Res Sq. 2023 Jul 14:rs.3.rs-3125859. doi: 10.21203/rs.3.rs-3125859/v1. Preprint.

ABSTRACT

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

PMID:37503019 | PMC:PMC10371084 | DOI:10.21203/rs.3.rs-3125859/v1

Heliyon. 2023 Jul 8;9(7):e18017. doi: 10.1016/j.heliyon.2023.e18017. eCollection 2023 Jul.

ABSTRACT

Statins have anticancer effects and may be used as anticancer agents via drug repositioning. In reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays, the internal reference gene must not be affected by any experimental conditions. As statins exert a wide range of effects on cells by inhibiting the mevalonate pathway, it is possible that statin treatment might alter the expression of housekeeping genes used as internal reference genes, thereby misleading the assessment of obtained gene expression data. Here, we evaluated the expression stability of internal reference genes in atorvastatin-treated cancer cell lines. We treated both statin-sensitive and statin-resistant cancer cell lines with atorvastatin at seven different concentrations and performed RT-qPCR on 15 housekeeping genes whose expression stability was then assessed using five different algorithms. In both statin-sensitive and statin-resistant cancer cell lines, atorvastatin affected the expression of certain internal reference genes in a dose-dependent and cancer cell line-dependent manner; therefore, caution should be exercised when comparing target gene expression between cells. Our findings emphasize the importance of the validation of internal reference genes in gene expression analyses in drug treatment-based cancer research.

PMID:37501994 | PMC:PMC10368838 | DOI:10.1016/j.heliyon.2023.e18017

Sci Rep. 2023 Jul 27;13(1):12192. doi: 10.1038/s41598-023-39382-1.

ABSTRACT

Infections by Entamoeba histolytica (E. histolytica) lead to considerable morbidity and mortality worldwide and treatment is reliant on a single class of drugs, nitroimidazoles. Treatment failures and intermittent reports of relapse from different parts of world indicate towards development of clinical drug resistance. In the present study, susceptibility testing of clinical isolates of E. histolytica was carried against metronidazole and tinidazole. Additionally, anti-amoebic property of active compounds of Andrographis paniculata was also evaluated. Prevalence of metronidazole resistance gene (nim) in patients attending hospital was also done to get comprehensive insight of present situation of drug resistance in E. histolytica. Mean inhibitory concentration 50 (IC50) value of E. histolytica isolates against metronidazole and tinidazole was 20.01 and 16.1 µM respectively. Andrographolide showed minimum mean IC50 value (3.06 µM). Significant percentage inhibition of E. histolytica isolates by andrographolide was seen as compared to metronidazole (p = 0.0495). None of E. histolytica isolates showed presence of nim gene. However, in stool samples from hospital attending population, prevalence of nimE gene was found to be 76.6% (69/90) and 62.2% (56/90) in diarrheal and non-diarrheal samples respectively. Inhibitory concentration of commonly used nitroimidazoles against clinical isolates of E. histolytica are on rise. Percentage inhibition of E. histolytica isolates by andrographolide was significantly higher than control drug metronidazole.

PMID:37500681 | PMC:PMC10374660 | DOI:10.1038/s41598-023-39382-1

Addict Biol. 2023 Aug;28(8):e13313. doi: 10.1111/adb.13313.

ABSTRACT

Cannabis use disorder (CUD) remains a significant public health issue globally, affecting up to one in five adults who use cannabis. Despite extensive research into the molecular underpinnings of the condition, there are no effective pharmacological treatment options available. Therefore, we sought to further explore genetic analyses to prioritise opportunities to repurpose existing drugs for CUD. Specifically, we aimed to identify druggable genes associated with the disorder, integrate transcriptomic/proteomic data and estimate genetic relationships with clinically actionable biochemical traits. Aggregating variants to genes based on genomic position, prioritised the phosphodiesterase gene PDE4B as an interesting target for drug repurposing in CUD. Credible causal PDE4B variants revealed by probabilistic finemapping in and around this locus demonstrated an association with inflammatory and other substance use phenotypes. Gene and protein expression data integrated with the GWAS data revealed a novel CUD associated gene, NPTX1, in whole blood and supported a role for hyaluronidase, a key enzyme in the extracellular matrix in the brain and other tissues. Finally, genetic correlation with biochemical traits revealed a genetic overlap between CUD and immune-related markers such as lymphocyte count, as well as serum triglycerides.

