Population Modeling of Tumor Kinetics and Overall Survival to Identify Prognostic and Predictive Biomarkers of Efficacy for Durvalumab in Patients With Urothelial Carcinoma. Issue 4 (17th January 2018)

• Record Type: Journal Article
• Authors: Zheng, Yanan; Narwal, Rajesh; Jin, ChaoYu; Baverel, Paul G.; Jin, Xiaoping; Gupta, Ashok; Ben, Yong; Wang, Bing; Mukhopadhyay, Pralay; Higgs, Brandon W.; Roskos, Lorin
• Is Part Of: Clinical pharmacology & therapeutics. Volume 103:Issue 4(2018)
• Page Start: 643
• Page End: 652
• Publication Date: 2018-01-17
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.986
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : Durvalumab is an anti‐PD‐L1 monoclonal antibody approved for patients with locally advanced or metastatic urothelial carcinoma (UC) that has progressed after platinum‐containing chemotherapy. A population tumor kinetic model, coupled with dropout and survival models, was developed to describe longitudinal tumor size data and predict overall survival in UC patients treated with durvalumab (NCT01693562) and to identify prognostic and predictive biomarkers of clinical outcomes. Model‐based covariate analysis identified liver metastasis as the most influential factor for tumor growth and immune‐cell PD‐L1 expression and baseline tumor burden as predictive factors for tumor killing. Tumor or immune‐cell PD‐L1 expression, liver metastasis, baseline hemoglobin, and albumin levels were identified as significant covariates for overall survival. These model simulations provided further insights into the impact of PD‐L1 cutoff values on treatment outcomes. The modeling framework can be a useful tool to guide patient selection and enrichment strategies for immunotherapies across various cancer indications.