Inflammatory Target prediction for the FDA-approved anticancer drugs using morgan fingerprint similarity-based methods

As a result of complicated interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, andenvironmental factors, most of the available drugs or multitarget therapies poses polypharmacologicaleffects. Cancer as a disease with multiple reasons, has been one of the main mortalities causes and drugdiscovery for it has been one of the most interested subjects during the years. One of the most well-knowncauses for cancer is inflammation and anti-inflammatory effect of available anticancer drugs could beregarded as a mechanism for their efficacy and potency. In this study we developed predictive models basedon the morgan fingerprint similarity search for anticancer drugs with focus on inflammatory targetA list of FDA approved anticancer drugs were generated and their fingerprint were calculated using RDKitpython module. The tanimoto index was calculated using the obtained fingerprints and the targets werepredicted based on the Chembl۲۵ target prediction mudule. The modified python code was utilized to predictthe 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 FDAapprovedanticancer drugs. The obtained results were clustered due to the synergistic, antagonistic and neutraltarget profiles for the investigated compounds. Litreture survey indicated the availabaility of experimentaland clinical evidences for most of the predicted targets, while some targets were not reported previously. Theresults showed that the developed rational method combined with similarity search method could be used tothe target prediction for FDA approved drugs.

کلیدواژه ها:

Polypharmacology ، Cancer ، Morgan fingerprint ، Chembl۲۵ ، Target prediction