Evaluation of tumor size response metrics to predict survival in oncology clinical trials

Clinical Pharmacology and Therapeutics

Volumn 95, Issue 4, 2014, Pages 386-393

  Bruno, R ^a   Mercier, F ^a   Claret, L ^a  

^a A Certara Company   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BEVACIZUMAB;

ARTICLE; CANCER INHIBITION; CANCER SURVIVAL; CLINICAL TRIAL (TOPIC); DRUG DEVELOPMENT; EXPERIMENTAL MODEL; HUMAN; LEARNING; LUNG NON SMALL CELL CANCER; ONCOLOGY; OVERALL SURVIVAL; PRIORITY JOURNAL; SIMULATION; TUMOR VOLUME;

ANTINEOPLASTIC AGENTS; CLINICAL TRIALS AS TOPIC; DRUG DESIGN; HUMANS; MODELS, BIOLOGICAL; NEOPLASMS; SURVIVAL ANALYSIS; TIME FACTORS;

EID: 84897022034     PISSN: 00099236     EISSN: 15326535     Source Type: Journal    
DOI: 10.1038/clpt.2014.4     Document Type: Article

References (75)

1. DiMasi, J.A., Reichert, J.M., Feldman, L. & Malins, A. Clinical approval success rates for investigational cancer drugs. Clin. Pharmacol. Ther. 94, 329-335 (2013.
2. Dowden, H.M. et al. Industry and regulatory performance in 2012: A year in review. Clin. Pharmacol. Ther. 94, 359-366 (2013.
3. Paul, S.M. et al. How to improve R&D productivity: The pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203-214 (2010.
4. US Food and Drug Administration. Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. Challenges and Opportunities Report-March 2004. http://www.fda.gov/ScienceResearch/ SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ ucm077262.htm. Accessed 12 December 2013.
5. Milligan, P.A. et al. Model-based drug development: A rational approach to efficiently accelerate drug development. Clin. Pharmacol. Ther. 93, 502-514 (2013.
6. Sharma, M.R., Maitland, M.L. & Ratain, M.J. Models of excellence: Improving oncology drug development. Clin. Pharmacol. Ther. 92, 548-550 (2012.
7. Eisenhauer, E.A. et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228-247 (2009.
8. Burzykowski, T. et al. Evaluation of tumor response, disease control, progression-free survival, and time to progression as potential surrogate end points in metastatic breast cancer. J. Clin. Oncol. 26, 1987-1992 (2008.
9. Buyse, M. et al. Progression-free survival is a surrogate for survival in advanced colorectal cancer. J. Clin. Oncol. 25, 5218-5224 (2007.
10. Villaruz, L.C. & Socinski, M.A. The clinical viewpoint: Definitions, limitations of RECIST, practical considerations of measurement. Clin. Cancer Res. 19, 2629-2636 (2013.
11. Michaelis, L.C. & Ratain, M.J. Measuring response in a post-RECIST world: From black and white to shades of grey. Nat. Rev. Cancer 6, 409-414 (2006.
12. Lavin, P.T. An alternative model for the evaluation of antitumor activity. Cancer Clin. Trials 4, 451-457 (1981.
13. Karrison, T.G., Maitland, M.L., Stadler, W.M. & Ratain, M.J. Design of phase II cancer trials using a continuous endpoint of change in tumor size: Application to a study of sorafenib and erlotinib in non small-cell lung cancer. J. Natl. Cancer Inst. 99, 1455-1461 (2007.
14. Sheiner, L.B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61, 275-291 (1997.
15. Gan, H.K., You, B., Pond, G.R. & Chen, E.X. Assumptions of expected benefits in randomized phase II trials evaluating systemic treatments for cancer. J. Natl. Cancer Inst. 104, 590-598 (2012.
16. Bruno, R., Mercier, F. & Claret, L. Model-based drug development in oncology: What's next? Clin. Pharmacol. Ther. 93, 303-305 (2013.
17. Wu, L., Hu, X.J & Wu, H. Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data. Biostatistics 9, 308-320 (2008.
18. Bonate, P.L. Modeling tumor growth in oncology. In Pharmacokinetics in Drug Development (eds. Bonate, P.L. & Howard, D.R.) 1-19 American Association of Pharmaceutical Scientists, http://link.springer.com/book/10.1007/ 978-1-4419-7937-7 (2011.
19. Bernard, A., Kimko, H., Mital, D. & Poggesi, I. Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development. Expert Opin. Drug Metab. Toxicol. 8, 1057-1069 (2012.
20. Suleiman, A.A., Nogova, L& Fuhr, U. Modeling NSCLC progression: Recent advances and opportunities available. AAPS J. 15, 542-550 (2013.
