Network pharmacology_SNA Presentation [Recovered].pptx

AndreeaIrina5 149 views 9 slides May 29, 2024
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About This Presentation

network pharmacology


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Network pharmacology Cruceru Irina Data Science

What is network pharmacology? Network Biology It is an interdisciplinary field comprising of genomics, proteomics, metabolomics, etc., that uses a holistic approach. Various types of biological networks are ecological, gene regulatory network , protein–protein interaction network , and metabolic network among others. Polypharmacology Polypharmacology is the study and development of pharmaceutical agents that act on multiple targets or biological pathways rather than focusing on a single target.

Network models for molecular interactions Networks are used in systems biology to represent the different types of relationships between biological entities such as genes, proteins, chemical compounds, and transcription factors. Computational measurements for the analysis of molecular interaction networks: Network statistics and topological features Modularity Motif analysis Types of networks commonly used Boolean networks Bayesian networks

Polypharmacology A lot of illnesses involve multiple pathways and biological processes → finding a very targeted drug may be difficult The concept of network biology was used to integrate data from DrugBank and OMIM, an online catalog of human genes and genetic disorders to understand the industry trends, the properties of drug targets, and to study how drug targets are related to disease-gene products. The first drug-target network was constructed, isolated and bipartite nodes were expected based on the existed current approach. A rich network of polypharmacology interactions between drugs and their targets has been observed instead. Traditionally, drugs are studied in isolation. ‘magic bullet’  'one gene, one drug, one disease '

Human polypharmacology interaction network representing relationships between proteins in chemical space The number of proteins in this network is 486 (nodes), with 3,636 polypharmacology relationships (edges) Paolini , G., Shapland , R., van Hoorn, W.  et al.  Global mapping of pharmacological space.  Nat Biotechnol   24 , 805–815 (2006). https://doi.org/10.1038/nbt1228

Applications Antibacterial polypharmacology Many effective antibiotics act by targeting multiple proteins simultaneously rather than individual proteins. Drug Discovery and Design Multi-Target Drug Development Lead Compound Identification Drug Repurposing Predicting Drug Side Effects Anticipating drug-drug interactions in multicomponent drug formulations Network Ethnopharmacology  Analyzing the logic behind traditional medicine systems

Network Ethnopharmacology “Dragons blood tablets” Made from resins of   Dracaena  spp.,  Daemonorops  spp.,  Croton  spp., and  Pterocarpus  spp., is an effective TCM for the treatment of colitis 48 compounds were identifies with ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method compounds were further screened for bioavailability followed by the systematic analysis of the known and putative targets for colitis

Putative DB targets-known colitis therapeutic targets protein-protein interaction (PPI) network. (A) The network between all targets and other human proteins. (B) The network of hub proteins in network (A). (C) The network of the major putative DB targets and the major known colitis therapeutic targets in network (B). Yellow spherical nodes indicate the putative targets; pink spherical nodes indicate the known therapeutic targets; purple spherical nodes indicate other human proteins that interact with putative targets or known therapeutic targets. Red edges in (C) indicate the PPIs of targets involved in the NOD-like receptor signaling pathway. Xu H, Zhang Y, Lei Y, Gao X, Zhai H, Lin N, et al. A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon’s blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology. PLoS One. 2014;9:e101432.

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