Front Pharmacol. 2024 Jun 11;15:1400029. doi: 10.3389/fphar.2024.1400029. eCollection 2024.

ABSTRACT

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

PMID:38919258 | PMC:PMC11196849 | DOI:10.3389/fphar.2024.1400029

Nat Commun. 2024 Jun 25;15(1):5378. doi: 10.1038/s41467-024-49620-3.

ABSTRACT

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen's application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

PMID:38918369 | DOI:10.1038/s41467-024-49620-3

Med Oncol. 2024 Jun 25;41(8):188. doi: 10.1007/s12032-024-02427-0.

ABSTRACT

FOXM1, a proto-oncogenic transcription factor, plays a critical role in cancer development and treatment resistance in cancers, particularly in breast cancer. Thus, this study aimed to identify potential FOXM1 inhibitors through computational screening of drug databases, followed by in vitro validation of their inhibitory activity against breast cancer cells. In silico studies involved pharmacophore modeling using the FOXM1 inhibitor, FDI-6, followed by virtual screening of DrugBank and Selleckchem databases. The selected drugs were prepared for molecular docking, and the crystal structure of FOXM1 was pre-processed for docking simulations. In vitro studies included MTT assays to assess cytotoxicity, and Western blot analysis to evaluate protein expression levels. Our study identified Pantoprazole and Rabeprazole as potential FOXM1 inhibitors through in silico screening and molecular docking. Molecular dynamics simulations confirmed stable interactions of these drugs with FOXM1. In vitro experiments showed both Pantoprazole and Rabeprazole exhibited strong FOXM1 inhibition at effective concentrations and that showed inhibition of cell proliferation. Rabeprazole showed the inhibitor activity at 10 µM in BT-20 and MCF-7 cell lines. Pantoprazole exhibited FOXM1 inhibition at 30 µM and in BT-20 cells and at 70 µM in MCF-7 cells, respectively. Our current study provides the first evidence that Rabeprazole and Pantoprazole can bind to FOXM1 and inhibit its activity and downstream signaling, including eEF2K and pEF2, in breast cancer cells. These findings indicate that rabeprazole and pantoprazole inhibit FOXM1 and breast cancer cell proliferation, and they can be used for FOXM1-targeted therapy in breast or other cancers driven by FOXM1.

PMID:38918225 | DOI:10.1007/s12032-024-02427-0

Cell. 2024 Jun 17:S0092-8674(24)00583-X. doi: 10.1016/j.cell.2024.05.039. Online ahead of print.

ABSTRACT

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

PMID:38917788 | DOI:10.1016/j.cell.2024.05.039

Front Med (Lausanne). 2024 Jun 10;11:1407912. doi: 10.3389/fmed.2024.1407912. eCollection 2024.

ABSTRACT

Drug repurposing is considered a valid approach to accelerate therapeutic solutions for rare diseases. However, it is not as widely applied as it could be, due to several barriers that discourage both industry and academic institutions from pursuing this path. Herein we present the case of an academic multicentre study that considered the repurposing of the old drug guanabenz as a therapeutic strategy in amyotrophic lateral sclerosis. The difficulties encountered are discussed as an example of the barriers that academics involved in this type of study may face. Although further development of the drug for this target population was hampered for several reasons, the study was successful in many ways. Firstly, because the hypothesis tested was confirmed in a sub-population, leading to alternative innovative solutions that are now under clinical investigation. In addition, the study was informative and provided new insights into the disease, which are now giving new impetus to laboratory research. The message from this example is that even a repurposing study with an old product has the potential to generate innovation and interest from industry partners, provided it is based on a sound rationale, the study design is adequate to ensure meaningful results, and the investigators keep the full clinical development picture in mind.

PMID:38915767 | PMC:PMC11194437 | DOI:10.3389/fmed.2024.1407912

BMC Musculoskelet Disord. 2024 Jun 24;25(1):490. doi: 10.1186/s12891-024-07589-6.

ABSTRACT

BACKGROUND: Ankylosing spondylitis (AS) with radiographic damage is more prevalent in men than in women. IL-17, which is mainly secreted from peripheral blood mononuclear cells (PBMCs), plays an important role in the development of AS. Its expression is different between male and female. However, it is still unclear whether sex dimorphism of IL-17 contribute to sex differences in AS.

METHODS: GSE221786, GSE73754, GSE25101, GSE181364 and GSE205812 datasets were collected from the Gene Expression Omnibus (GEO) database. Differential expressed genes (DEGs) were analyzed with the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods. CIBERSORTx and EcoTyper algorithms were used for immune infiltration analyses. Machine learning based on the XGBoost algorithm model was used to identify the impact of DEGs. The Connectivity Map (CMAP) database was used as a drug discovery tool for exploring potential drugs based on the DEGs.

