Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. (FDA) CZC-25146 approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics CZC-25146 approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs. 1 Introduction Traditional drug development is costly and labor-intensive and scientists CZC-25146 are devoted to finding an alternative way to facilitate the drug discovery process. Drug repositioning finding new therapeutic uses for existing drugs is one of the most efficient and efficacious approaches to speed drug discovery. With the advance in computational technology computational drug repositioning has shown its advantage as many studies been published recently. Ye et al. [1] explored a disease-oriented strategy for evaluating the relationship between drugs and disease on the basis of CZC-25146 their pathway profile; Napolitano et al. [2] investigated machine-learning algorithms to predict drug repositioning; Li and Lu[3] presented an approach for identifying potential new indications of an existing drug through its relation to similar drugs. Butte’s lab has reported their efforts on computational drug repurposing by exploring gene expression data CZC-25146 [4 5 These studies drew on different technologies to address the problem of computational drug repositioning. However none of them attempted to leverage data from emerging pharmacogenomics (PGx) studies in an integrated and transformable manner and explore Semantic Web technology as core implementation tool to address drug repositioning which is our proposed aim for this study. PGx study investigates how CZC-25146 genetic variations affect drug responses for the individual patient consequently high volume of PGx information including relations among drugs genes single nucleotide polymorphisms (SNPs) etc. has been accumulated. The overarching goal of this study was to provide PGx profiles for FDA approved breast cancer drugs (BCDs) by leveraging informatics approaches and Semantic Web technologies and ultimately to facilitate oncology-relevant biomedical and clinical studies and to support breast cancer Klf6 drug repositioning. Currently in the PGx world different formats are being used for different data resources which is the main obstacle to integration of PGx data to support development of relevant applications. Different formats might be preferred to represent scientific data based on the nature of the source the way the data are to be queried or visualized or the type of analyses to be performed. Traditionally investigators have relied heavily on tools such as Excel spreadsheets and relational databases to store and represent their research findings. However these tools lack interoperability and capability to make inferences. In contrast Semantic Web technology can manage scientific data in a more integrative and intelligent way. It is “a rigorous mechanism for defining and linking data using Web protocols in such a way that the data can be used by machines not just for display but also for automation integration and reuse across various applications”[6]. Web Ontology Language (OWL) as a Semantic Web standard can formally represent domain knowledge; it “organizes concepts or entities within classification (specialization or “is-a”) hierarchies that provide for inheritance of attributes”[7]. Reusing existing resources in an integrative manner is essential but exploring new associations is much more challenging. A Semantic Web reasoner enables identification of new BCD PGx associations with an ultimate goal of repositioning BCDs. Dumontier [10] has demonstrated some advantages by expressing PGX data PharmGKB in OWL for personalized medicine purpose. Additionally novel PGx information may be detected from a chemical perspective..