PREDICTION OF DRUG-DRUG INTERACTIONS FROM CHEMOGENOMIC AND GENE-GENE INTERACTIONS AND ANALYSIS OF DRUG-DRUG INTERACTIONS
Biological Sciences and Bioengineering, MSc. Thesis 2013
Asst. Prof. Murat Çokol (Thesis Supervisor), Prof. Dr. Hikmet Budak, Prof. Dr. Uğur Osman Sezerman, Asst. Prof. Erdal Toprak, Assoc. Prof. Berrin Yanıkoğlu
Date &Time: July 04th, 2013 - 15:00
Place: FENS L062
Keywords: Chemogenomics, DrugBank, drug interactions, drug interaction prediction, genetic interactions, high throughput screening, biological networks
The interactions between multiple drugs administered to an organism concurrently, whether in the form of synergy or antagonism, are of clinical relevance. Moreover, un-derstanding the mechanisms and nature of drug-drug interactions is of great practical and theoretical interest. Work has previously been done on gene-gene and gene-drug interactions, but the prediction and rationalization of drug-drug interactions from this data is not straightforward. We present a strategy for attacking this problem and producing a computational solution. Our approach makes use of published work on large-scale genetic, chemogenomic and drug-drug interactions in order to find compound pairs that are likely to interact synergistically or antagonistically with each other in S. cerevisiae. We defined gene sets whose heterozygous deletion confers sensitivity to a drug as ‘drug target candidates.’ For each drug pair whose interaction is known in S. cerevisiae, we found the number of genetic interactions between each drug’s ‘target candidates.’ We examined whether genetic interaction frequency between ‘drug target candidates’ is different than overall genetic interaction frequency. We attempted to use this as a basis for prediction of drug-drug interactions, and experimentally tested some of the interactions.
Additionally, we have also analyzed the DrugBank database of drug-drug interactions. DrugBank includes data about the interactions of clinically used drugs in human pa-tients, which is supplied in natural language format. We have standardized this data by a process of manual curation, and produced a large dataset of machine-readable human drug-drug interaction data. We also present some analyses performed on this dataset.