RESULTS: According to immune infiltration analyses, T cells accounted for the largest proportion of IL-17-secreting PBMCs, and KEGG analyses suggested an enhanced activation of mast cells among male AS patients, whereas the expression of TNF was higher in female AS patients. Other signaling pathways, including those involving metastasis-associated 1 family member 3 (MAT3) or proteasome, were found to be more activated in male AS patients. Regarding metabolic patterns, oxidative phosphorylation pathways and lipid oxidation were significantly upregulated in male AS patients. In XGBoost algorithm model, DEGs including METRN and TMC4 played important roles in the disease process. we integrated the CMAP database for systematic analyses of polypharmacology and drug repurposing, which indicated that atorvastatin, famciclocir, ATN-161 and taselisib may be applicable to the treatment of AS.

CONCLUSIONS: We analyzed the sex dimorphism of IL-17-secreting PBMCs in AS. The results showed that mast cell activation was stronger in males, while the expression of TNF was higher in females. In addition, through machine learning and the CMAP database, we found that genes such as METRN and TMC4 may promote the development of AS, and drugs such as atorvastatin potentially could be used for AS treatment.

PMID:38914997 | DOI:10.1186/s12891-024-07589-6

Expert Rev Anticancer Ther. 2024 Jun 24. doi: 10.1080/14737140.2024.2372336. Online ahead of print.

ABSTRACT

INTRODUCTION: . The pharmacological treatment of cancer has evolved from cytotoxic to molecular targeted therapy. The median survival gains of 124 drugs approved by the FDA from 2003 to 2021 is 2.8 months. Targeted therapy is based on the somatic mutation theory, which has some paradoxes and limitations. While efforts of targeted therapy must continue, we must study newer approaches that could advance therapy and affordability for patients.

AREAS COVERED: This work briefly overviews how cancer therapy has evolved from cytotoxic chemotherapy to current molecular-targeted therapy. The limitations of the one-target, one-drug approach considering cancer as a robust system and the basis for multitargeting approach with polypharmacotherapy using repurposing drugs.

EXPERT OPINION: Multitargeted polypharmacotherapy for cancer with repurposed drugs should be systematically investigated in preclinical and clinical studies. Remarkably, most of these proposed drugs already have a long history in the clinical setting, and their safety is known. In principle, the risk of their simultaneous administration should not be greater than that of a first-in-human phase I study as long as the protocol is developed with strict vigilance to detect early possible side effects from their potential interactions. Research on cancer therapy should go beyond the prevailing paradigm targeted therapy.

PMID:38913911 | DOI:10.1080/14737140.2024.2372336

Bioinformatics. 2024 Jun 24:btae349. doi: 10.1093/bioinformatics/btae349. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages.

RESULTS: We propose a new solution, HGTDR (Heterogeneous Graph Transformer for Drug Repurposing), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: 1) constructing a heterogeneous knowledge graph, 2) utilizing a heterogeneous graph transformer network, and 3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top ten drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations.

AVAILABILITY: The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

PMID:38913860 | DOI:10.1093/bioinformatics/btae349

J Mol Model. 2024 Jun 24;30(7):227. doi: 10.1007/s00894-024-05990-5.

ABSTRACT

CONTEXT: Breast cancer stem cells (BCSCs) are a small subset of cells within breast tumors with characteristics similar to normal stem cells. Despite advancements in chemotherapy and targeted therapy for breast cancer, the prognosis for breast cancer patients has remained poor due to drug resistance, reoccurrence, and metastasis. Growing evidence suggests that deregulation of the self-renewal pathways, like the Wnt signaling pathway mediated by β-catenin, plays a crucial role in the survival of breast cancer stem cells. Targeting the Wnt signaling pathway in breast cancer stem cells offers a promising avenue for developing effective therapeutic strategies targeting these cells, potentially leading to improved patient outcomes and reduced tumor recurrence.

METHODS: For this purpose, we have screened a 1615 FDA-approved drug library against our target protein, β-catenin, which is involved in the Wnt signaling pathway using molecular docking analysis, molecular dynamics (MD) simulations, and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations.

RESULTS: Molecular docking studies showed that the Lumacaftor- β-catenin complex had the lowest docking score of - 8.7 kcal/mol towards β-catenin protein than the reference inhibitor. Molecular dynamic simulations and MM/PBSA calculations were also performed for the Lumacaftor-β-catenin complex to establish the stability of the interactions involved. Considering its promising attributes and encouraging results, Lumacaftor holds significant potential as a novel therapeutic option to target BCSCs. This study opens avenues for further investigation and may pave the way for developing therapeutic potential in breast cancer treatment. Further confirmation is warranted through in vitro and clinical studies to validate the findings of this study.

PMID:38913211 | DOI:10.1007/s00894-024-05990-5

Front Pharmacol. 2024 Jun 7;15:1397602. doi: 10.3389/fphar.2024.1397602. eCollection 2024.

ABSTRACT

Drug repurposing (repositioning) is a dynamically-developing area in the search for effective therapy of infectious diseases. Repositioning existing drugs with a well-known pharmacological and toxicological profile is an attractive method for quickly discovering new therapeutic indications. The off-label use of drugs for infectious diseases requires much less capital and time, and can hasten progress in the development of new antimicrobial drugs, including antibiotics. The use of drug repositioning in searching for new therapeutic options has brought promising results for many viral infectious diseases, such as Ebola, ZIKA, Dengue, and HCV. This review describes the most favorable results for repositioned drugs for the treatment of bacterial infections. It comprises publications from various databases including PubMed and Web of Science published from 2015 to 2023. The following search keywords/strings were used: drug repositioning and/or repurposing and/or antibacterial activity and/or infectious diseases. Treatment options for infections caused by multidrug-resistant bacteria were taken into account, including methicillin-resistant staphylococci, multidrug-resistant Mycobacterium tuberculosis, or carbapenem-resistant bacteria from the Enterobacteriaceae family. It analyses the safety profiles of the included drugs and their synergistic combinations with antibiotics and discusses the potential of antibacterial drugs with antiparasitic, anticancer, antipsychotic effects, and those used in metabolic diseases. Drug repositioning may be an effective response to public health threats related to the spread of multidrug-resistant bacterial strains and the growing antibiotic resistance of microorganisms.

PMID:38910882 | PMC:PMC11193365 | DOI:10.3389/fphar.2024.1397602

J Chem Inf Model. 2024 Jun 22. doi: 10.1021/acs.jcim.3c01905. Online ahead of print.

ABSTRACT

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

PMID:38907989 | DOI:10.1021/acs.jcim.3c01905

Biomed Pharmacother. 2024 Jun 20;177:116991. doi: 10.1016/j.biopha.2024.116991. Online ahead of print.

ABSTRACT

Macropinocytosis is a cellular process that enables cells to engulf extracellular material, such as nutrients, growth factors, and even whole cells. It is involved in several physiological functions as well as pathological conditions. In cancer cells, macropinocytosis plays a crucial role in promoting tumor growth and survival under nutrient-limited conditions. In particular KRAS mutations have been identified as main drivers of macropinocytosis in pancreatic, breast, and non-small cell lung cancers. We performed a high-content screening to identify inhibitors of macropinocytosis in pancreatic ductal adenocarcinoma (PDAC)-derived cells, aiming to prevent nutrient scavenging of PDAC tumors. The screening campaign was conducted in a well-known pancreatic KRAS-mutated cell line (MIAPaCa-2) cultured under nutrient deprivation and using FITC-dextran to precisely quantify macropinocytosis. We assembled a collection of 3584 small molecules, including drugs approved by the Food and Drug Administration (FDA), drug-like molecules against molecular targets, kinase-targeted compounds, and molecules designed to hamper protein-protein interactions. We identified 28 molecules that inhibited macropinocytosis, with potency ranging from 0.4 to 29.9 μM (EC50). A few of them interfered with other endocytic pathways, while 11 compounds did not and were therefore considered specific "bona fide" macropinocytosis inhibitors and further characterized. Four compounds (Ivermectin, Tyrphostin A9, LY2090314, and Pyrvinium Pamoate) selectively hampered nutrient scavenging in KRAS-mutated cancer cells. Their ability to impair albumin-dependent proliferation was replicated both in different 2D cell culture systems and 3D organotypic models. These findings provide a new set of compounds specifically targeting macropinocytosis, which could have therapeutic applications in cancer and infectious diseases.

PMID:38906021 | DOI:10.1016/j.biopha.2024.116991

Front Pharmacol. 2024 Jun 5;15:1431975. doi: 10.3389/fphar.2024.1431975. eCollection 2024.

NO ABSTRACT

PMID:38904005 | PMC:PMC11188425 | DOI:10.3389/fphar.2024.1431975

Sci Rep. 2024 Jun 20;14(1):14208. doi: 10.1038/s41598-024-61541-1.

