Epigenomes. 2024 Apr 1;8(2):14. doi: 10.3390/epigenomes8020014.

ABSTRACT

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.

PMID:38651367 | DOI:10.3390/epigenomes8020014

Front Oncol. 2024 Apr 8;14:1376515. doi: 10.3389/fonc.2024.1376515. eCollection 2024.

ABSTRACT

BACKGROUND: The role of drug repositioning in the treatment of ovarian cancer has received increasing attention. Although promising results have been achieved, there are also major controversies.

METHODS: In this study, we conducted a drug-target Mendelian randomisation (MR) analysis to systematically investigate the reported effects and relevance of traditional drugs in the treatment of ovarian cancer. The inverse-variance weighted (IVW) method was used in the main analysis to estimate the causal effect. Several MR methods were used simultaneously to test the robustness of the results.

RESULTS: By screening 31 drugs with 110 targets, FNTA, HSPA5, NEU1, CCND1, CASP1, CASP3 were negatively correlated with ovarian cancer, and HMGCR, PLA2G4A, ITGAL, PTGS1, FNTB were positively correlated with ovarian cancer.

CONCLUSION: Statins (HMGCR blockers), lonafarnib (farnesyltransferase inhibitors), the anti-inflammatory drug aspirin, and the anti-malarial drug adiponectin all have potential therapeutic roles in ovarian cancer treatment.

PMID:38651149 | PMC:PMC11033362 | DOI:10.3389/fonc.2024.1376515

Front Microbiol. 2024 Apr 8;15:1382953. doi: 10.3389/fmicb.2024.1382953. eCollection 2024.

ABSTRACT

Arenaviruses belonging to the Arenaviridae family, genus mammarenavirus, are enveloped, single-stranded RNA viruses primarily found in rodent species, that cause severe hemorrhagic fever in humans. With high mortality rates and limited treatment options, the search for effective antivirals is imperative. Current treatments, notably ribavirin and other nucleoside inhibitors, are only partially effective and have significant side effects. The high lethality and lack of treatment, coupled with the absence of vaccines for all but Junín virus, has led to the classification of these viruses as Category A pathogens by the Centers for Disease Control (CDC). This review focuses on entry inhibitors as potential therapeutics against mammarenaviruses, which include both New World and Old World arenaviruses. Various entry inhibition strategies, including small molecule inhibitors and neutralizing antibodies, have been explored through high throughput screening, genome-wide studies, and drug repurposing. Notable progress has been made in identifying molecules that target receptor binding, internalization, or fusion steps. Despite promising preclinical results, the translation of entry inhibitors to approved human therapeutics has faced challenges. Many have only been tested in in vitro or animal models, and a number of candidates showed efficacy only against specific arenaviruses, limiting their broader applicability. The widespread existence of arenaviruses in various rodent species and their potential for their zoonotic transmission also underscores the need for rapid development and deployment of successful pan-arenavirus therapeutics. The diverse pool of candidate molecules in the pipeline provides hope for the eventual discovery of a broadly effective arenavirus antiviral.

PMID:38650890 | PMC:PMC11033450 | DOI:10.3389/fmicb.2024.1382953

Bioinformatics. 2024 Apr 22:btae271. doi: 10.1093/bioinformatics/btae271. Online ahead of print.

ABSTRACT

MOTIVATION: Accurate inference of potential Drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance.

RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multi-layer perceptrons (MLPs) to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach are expected to offer valuable insights for furthering drug repurposing and personalized medicine research.

AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:38648052 | DOI:10.1093/bioinformatics/btae271

J Cancer Res Clin Oncol. 2024 Apr 22;150(4):207. doi: 10.1007/s00432-024-05741-2.

ABSTRACT

PURPOSE: Colon cancer is a prevalent cancer globally, representing approximately 10% of all cancer cases and accounting for 10% of all cancer-related deaths. Therefore, finding new therapeutic methods with high efficiency will be very valuable. Cromolyn (C), a common anti-allergic and mast cell membrane stabilizing drug, has recently shown valuable anti-cancer effects in several studies. This study was designed to investigate the anti-cancer activity of cromolyn on colon cancer in vitro and in vivo and to determine values such as selectivity index and survival effect.

METHODS: HT-29 (colon cancer) and MCF-10 (normal epithelial) cell lines were treated with C and Doxorubicin (DOX; Positive control). IC50 values and the effects of C and DOX on apoptosis were explored using methyl thiazole diphenyl-tetrazolium bromide (MTT) assay and Annexin V/PI Apoptosis Assay Kit. To investigate in an animal study, colon cancer was subcutaneously induced by CT26 cells (mouse colon cancer) in bulb/c mice. Mice were treated with 0.05 LD50 intraperitoneal every other day for 35 days. After the death of mice, tumor volume, tumor weight, and survival rate were evaluated.

