C. Laouénan (1,2), J. Guedj (1,3), F. Mentré (1,2)
(1) Univ Paris Diderot, Sorbonne Paris Cité, INSERM, UMR 738, Paris, France; (2) AP-HP, Hosp Bichat, Service de Biostatistique, Paris, France; (3) Los Alamos National Laboratory, New Mexico, USA
Objectives: 2011 has marked a milestone in HCV therapy with the approval of two protease inhibitors (PI), telaprevir (TVR) and boceprevir (BOC), in addition to current treatment. However the antiviral potency of BOC has never been estimated. Ongoing MODCUPIC – ANRS trial will provide for the first time a precise description of viral kinetics under tritherapy in real conditions of use. The objectives of our study were to evaluate by simulation i) the estimation performance for the chosen MODCUPIC’s design ii) the power to detect a difference of potency between TVR and BOC using an HCV dynamic model.
Methods: The Neumann et al. [1] biphasic viral kinetics model considers initial viral load (VL), free virion and infected cells clearance rate (c and δ), and antiviral potencies (ɛ) which is the percentage of blockage of virion production. Values of parameters and their inter-individual variations were those proposed by Guedj et al. [2]. A treatment effect was added on ɛ (assuming εTVR = 99.9% and εBOC = 99%, 99.5% or 99.8%). We simulated 500 datasets with additive error for log10 VL using MODCUPIC’s design (30 patients per PI and VL measurement at 0, 0.33, 1, 2, 3, 7 and 14 days). Nonlinear mixed-effects models (NLMEM) were used to estimate parameters using the extended SAEM algorithm in MONOLIX v4.1 that take into account below limit of detection data [3-4]. Relative bias (RB) and relative root mean square error (RRMSE) of the estimated parameters were computed. We performed a Wald test to detect a difference between PIs.
Results: With 30 patients per PI, all parameters were well estimated with small RB and RRMSE. For example, with εTVR = 99.9% and εBOC = 99%, c was estimated with RB = 0.2% and RRMSE = 4.1% and ε with RB = -0.01% and RRMSE = 0.2%. Power to detect a difference between ε were 100%, 100% and 94% with εBOC = 99%, 99.5% and 99.8% respectively. With εTVR = 99.9% and εBOC = 99% but in absence of datapoints at 0.33 and 1d, ε remained precisely estimated with RB = -0.02% and RRMSE = 0.2%, but the estimation of c was degraded with RB = 35.9% and RRMSE = 80.9%. However, without these datapoints power remained very high (100%, 100% and 89% respectively).
Conclusions: The use of viral dynamic modeling approaches along with NLMEM in our simulation study validates a priori the design of MODCUPIC’s trial to estimate parameters and to compare the antiviral potency of TVR and BOC, even with sparse initial sampling. The study of the test properties is ongoing.
References:
[1] Neumann AU, Lam NP, Dahari H, Gretch DR, Wiley TE, Layden TJ, et al. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha therapy. Science. 1998;282(5386);103-107.
[2] Guedj J, Perelson AS. Second-phase hepatitis C virus RNA decline during telaprevir-based therapy increases with drug effectiveness: implications for treatment duration. Hepatology. 2011;53(6);1801-1808.Â
[3] Kuhn E, Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Comput Stat Data Anal, 2005;49:1020-1038.
[4] Samson A, Lavielle M and Mentré F. Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model. Comput Stat Data Anal, 2006;51;1562-1574.
Reference: PAGE 21 () Abstr 2473 [www.page-meeting.org/?abstract=2473]
Poster: Estimation methods