21. Bender, B.C., Schindler, E. & Friberg, L.E. Population pharmacokinetic pharmacodynamic modelling in oncology: A tool for predicting clinical response. Br. J. Clin. Pharmacol. (2013); e-pub ahead of print 18 October 2013.
22. Stein, W.D. et al. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: The growth rate constant as an indicator of therapeutic efficacy. Clin. Cancer Res. 17, 907-917 (2011.
23. Jonsson, F. et al. A longitudinal tumor growth inhibition model based on serum M-protein levels in patients with multiple myeloma treated by dexamethasone [abstr 1705]. http://www.page-meeting.org/default. asp?abstract=1705 (2010.
24. Wilbaux, M., You, B., Hénin, E., Colomban, O., Freyer, G. & Tod, M. Population K-PD joint modeling of tumor size and CA 125 kinetics after chemotherapy in relapsed ovarian cancer (ROC) patients (Abstr 2587). http://www.pagemeeting. org/default.asp?abstract=2587 (2012.
25. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89-95 (2001.
26. Piessevaux, H. et al. Use of early tumor shrinkage to predict long-Term outcome in metastatic colorectal cancer treated with cetuximab. J. Clin. Oncol. 31, 3764-3775 (2013.
27. An, M.W. et al. Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials. Clin. Cancer Res. 17, 6592-6599 (2011.
28. Lara, P.N. Jr et al.; Southwest Oncology Group. Disease control rate at 8 weeks predicts clinical benefit in advanced non-small-cell lung cancer: Results from Southwest Oncology Group randomized trials. J. Clin. Oncol. 26, 463-467 (2008.
29. Claret, L. et al. Prediction of overall survival or progression free survival by disease control rate at week 8 is independent of ethnicity: Western versus Chinese patients with first-line non-small cell lung cancer treated with chemotherapy with or without bevacizumab. J. Clin. Pharmacol. (2013); e-pub ahead of print 3 October 2013.
30. Rotshteyn, Y., Mercier, F., Bruno, R., Stambler, N., Israel, R.J. & Wong, V. Correlation of PSMA ADC exposure with reduction in tumor growth rate determined using serial PSA measurements from a phase I clinical trial [abstr e16047]. J. Clin. Oncol. http://meetinglibrary.asco.org/content/112918-132 (2013.
31. Marchand, M., Claret, L., Losic, N., Puchalski, T.A& Bruno, R. Population pharmacokinetics and exposure-response analyses to support dose selection of daratumumab in multiple myeloma patients (abstr 2668). http://www. page-meeting.org/?abstract=2668 (2013.
32. Ferté, C. et al. Tumor Growth Rate Provides Useful Information to Evaluate Sorafenib and Everolimus Treatment in Metastatic Renal Cell Carcinoma Patients: An Integrated Analysis of the TARGET and RECORD Phase 3 Trial Data. Eur. Urol. (2013); e-pub ahead of print 15 August 2013.
33. Retout, S., Phipps, A., Geho, D., Tessier, J., Nichols, G.L. & Bottino, D. A modelbased approach to optimize detection of treatment effects in early oncology trials [abstr e13508]. J. Clin. Oncol. 31 (2013.
34. Houk, B.E. Predictive capability of a Food and Drug Administration overall survival model in non-small cell lung cancer in two Phase II trials utilizing different anti-cancer agents [abstr PII-70]. Clin. Pharmacol. Ther. 85(Suppl. 1), S58 (2009.
35. Bruno, R. et al. Simulation of clinical outcome for pomalidomide plus low-dose dexamethasone in patients with refractory multiple myeloma based on week 8 M-protein response [abstr 1881]. Blood 118, 1881 (2011.
36. Claret, L. et al. Model-based prediction of phase II overall survival in colorectal cancer on the basis of phase II tumor dynamics. J. Clin. Oncol. 27, 4103-4108 (2009.
37. Sallas, W., Stein, A., Wang, W. & Sarr, C. Assessment of published population models for prediction of Phase 3 overall survival in non-small-cell lung cancer patients based on Phase 2 tumor shrinkage [abstract]. http://www.go-Acop. org/sites/default/files/webform/posters/William-Sallas.pdf (2011.
