OBJECTIVE: To develop a population pharmacokinetic/pharmacodynamic model describing the relationship between motesanib exposure and tumor response in a phase 2 study of motesanib in patients with advanced differentiated thyroid cancer or medullary thyroid cancer. METHODS: Data from patients (n = 184) who received motesanib 125 mg once daily were used for population pharmacokinetic/pharmacodynamic modeling. Motesanib concentrations were fitted to a 2-compartment population pharmacokinetic model. Observed change in tumor size was the drug response measure for the pharmacodynamic model. Exposure measures in the pharmacokinetic/pharmacodynamic model included dose, plasma concentration profile, or steady-state area under the concentration versus time curve (AUC( ss )). A longitudinal exposure-tumor response model of drug effect on tumor growth dynamics was used. RESULTS: Motesanib oral clearance in patients with medullary thyroid cancer was 67% higher than in patients with differentiated thyroid cancer patients (73.7 vs. 44 L/h). Patients' disease type (medullary thyroid cancer vs. differentiated thyroid cancer) was the most important covariate for explaining interpatient variability in clearance. The objective response rates were 14 versus 2% for differentiated thyroid cancer and medullary thyroid cancer, respectively. Motesanib exposure measures (AUC( ss ) or concentration profile) were better predictors of tumor response than motesanib dose. The estimated motesanib concentration yielding tumor stasis (1.9 ng/mL) was lower than the observed trough concentrations in differentiated thyroid cancer and medullary thyroid cancer patients. CONCLUSIONS: Differences in motesanib pharmacokinetics likely explain the difference in tumor response observed between differentiated thyroid cancer and medullary thyroid cancer patients. The population pharmacokinetic/pharmacodynamic model provides a tool for predicting tumor response to the drug to support the dosing regimen of motesanib in thyroid cancer patients.
OBJECTIVE: To develop a population pharmacokinetic/pharmacodynamic model describing the relationship between motesanib exposure and tumor response in a phase 2 study of motesanib in patients with advanced differentiated thyroid cancer or medullary thyroid cancer. METHODS: Data from patients (n = 184) who received motesanib 125 mg once daily were used for population pharmacokinetic/pharmacodynamic modeling. Motesanib concentrations were fitted to a 2-compartment population pharmacokinetic model. Observed change in tumor size was the drug response measure for the pharmacodynamic model. Exposure measures in the pharmacokinetic/pharmacodynamic model included dose, plasma concentration profile, or steady-state area under the concentration versus time curve (AUC( ss )). A longitudinal exposure-tumor response model of drug effect on tumor growth dynamics was used. RESULTS:Motesanib oral clearance in patients with medullary thyroid cancer was 67% higher than in patients with differentiated thyroid cancerpatients (73.7 vs. 44 L/h). Patients' disease type (medullary thyroid cancer vs. differentiated thyroid cancer) was the most important covariate for explaining interpatient variability in clearance. The objective response rates were 14 versus 2% for differentiated thyroid cancer and medullary thyroid cancer, respectively. Motesanib exposure measures (AUC( ss ) or concentration profile) were better predictors of tumor response than motesanib dose. The estimated motesanib concentration yielding tumor stasis (1.9 ng/mL) was lower than the observed trough concentrations in differentiated thyroid cancer and medullary thyroid cancerpatients. CONCLUSIONS: Differences in motesanib pharmacokinetics likely explain the difference in tumor response observed between differentiated thyroid cancer and medullary thyroid cancerpatients. The population pharmacokinetic/pharmacodynamic model provides a tool for predicting tumor response to the drug to support the dosing regimen of motesanib in thyroid cancerpatients.
Authors: Zinnia P Parra-Guillen; Pedro Berraondo; Emmanuel Grenier; Benjamin Ribba; Iñaki F Troconiz Journal: AAPS J Date: 2013-04-19 Impact factor: 4.009
Authors: E E W Cohen; M Tortorici; S Kim; A Ingrosso; Y K Pithavala; P Bycott Journal: Cancer Chemother Pharmacol Date: 2014-10-15 Impact factor: 3.333
Authors: Jian-Feng Lu; Erik Rasmussen; Beth Y Karlan; Ignace B Vergote; Lynn Navale; Mita Kuchimanchi; Rebeca Melara; Daniel E Stepan; David M Weinreich; Yu-Nien Sun Journal: Cancer Chemother Pharmacol Date: 2012-01-01 Impact factor: 3.333
Authors: E K Hansson; M A Amantea; P Westwood; P A Milligan; B E Houk; J French; M O Karlsson; L E Friberg Journal: CPT Pharmacometrics Syst Pharmacol Date: 2013-11-20