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Pharmacol Ther. 2013 Jul;139(1):60-70. doi: 10.1016/j.pharmthera.2013.04.004. Epub 2013 Apr 6.

Functional genomics of pain in analgesic drug development and therapy.

Author information

1
Institute of Clinical Pharmacology, Goethe-University Hospital, Theodor Stern Kai 7, D-60590 Frankfurt am Main, Germany j.loetsch@em.uni-frankfurt.de

Abstract

Advances in genomic research have led to the clarification of the detailed involvement of gene products in biological pathways and these are being increasingly exploited in strategies for drug discovery and repurposing. Concomitant developments in informatics have resulted in the acquisition of complex gene information through the application of computational analysis of molecular interaction networks. This approach enables the acquired knowledge on hundreds of genes to be used to view molecular disease mechanisms from a genetic point of view. By analyzing 410 genes which control the complex process of pain, we show by computational analysis, based on functional annotations to pain-related genes, that 12 clearly circumscribed functional areas are essential for pain perception and thus for analgesic drug development. The genetics perspective revealed that future development strategies should focus on substances modulating intracellular signal transduction, ion transport and anatomical structure development. These processes are involved in the genetic-based absence of pain and therefore, provide promising fields for curative or preventive treatments. In contrast, interactions with G-protein coupled receptor pathways seem merely to provide symptomatic, not preventative relief of pain. In addition, biological functions accessed either by analgesic drugs or microRNAs suggest that synergistic therapies may be a future direction for drug development. With modern computational functional genomics, it is possible to exploit genetic information from increasingly available data sets on complex diseases, such as pain, and offers a new insight into drug development and therapy which is complementary to pathway-centered approaches.

[Indexed for MEDLINE]

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