38. Claret, L., Lu, J.F., Bruno, R., Hsu, C.P., Hei, Y.J. & Sun, Y.N. Simulations using a drug-disease modeling framework and phase II data predict phase II survival outcome in first-line non-small-cell lung cancer. Clin. Pharmacol. Ther. 92, 631-634 (2012.
39. Wang, Y. et al. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin. Pharmacol. Ther. 86, 167-174 (2009.
40. Claret, L. et al. Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer. J. Clin. Oncol. 31, 2110-2114 (2013.
41. Yang, J. et al. The combination of exposure-response and case-control analyses in regulatory decision making. J. Clin. Pharmacol. 53, 160-166 (2013.
42. Lu, J.F. et al. Exposure-response relationship of AMG 386 in combination with weekly paclitaxel in recurrent ovarian cancer and its implication for dose selection. Cancer Chemother. Pharmacol. 69, 1135-1144 (2012.
43. Quartino, A.L. et al. Evaluation of tumor size metrics to predict survival in advanced gastric cancer (abstr 2812). http://www.page-meeting. org/?abstract=2812 (2013.
44. Hansson, E.K. et al. PKPD modeling of VEGF, sVEGFR-2,-3 and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst. Pharmacol. 2, e84 (2013.
45. Hansson, E.K. et al. PKPD modeling of predictors for adverse effects and overall survival in sunitinib-Treated patients with GIST. CPT Pharmacometrics Syst. Pharmacol. 2, e85 (2013.
46. Bruno, R. et al. Simulations to Assess Phase II Noninferiority Trials of Different Doses of Capecitabine in Combination With Docetaxel for Metastatic Breast Cancer. CPT Pharmacometrics Syst. Pharmacol. 1, e19 (2012.
47. Claret, L., Schaedeli Stark, F., Sirzen, F., Gieschke, R& Bruno, R. A modeling framework to simulate Xeloda dose intensity and survival in colorectal cancer (abstr 1312). http://www.page-meeting.org/?abstract=1312 (2008.
48. Claret, L., Lu, J.F., Sun, Y.N. & Bruno, R. Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer. Cancer Chemother. Pharmacol. 66, 1141-1149 (2010.
49. Sharma, M.R. et al. Resampling phase II data to assess phase II trial designs and endpoints. Clin. Cancer Res. 18, 2309-2315 (2012.
50. Kaiser, L.D. Tumor burden modeling versus progression-free survival for phase II decision making. Clin. Cancer Res. 19, 314-319 (2013.
51. Maitland, M.L. et al. Estimation of renal cell carcinoma treatment effects from disease progression modeling. Clin. Pharmacol. Ther. 93, 345-351 (2013.
52. Rubinstein, L.V., Dancey, J.E., Korn, E.L., Smith, M.A. & Wright, J.J. Early average change in tumor size in a phase 2 trial: Efficient endpoint or false promise? J. Natl. Cancer Inst. 99, 1422-1423 (2007.
53. Claret, L., Andre, V., de Alwis, D. & Bruno, R. Modeling and simulation to assess the use of change in tumor size as primary endpoint in phase II studies in oncology [abstr 1386]. http://www.page-meeting.org/? abstract=1386 (2008.
54. Talbot, D.C. et al. A randomized open-label phase II study evaluating antitumor activity of the survivin antisense oligonucleotide LY2181308 in combination with docetaxel for second-line treatment of patients with nonsmall cell lung cancer using change in tumor size [abstr 8051]. J. Clin. Oncol. 31 (suppl. abstr 8051) (2013.
55. French, J., Polhamus, D. & Gastonguay, M. Can methods based on existing models really aid decision making in non-small-cell lung cancer (NSCLC) trials? [abstr 2925]. http://www.page-meeting.org/?abstract=2925 (2013.
56. Claret, L et al. Evaluation of tumor size response metrics to predict survival and progression free survival in first line metastatic colorectal cancer [abstr 2328]. http://www.page-meeting.org/?abstract=2328 (2012.
57. Draft policy 70: Publication and access to clinical-Trial data http://www.ema. europa.eu/ema/doc-index.jsp?curl=pages/includes/document/ document-detail.jsp?webContentId=WC500144730&murl=menus/document-library/ document-library.jsp&mid=0b01ac058009a3dc. Accessed 10 December 2013.
58. Qualification opinion of a novel data driven model of disease progression and trial evaluation in mild and moderate Alzheimer's disease http://www.ema. europa.eu/docs/en-GB/document-library/Regulatory-And-procedural-guideline/2013/ 10/WC500151309.pdf. Accessed 10 December 2013.
