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A novel machine learning model for prediction of ICI responsive patients based on ICI pharmacokinetics

TS-075183 — Problem Current eligibility biomarkers for Immune checkpoint inhibitors (ICIs) poorly predict patient’s response. Drug clearance, both at baseline and over time, is a stronger predictor of ICI effectiveness, however the current non-linear mixed effects modeling (NLME) approach for estimating …

College: College of Pharmacy
Inventors: Phelps, Mitchell "Mitch"; Adeluola, Adeoluwa; Coss, Christopher; Kim, Kyeongmin; Mo, Xiaokui "Molly"; Owen, Dwight
Licensing Officer: Hampton, Andrew

Addressing the Challenges in HCC Treatment with Innovative Technology

TS-062690 — Novel niclosamide analogues with improved bioavailability and mitigated dose-limiting toxicities.

Hepatocellular carcinoma (HCC) poses a significant global health challenge, being a predominant form of liver cancer with high mortality rates. The disease exhibits sexual dimorphism, disproportionately affecting males. The Need Despite the crucial role of androgen receptors (AR) in HCC progress…

College: College of Pharmacy
Inventors: Coss, Christopher; Cheng, Jeffrey; Cheng, Xiaolin; Kulp, Samuel; Li, Tom; Montgomery, Emma; Xing, Sam
Licensing Officer: Willson, Christopher