Modeling of longitudinal tumor size data in clinical oncology studies of drugs in combination
N. Frances (1), L. Claret (2), F. Schaedeli Stark (3), R. Bruno (2), A. Iliadis (1)
(1) School of Pharmacy, Université de la Méditerranée, Marseille, France (2) Pharsight Corp., Mountain View, USA (3) Hoffman-La Roche, Basel, Switzerland
Objectives: The analysis of tumor size measurements, obtained in clinical studies involving combination chemotherapy, remains an open modeling problem. We used retrospective clinical data in metastatic breast cancer in order to investigate whether the contribution to the anti-tumor effect of each compound in a combination setting can be estimated 1) from combination data with or without single agent data, and 2) from datasets with a limited number of patients.
Methods: Data concerning tumor size measurements and treatments characteristics were available for docetaxel (D, n=223), capecitabine (C, n=168) [1, 2] given as single agents and their combination (D+C, n=222) . The developed model is an extension of already presented disturbed growth models [4, 5] and it is based on the following hypotheses: 1) Tumor growth is exponential or Gompertz; 2) K-PD model describes administration protocols; 3) Resistance is materialized by exponential decline of cell-kill rate; 4) Drugs are combined either in a linear, or Emax, or Weibull model involving a drug interaction term. Population analyses were performed using NONMEM Version 6 within a MATLAB environment. The models were validated using posterior predictive checks.
Results: In the developed models, over-parameterization was the most frequent problem. K-PD models involve only one parameter expressing the dynamics of drug amounts in the cell-kill rate formulation. This parameter was obtained for D and C from the single agent studies and was fixed in the analysis using the combination data only. When using the combination data only, the contribution of each drug to the anti-tumor effect was accurately estimated and the estimates were consistent with those obtained using single-agent data. The effect of the 2 drugs was found to be additive with no drug interaction term. Situation #2 is still under investigation.
Conclusions: Using combination data, the tumor size dynamic model parameters were successfully estimated. Further investigations are in progress for assessing the minimum required extent and type of clinical data for evaluating drug combinations in oncology. This model will be part of a modeling framework to simulate expected clinical response of new compounds and to support end-of-phase II decisions and design of phase III studies .
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