Int J Mol Sci. 2023 Sep 15;24(18):14142. doi: 10.3390/ijms241814142.

ABSTRACT

Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

PMID:37762445 | DOI:10.3390/ijms241814142

Int J Mol Sci. 2023 Sep 14;24(18):14099. doi: 10.3390/ijms241814099.

ABSTRACT

Primary and secondary non-response affects approximately 50% of patients with Crohn's disease treated with anti-tumour necrosis factor (TNF) monoclonal antibodies. To date, very little single cell research exists regarding drug repurposing in Crohn's disease. We aimed to elucidate the cellular phenomena underlying resistance to anti-TNF therapy in patients with Crohn's disease and to identify potential drug candidates for these patients. Single-cell transcriptome analyses were performed using data (GSE134809) from the Gene Expression Omnibus and Library of Integrated Network-Based Cellular Signatures L1000 Project. Data aligned to the Genome Reference Consortium Human Build 38 reference genome using the Cell Ranger software were processed using the Seurat package. To capture significant functional terms, gene ontology functional enrichment analysis was performed on the marker genes. For biological analysis, 93,893 cells were retained (median 20,163 genes). Through marker genes, seven major cell lineages were identified: B-cells, T-cells, natural killer cells, monocytes, endothelial cells, epithelial cells, and tissue stem cells. In the anti-TNF-resistant samples, the top 10 differentially expressed genes were HLA-DQB-1, IGHG1, RPS23, RPL7A, ARID5B, LTB, STAT1, NAMPT, COTL1, ISG20, IGHA1, IGKC, and JCHAIN, which were robustly distributed in all cell lineages, mainly in B-cells. Through molecular function analyses, we found that the biological functions of both monocyte and T-cell groups mainly involved immune-mediated functions. According to multi-cluster drug repurposing prediction, vorinostat is the top drug candidate for patients with anti-TNF-refractory Crohn's disease. Differences in cell populations and immune-related activity within tissues may influence the responsiveness of Crohn's disease to anti-TNF agents. Vorinostat may serve as a promising novel therapy for anti-TNF-resistant Crohn's disease.

PMID:37762402 | DOI:10.3390/ijms241814099

Int J Mol Sci. 2023 Sep 12;24(18):13972. doi: 10.3390/ijms241813972.

ABSTRACT

ATP-binding cassette transporters, including ABCB1 (P-glycoprotein) and ABCG2 (BCRP/MXR/ABCP), are pivotal in multidrug resistance (MDR) development in cancer patients undergoing conventional chemotherapy. The absence of approved therapeutic agents for multidrug-resistant cancers presents a significant challenge in effectively treating cancer. Researchers propose repurposing existing drugs to sensitize multidrug-resistant cancer cells, which overexpress ABCB1 or ABCG2, to conventional anticancer drugs. The goal of this study is to assess whether furmonertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor overcomes drug resistance mediated by ABCB1 and ABCG2 transporters. Furmonertinib stands out due to its ability to inhibit drug transport without affecting protein expression. The discovery of this characteristic was validated through ATPase assays, which revealed interactions between furmonertinib and ABCB1/ABCG2. Additionally, in silico docking of furmonertinib offered insights into potential interaction sites within the drug-binding pockets of ABCB1 and ABCG2, providing a better understanding of the underlying mechanisms responsible for the reversal of MDR by this repurposed drug. Given the encouraging results, we propose that furmonertinib should be explored as a potential candidate for combination therapy in patients with tumors that have high levels of ABCB1 and/or ABCG2. This combination therapy holds the potential to enhance the effectiveness of conventional anticancer drugs and presents a promising strategy for overcoming MDR in cancer treatment.

PMID:37762275 | DOI:10.3390/ijms241813972

J Am Med Inform Assoc. 2023 Sep 27:ocad186. doi: 10.1093/jamia/ocad186. Online ahead of print.

ABSTRACT

OBJECTIVE: Identifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing. Toward that aim, we utilized an integrative knowledge graph to construct clusters of rare diseases.