ABSTRACT

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

PMID:38902252 | DOI:10.1038/s41598-024-61541-1

Arch Microbiol. 2024 Jun 20;206(7):312. doi: 10.1007/s00203-024-04039-8.

ABSTRACT

Dengue virus (DENV) is the leading cause of numerous deaths every year due to its high infectivity. In this study we have tried to target the DENV envelope protein receptor binding domain, the region crucial for binding to host receptors which leads to membrane fusion and entry of the viral genome into the human host cell. We have taken 13 known FDA approved antiviral therapeutic antibodies from therapeutic antibody database and tried to repurpose them against the DENV envelope protein. Based on the humanness analysis, 10 antibodies were selected against the DENV envelope protein. Computational affinity maturation of the 10 selected antibodies was performed to increase their binding affinity and specificity against the DENV envelope protein which ultimately led to 8 mutant antibodies having better binding affinity than the native ones. Molecular Dynamics (MD) simulation shows that, the stability of the complexes involving both the native and mutant antibodies were found to be the same although the binding energy between the protein and the respective antibodies was seen to improve upon computational affinity maturation. Contact analyses show similar robustness of the interaction for both the mutant and native antibodies during complex formation with the DENV envelope protein. This has led to the selection of total 18 antibodies including 10 natural and 8 affinity matured mutants which have a high probability of interacting with the DENV envelope protein. Finally, based on all these analyses along with heated MD simulation, Bamlanivimab, Etesivimab and Tixagevimab with a mutation of residue 100 of the heavy chain from serine to tyrosine were selected as prospective therapeutic antibodies to combat DENV infection. This study may open a new avenue in designing therapeutics to combat Dengue viral infection.

PMID:38900285 | DOI:10.1007/s00203-024-04039-8

Life Sci. 2024 Jun 17:122844. doi: 10.1016/j.lfs.2024.122844. Online ahead of print.

ABSTRACT

AIMS: Leishmaniasis, caused by the protozoan parasite poses a significant health burden globally. With a very few specific drugs, increased drug resistance it is important to look for drug repurposing along with the identification of pre-clinical candidates against visceral leishmaniasis. This study aims to identify potential drug candidates against visceral leishmaniasis by targeting leishmanial MAP kinases and screening FDA approved protein kinase inhibitors.

MATERIALS AND METHODS: MAP kinases were identified from the Leishmania genome. 12 FDA approved protein kinase inhibitors were screened against Leishmania MAP kinases. Binding affinity, ADME and toxicity of identified drug candidates were profiled. The anti-proliferative effects and mechanism of action were assessed in Leishmania, including changes in cell morphology, flagellar length, cell cycle progression, reactive oxygen species (ROS) generation, and intra-macrophage parasitic burden.

KEY FINDINGS: 23 MAP kinases were identified from the Leishmania genome. Sorafenib and imatinib emerged as repurposable drug candidates and demonstrated excellent anti-proliferative effects in Leishmania. Treatment with these inhibitors resulted in significant changes in cell morphology, flagellar length, and cell cycle arrest. Furthermore, sorafenib and imatinib promoted ROS generation and reduced intra-macrophage parasitic burden, and elicited anti-leishmanial activity in in vivo experimental VL models.

SIGNIFICANCE: Collectively, these results imply involvement of MAP kinases in infectivity and survival of the parasite and can pave the avenue for repurposing sorafenib and imatinib as anti-leishmanial agents. These findings contribute to the exploration of new treatment options for visceral leishmaniasis, particularly in the context of emerging drug resistance.

PMID:38897344 | DOI:10.1016/j.lfs.2024.122844

Ann Plast Surg. 2024 Jun 18. doi: 10.1097/SAP.0000000000004007. Online ahead of print.

ABSTRACT

BACKGROUND: Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offered. However, these interventions are not devoid of risks and are associated with postinflammatory hyperpigmentation. In the quest for novel therapeutic potentials, this study aims to investigate computational methods in the identification of new targeted therapies in the treatment of hyperpigmentation.

METHODS: We used a comprehensive approach, which integrated text mining, interpreting gene lists through enrichment analysis and integration of diverse biological information (GeneCodis), protein-protein association networks and functional enrichment analyses (STRING), and plug-in network centrality parameters (Cytoscape) to pinpoint genes closely associated with hyperpigmentation. Subsequently, analysis of drug-gene interactions to identify potential drugs (Cortellis) was utilized to select drugs targeting these identified genes. Lastly, we used Deep Learning Based Drug Repurposing Toolkit (DeepPurpose) to conduct drug-target interaction predictions to ultimately identify candidate drugs with the most promising binding affinities.

