The efficiency of multi-target drugs: the network approach might help drug design

Abstract

Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug design paradigm will depend not only on a new generation of computer models to identify the correct multiple hits and their multi-fitting, low-affinity drug candidates but also on more efficient in vivo testing.

6 pages, 2 figures, 1 box, 38 references

References in corpus (5)

• Emergence of scaling in random networks
• The structure and function of complex networks
• Efficient Behavior of Small-World Networks
• Multiple, weak hits confuse complex systems: A transcriptional regulatory network as an example
• Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins

Cited by in corpus (15)

• Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review
• Allo-network drugs: harnessing allostery in cellular networks
• Multiple, weak hits confuse complex systems: A transcriptional regulatory network as an example
• Stress-induced rearrangements of cellular networks: consequences for protection and drug design
• Chaperones as integrators of cellular networks: Changes of cellular integrity in stress and diseases
• Aging cellular networks: chaperones as major participants
• Drug-therapy networks and the predictions of novel drug targets
• Adaptation and learning of molecular networks as a description of cancer development at the systems-level: Potential use in anti-cancer therapies
• Random matrix analysis of localization properties of Gene co-expression network
• Control of asymmetric Hopfield networks and application to cancer attractors
• Perturbation centrality and Turbine: a novel centrality measure obtained using a versatile network dynamics tool
• Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems
• How to design multi-target drugs: Target search options in cellular networks
• Potential application of network descriptions for understanding conformational changes and protonation states of ABC transporters
• Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases