Literature DB >> 20872147

Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer.

Laurent Claret1, Jian-Feng Lu, Yu-Nien Sun, René Bruno.   

Abstract

PURPOSE: To develop a modeling framework that simulates clinical endpoints (objective response rate and progression-free survival) to support development of motesanib. The framework was evaluated using results from a phase 2 study of motesanib in thyroid cancer.
METHODS: Models of probability and duration of dose modifications and overall survival were developed using data from 93 patients with differentiated thyroid cancer and 91 patients with medullary thyroid cancer, who received motesanib 125 mg once daily. The models, combined with previously developed population pharmacokinetic and tumor growth inhibition models, were assessed in predicting dose intensity, tumor size over time, objective response rate, and progression-free survival. Dose-response simulations were performed in patients with differentiated thyroid cancer.
RESULTS: The predicted objective response rate and median progression-free survival in patients with differentiated thyroid cancer was 15.0% (95% prediction interval, 7.5%-23.7%) and 40 weeks (95% prediction interval, 32-49 weeks), respectively, compared with the observed objective response rate of 14.0% and median progression-free survival of 40 weeks. The simulated median objective response rate increased with motesanib starting dose from 13.5% at 100 mg once daily to 38.0% at 250 mg once daily. However, simulated median progression-free survival was independent of starting dose, ranging from 40.5 weeks (95% prediction interval, 38.6-46.9 weeks) at 100 mg once daily to 40.0 weeks (95% prediction interval, 38.6-46.8 weeks) at 250 mg once daily.
CONCLUSIONS: Dose-response simulations confirmed the appropriateness of 125-mg once-daily dosing; no clinically relevant improvement in progression-free survival would be obtained by dose intensification. This modeling framework represents an important tool to simulate clinical response and support clinical development decisions.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20872147     DOI: 10.1007/s00280-010-1449-z

Source DB:  PubMed          Journal:  Cancer Chemother Pharmacol        ISSN: 0344-5704            Impact factor:   3.333


  16 in total

Review 1.  Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications.

Authors:  Núria Buil-Bruna; José-María López-Picazo; Salvador Martín-Algarra; Iñaki F Trocóniz
Journal:  Oncologist       Date:  2015-12-14

2.  Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.

Authors:  Lili Zhao; Susan Murray; Laura H Mariani; Wenjun Ju
Journal:  Stat Med       Date:  2020-07-27       Impact factor: 2.373

3.  Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers.

Authors:  Lili Zhao; Dai Feng; Brian Neelon; Marc Buyse
Journal:  Stat Med       Date:  2015-01-29       Impact factor: 2.373

4.  Predicting Overall Survival and Progression-Free Survival Using Tumor Dynamics in Advanced Breast Cancer Patients.

Authors:  Hyeong-Seok Lim; Wan Sun; Kourosh Parivar; Diane Wang
Journal:  AAPS J       Date:  2019-01-30       Impact factor: 4.009

5.  Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer.

Authors:  Chiara Zecchin; Ivelina Gueorguieva; Nathan H Enas; Lena E Friberg
Journal:  Br J Clin Pharmacol       Date:  2016-06-08       Impact factor: 4.335

6.  Simulations to Assess Phase II Noninferiority Trials of Different Doses of Capecitabine in Combination With Docetaxel for Metastatic Breast Cancer.

Authors:  R Bruno; L Lindbom; F Schaedeli Stark; P Chanu; F Gilberg; N Frey; L Claret
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2012-12-26

7.  Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin.

Authors:  J G C van Hasselt; A Gupta; Z Hussein; J H Beijnen; J H M Schellens; A D R Huitema
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-06-30

8.  Exposure-response analysis of rilotumumab in gastric cancer: the role of tumour MET expression.

Authors:  M Zhu; R Tang; S Doshi; K S Oliner; S Dubey; Y Jiang; R C Donehower; T Iveson; E Y Loh; Y Zhang
Journal:  Br J Cancer       Date:  2015-01-13       Impact factor: 7.640

9.  PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST.

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

10.  A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis.

Authors:  B Ribba; N H Holford; P Magni; I Trocóniz; I Gueorguieva; P Girard; C Sarr; M Elishmereni; C Kloft; L E Friberg
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2014-05-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.