Antiviral drug development by targeting RNA binding site, oligomerization and nuclear export of influenza nucleoprotein

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 282, P. 136996 - 136996

Published: Nov. 1, 2024

Language: Английский

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Proteolysis Targeting Chimeras (PROTACs) based on celastrol induce multiple protein degradation for triple-negative breast cancer treatment

European Journal of Pharmaceutical Sciences, Journal Year: 2023, Volume and Issue: 192, P. 106624 - 106624

Published: Oct. 28, 2023

The pursuit of single drugs targeting multiple targets has become a prominent trend in modern cancer therapeutics. Natural products, known for their multi-targeting capabilities, accessibility, and cost-effectiveness, hold great potential the development multi-target drugs. However, therapeutic efficacy is often hindered by complex structural modifications limited anti-tumor activity. In this study, we present novel approach using celastrol (CST)-based Proteolysis Targeting Chimeras (PROTACs) breast therapy. Through rational design, have successfully developed compound 6a, potent protein degrader capable selectively degrading GRP94 CDK1/4 tumor cells via endogenous ubiquitin-proteasome system. Furthermore, 6a demonstrated remarkable inhibitory effects on cell proliferation migration, induction apoptosis 4T1 through cycle arrest activation Bcl-2/Bax/cleaved Caspase-3 apoptotic pathway. vivo administration effectively suppressed growth with an acceptable safety profile. Our findings suggest that CST-based PROTACs described herein can be readily extended to other natural offering avenue product-based treatment.

Language: Английский

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Computational Advancements in Cancer Combination Therapy Prediction

JCO Precision Oncology, Journal Year: 2023, Volume and Issue: 7

Published: Sept. 1, 2023

Given the high attrition rate of de novo drug discovery and limited efficacy single-agent therapies in cancer treatment, combination therapy prediction through silico repurposing has risen as a time- cost-effective alternative for identifying novel potentially efficacious cancer. The purpose this review is to provide an introduction computational methods summarize recent studies that implement each these methods. A systematic search PubMed database was performed, focusing on published within past 10 years. Our included reviews articles ongoing retrospective studies. We prioritized with findings suggest considerations improving over providing meta-analysis all currently available Computational used research include networks, regression-based machine learning, classifier learning models, deep approaches. Each method class its own advantages disadvantages, so careful consideration needed determine most suitable when designing method. Future directions improve current technology incorporation disease pathobiology, characteristics, patient multiomics data, drug-drug interactions maximally tolerable regimens As their capability integrate patient, drug, more comprehensive models can be developed accurately predict safe other complex diseases.

Language: Английский

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Unlocking biological insights from differentially expressed Genes: Concepts, methods, and future perspectives

Journal of Advanced Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting significance large DEG lists challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such network module knowledge graphs, drug repurposing, cell marker discovery, trajectory communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically thoroughly mining information within remain lacking.

Language: Английский

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Computational Advancements in Drug Repurposing for Cancer Combination Therapy Prediction

Published: May 23, 2023

As cancer remains resistant to several modes of treatment, novel therapeutics are still under active investigation overcome treatment inefficacy in cancer. Given the high attrition rate de novo drug discovery, screening, and repurposing have offered time- cost-effective alternative strategies for identification potentially effective therapeutics. In contrast large-scale screens, computational approaches leverage increasing amounts biomedical data predict candidate therapeutic agents prior testing biological models. Current studies therapy prediction increasingly focused on combination therapies, as therapies numerous advantages over monotherapies. These include increased effect from synergistic interactions, reduced toxicity lowered doses, a risk resistance due multiple non-overlapping mechanisms action. This review provides summary classes methods used research, including networks, regression-based machine learning, classifier learning models, deep approaches, with goal presenting current progress field, particularly non-computational biologists. We conclude by discussing need further advancements technologies that incorporate disease mechanisms, characteristics, multi-omics data, clinical considerations generate patient-specific combinations, holistic integration will inevitably result optimal targeted

Language: Английский

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Transcriptomic Profiling Unveils Novel Therapeutic Options for Drug-Resistant Temporal Lobe Epilepsy

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 26, 2024

ABSTRACT Background Epilepsy drug treatments fail in 25-30% of patients, who then develop resistance. Temporal lobe epilepsy is the most prevalent subtype associated with Classical discovery a long and extremely costly process high rate failure clinical trials. Drug repurposing more cost- time-effective strategy. Hence, main objective this study to propose candidates for treatment drug-resistant temporal (DR-TLE) through based on transcriptomic profiling. Methods Total RNA-sequencing (RNA-Seq) was performed 45 formalin-fixed paraffin-embedded (FFPE) hippocampi DR-TLE patients 36 FFPE post-mortem biobank donors. RNA-Seq carried out an Illumina NovaSeq 6000 platform 100bp paired-end. analysis top against these databases: Pandrugs2, PharmOmics, DGIdb, ToppGene, L1000CDS 2 Connectivity Map. Results We found 887 genes differentially expressed between controls. observed 74 potential at least two independent databases. Of these, we selected only 11 which can cross blood-brain barrier: cobimetinib, panobinostat, melphalan, rucaparib, alectinib, ponatinib, danazol, carboplatin, vandetanib, erlotinib, gefitinib. After analyzing their safety efficacy profile previous publications, provide list 5 candidates. Conclusions therefore panobinostat as therapies differential

Language: Английский

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The current and future use of precision nutrition

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 133 - 140

Published: Jan. 1, 2024

Language: Английский

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Signature Search Polestar: A comprehensive drug repurposing method evaluation assistant for customized oncogenic signature

Bioinformatics, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 29, 2024

Abstract Summary The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, been instrumental anti-cancer drug screening and identifying mechanisms action without relying on prior knowledge. However, various studies found that different SSMs exhibit varying performance across datasets. In addition, size signature can also significantly impact result repurposing. Therefore, finding optimal customized specific disease remains challenge. To address this, we introduce Search Polestar (SSP), webserver integrating largest datasets drugs from LINCS L1000 with five state-of-the-art (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, Application. Benchmark uses two indices, Area Under Curve Enrichment Score, based annotations evaluate at sizes. applicable when are insufficient, score self-retrieval evaluation. Application strategies, single method, SS_all, SS_cross, allowing users freely utilize tailored Availability implementation is free https://web.biotcm.net/SSP/. current version archived https://doi.org/10.6084/m9.figshare.26524741.v1, directly use or customize their own webserver. Supplementary information data available Bioinformatics online.

Language: Английский

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Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of United States Food and Drug Administration (FDA) approved drugs used GBM deliver satisfactory survival improvement. This study presents novel computational pipeline by utilizing gene expression data analysis drug repurposing to address challenges in disease development, particularly focusing GBM. The Gene Expression Profile (GGEP) was constructed multi-omics identify reversal GGEP from Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized candidates via hierarchical clustering their signatures quantification strength calculating two self-defined indices based genes' log

Language: Английский

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Research on machine learning based processing strategies for large-scale datasets

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract In this paper, we first mine the interconnections between data in large-scale datasets through association rule models machine learning and then perform T -time K-Means clustering on mined to realize integration. On basis, a classification prediction model based an enhanced ChebNet is proposed, which combines efficient feature extraction capability of graph convolutional neural network accurate advantage big analysis effectively processing sets. Taking tobacco production monitoring as example, performs well predicting correlation cigarette sensory indexes, especially when sliding window size 30 jump step 1. The performance reaches optimal, provides strong support for quality control production, capable production.

Language: Английский

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