MATERIALS AND METHODS: Data on 3242 rare diseases were extracted from the National Center for Advancing Translational Science Genetic and Rare Diseases Information center internal data resources. The rare disease data enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data, and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were trained and clustered. We validated the disease clusters through semantic similarity and feature enrichment analysis.

RESULTS: Thirty-seven disease clusters were created with a mean size of 87 diseases. We validate the clusters quantitatively via semantic similarity based on the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters are highly related.

DISCUSSION: We demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and drugs are enumerated for follow-up efforts.

CONCLUSION: We lay out a method for clustering rare diseases using graph node embeddings. We develop an easy-to-maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems.

PMID:37759342 | DOI:10.1093/jamia/ocad186

Hum Gene Ther. 2023 Sep 27. doi: 10.1089/hum.2021.222. Online ahead of print.

ABSTRACT

Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effective for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and GEO expression profiles respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by Graph Neural Networks (GNN). An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and differential expression (DE) genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR.

PMID:37756369 | DOI:10.1089/hum.2021.222

Mol Diagn Ther. 2023 Sep 27. doi: 10.1007/s40291-023-00676-9. Online ahead of print.

ABSTRACT

Precision medicine is an old concept, but it is not widely applied across human health conditions as yet. Numerous attempts have been made to apply precision medicine in epilepsy, this has been based on a better understanding of aetiological mechanisms and deconstructing disease into multiple biological subsets. The scope of precision medicine is to provide effective strategies for treating individual patients with specific agent(s) that are likely to work best based on the causal biological make-up. We provide an overview of the main applications of precision medicine in epilepsy, including the current limitations and pitfalls, and propose potential strategies for implementation and to achieve a higher rate of success in patient care. Such strategies include establishing a definition of precision medicine and its outcomes; learning from past experiences, from failures and from other fields (e.g. oncology); using appropriate precision medicine strategies (e.g. drug repurposing versus traditional drug discovery process); and using adequate methods to assess efficacy (e.g. randomised controlled trials versus alternative trial designs). Although the progress of diagnostic techniques now allows comprehensive characterisation of each individual epilepsy condition from a molecular, biological, structural and clinical perspective, there remain challenges in the integration of individual data in clinical practice to achieve effective applications of precision medicine in this domain.

PMID:37755653 | DOI:10.1007/s40291-023-00676-9

Membranes (Basel). 2023 Sep 17;13(9):800. doi: 10.3390/membranes13090800.

ABSTRACT

High levels of oxidative stress are implicated in hypoxia, a physiological response to low levels of oxygen. Evidence supports a connection between this response and depression. Previous studies indicate that tryptophan hydroxylase can be negatively affected in hypoxia, impairing serotonin synthesis and downstream pathways. Some studies also hypothesize that increasing hypoxia-inducible factor-1 (HIF-1) levels may be a new therapeutic modality for depression. Hence, this study delved into the influence of hypoxia on the cellular response to drugs designed to act in depression. By the induction of hypoxia in SH-SY5Y cells through a hypoxia incubator chamber or Cobalt Chloride treatment, the effect of Mirtazapine, an antidepressant, and other drugs that interact with serotonin receptors (TCB-2, Dextromethorphan, Ketamine, Quetiapine, Scopolamine, Celecoxib, and Lamotrigine) on SH-SY5Y cellular viability and morphology was explored. The selection of drugs was initially conducted by literature search, focusing on compounds with established potential for employment in depression therapy. Subsequently, we employed in silico approaches to forecast their ability to traverse the blood-brain barrier (BBB). This step was particularly pertinent as we aimed to assess their viability for inducing potential antidepressant effects. The effect of these drugs in hypoxia under the inhibition of HIF-1 by Echinomycin was also tested. Our results revealed that all the potential repurposed drugs promoted cell viability, especially when hypoxia was chemically induced. When combined with Echinomycin, all drugs decreased cellular viability, possibly by the inability to interact with HIF-1.

