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Enregistrement W4405215139 · doi:10.3389/fphar.2024.1535754

Editorial: Systems pharmacology in the spotlight: trending technologies in network biology and drug discovery

2024· editorial· en· W4405215139 sur OpenAlexaff
Raghuveera Kumar Goel, Kiven Erique Lukong, Andrew Emili

Notice bibliographique

RevueFrontiers in Pharmacology · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueBiological Stains and Phytochemicals
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésSystems pharmacologyMALAT1MedicinePharmacologyProstate cancerDrug discoveryBioinformaticsDrugComputational biologyCancer researchCancerBiologyInternal medicineGene

Résumé

récupéré en direct d'OpenAlex

Lin et al. (2023) employed an integrative approach combining metabolomics and proteomics to identify biomarkers associated with hemodialysis in end-stage kidney disease (ESKD). Their analysis uncovered significant alterations in metabolic and protein profiles, providing insights into the physiological changes induced by hemodialysis. These findings have the potential to guide the development of targeted therapies and improve patient outcomes in ESKD management.Mechanisms of Action of Sappan Lignum for Prostate Cancer Treatment: Li et al. ( 2024) investigated the antitumor properties of Sappan Lignum, derived from Caesalpinia sappan L., against prostate cancer (PCa). The study identified 21 major active compounds within Sappan Lignum and their 32 highly probable target proteins mapped from 821 differentially expressed genes associated with PCa. Through network pharmacology and molecular docking, they pinpointed eight key targets, notably BCL-2 and CCNB1, with the p53 signaling pathway being central to the herb's antitumor effects. In vitro experiments demonstrated that 3-deoxysappanchalcone (3-DSC), a principal component, inhibited PCa cell proliferation, migration, and induced apoptosis, primarily by modulating the p53/p21/CDC2/CCNB1 pathway. These findings underscore Sappan lignum's promise as a multi-targeted therapeutic agent for PCa. 2024) study explores the underlying mechanisms of action of Luoshi Neiyi prescription (LSNYP) for treating endometriosis, utilizing serum pharmacochemistry and network pharmacology approaches. By identifying active components and their targets, the research highlights LSNYP's regulation of hypoxia and inflammation-related pathways, notably the HIF1A/EZH2/ANTXR2 axis. Experimental validation demonstrated that LSNYP modulates key cellular proteins, suggesting its therapeutic potential in endometriosis management. This integrative approach provides a scientific basis for the therapeutic effects of LNP in endometriosis management.Long Mu Qing Xin Mixture for ADHD: A Multi-Methodological Study: Li et al. ( 2023) investigated the formulated medicine, Long Mu Qing Xin Mixture (LMQXM) for treating attention deficit hyperactivity disorder (ADHD). Network pharmacology and molecular docking approaches identified key components; beta-sitosterol, stigmasterol, rhynchophylline, baicalein, and formononetin, that interact with dopamine receptors DRD1 and DRD2. In vivo experiments confirmed that LMQXM modulates the dopamine and cAMP signaling pathways, suggesting its therapeutic potential for ADHD Systems Biology Approach to Glioblastoma Multiforme: This study (Alqahtani et al., 2024) highlighted matrix metallopeptidase 9 (MMP9) as a critical molecular target in glioblastoma multiforme (GBM) pathophysiology. By prioritizing molecular compounds sourced from drug-ligand interaction databases and molecular docking, carmustine, lomustine, marimastat, and temozolomide demonstrated significant binding affinities to MMP9, suggesting their utility in modulating GBM's molecular network for enhanced therapeutic outcomes.Desert Flora in Cancer Therapy: Network Pharmacology and Molecular Modeling: Alblihy ( 2024) systematically explored the anticancer potential of phytochemical compounds derived from Arabian flora using network pharmacology and molecular modeling. The work highlighted multiple pathways through which these natural compounds exert their effects, including apoptosis, cell cycle arrest, and inhibition of metastasis. The study emphasizes the importance of integrating traditional knowledge with modern computational techniques to discover novel therapeutic agents in ovarian cancer.GHRP-6 Mitigates Doxorubicin-Induced Cardiotoxicity: Berlanga-Acosta et al. (2024) explored the cardioprotective effects of Growth Hormone Releasing Peptide-6 (GHRP-6) against doxorubicin (Dox)-induced cardiotoxicity. Through echocardiography, histopathology, and molecular analyses, the study found that GHRP-6 administration alongside Dox prevented myocardial fiber degradation and ventricular dilation, thereby preserving left ventricular systolic function. Mechanistically, GHRP-6 enhanced antioxidant defenses, upregulated the pro-survival gene Bcl-2, and maintained mitochondrial integrity in cardiomyocytes. These findings suggest that GHRP-6 activates pro-survival mechanisms, offering a potential therapeutic strategy to mitigate Dox-induced cardiac damage.Collectively, these studies underscore four key tools of systems pharmacology for unraveling complex disease mechanisms and identifying potential therapeutic agents: high-throughput screening, biomolecular network modeling, simulated structural docking, and experimental validation. By integrating computational and experimental approaches, researchers can accelerate the discovery of novel treatments and deepen our understanding of disease pathology.The future of generative AI within systems pharmacology is poised to be transformative. Generative AI holds the potential to automate the design of therapeutic molecules and simulate their interactions across diverse biological systems, significantly reducing the time and cost required to move from concept to clinical trials. By modeling vast chemical spaces in predicting drug-target interactions and synergizing systems-level data such as multi-omics and dynamic network analyses, generative AI can enhance drug optimizations as well as evaluate safety and efficacy through iterative simulations on virtual patients. By democratizing drug discovery and fostering interdisciplinary collaboration, generative AI promises a more efficient and sustainable future for systems pharmacology.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,065

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,022
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0040,004
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,004
Communication savante0,0080,007
Science ouverte0,0050,002
Intégrité de la recherche0,0160,024
Charge utile insuffisante (le modèle a refusé de juger)0,0200,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,313
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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