Miro Eigenmann (1), Nicolas Frances (2) and Antje Walz (3)
Roche Innovation Centre Basel, Hoffmann-La Roche Ltd., Basel
Objectives: It is reported that most patients under tyrosine kinase inhibitor (TKI) treatment will eventually develop resistance versus TKI drugs [1-3]. The aim of this work is to develop a semi-mechanistic PK/PD model including a resistance mechanism under TKI treatment (Erlotinib and Gefitinib) in tumor xenograft mice.
Methods: Tumor growth inhibition (TGI) experiments were conducted in primary patient tumor (LXF A677) bearing mice receiving Erlotinib or Gefitinib treatment. The mice were randomized into treatment groups, control, 6.25mg/kg, 25mg/kg or 100mg/kg for each drug and treatment was daily orally administered for 14 days. Tumor volume was monitored over 30 days and sparse plasma PK data were collected.
A semi-mechanistic PK/PD model involving adaptive resistance mechanism was developed based on published evidence on resistance emergence after TKI administration [3]. The performance of the resistance model was compared to a simple direct effect model [4]. The model development and parameter estimation was done in Monolix v4.3.2. The models were evaluated in terms of parameter estimation precision, residual error, Akaike information criterion and visual predictive checks. Simulation analyses were performed in Berkeley Madonna v8.3.18.
Results: Pre-clinical data upon TKI treatment were better described by a model involving a resistance mechanism as compared to a simple direct effect TGI model. The resistance model suggests selection of resistant cells upon TKI treatment (acquired resistance).The initial fraction of resistant cells is assumed to be zero in treatment naïve mice. Growth rate of the resistant cell population was estimated to be 1.18 times slower than in the parental cell population. Simulations show the impact of the dosing regimen on total tumor and the emergence of the resistant cell population.
Conclusions: This modeling exercise supports and describes the dynamic of previously reported resistance upon TKI treatment. Adding a mechanism for adaptive resistance to a TGI model allows for a more precise estimation of potency parameters and to better support compound development. Estimated resistance related parameters and simulation studies are in line with findings by Chmielecki et al. [3]. This model could also be used in optimizing the administration protocol [5, 6].
References:
[1] Mok, T.S., et al., Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. The New England journal of medicine, 2009. 361(10): p. 947-57.
[2] Maemondo, M., et al., Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. The New England journal of medicine, 2010. 362(25): p. 2380-8. [3] Chmielecki, J., et al., Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Science translational medicine, 2011. 3(90): p. 90ra59.
[4] Jusko, W., Pharmacodynamics of Chemotherapeutic Effects: Dose-Time-Response Relationships for Phase-Nonspecific Agents. Journal of pharmaceutical sciences, 1971. 60(6): p. 892-895.
[5] Foo, J., et al., Effects of pharmacokinetic processes and varied dosing schedules on the dynamics of acquired resistance to erlotinib in EGFR-mutant lung cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2012. 7(10): p. 1583-93.
[6] Foo, J. and F. Michor, Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies. PLoS computational biology, 2009. 5(11): p. e1000557.
Reference: PAGE 24 () Abstr 3439 [www.page-meeting.org/?abstract=3439]
Poster: Drug/Disease modeling - Oncology