PMID:37755222 | DOI:10.3390/membranes13090800

Chem Biodivers. 2023 Sep 26:e202300998. doi: 10.1002/cbdv.202300998. Online ahead of print.

ABSTRACT

Based on the research strategy of "drug repurposing", a series of derivatives and marketed drugs that containing salicylic acid skeleton were tested for their antibacterial activities against phytopathogens. Salicylic acid can not only regulate some important growth metabolism of plants, but also induce plant disease resistance. The bioassay results showed that the salicylamides exhibited excellent antibacterial activity. Especially, oxyclozanide showed the best antibacterial effect against Xanthomonas oryzae, Xanthomonas axonopodis pv. citri and Pectobacterium atroseptica with MICs of 0.78, 3.12 and 12.5 μg.mL-1, respectively. In vivo experiments with rice bacterial leaf blight had further demonstrated that oxyclozanide exhibited stronger antibacterial activity than the commercial bactericide, thiodiazole copper. Oxyclozanide could induce plant defense responses through the determination of salicylic acid content and the activities of defense-related enzymes including CAT, POD, and SOD in rice. The preliminarily antibacterial mechanism study indicated that oxyclozanide exhibited the antibacterial activity by disrupting cell integrity and reducing bacterial pathogenicity. Additionally, oxyclozanide could induce plant defense responses through the determination of salicylic acid content.

PMID:37755070 | DOI:10.1002/cbdv.202300998

Gut Microbes. 2023 Dec;15(2):2256695. doi: 10.1080/19490976.2023.2256695. Epub 2023 Sep 25.

ABSTRACT

The intestinal pathogen Clostridioides difficile is the leading cause of antibiotic-associated diarrhea and pseudomembranous colitis in humans. The symptoms of C. difficile-associated diseases (CDADs) are directly associated with the pathogen's toxins TcdA and TcdB, which enter host cells and inactivate Rho and/or Ras GTPases by glucosylation. Membrane cholesterol is crucial during the intoxication process of TcdA and TcdB, and likely involved during pore formation of both toxins in endosomal membranes, a key step after cellular uptake for the translocation of the glucosyltransferase domain of both toxins from endosomes into the host cell cytosol. The licensed drug amiodarone, a multichannel blocker commonly used in the treatment of cardiac dysrhythmias, is also capable of inhibiting endosomal acidification and, as shown recently, cholesterol biosynthesis. Thus, we were keen to investigate in vitro with cultured cells and human intestinal organoids, whether amiodarone preincubation protects from TcdA and/or TcdB intoxication. Amiodarone conferred protection against both toxins independently and in combination as well as against toxin variants from the clinically relevant, epidemic C. difficile strain NAP1/027. Further mechanistic studies suggested that amiodarone's mode-of-inhibition involves also interference with the translocation pore of both toxins. Our study opens the possibility of repurposing the licensed drug amiodarone as a novel pan-variant antitoxin therapeutic in the context of CDADs.

PMID:37749884 | DOI:10.1080/19490976.2023.2256695

Orphanet J Rare Dis. 2023 Sep 25;18(1):301. doi: 10.1186/s13023-023-02876-2.

ABSTRACT

BACKGROUND: Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data.

METHODS: We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM.

RESULTS: We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM.

CONCLUSION: Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

PMID:37749605 | DOI:10.1186/s13023-023-02876-2

bioRxiv. 2023 Sep 14:2023.09.12.557235. doi: 10.1101/2023.09.12.557235. Preprint.

ABSTRACT

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

PMID:37745452 | PMC:PMC10515830 | DOI:10.1101/2023.09.12.557235

Front Pharmacol. 2023 Sep 7;14:1264842. doi: 10.3389/fphar.2023.1264842. eCollection 2023.

