Literature DB >> 21599620

Why so few drug targets: a mathematical explanation?

Kyaw Tun1, Marta Menghini, Lina D'Andrea, Pawan Dhar, Hiroshi Tanaka, Alessandro Giuliani.   

Abstract

The apparently paradoxical lack of correlation between the huge increase in the discovery of new potential drug targets made possible by the post-genomic sciences and new drugs development has stimulated many different interpretations. Here we illustrate the general principle of redundancy of biological pathways on hand of simplified mathematical approaches applied to different models of biological regulation. The simulation was based on the analysis of the 'degree of autonomy' of network architectures in which the possibility for an external stimulus (e.g. a drug) impinging into a specific node to be sensed by the entire network, and eventually amplified up to a macroscopic consequence, was demonstrated to be limited to strictly linear pathways. The implications of such a result for poly-pharmacology and computational approaches to drug development are described as well.

Mesh:

Substances:

Year:  2011        PMID: 21599620     DOI: 10.2174/157340911796504297

Source DB:  PubMed          Journal:  Curr Comput Aided Drug Des        ISSN: 1573-4099            Impact factor:   1.606


  3 in total

1.  Why network approach can promote a new way of thinking in biology.

Authors:  Alessandro Giuliani; Simonetta Filippi; Marta Bertolaso
Journal:  Front Genet       Date:  2014-04-08       Impact factor: 4.599

2.  Network Intervention, a Method to Address Complex Therapeutic Strategies.

Authors:  Chi Zhang; Wei Zhou; Dao-Gang Guan; Yong-Hua Wang; Ai-Ping Lu
Journal:  Front Pharmacol       Date:  2018-07-12       Impact factor: 5.810

Review 3.  Resolution of Complex Issues in Genome Regulation and Cancer Requires Non-Linear and Network-Based Thermodynamics.

Authors:  Jekaterina Erenpreisa; Alessandro Giuliani
Journal:  Int J Mol Sci       Date:  2019-12-29       Impact factor: 5.923

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.