Prog Mol Biol Transl Sci. 2024;205:277-302. doi: 10.1016/bs.pmbts.2024.03.028. Epub 2024 Apr 2.

ABSTRACT

The field of drug repurposing is gaining attention as a way to introduce pharmaceutical agents with established safety profiles to new patient populations. This approach involves finding new applications for existing drugs through observations or deliberate efforts to understand their mechanisms of action. Recent advancements in bioinformatics and pharmacology, along with the availability of extensive data repositories and analytical techniques, have fueled the demand for novel methodologies in pharmaceutical research and development. To facilitate systematic drug repurposing, various computational methodologies have emerged, combining experimental techniques and in silico approaches. These methods have revolutionized the field of drug discovery by enabling the efficient repurposing of screens. However, establishing an ideal drug repurposing pipeline requires the integration of molecular data accessibility, analytical proficiency, experimental design expertise, and a comprehensive understanding of clinical development processes. This chapter explores the key methodologies used in systematic drug repurposing and discusses the stakeholders involved in this field. It emphasizes the importance of strategic alliances to enhance the success of repurposing existing compounds for new indications. Additionally, the chapter highlights the current benefits, considerations, and challenges faced in the repurposing process, which is pursued by both biotechnology and pharmaceutical companies. Overall, drug repurposing holds great promise in expanding the use of existing drugs and bringing them to new patient populations. With the advancements in computational methodologies and the collaboration of various stakeholders, this approach has the potential to accelerate drug development and improve patient outcomes.

PMID:38789184 | DOI:10.1016/bs.pmbts.2024.03.028

Prog Mol Biol Transl Sci. 2024;205:259-275. doi: 10.1016/bs.pmbts.2024.03.024. Epub 2024 Apr 9.

ABSTRACT

Medications that are currently on the market and have proven therapeutic usage can have new therapeutic indications discovered through a process called drug repurposing, which is also called drug repositioning. This approach presents a viable method for drug developers and pharmaceutical companies to discern novel targets for FDA-approved medications. Drug repurposing presents several advantages, including reduced time consumption, lower costs, and diminished risk of failure. Sildenafil, commonly known as Viagra, serves as a notable illustration of a repurposed pharmaceutical agent, initially developed and introduced to the market as an antianginal medication. However, in the current context, its application has been redirected towards serving as a pharmaceutical intervention for the treatment of erectile dysfunction. Comparably, a multitude of pharmaceutical agents exist that have demonstrated efficacy in repurposing for therapeutic management of various clinical conditions. Focusing on the historical use of repurposed pharmaceuticals and their present state of application in disease therapies, this chapter seeks to offer a thorough review of drug repurposing methodologies. Furthermore, the rules and regulations that control the repurposing of drugs will be covered in detail in this chapter.

PMID:38789183 | DOI:10.1016/bs.pmbts.2024.03.024

Nucleic Acids Res. 2024 May 23:gkae388. doi: 10.1093/nar/gkae388. Online ahead of print.

ABSTRACT

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

PMID:38783119 | DOI:10.1093/nar/gkae388

J Biomed Inform. 2024 May 21:104658. doi: 10.1016/j.jbi.2024.104658. Online ahead of print.

ABSTRACT

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB.

METHODS: We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts.

RESULTS: Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct.

CONCLUSION: These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.

PMID:38782169 | DOI:10.1016/j.jbi.2024.104658

PLoS One. 2024 May 23;19(5):e0299685. doi: 10.1371/journal.pone.0299685. eCollection 2024.

