1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Inflammatory Target prediction for the FDA-approved anticancer drugs using morgan fingerprint similarity-based methods
Paper ID : 1357-ICB10
Authors:
Somaieh Soltani *1, Andrease Bender2, Gerhard Wolber3, David Schaller4
1Pharmacy Faculty, Tabriz University of Medical Sciences, Tabriz, Iran
2Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
3Pharmaceutical and Medicinal Chemistry, Freie Universitat Berlin, Konigin-Luise-Str. 2+4, 14195 Berlin, Germany
4In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
Abstract:
As a result of complicated interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors, most of the available drugs or multitarget therapies poses polypharmacological effects. Cancer as a disease with multiple reasons, has been one of the main mortalities causes and drug discovery for it has been one of the most interested subjects during the years. One of the most well-known causes for cancer is inflammation and anti-inflammatory effect of available anticancer drugs could be regarded as a mechanism for their efficacy and potency. In this study we developed predictive models based on the morgan fingerprint similarity search for anticancer drugs with focus on inflammatory target [5]
A list of FDA approved anticancer drugs were generated and their fingerprint were calculated using RDKit python module. The tanimoto index was calculated using the obtained fingerprints and the targets were predicted based on the Chembl25 target prediction mudule. The modified python code was utilized to predict the target profile for the investigated compounds, while the target list was limited to the inflammatory targets. The results indicated an interesting profile of anti-inflammatory predicted targets for the available FDA-approved anticancer drugs. The obtained results were clustered due to the synergistic, antagonistic and neutral target profiles for the investigated compounds. Litreture survey indicated the availabaility of experimental and clinical evidences for most of the predicted targets, while some targets were not reported previously. The results showed that the developed rational method combined with similarity search method could be used to the target prediction for FDA approved drugs.
Keywords:
Polypharmacology, Cancer, Morgan fingerprint, Chembl25, Target prediction
Status : Paper Accepted (Oral Presentation)