title: Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics.
abstract: OBJECTIVE
  We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials.
  
  
  METHODS
  We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC.
  
  
  RESULTS
  The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine.
  
  
  CONCLUSIONS
  The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.
authors.affiliation: Pharsight Corporation, Mountain View, CA, USA.
authors.forename: Laurent
authors.initials: L
authors.lastname: Claret
authors.forename: Pascal
authors.initials: P
authors.lastname: Girard
authors.forename: Paulo M
authors.initials: PM
authors.lastname: Hoff
authors.forename: Eric
authors.initials: E
authors.lastname: Van Cutsem
authors.forename: Klaas P
authors.initials: KP
authors.lastname: Zuideveld
authors.forename: Karin
authors.initials: K
authors.lastname: Jorga
authors.forename: Jan
authors.initials: J
authors.lastname: Fagerberg
authors.forename: René
authors.initials: R
authors.lastname: Bruno
mesh_terms: D000964:Antimetabolites, Antineoplastic / Q000627:therapeutic use*; D000069287:Capecitabine; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D015179:Colorectal Neoplasms / Q000188:drug therapy* / Q000401:mortality* / Q000473:pathology; D003198:Computer Simulation; D003661:Decision Support Techniques; D003841:Deoxycytidine / Q000031:analogs & derivatives / Q000627:therapeutic use; D019008:Drug Resistance, Neoplasm; D005472:Fluorouracil / Q000031:analogs & derivatives / Q000627:therapeutic use; D006801:Humans; D008954:Models, Biological; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012189:Retrospective Studies; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000964:Antimetabolites, Antineoplastic; D003841:Deoxycytidine; D000069287:Capecitabine; D005472:Fluorouracil