ABSTRACT

Nischarin was reported to be a tumor suppressor that plays a critical role in breast cancer initiation and progression, and a positive prognostic marker in breast, ovarian and lung cancers. Our group has found that nischarin had positive prognostic value in female melanoma patients, but negative in males. This opened up a question whether nischarin has tumor type-specific and sex-dependent roles in cancer progression. In this study, we systematically examined in the public databases the prognostic value of nischarin in solid tumors, regulation of its expression and associated signaling pathways. We also tested the effects of a nischarin agonist rilmenidine on cancer cell viability in vitro. Nischarin expression was decreased in tumors compared to the respective healthy tissues, most commonly due to the deletions of the nischarin gene and promoter methylation. Unlike in healthy tissues where it was located in the cytoplasm and at the membrane, in tumor tissues nischarin could also be observed in the nuclei, implying that nuclear translocation may also account for its cancer-specific role. Surprisingly, in several cancer types high nischarin expression was a negative prognostic marker. Gene set enrichment analysis showed that in tumors in which high nischarin expression was a negative prognostic marker, signaling pathways that regulate stemness were enriched. In concordance with the findings that nischarin expression was negatively associated with pathways that control cancer growth and progression, nischarin agonist rilmenidine decreased the viability of cancer cells in vitro. Taken together, our study lays a ground for functional studies of nischarin in a context-dependent manner and, given that nischarin has several clinically approved agonists, provides rationale for their repurposing, at least in tumors in which nischarin is predicted to be a positive prognostic marker.

PMID:38781180 | DOI:10.1371/journal.pone.0299685

Phys Chem Chem Phys. 2024 May 23. doi: 10.1039/d4cp00576g. Online ahead of print.

ABSTRACT

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Considering the safety and druggability of FDA-approved drugs, virtual screening of the database was performed to discover Mcl1 inhibitors, and the hit was subsequently validated via TR-FRET, cytotoxicity, and flow cytometry assays. To reveal the binding characteristics shared by the hit and a typical Mcl1 selective inhibitor, we employed quantum mechanics and molecular mechanics (QM/MM) calculations, umbrella sampling, and metadynamics in this work. The combined studies suggested that fluvastatin had promising cell inhibitory potency and was suitable for further investigation. We believe that this research will shed light on the discovery of novel Mcl1 inhibitors that can be used as a supplemental treatment against leukemia and provide a possible method to improve the accuracy of drug repurposing with limited computational resources while balancing the costs of experimentation well.

PMID:38780456 | DOI:10.1039/d4cp00576g

Acta Oncol. 2024 May 23;63:385-391. doi: 10.2340/1651-226X.2024.34791.

ABSTRACT

BACKGROUND: In the two European Union (EU)-funded projects, PCM4EU (Personalized Cancer Medicine for all EU citizens) and PRIME-ROSE (Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials), we aim to facilitate implementation of precision cancer medicine (PCM) in Europe by leveraging the experience from ongoing national initiatives that have already been particularly successful.

PATIENTS AND METHODS: PCM4EU and PRIME-ROSE gather 17 and 24 partners, respectively, from 19 European countries. The projects are based on a network of Drug Rediscovery Protocol (DRUP)-like clinical trials that are currently ongoing or soon to start in 11 different countries, and with more trials expected to be established soon. The main aims of both the projects are to improve implementation pathways from molecular diagnostics to treatment, and reimbursement of diagnostics and tumour-tailored therapies to provide examples of best practices for PCM in Europe.

RESULTS: PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024.

CONCLUSION: European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients. ble from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024.

CONCLUSION: European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients.

PMID:38779910 | DOI:10.2340/1651-226X.2024.34791

Front Endocrinol (Lausanne). 2024 May 8;15:1365321. doi: 10.3389/fendo.2024.1365321. eCollection 2024.

ABSTRACT

BACKGROUND: Adrenocortical carcinoma (ACC) is an aggressive endocrine malignancy with limited therapeutic options. Treating advanced ACC with mitotane, the cornerstone therapy, remains challenging, thus underscoring the significance to predict mitotane response prior to treatment and seek other effective therapeutic strategies.

OBJECTIVE: We aimed to determine the efficacy of mitotane via an in vitro assay using patient-derived ACC cells (PDCs), identify molecular biomarkers associated with mitotane response and preliminarily explore potential agents for ACC.

METHODS: In vitro mitotane sensitivity testing was performed in 17 PDCs and high-throughput screening against 40 compounds was conducted in 8 PDCs. Genetic features were evaluated in 9 samples using exomic and transcriptomic sequencing.