RESULTS: C selectively and significantly suppressed the proliferation of cancer cells in a dose-dependent manner. The IC50 values for the MCF-10 and HT29 cell lines were 7.33 ± 0.78 μM and 2.33 ± 0.6 μM, respectively. Notably, the selective index (SI) highlighted that C displayed greater selectivity in inhibiting cancer cell growth compared to DOX, with SI values of 3.15 and 2.60, respectively. C exhibited higher effectiveness and selectivity in inducing apoptosis in cancer cells compared to DOX, with a significant p-value (61% vs. 52%, P-value ≤ 0.0001). Also, in mice bearing colon cancer, C reduced the tumor volume (6317 ± 1685mm3) and tumor weight (9.8 ± 1.6 g) compared to the negative control group (weight 12.45 ± 0.9 g; volume 7346 ± 1077) but these values were not statistically significant (P ≤ 0.05).

CONCLUSION: Our study showed that cromolyn is a selective and strong drug in inhibiting the proliferation of colon cancer cells. Based on our results, the efficacy of C in vitro analysis (MTT assays and apoptosis), as well as animal studies is competitive with the FDA-approved drug doxorubicin. C is very promising as a low-complication and good-efficacy drug for cancer drug repositioning. This requires clinical research study designs to comprehensively evaluate its anti-cancer effects.

PMID:38647571 | DOI:10.1007/s00432-024-05741-2

Brief Bioinform. 2024 Mar 27;25(3):bbae172. doi: 10.1093/bib/bbae172.

ABSTRACT

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

PMID:38647153 | PMC:PMC11033846 | DOI:10.1093/bib/bbae172

Brief Bioinform. 2024 Mar 27;25(3):bbae108. doi: 10.1093/bib/bbae108.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML).

METHODS: We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects.

RESULTS: Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis.

CONCLUSION: Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.

PMID:38647152 | PMC:PMC11033730 | DOI:10.1093/bib/bbae108

Patterns (N Y). 2024 Mar 5;5(4):100949. doi: 10.1016/j.patter.2024.100949. eCollection 2024 Apr 12.

ABSTRACT

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.

PMID:38645769 | PMC:PMC11026976 | DOI:10.1016/j.patter.2024.100949

medRxiv [Preprint]. 2024 Apr 9:2024.04.06.24305166. doi: 10.1101/2024.04.06.24305166.

ABSTRACT

There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable fit. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.

PMID:38645045 | PMC:PMC11030494 | DOI:10.1101/2024.04.06.24305166

Front Cell Neurosci. 2024 Apr 4;18:1367838. doi: 10.3389/fncel.2024.1367838. eCollection 2024.

ABSTRACT

Variants in KCNT1 are associated with a wide spectrum of epileptic phenotypes, including epilepsy of infancy with migrating focal seizures (EIMFS), non-EIMFS developmental and epileptic encephalopathies, autosomal dominant or sporadic sleep-related hypermotor epilepsy, and focal epilepsy. Here, we describe a girl affected by drug-resistant focal seizures, developmental delay and behavior disorders, caused by a novel, de novo heterozygous missense KCNT1 variant (c.2809A > G, p.S937G). Functional characterization in transiently transfected Chinese Hamster Ovary (CHO) cells revealed a strong gain-of-function effect determined by the KCNT1 p.S937G variant compared to wild-type, consisting in an increased maximal current density and a hyperpolarizing shift in current activation threshold. Exposure to the antidepressant drug fluoxetine inhibited currents expressed by both wild-type and mutant KCNT1 channels. Treatment of the proband with fluoxetine led to a prolonged electroclinical amelioration, with disappearance of seizures and better EEG background organization, together with an improvement in behavior and mood. Altogether, these results suggest that, based on the proband's genetic and functional characteristics, the antidepressant drug fluoxetine may be repurposed for the treatment of focal epilepsy caused by gain-of-function variants in KCNT1. Further studies are needed to verify whether this approach could be also applied to other phenotypes of the KCNT1-related epilepsies spectrum.

PMID:38644974 | PMC:PMC11027738 | DOI:10.3389/fncel.2024.1367838

Front Oncol. 2024 Apr 4;14:1403778. doi: 10.3389/fonc.2024.1403778. eCollection 2024.

