACT

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

PMID:37380943 | DOI:10.1186/s12859-023-05393-y

PLoS Negl Trop Dis. 2023 Jun 28;17(6):e0011443. doi: 10.1371/journal.pntd.0011443. Online ahead of print.

ABSTRACT

Between January and March 2022, WHO conducted a global online survey to collect data on diagnostic capacities and treatment practices in different settings for four implantation mycoses: eumycetoma, actinomycetoma, cutaneous sporotrichosis and chromoblastomycosis. The survey investigated the type of diagnostic methods available in countries at various health system levels (tertiary, secondary, primary level) and the medicines used to treat implantation mycoses, with a view to understanding the level of drug repurposing for treatment of these diseases. 142 respondents from 47 countries, including all continents, contributed data: 60% were from middle-income countries, with 59% working at the tertiary level of the health system and 30% at the secondary level. The results presented in this article provide information on the current diagnostic capacity and treatment trends for both pharmacological and non-pharmacological interventions. In addition, the survey provides insight on refractory case rates, as well as other challenges, such as availability and affordability of medicines, especially in middle-income countries. Although the study has limitations, the survey-collected data confirms that drug repurposing is occurring for all four surveyed implantation mycoses. The implementation of an openly accessible global and/or a national treatment registry for implantation mycoses could contribute to address the gaps in epidemiological information and collect valuable observational data to inform treatment guidelines and clinical research.

PMID:37379338 | DOI:10.1371/journal.pntd.0011443