Investigational New Drugs

, Volume 32, Issue 6, pp 1316–1319

Contributions from emerging transcriptomics technologies and computational strategies for drug discovery


DOI: 10.1007/s10637-014-0081-x

Cite this article as:
Kadioglu, O. & Efferth, T. Invest New Drugs (2014) 32: 1316. doi:10.1007/s10637-014-0081-x


Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of “-omics” data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval.


Computational biology Connectivity map Deep sequencing Drug discovery Drug repositioning Genome-wide association studies -omics technologies Systems biology Whole exon sequencing 



Encyclopedia of DNA elements


Genome wide association studies


Online mendelian ınheritance ın men


Research and development


Single nucleotide polymorphism


Whole exome sequencing

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Pharmaceutical Biology, Institute of Pharmacy and BiochemistryUniversity of MainzMainzGermany