PMID:37500481 | DOI:10.1111/adb.13313

Front Pharmacol. 2023 Jul 11;14:1202287. doi: 10.3389/fphar.2023.1202287. eCollection 2023.

NO ABSTRACT

PMID:37497109 | PMC:PMC10367545 | DOI:10.3389/fphar.2023.1202287

Front Genet. 2023 Jul 11;14:1185790. doi: 10.3389/fgene.2023.1185790. eCollection 2023.

ABSTRACT

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method. Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method. Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

PMID:37496714 | PMC:PMC10366691 | DOI:10.3389/fgene.2023.1185790

Comput Biol Med. 2023 Jun 22;164:107131. doi: 10.1016/j.compbiomed.2023.107131. Online ahead of print.

ABSTRACT

Accurately predicting compound-protein binding affinity is a crucial task in drug discovery. Computational models offer the advantages of short time, low cost and safety compared to traditional drug development. Pocket is the key binding region of the protein, which provides invaluable information for drug repositioning and drug design. In this study, we propose an ensemble learning model, called StackCPA, to predict the compound-protein binding affinity. The model integrates multi-scale features of protein pocket and compound through a transfer learning strategy. The protein pocket is described in a fine-grained way by atomic level, residue level and subdomain level. The proposed model StackCPA is evaluated on three binding affinity benchmark datasets. The experiment results show that StackCPA achieves the best performance on all the three datasets in comparison with other state-of-the-art deep learning models. The ablation study shows that the protein pocket can provide sufficient information for affinity prediction and its multi-scale features enable the model to further improve the prediction performance. In addition, the case study for epidermal growth factor receptor erbB1 (EGFR) indicates that StackCPA could serve as an effective tool for drug repurposing. Source codes and data of StackCPA are available at https://github.com/CSUBioGroup/StackCPA.

PMID:37494820 | DOI:10.1016/j.compbiomed.2023.107131

Cell Death Dis. 2023 Jul 26;14(7):468. doi: 10.1038/s41419-023-05955-1.

ABSTRACT

Despite high initial response rates to targeted kinase inhibitors, the majority of patients suffering from metastatic melanoma present with high relapse rates, demanding for alternative therapeutic options. We have previously developed a drug repurposing workflow to identify metabolic drug targets that, if depleted, inhibit the growth of cancer cells without harming healthy tissues. In the current study, we have applied a refined version of the workflow to specifically predict both, common essential genes across various cancer types, and melanoma-specific essential genes that could potentially be used as drug targets for melanoma treatment. The in silico single gene deletion step was adapted to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance. Based on publicly available, and in-house, large-scale transcriptomic data metabolic models for melanoma were reconstructed enabling the prediction of 28 candidate drugs and estimating their respective efficacy. Twelve highly efficacious drugs with low half-maximal inhibitory concentration values for the treatment of other cancers, which are not yet approved for melanoma treatment, were used for in vitro validation using melanoma cell lines. Combination of the top 4 out of 6 promising candidate drugs with BRAF or MEK inhibitors, partially showed synergistic growth inhibition compared to individual BRAF/MEK inhibition. Hence, the repurposing of drugs may enable an increase in therapeutic options e.g., for non-responders or upon acquired resistance to conventional melanoma treatments.

PMID:37495601 | DOI:10.1038/s41419-023-05955-1

Elife. 2023 Jul 26;12:RP86961. doi: 10.7554/eLife.86961.

ABSTRACT

Systems genetics has begun to tackle the complexity of insulin resistance by capitalising on computational advances to study high-diversity populations. 'Diversity Outbred in Australia (DOz)' is a population of genetically unique mice with profound metabolic heterogeneity. We leveraged this variance to explore skeletal muscle's contribution to whole-body insulin action through metabolic phenotyping and skeletal muscle proteomics of 215 DOz mice. Linear modelling identified 553 proteins that associated with whole-body insulin sensitivity (Matsuda Index) including regulators of endocytosis and muscle proteostasis. To enrich for causality, we refined this network by focusing on negatively associated, genetically regulated proteins, resulting in a 76-protein fingerprint of insulin resistance. We sought to perturb this network and restore insulin action with small molecules by integrating the Broad Institute Connectivity Map platform and in vitro assays of insulin action using the Prestwick chemical library. These complementary approaches identified the antibiotic thiostrepton as an insulin resistance reversal agent. Subsequent validation in ex vivo insulin-resistant mouse muscle and palmitate-induced insulin-resistant myotubes demonstrated potent insulin action restoration, potentially via upregulation of glycolysis. This work demonstrates the value of a drug-centric framework to validate systems-level analysis by identifying potential therapeutics for insulin resistance.

PMID:37494090 | DOI:10.7554/eLife.86961

BioDrugs. 2023 Jul 26. doi: 10.1007/s40259-023-00617-2. Online ahead of print.

ABSTRACT

Clostridioides difficile is one of the most important causes of healthcare-associated diarrhea. The high incidence and recurrence rates of C. difficile infection, as well as its associated morbidity and mortality, are great concerns. The most common complication of C. difficile infection is recurrence, with rates of 20-30% after a primary infection and 60% after three or more episodes. Medical management of recurrent C. difficile infection involves a choice of therapy that is different from the antibiotic used in the primary episode. Patients with recurrent C. difficile infection also benefit from fecal microbiota transplantation or standardized microbiome restoration therapies (approved or experimental) to restore eubiosis. In contrast to antibiotics, microbiome restoration therapies restore a normal gut flora and eliminate C. difficile colonization and infection. Fecal microbiota transplantation in recurrent C. difficile infection has demonstrated higher success rates than vancomycin, fidaxomicin, or placebo. Fecal microbiota transplantation has traditionally been considered safe, with the most common adverse reactions being abdominal discomfort, and diarrhea, and rare serious adverse events. Significant heterogeneity and a lack of standardization regarding the process of preparation, and administration of fecal microbiota transplantation remain a major pitfall. Standardized microbiome-based therapies provide a promising alternative. In the ECOSPOR III trial of SER-109, an oral formulation of bacterial spores, a significant reduction in the recurrence rate (12%) was observed compared with placebo (40%). In the phase III PUNCH CD3 trial, RBX2660 also demonstrated high efficacy rates of 70.6% versus 57.5%. Both these agents are now US Food and Drug Administration approved for recurrent C. difficile infection. Other standardized microbiome-based therapies currently in the pipeline are VE303, RBX7455, and MET-2. Antibiotic neutralization strategies, vaccines, passive monoclonal antibodies, and drug repurposing are other therapeutic strategies being explored to treat C. difficile infection.

PMID:37493938 | DOI:10.1007/s40259-023-00617-2

Neurotherapeutics. 2023 Jul 26. doi: 10.1007/s13311-023-01406-z. Online ahead of print.

ABSTRACT

Pathogenic changes to TAR DNA-binding protein 43 (TDP-43) leading to alteration of its homeostasis are a common feature shared by several progressive neurodegenerative diseases for which there is no effective therapy. Here, we developed Drosophila lines expressing either wild type TDP-43 (WT) or that carrying an Amyotrophic Lateral Sclerosis /Frontotemporal Lobar Degeneration-associating G384C mutation that recapitulate several aspects of the TDP-43 pathology. To identify potential therapeutics for TDP-43-related diseases, we implemented a drug repurposing strategy that involved three consecutive steps. Firstly, we evaluated the improvement of eclosion rate, followed by the assessment of locomotive functions at early and late developmental stages. Through this approach, we successfully identified fingolimod, as a promising candidate for modulating TDP-43 toxicity. Fingolimod exhibited several beneficial effects in both WT and mutant models of TDP-43 pathology, including post-transcriptional reduction of TDP-43 levels, rescue of pupal lethality, and improvement of locomotor dysfunctions. These findings provide compelling evidence for the therapeutic potential of fingolimod in addressing TDP-43 pathology, thereby strengthening the rationale for further investigation and consideration of clinical trials. Furthermore, our study demonstrates the utility of our Drosophila-based screening pipeline in identifying novel therapeutics for TDP-43-related diseases. These findings encourage further scale-up screening endeavors using this platform to discover additional compounds with therapeutic potential for TDP-43 pathology.