ABSTRACT

Dimethyl fumarate (DMF) is an FDA-approved drug for the treatment of psoriasis and multiple sclerosis. DMF is known to stabilize the transcription factor Nrf2, which in turn induces the expression of antioxidant response element genes. It has also been shown that DMF influences autophagy and participates in the transcriptional control of inflammatory factors by inhibiting NF-κB and its downstream targets. DMF is receiving increasing attention for its potential to be repurposed for several diseases. This versatile molecule is indeed able to exert beneficial effects on different medical conditions through a pleiotropic mechanism, in virtue of its antioxidant, immunomodulatory, neuroprotective, anti-inflammatory, and anti-proliferative effects. A growing number of preclinical and clinical studies show that DMF may have important therapeutic implications for chronic diseases, such as cardiovascular and respiratory pathologies, cancer, eye disorders, neurodegenerative conditions, and systemic or organ specific inflammatory and immune-mediated diseases. This comprehensive review summarizes and highlights the plethora of DMF's beneficial effects and underlines its repurposing opportunities in a variety of clinical conditions.

PMID:37745068 | PMC:PMC10512734 | DOI:10.3389/fphar.2023.1264842

Front Pharmacol. 2023 Sep 7;14:1257700. doi: 10.3389/fphar.2023.1257700. eCollection 2023.

ABSTRACT

Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.

PMID:37745051 | PMC:PMC10512468 | DOI:10.3389/fphar.2023.1257700

ACS Omega. 2023 Sep 5;8(37):34084-34090. doi: 10.1021/acsomega.3c05138. eCollection 2023 Sep 19.

ABSTRACT

In tropical and subtropical areas, malaria stands as a profound public health challenge, causing an estimated 247 million cases worldwide annually. Given the absence of a viable vaccine, the timely and effective treatment of malaria remains a critical priority. However, the growing resistance of parasites to currently utilized drugs underscores the critical need for the identification of new antimalarial therapies. Here, we aimed to identify potential new drug candidates against Plasmodium falciparum, the main causative agent of malaria, by analyzing the transcriptomes of different life stages of the parasite and identifying highly expressed genes. We searched for genes that were expressed in all stages of the parasite's life cycle, including the asexual blood stage, gametocyte stage, liver stage, and sexual stages in the insect vector, using transcriptomics data from publicly available databases. From this analysis, we found 674 overlapping genes, including 409 essential ones. By searching through drug target databases, we discovered 70 potential drug targets and 75 associated bioactive compounds. We sought to expand this analysis to similar compounds to known drugs. So, we found a list of 1557 similar compounds, which we predicted as actives and inactives using previously developed machine learning models against five life stages of Plasmodium spp. From this analysis, two compounds were selected, and the reactions were experimentally evaluated. The compounds HSP-990 and silvestrol aglycone showed potent inhibitory activity at nanomolar concentrations against the P. falciparum 3D7 strain asexual blood stage. Moreover, silvestrol aglycone exhibited low cytotoxicity in mammalian cells, transmission-blocking potential, and inhibitory activity comparable to those of established antimalarials. These findings warrant further investigation of silvestrol aglycone as a potential dual-acting antimalarial and transmission-blocking candidate for malaria control.

PMID:37744849 | PMC:PMC10515587 | DOI:10.1021/acsomega.3c05138

ACS Omega. 2023 Sep 6;8(37):34160-34170. doi: 10.1021/acsomega.3c05425. eCollection 2023 Sep 19.

ABSTRACT

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

PMID:37744812 | PMC:PMC10515398 | DOI:10.1021/acsomega.3c05425

Front Chem. 2023 Sep 8;11:1250043. doi: 10.3389/fchem.2023.1250043. eCollection 2023.

