Jérémy Seurat (1), Pascal Girard (2), Vishnu Dutt Sharma (3), Kosalaram Goteti (3), France Mentré (1)
(1)IAME, INSERM, UMR 1137, University Paris Diderot, Paris, France (2)Merck Institute for Pharmacometrics, Merck Serono S.A., Lausanne, Switzerland (3)EMD Serono R&D Institute, Billerica, MA, USA
Objectives: In oncology, there is a growing interest in the use of combination therapies in early clinical trials, but most of the time only monotherapy data from each agent and no data from combination agents are available at early stage to evaluate trial design performances and optimize them. Literature evidences suggest that early tumor shrinkage (ETS) is a good predictor of overall survival [1]. The aim of this analysis was to compare in silico several combination designs of drug M with cetuximab (C) in the treatment of solid tumors and to define the appropriate dose of C, assuming dose M is fixed. The performances, type I error (α) and power, of several one-stage designs were compared to test the superiority of the combination C+M to C alone using modeling and simulation of exposure-tumor growth inhibition (TGI).
Methods: Clinical trial simulations were performed, using an exposure-TGI model [2]. The effect of C exposure on progression of tumor size was modeled as described in [3], following a dose of 500 mg/m2 every 2 weeks (Q2W). Different combination effects of M and C (no effect/additive/synergistic) were explored, using a global pharmacodynamic model, assuming exposure independent effect of M [4]. 1, 2 and 4 arm designs, all composed of 60 patients in total were evaluated. For each treatment arm of the combination design described, 500 datasets with tumor sizes at baseline and weeks 2, 4, 6 and 8 were simulated. The data for all arms were fitted using SAEM algorithm in Monolix 2018R2 [5] to obtain individual ETS predictions at week 8 (ETS8). Comparison test were performed on predicted and observed ETS8 (with residual variability) between the different arms.
In the 1 arm design, 60 patients received C+M and individual ETS8 were compared to a ‘reference’ ETS8 for C only, using a one sample Wilcoxon test. Different historical values (wrong or reliable) for this reference ETS8 were used. Then, randomized trial with 2 arms, C and C+M, of 30 patients were simulated and ETS8 were compared using a two-sample Wilcoxon test. In the 4 arm designs, in addition to C500 and C500+M, where dose of C is 500 mg/m2 Q2W, two combination arms with lower dose of C: C400+M and C200+M were evaluated where each arm was composed of 15 patients. First, a global Kruskal-Wallis test was performed to compare the 4 arms. If the global test was significant, a Dunnett test was performed to test each combo arm to reference C alone.
Results: When the ETS8 reference value for C only is adequate, the 1 arm design has the maximum power (98% for additivity of M on C and 100% for synergy). There is, however, a strong inflation of α when no effect of M is simulated on top of C in case of a wrong historical reference: for instance, α is 34% if reference ETS8 is 14% lower than true one. With the 2 arm randomized design, the power of the Wilcoxon test is 69% in case of additivity assumption and 99% for synergy. The power is lower for the tests based on ETS8 computed from observations only (67% and 97%, respectively). With the 4 arm design, the power of the global test is 52% for additivity and 77% for synergy. With the Dunnett test, C500+M was found better than C500, with power of 26% for additivity and 65% for synergy. The superiority of a lower dose combo was respectively 13% and 52% with C400+M, and respectively 5% and 19% with C200+M. As expected, the power of 4 arms design, with a total number of 60 patients, is lower than the power of 2 arms design, but it allows to explore more aspects of the drugs combination.
Conclusions: This work highlights the strengths and weaknesses of the different early clinical combination designs in ETS, in the context where we have fixed dose of one already approved agent and different doses of another. The 1 arm design shows a better power of tests than 2 or 4 arms, but implies strong assumptions on the historical reference value, leading to strong inflation of type I error in case of under-estimated reference. Choosing a 2 or a 4 arms depends on the objective of the study: a 2 arms design is preferable than a 4 arms to reach a good power of statistical tests, but a 4 arms design allows a better understanding of the dose-exposure relationship and thus a better dose selection. An extension of this work is to perform model-based adaptive two-stage designs [6,7] using the Fisher Information Matrix to optimize the second stage of the study, where arms could also be added or dropped at the end of first stage.
References:
[1] Heinemann V, Stintzing S, Modest DP, Giessen-Jung C, Michl M, Mansmann UR. Early tumour shrinkage (ETS) and depth of response (DpR) in the treatment of patients with metastatic colorectal cancer (mCRC). Eur J Cancer Oxf Engl 1990. 2015;51:1927–36.
[2] Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, et al. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. J Clin Oncol Off J Am Soc Clin Oncol. 2009;27:4103–8.
[3] Girard P, Brodowicz T, Kovar A, Brockhaus B, Zühlsdorf M, Schlichting M, et al. Drug-disease model of tumor size and overall survival in metastatic colorectal cancer patients treated with cetuximab administered weekly or every second week. European Cancer Congress 2013, Amsterdam, Abstract # 2425
[4] Wicha SG, Chen C, Clewe O, Simonsson USH. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat Commun. 2017;8:2129.
[5] Kuhn E, Lavielle M. Maximum Likelihood Estimation in Nonlinear Mixed Effects Models. Comput Stat Data Anal. 2005;49:1020–1038.
[6] Foo LK, Duffull S. Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies. Pharm Res. 2012;29:1530–43.
[7] Pierrillas PB, Fouliard S, Chenel M, Hooker AC, Friberg LE, Karlsson MO. Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology. AAPS J. 2018;20:39.
Reference: PAGE 28 (2019) Abstr 9046 [www.page-meeting.org/?abstract=9046]
Poster: Methodology - New Modelling Approaches