RESULTS: PDCs exhibited variable sensitivity to mitotane treatment. The median cell viability inhibition rate was 48.4% (IQR: 39.3-59.3%) and -1.2% (IQR: -26.4-22.1%) in responders (n=8) and non-responders (n=9), respectively. Median IC50 and AUC were remarkably lower in responders (IC50: 53.4 µM vs 74.7 µM, P<0.0001; AUC: 158.0 vs 213.5, P<0.0001). Genomic analysis revealed CTNNB1 somatic alterations were only found in responders (3/5) while ZNRF3 alterations only in non-responders (3/4). Transcriptomic profiling found pathways associated with lipid metabolism were upregulated in responder tumors whilst CYP27A1 and ABCA1 expression were positively correlated to in vitro mitotane sensitivity. Furthermore, pharmacologic analysis identified that compounds including disulfiram, niclosamide and bortezomib exhibited efficacy against PDCs.

CONCLUSION: ACC PDCs could be useful for testing drug response, drug repurposing and guiding personalized therapies. Our results suggested response to mitotane might be associated with the dependency on lipid metabolism. CYP27A1 and ABCA1 expression could be predictive markers for mitotane response, and disulfiram, niclosamide and bortezomib could be potential therapeutics, both warranting further investigation.

PMID:38779454 | PMC:PMC11109426 | DOI:10.3389/fendo.2024.1365321

Front Oncol. 2024 May 8;14:1407511. doi: 10.3389/fonc.2024.1407511. eCollection 2024.

ABSTRACT

The diagnosis of thyroid cancer (TC) has increased dramatically in recent years. Papillary TC is the most frequent type and has shown a good prognosis. Conventional treatments for TC are surgery, hormonal therapy, radioactive iodine, chemotherapy, and targeted therapy. However, resistance to treatments is well documented in almost 20% of all cases. Genomic sequencing has provided valuable information to help identify variants that hinder the success of chemotherapy as well as to determine which of those represent potentially druggable targets. There is a plethora of targeted therapies for cancer, most of them directed toward point mutations; however, chromosomal rearrangements that generate fusion genes are becoming relevant in cancer but have been less explored in TC. Therefore, it is relevant to identify new potential inhibitors for genes that are recurrent in the formation of gene fusions. In this review, we focus on describing potentially druggable variants and propose both point variants and fusion genes as targets for drug repositioning in TC.

PMID:38779099 | PMC:PMC11109414 | DOI:10.3389/fonc.2024.1407511

Comput Biol Chem. 2024 May 11;111:108093. doi: 10.1016/j.compbiolchem.2024.108093. Online ahead of print.

ABSTRACT

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

PMID:38772047 | DOI:10.1016/j.compbiolchem.2024.108093

Parasitol Res. 2024 May 21;123(5):215. doi: 10.1007/s00436-024-08236-8.

ABSTRACT

Schistosomiasis is a neglected tropical disease associated with considerable morbidity. Praziquantel (PZQ) is effective against adult schistosomes, yet, it has little effect on juvenile stages, and PZQ resistance is emerging. Adopting the drug repurposing strategy as well as assuming enhancing the efficacy and lessening the doses and side effects, the present study aimed to investigate the in vivo therapeutic efficacy of the widely used antiarrhythmic, amiodarone, and diuretic, spironolactone, and combinations of them compared to PZQ. Mice were infected by Schistosoma mansoni "S. mansoni" cercariae (Egyptian strain), then they were divided into two major groups: Early- [3 weeks post-infection (wpi)] and late- [6 wpi] treated. Each group was subdivided into seven subgroups: positive control, PZQ, amiodarone, spironolactone, PZQ combined with amiodarone, PZQ combined with spironolactone, and amiodarone combined with spironolactone-treated groups. Among the early-treated groups, spironolactone had the best therapeutic impact indicated by a 69.4% reduction of total worm burden (TWB), 38.6% and 48.4% reduction of liver and intestine egg load, and a significant reduction of liver granuloma number by 49%. Whereas, among the late-treated groups, amiodarone combined with PZQ was superior to PZQ alone evidenced by 96.1% reduction of TWB with the total disappearance of female and copula in the liver and intestine, 53.1% and 84.9% reduction of liver and intestine egg load, and a significant reduction of liver granuloma number by 67.6%. Comparatively, spironolactone was superior to PZQ and amiodarone in the early treatment phase targeting immature stages, while amiodarone had a more potent effect when combined with PZQ in the late treatment phase targeting mature schistosomes.

