Sanju Sinha, Assistant Professor at Sanford Burnham Prebys, shared a post on X about a paper he co-authored with colleagues published in NPJ Precision Oncology:
“Most current drug discovery efforts is structure-based eg. create small molecules or antibodies that best binds X. However, a drug may not drive its efficacy from its strongest binder. Taking a step away from structure-paradigm, we reason that if a CRISPR knockout of a gene mimics a drug’s effects across cancer cell lines, that gene is likely the drug’s target. This was done in Eytan Ruppin lab in collaboration with Ani Deshpande lab and Ben-David lab.
Using this principle, we integrated drug and crispr profiles from 1000s of drugs to find their context specific targets (different cancers or when known target is not expressed but drug is yet killing cancer cells).
We call this tool DeepTarget. We show that this approach outperforms current structure based methods (AF3, RF, Chai) to find drug’s target in a genome-wide search, when we had no information on what the target might be. We benchmarked in eight gold-standard drug-target pairs. It took us months to get this benchmarks (we hope this benchmark helps the field).
We present two experimentally validated cases and pls see the paper for this (link at the end). An intriguing observation is that we had many cases where we have many small molecules targeting the same gene (eg. EGFR) and we found that small molecules with higher predicted target specificity show greater clinical advancement.
Title: DeepTarget predicts anti-cancer mechanisms of action of small molecules by integrating drug and genetic screens
Authors: Sanju Sinha, Neelam Sinha, Marlenne Perales, Adi Tarrab, Trinh Nguyen, Lihe Liu, Thomas Cantore, Kyle Alvarez, Sumeet Patiyal, Sumit Mukherjee, Sanna Madan, Kevin Tharp, Jianhua Zhao, Ranjit Kumar, Greg Flanigan, John A. Beutler, Barry R. O’Keefe, Daoud Meerzaman, Uri Ben-David, Aniruddha J. Deshpande, Eytan Ruppin
You can read the full article in NPJ Precision Oncology.
More posts featuring Sanju Sinha.