RESULTS: Thirty-four hyperpigmentation-related genes were identified by text mining. Eight key genes were highlighted by utilizing GeneCodis, STRING, Cytoscape, gene enrichment, and protein-protein interaction analysis. Thirty-five drugs targeting hyperpigmentation-associated genes were identified by Cortellis, and 29 drugs, including 16 M2PK1 inhibitors, 11 KRAS inhibitors, and 2 BRAF inhibitors were recommended by DeepPurpose.

CONCLUSIONS: The study highlights the promise of advanced computational methodology for identifying potential treatments for hyperpigmentation.

PMID:38896860 | DOI:10.1097/SAP.0000000000004007

Arch Pharm (Weinheim). 2024 Jun 19:e2400267. doi: 10.1002/ardp.202400267. Online ahead of print.

ABSTRACT

Energy-coupling factor transporters (ECFTs) are membrane-bound ATP-binding cassette (ABC) transporters in prokaryotes that are found in pathogens against which novel antibiotics are urgently needed. To date, just 54 inhibitors of three molecular-structural classes with mostly weak inhibitory activity are known. Target repurposing is a strategy that transfers knowledge gained from a well-studied protein family to under-studied targets of phylogenetic relation. Forty-eight human ABC transporters are known that may harbor structural motifs similar to ECFTs to which particularly multitarget compounds may bind. We assessed 31 multitarget compounds which together target the entire druggable human ABC transporter proteome against ECFTs, of which nine showed inhibitory activity (hit rate 29.0%) and four demonstrated moderate to strong inhibition of an ECFT (IC50 values between 4.28 and 50.2 µM) as well as antibacterial activity against ECFT-expressing Streptococcus pneumoniae. Here, ivermectin was the most potent candidate (MIC95: 22.8 µM), and analysis of five ivermectin derivatives revealed moxidectin as one of the most potent ECFT-targeting antibacterial agents (IC50: 2.23 µM; MIC95: 2.91 µM). Distinct molecular-structural features of avermectins and derivatives as well as the differential biological response of the hit compounds in general provided first indications with respect to the structure-activity relationships and mode of action, respectively.

PMID:38896404 | DOI:10.1002/ardp.202400267

bioRxiv [Preprint]. 2024 Jun 9:2024.06.06.597745. doi: 10.1101/2024.06.06.597745.

ABSTRACT

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

AUTHOR SUMMARY: This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

PMID:38895485 | PMC:PMC11185689 | DOI:10.1101/2024.06.06.597745

bioRxiv [Preprint]. 2024 Jun 6:2024.06.04.595773. doi: 10.1101/2024.06.04.595773.

ABSTRACT

Neuroinflammation is a pathological feature of many neurodegenerative diseases, including Alzheimer's disease (AD) 1,2 and amyotrophic lateral sclerosis (ALS) 3 , raising the possibility of common therapeutic targets. We previously established that cytoplasmic double-stranded RNA (cdsRNA) is spatially coincident with cytoplasmic pTDP-43 inclusions in neurons of patients with C9ORF72-mediated ALS 4 . CdsRNA triggers a type-I interferon (IFN-I)-based innate immune response in human neural cells, resulting in their death 4 . Here, we report that cdsRNA is also spatially coincident with pTDP-43 cytoplasmic inclusions in brain cells of patients with AD pathology and that type-I interferon response genes are significantly upregulated in brain regions affected by AD. We updated our machine-learning pipeline DRIAD-SP (Drug Repurposing In Alzheimer's Disease with Systems Pharmacology) to incorporate cryptic exon (CE) detection as a proxy of pTDP-43 inclusions and demonstrated that the FDA-approved JAK inhibitors baricitinib and ruxolitinib that block interferon signaling show a protective signal only in cortical brain regions expressing multiple CEs. Furthermore, the JAK family member TYK2 was a top hit in a CRISPR screen of cdsRNA-mediated death in differentiated human neural cells. The selective TYK2 inhibitor deucravacitinib, an FDA-approved drug for psoriasis, rescued toxicity elicited by cdsRNA. Finally, we identified CCL2, CXCL10, and IL-6 as candidate predictive biomarkers for cdsRNA-related neurodegenerative diseases. Together, we find parallel neuroinflammatory mechanisms between TDP-43 associated-AD and ALS and nominate TYK2 as a possible disease-modifying target of these incurable neurodegenerative diseases.

PMID:38895380 | PMC:PMC11185596 | DOI:10.1101/2024.06.04.595773