PMID:37493896 | DOI:10.1007/s13311-023-01406-z

Rev Soc Bras Med Trop. 2023 Jul 24;56:e01212023. doi: 10.1590/0037-8682-0121-2023. eCollection 2023.

ABSTRACT

There is a consensus that the antifungal repertoire for the treatment of cryptococcal infections is limited. Standard treatment involves the administration of an antifungal drug derived from natural sources (i.e., amphotericin B) and two other drugs developed synthetically (i.e., flucytosine and fluconazole). Despite treatment, the mortality rates associated with fungal cryptococcosis are high. Amphotericin B and flucytosine are toxic, require intravenous administration, and are usually unavailable in low-income countries because of their high cost. However, fluconazole is cost-effective, widely available, and harmless with regard to its side effects. However, fluconazole is a fungistatic agent that has contributed considerably to the increase in fungal resistance and frequent relapses in patients with cryptococcal meningitis. Therefore, there is an unquestionable need to identify new alternatives or adjuvants to conventional drugs for the treatment of cryptococcosis. A potential antifungal agent should be able to kill cryptococci and "bypass" the virulence mechanism of the yeast. Furthermore, it should have fungicidal action, low toxicity, high selectivity, easily penetrate the central nervous system, and widely available. In this review, we describe cryptococcosis, its conventional therapy, and failures arising from the use of drugs traditionally considered to be the reference standard. Additionally, we present the approaches used for the discovery of new drugs to counteract cryptococcosis, ranging from the conventional screening of natural products to the inclusion of structural modifications to optimize anticryptococcal activity, as well as drug repositioning and combined therapies.

PMID:37493736 | DOI:10.1590/0037-8682-0121-2023

mBio. 2023 Jul 26:e0137623. doi: 10.1128/mbio.01376-23. Online ahead of print.

ABSTRACT

The fungal pathogen Candida auris represents a severe threat to hospitalized patients. Its resistance to multiple classes of antifungal drugs and ability to spread and resist decontamination in healthcare settings make it especially dangerous. We screened 1,990 clinically approved and late-stage investigational compounds for the potential to be repurposed as antifungal drugs targeting C. auris and narrowed our focus to five Food and Drug Administration (FDA)-approved compounds with inhibitory concentrations under 10 µM for C. auris and significantly lower toxicity to three human cell lines. These compounds, some of which had been previously identified in independent screens, include three dihalogenated 8-hydroxyquinolines: broxyquinoline, chloroxine, and clioquinol. A subsequent structure-activity study of 32 quinoline derivatives found that 8-hydroxyquinolines, especially those dihalogenated at the C5 and C7 positions, were the most effective inhibitors of C. auris. To pursue these compounds further, we exposed C. auris to clioquinol in an extended experimental evolution study and found that C. auris developed only twofold to fivefold resistance to the compound. DNA sequencing of resistant strains and subsequent verification by directed mutation in naive strains revealed that resistance was due to mutations in the transcriptional regulator CAP1 (causing upregulation of the drug transporter MDR1) and in the drug transporter CDR1. These mutations had only modest effects on resistance to traditional antifungal agents, and the CDR1 mutation rendered C. auris more susceptible to posaconazole. This observation raises the possibility that a combination treatment involving an 8-hydroxyquinoline and posaconazole might prevent C. auris from developing resistance to this established antifungal agent. IMPORTANCE The rapidly emerging fungal pathogen Candida auris represents a growing threat to hospitalized patients, in part due to frequent resistance to multiple classes of antifungal drugs. We identify a class of compounds, the dihalogenated 8-hydroxyquinolines, with broad fungistatic ability against a diverse collection of 13 strains of C. auris. Although this compound has been identified in previous screens, we extended the analysis by showing that C. auris developed only modest twofold to fivefold increases in resistance to this class of compounds despite long-term exposure; a noticeable difference from the 30- to 500-fold increases in resistance reported for similar studies with commonly used antifungal drugs. We also identify the mutations underlying the resistance. These results suggest that the dihalogenated 8-hydroxyquinolines are working inside the fungal cell and should be developed further to combat C. auris and other fungal pathogens. Lohse and colleagues characterize a class of compounds that inhibit the fungal pathogen C. auris. Unlike many other antifungal drugs, C. auris does not readily develop resistance to this class of compounds.