PMID:38771511 | DOI:10.1007/s00436-024-08236-8

Front Genet. 2024 May 6;15:1381997. doi: 10.3389/fgene.2024.1381997. eCollection 2024.

ABSTRACT

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

PMID:38770418 | PMC:PMC11102998 | DOI:10.3389/fgene.2024.1381997

BMC Bioinformatics. 2024 May 20;25(1):196. doi: 10.1186/s12859-024-05806-6.

ABSTRACT

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects.

RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features.

CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

PMID:38769492 | DOI:10.1186/s12859-024-05806-6

Nat Commun. 2024 May 20;15(1):4260. doi: 10.1038/s41467-024-48143-1.

ABSTRACT

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

PMID:38769300 | DOI:10.1038/s41467-024-48143-1

Anticancer Agents Med Chem. 2024 May 17. doi: 10.2174/0118715206304918240509111700. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repurposing in oncology promises a high impact on many patients through its ability to provide access to novel, fast-tracked treatments. Previous studies have demonstrated that depression may influence tumor progression. Anti-proliferative activity of certain antidepressants, mainly selective serotonin reuptake inhibitors (SSRIs), are reported in the literature.

OBJECTIVE: This study was conducted to repurpose selective serotonin reuptake inhibitors (SSRIs) in treating breast cancers, and it merits the pursuit of drug repositioning in oncology.

EXPERIMENTAL: Changes in cell morphology were studied using DAPI staining, and the Annexin V/PI method was employed for apoptotic analysis. The expression of specific genes involved in cancer progression was also analyzed via RT-PCR. Caspase-3 activation was observed through fluorometric assay.

RESULTS: We have identified that sertraline hydrochloride most effectively inhibited the growth of breast cancer cell cells in vitro. Preliminary mechanistic studies demonstrated that the cytotoxicity of sertraline hydrochloride was possibly through the induction of apoptosis, as inferred from enhanced nuclear fragmentation, flow cytometric data, and caspase-3/7 activation. Gene expression analysis also showed an increased expression of proapoptotic Bax and a slight decrease in oncogene c-myc in the presence of sertraline hydrochloride.

CONCLUSION: In conclusion, our data suggest that sertraline hydrochloride, an antidepressant, can potentially be used for the treatment of breast cancer.

PMID:38766835 | DOI:10.2174/0118715206304918240509111700

Front Pharmacol. 2024 May 3;15:1403176. doi: 10.3389/fphar.2024.1403176. eCollection 2024.

NO ABSTRACT

PMID:38766630 | PMC:PMC11099202 | DOI:10.3389/fphar.2024.1403176

Brief Bioinform. 2024 Mar 27;25(3):bbae238. doi: 10.1093/bib/bbae238.

ABSTRACT

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

PMID:38762789 | DOI:10.1093/bib/bbae238

Transl Psychiatry. 2024 May 18;14(1):204. doi: 10.1038/s41398-024-02915-x.

ABSTRACT

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

PMID:38762535 | DOI:10.1038/s41398-024-02915-x

Int J Biol Macromol. 2024 May 16:132468. doi: 10.1016/j.ijbiomac.2024.132468. Online ahead of print.

ABSTRACT

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

PMID:38761900 | DOI:10.1016/j.ijbiomac.2024.132468

Technol Health Care. 2024 Apr 25. doi: 10.3233/THC-248005. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately.

OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance.

METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning.

RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods.

CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

PMID:38759038 | DOI:10.3233/THC-248005