Pierrillas Philippe (1), Hénin Emilie (1), Chenel Marylore (2), Amiel Magali (3), Bouzom François (3), Tod Michel (1)
(1) Université Claude Bernard, Lyon, France ; EMR 3738 Ciblage Thérapeutique en Oncologie, (2) Clinical Pharmacokinetic Department, Institut de Recherches Internationales Servier, Suresnes, France, (3) Technologie Servier, Orléans, France
Objectives: Bridging the gap between preclinical and clinical settings by anticipating human efficacy could be promising to improve drug development. This work focused on strategies based on modeling of preclinical data to anticipate human behavior of a new pro-apoptotic S compound.
Methods: Data from in vitro assays, PK, biomarker and tumor growth inhibition (TGI) studies in mice were considered. PK and response data from a phase 1 trial were collected (8 dose levels). Based on semi-mechanistic PK-PD models in mice and PBPK extrapolation, several PD extrapolation strategies were elaborated:
- Rocchetti approach (ROC) [1]
- Orthogonal Rocchetti approach (oROC) modifying the original ROC using an orthogonal regression
- Consistent across species approach (CAS) bringing out an efficacy parameter assumed consistent across species
Scaling species-specific parameters approach (SSP) assuming the concentration-TGI link is the same in mice as in humans, provided allometric scaling.
Results: S PK was best described by a 2-compartment disposition model with both saturable absorption and elimination. Tumor growth was modeled by a Koch model [2] inhibited by an all-or-none effect of caspase, defining a plasma concentration threshold for apoptosis, CTHRE. An hybrid PBPK approach [3] was applied to extrapolate the nonlinear PK and was well qualified on rat and dog data. Human observations were well predicted. oROC gave similar fitting performances as ROC on the 10 drugs from the original work [1]. ROC and oROC predict respectively a S clinical dose of 1470 and 2020mg. CAS was built on a relationship between TGI and the time that concentration remains above CTHRE, assumed to be consistent across species as time above Minimal Inhibitory Concentration and therapeutic efficacy for antibiotics [4-6]. Simulations with a negligible natural tumor growth assumption revealed a median response about 65% at 900mg whereas a mice-equivalent natural tumor growth assumption led to a median response of 23% at 900mg. SSP predicted a dose of 700mg to get a median response of 60%.
Conclusions: While empirical methods only predict a dose level, results of mechanism based strategies are preliminary and do not seem to be invalidated: proportion of patients responding to treatment is increasing with the dose while patients can respond from the first dose level. Those strategies seem to be more informative for the follow-up of a clinical study, highlighting the potential interest of such approaches.
References:
[1] Rocchetti M, Simeoni M, Pesenti E, De Nicolao G, Poggesi I (2007) Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur J Cancer 43 (12):1862-1868. doi:S0959-8049(07)00380-2 [pii] 10.1016/j.ejca.2007.05.011
[2] Koch G, Walz A, Lahu G, Schropp J (2009) Modeling of tumor growth and anticancer effects of combination therapy. Journal of pharmacokinetics and pharmacodynamics 36 (2):179-197. doi:10.1007/s10928-009-9117-9
[3] Sayama H, Komura H, Kogayu M, Iwaki M (2013) Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics. Journal of pharmaceutical sciences 102 (11):4193-4204. doi:10.1002/jps.23726
[4] Craig WA (1998) Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis 26 (1):1-10; quiz 11-12
[5] Andes D, Marchillo K, Stamstad T, Conklin R (2003) In vivo pharmacodynamics of a new triazole, ravuconazole, in a murine candidiasis model. Antimicrob Agents Chemother 47 (4):1193-1199
[6] Craig WA, Andes D (1996) Pharmacokinetics and pharmacodynamics of antibiotics in otitis media. Pediatr Infect Dis J 15 (3):255-259
Reference: PAGE 25 () Abstr 5907 [www.page-meeting.org/?abstract=5907]
Poster: Drug/Disease modeling - Oncology