PMID:37493629 | DOI:10.1128/mbio.01376-23

Expert Opin Drug Discov. 2023 Jul 25:1-13. doi: 10.1080/17460441.2023.2239700. Online ahead of print.

ABSTRACT

INTRODUCTION: Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED: This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION: Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

PMID:37489516 | DOI:10.1080/17460441.2023.2239700

CNS Spectr. 2023 Jul 25:1-13. doi: 10.1017/S1092852923002365. Online ahead of print.

NO ABSTRACT

PMID:37489503 | DOI:10.1017/S1092852923002365

Cell Commun Signal. 2023 Jul 24;21(1):184. doi: 10.1186/s12964-023-01190-5.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) is a highly prevalent cancer type with limited targeted therapies available and 5-year survival rate, particularly for late-stage patients. There have been numerous attempts to repurpose drugs to tackle this problem. It has been reported that autophagy inducers could augment the effect of certain chemotherapeutic agents by enhancing immunogenic cell death (ICD).

METHODS: In this study, we employed bioinformatics tools to identify thioridazine (THD), an antipsychotic drug, and found that it could induce autophagy and ICD in CRC. Then in vitro and in vivo experiments were performed to further elucidate the molecular mechanism of THD in CRC.

RESULTS: THD was found to induce endoplasmic reticulum (ER) stress in CRC cells by activating the eIF2α/ATF4/CHOP axis and facilitating the accumulation of secretory autophagosomes, leading to ICD. In addition, THD showed a remarkable ICD-activating effect when combined with oxaliplatin (OXA) to prevent tumor progression in the mouse model.

CONCLUSIONS: Together, our findings suggest that the repurposed function of THD in inhibiting CRC involves the upregulation of autophagosomes and ER stress signals, promoting the release of ICD markers, and providing a potential candidate to enhance the clinical outcome for CRC treatment. Video Abstract.

PMID:37488534 | PMC:PMC10364410 | DOI:10.1186/s12964-023-01190-5

Drugs Aging. 2023 Aug;40(8):703-719. doi: 10.1007/s40266-023-01042-4. Epub 2023 Jul 24.

ABSTRACT

Sarcopenia, the age-related loss of muscle strength and mass or quality, is a common condition with major adverse consequences. Although the pathophysiology is incompletely understood, there are common mechanisms between sarcopenia and the phenomenon of accelerated ageing seen in diabetes mellitus. Drugs currently used to treat type 2 diabetes mellitus may have mechanisms of action that are relevant to the prevention and treatment of sarcopenia, for those with type 2 diabetes and those without diabetes. This review summarises shared pathophysiology between sarcopenia and diabetes mellitus, including the effects of advanced glycation end products, mitochondrial dysfunction, chronic inflammation and changes to the insulin signalling pathway. Cellular and animal models have generated intriguing, albeit mixed, evidence that supports possible beneficial effects on skeletal muscle function for some classes of drugs used to treat diabetes, including metformin and SGLT2 inhibitors. Most human observational and intervention evidence for the effects of these drugs has been derived from populations with type 2 diabetes mellitus, and there is a need for intervention studies for older people with, and at risk of, sarcopenia to further investigate the balance of benefit and risk in these target populations. Not all diabetes treatments will be safe to use in those without diabetes because of variable side effects across classes. However, some agents [including glucagon-like peptide (GLP)-1 receptor agonists and SGLT2 inhibitors] have already demonstrated benefits in populations without diabetes, and it is these agents, along with metformin, that hold out the most promise for further investigation in sarcopenia.

PMID:37486575 | PMC:PMC10371965 | DOI:10.1007/s40266-023-01042-4

Comput Biol Med. 2023 Jul 18;164:107261. doi: 10.1016/j.compbiomed.2023.107261. Online ahead of print.

ABSTRACT

Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.

PMID:37487382 | DOI:10.1016/j.compbiomed.2023.107261

Comput Biol Chem. 2023 Jul 21;106:107934. doi: 10.1016/j.compbiolchem.2023.107934. Online ahead of print.

ABSTRACT

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

PMID:37487250 | DOI:10.1016/j.compbiolchem.2023.107934

Adv Exp Med Biol. 2023;1424:201-211. doi: 10.1007/978-3-031-31982-2_22.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

PMID:37486495 | DOI:10.1007/978-3-031-31982-2_22