NO ABSTRACT

PMID:38638856 | PMC:PMC11024629 | DOI:10.3389/fonc.2024.1403778

Signal Transduct Target Ther. 2024 Apr 19;9(1):92. doi: 10.1038/s41392-024-01808-1.

ABSTRACT

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.

PMID:38637540 | DOI:10.1038/s41392-024-01808-1

Comput Biol Med. 2024 Apr 15;174:108444. doi: 10.1016/j.compbiomed.2024.108444. Online ahead of print.

ABSTRACT

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

PMID:38636325 | DOI:10.1016/j.compbiomed.2024.108444

J Med Internet Res. 2024 Apr 18;26:e46777. doi: 10.2196/46777.

ABSTRACT

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

PMID:38635981 | DOI:10.2196/46777

Drug Dev Res. 2024 May;85(3):e22184. doi: 10.1002/ddr.22184.

ABSTRACT

Alzheimer's disease (AD), one of the main causes of dementia, is a neurodegenerative disorder. Cholinesterase inhibitors are used in the treatment of AD, but prolonged use of these drugs can lead to serious side effects. Drug repurposing is an approach that aims to reveal the effectiveness of drugs in different diseases beyond their clinical uses. In this work, we investigated in vitro and in silico inhibitory effects of 11 different drugs on cholinesterases. The results showed that trimebutine, theophylline, and levamisole had the highest acetylcholinesterase inhibitory actions among the tested drugs, and these drugs inhibited by 68.70 ± 0.46, 53.25 ± 3.40, and 44.03 ± 1.20%, respectively at 1000 µM. In addition, these drugs are bound to acetylcholinesterase via competitive manner. Molecular modeling predicted good fitness in acetylcholinesterase active site for these drugs and possible central nervous system action for trimebutine. All of these results demonstrated that trimebutine was determined to be the drug with the highest potential for use in AD.

PMID:38634273 | DOI:10.1002/ddr.22184

Front Aging Neurosci. 2024 Mar 26;16:1368291. doi: 10.3389/fnagi.2024.1368291. eCollection 2024.

ABSTRACT

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

PMID:38633982 | PMC:PMC11022852 | DOI:10.3389/fnagi.2024.1368291

ACS Pharmacol Transl Sci. 2024 Apr 1;7(4):915-932. doi: 10.1021/acsptsci.3c00361. eCollection 2024 Apr 12.

ABSTRACT

Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.

PMID:38633585 | PMC:PMC11019736 | DOI:10.1021/acsptsci.3c00361

Front Genet. 2024 Apr 3;15:1374867. doi: 10.3389/fgene.2024.1374867. eCollection 2024.

NO ABSTRACT

PMID:38633405 | PMC:PMC11021751 | DOI:10.3389/fgene.2024.1374867

Clin Transl Med. 2024 Apr;14(4):e1657. doi: 10.1002/ctm2.1657.

ABSTRACT

PURPOSE: Systematic repurposing of approved medicines for another indication may accelerate drug development in oncology. We present a strategy combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer.

METHODS: Tumours were sequenced with the Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne CDx panel. Mutations were screened by two medical oncologists and pathogenic mutations were categorised referencing literature. Variants of unknown significance were classified as potentially pathogenic using plausible mechanisms and computational prediction of pathogenicity. Gain of function (GOF) mutations were evaluated through repurposing databases Probe Miner (PM), Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. GOF mutations were repurposing events if identified in PM, not indexed in TOPOGRAPH and excluding mutations with a known Food and Drug Administration (FDA)-approved biomarker. The computational repurposing approach was validated by evaluating its ability to identify FDA-approved biomarkers. The total repurposable genome was identified by evaluating all possible gene-FDA drug-approved combinations in the PM dataset.

RESULTS: The computational repurposing approach was accurate at identifying FDA therapies with known biomarkers (94%). Using next-generation sequencing molecular reports (n = 94), a meaningful percentage of patients (14%) could have an off-label therapeutic identified. The frequency of theoretical drug repurposing events in The Cancer Genome Atlas pan-cancer dataset was 73% of the samples in the cohort.

CONCLUSION: A computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with no access to standard therapies. Further validation is needed to confirm a precision oncology approach using drug repurposing.

PMID:38629623 | DOI:10.1002/ctm2.1657

Curr Drug Discov Technol. 2024;21(1):e101023222023. doi: 10.2174/0115701638253929230922115127.

ABSTRACT

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

PMID:38629171 | DOI:10.2174/0115701638253929230922115127