From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells
Esraa A Salim, Xiaojia Ji, Michael Tarpley, Maria S Dixon, Weifan Zheng, John E Scott, Kevin P Williams · 2026
Read From in silico prediction to experimental validation: Identification of drugs and novel synergistic combinations that inhibit growth of inflammatory breast cancer cells by Esraa A Salim, Xiaojia Ji, Michael Tarpley, Maria S Dixon, Weifan Zheng, John E Scott, Kevin P Williams (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/plos-pone-0339543.txt.
Description
Esraa A Salim led a team of seven authors hunting drug combinations against a rare breast cancer, published in PLOS ONE in 2026. Inflammatory breast cancer is uncommon and aggressive, and new treatment options are badly needed. The team began with computer predictions, then tested the best candidates in real cancer cells.
Salim's pipeline identified drugs and paired combinations that slow the growth of these cancer cells, with synergy between partners. Moving from in silico screening to lab validation in one study strengthens the claims. The candidates now merit further testing toward clinical use. For patients with few options, a disciplined route from computation to bench evidence is exactly how new therapies start.
Catalog record
| CREATOR | Esraa A Salim, Xiaojia Ji, Michael Tarpley, Maria S Dixon, Weifan Zheng, John E Scott, Kevin P Williams |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 115994 |
| LICENSE | CC_BY |
| RIGHTS | CC BY |
| SOURCE | PLOS |