ABSTRACT

Quercetin (QR) is a natural flavonol compound widely distributed in the plant kingdom with extensive pharmacological effects. To find the potential clinical indications of QR, 156 differentially expressed genes (DEGs) regulated by QR were obtained from the Gene Expression Omnibus database, and new potential pharmacological effects and clinical indications of QR were repurposed by integrating compounds with similar gene perturbation signatures and associated-disease signatures to QR based on the Connectivity Map and Coexpedia platforms. The results suggested QR has mainly potential therapeutic effects on multiple sclerosis (MS), osteoarthritis, type 2 diabetes mellitus, and acute leukemia. Then, MS was selected for subsequent animal experiments as a representative potential indication, and it found that QR significantly delays the onset time of classical MS model animal mice and ameliorates the inflammatory infiltration and demyelination in the central nervous system. Combined with network pharmacology technology, the therapeutic mechanism of QR on MS was further demonstrated to be related to the inhibition of the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β, IFN-γ, IL-17A, and IL-2) related to TNF-α/TNFR1 signaling pathway. In conclusion, this study expanded the clinical indications of QR and preliminarily confirmed the therapeutic effect and potential mechanism of QR on MS.

PMID:37744058 | PMC:PMC10514366 | DOI:10.3389/fchem.2023.1250043

J Biol Chem. 2023 Sep 22:105279. doi: 10.1016/j.jbc.2023.105279. Online ahead of print.

ABSTRACT

Thermal proteome profiling (TPP) has significantly advanced the field of drug discovery by facilitating proteome-wide identification of drug targets and off-targets. However, TPP has not been widely applied for high-throughput drug screenings, since the method is labor intensive and requires a lot of measurement time on a mass spectrometer. Here, we present Single-tube TPP with Uniform Progression (STPP-UP), which significantly reduces both the amount of required input material and measurement time, while retaining the ability to identify drug targets for compounds of interest. By using incremental heating of a single sample, changes in protein thermal stability across a range of temperatures can be assessed, while alleviating the need to measure multiple samples heated to different temperatures. We demonstrate that STPP-UP is able to identify the direct interactors for anti-cancer drugs in both human and mice cells. In summary, the STPP-UP methodology represents a useful tool to advance drug discovery and drug repurposing efforts.

PMID:37742922 | DOI:10.1016/j.jbc.2023.105279

Biomed Pharmacother. 2023 Sep 22;167:115539. doi: 10.1016/j.biopha.2023.115539. Online ahead of print.

ABSTRACT

Among different strategies to develop novel therapies, drug repositioning (aka repurposing) aims at identifying new uses of an already approved or investigational drug. This approach has the advantages of availability of the extensive pre-existing knowledge of the drug's safety, pharmacology and toxicology, manufacturing and formulation. It provides advantages to the risk-versus-rewards trade-off as compared to the costly and time-consuming de novo drug discovery process. Clofazimine, a red-colored synthetic derivative of riminophenazines initially isolated from lichens, was first synthesized in the 1950 s, and passed through several phases of repositioning in its history as a drug. Being initially developed as an anti-tuberculosis treatment, it was repurposed for the treatment of leprosy, prior to re-repositioning for the treatment of multidrug-resistant tuberculosis and other infections. Since 1990 s, reports on the anticancer properties of clofazimine, both in vitro and in vivo, started to appear. Among the diverse mechanisms of action proposed, the activity of clofazimine as a specific inhibitor of the oncogenic Wnt signaling pathway has recently emerged as the promising targeting mechanism of the drug against breast, colon, liver, and other forms of cancer. Seventy years after the initial discovery, clofazimine's journey as a drug finding new applications continues, serving as a colorful illustration of drug repurposing in modern pharmacology.

PMID:37742606 | DOI:10.1016/j.biopha.2023.115539

Biomed Pharmacother. 2023 Sep 21:115388. doi: 10.1016/j.biopha.2023.115388. Online ahead of print.

NO ABSTRACT

PMID:37741799 | DOI:10.1016/j.biopha.2023.115388

Am J Hum Genet. 2023 Sep 14:S0002-9297(23)00313-0. doi: 10.1016/j.ajhg.2023.08.018. Online ahead of print.

ABSTRACT

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

PMID:37741276 | DOI:10.1016/j.ajhg.2023.08.018