59. Houk, B.E., Bello, C.L., Poland, B., Rosen, L.S., Demetri, G.D. & Motzer, R.J. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: Results of a pharmacokinetic/ pharmacodynamic meta-Analysis. Cancer Chemother. Pharmacol. 66, 357-371 (2010.
60. Lu, J.F. et al. Population pharmacokinetic/pharmacodynamic modeling for the time course of tumor shrinkage by motesanib in thyroid cancer patients. Cancer Chemother. Pharmacol. 66, 1151-1158 (2010.
61. Frances, N., Claret, L., Bruno, R. & Iliadis, A. Tumor growth modeling from clinical trials reveals synergistic anticancer effect of the capecitabine and docetaxel combination in metastatic breast cancer. Cancer Chemother. Pharmacol. 68, 1413-1419 (2011.
62. Zhang, J. & Koch, K. Population analysis of tumor growth inhibition and progression-free survival following lapatinib treatment in patients with advanced/metastatic breast cancer [abstr 2390]. http://www.page-meeting. org/?abstract=2390 (2012.
63. Stein, A. et al. Treatment with bevacizumab and FOLFOXIRI in patients with advanced colorectal cancer: Presentation of two novel trials (CHARTA and PERIMAX) and review of the literature. BMC Cancer 12, 356 (2012.
64. Chanu, P et al. PK/PD relationship of the monoclonal anti-BAFF antibody tabalumab in combination with bortezomib in patients with previously treated multiple myeloma: Comparison of serum M-protein and serum free light chains as predictor of progression free survival (abstr 2732). http:// www.page-meeting.org/?abstract=2732 (2013.
65. Girard, P., Kovar, A., Brockhaus, B., Zuehlsdorf, M., Schlichting, M & Munafo, A. Tumor size model and survival analysis of cetuximab and various cytotoxics in patients treated for metastatic colorectal cancer (abstr 2902). http://www. page-meeting.org/default.asp?abstract=2902 (2013.
66. Tham, L.S. et al. A pharmacodynamic model for the time course of tumor shrinkage by gemcitabine + carboplatin in non-small cell lung cancer patients. Clin. Cancer Res. 14, 4213-4218 (2008.
67. Riba, B. et al. A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy. Clin. Cancer Res. 18, 5071-5080 (2012.
68. Mazzocco, P., Kaloshi, G., Ricard, D., Delattre, J.Y., Ducray, F. & Ribba, B. Modeling tumor dynamics and overall survival in patients with low-grade gliomas treated with temozolomide [abstr 2936]. http://www. pagemeeting. org/?abstract=2936 (2013.
69. Stein, W.D et al. Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: A novel strategy for evaluation of clinical trial data. Oncologist 13, 1046-1054 (2008.
70. Stein, W.D., Yang, J., Bates, S.E & Fojo, T. Bevacizumab reduces the growth rate constants of renal carcinomas: A novel algorithm suggests early discontinuation of bevacizumab resulted in a lack of survival advantage. Oncologist 13, 1055-1062 (2008.
71. Stein, W.D. et al. Analyzing the pivotal trial that compared sunitinib and IFN-A in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin. Cancer Res. 18, 2374-2381 (2012.
72. Claret, L., Mancini, P., Sebastien, B., Veyrat-Follet, C. & Bruno, R. Model-based estimates of tumor growth inhibition metrics to predict for overall survival in first-line non-small cell lung cancer [abstr e19049]. J. Clin. Oncol 31 (2013.
73. Claret, L., Bruno, R., Lu, J.F., Sun, Y.N. & Hsu, C.P. Exploratory modeling and simulation to support development of motesanib in Asian patients with non-small cell lung cancer based on MONET1 study results. Clin. Pharmacol. Ther. (in press.
74. Stein, A. et al.; RECORD-1 Trial Study Group. Survival prediction in everolimustreated patients with metastatic renal cell carcinoma incorporating tumor burden response in the RECORD-1 trial. Eur. Urol. 64, 994-1002 (2013.
75. Lindbom, L., Claret, L., Andre, V., Cleverly, A., de Alwis, D. & Bruno, R. A drug independent tumor size reduction-survival model in advanced ovarian cancer to support early clinical development decisions. Am. Conf. Pharmacomet. http://2009.go-Acop.org/sites/all/assets/webform/ACoP%202009%20 Poster%20-%20Lars%20Lindbom.pdf (2009.