Benjamin K Schneider (1), Bruno Bieth (2), Arnaud Boyer (3, 4), Joseph Ciccolini (3), Fabrice Barlesi (4), Kenneth Wang (5), Sebastien Benzekry (1, 6, *) and Jonathan P Mochel (1, *)
(1) SMART Pharmacology, Iowa State University College of Veterinary Medicine, Ames, IA, U.S.A; (2) Pharmacometrics Modeling & Simulation, Novartis Pharmaceuticals, Basel, Switzerland; (3) SMARTc Unit, Centre de Recherche en Cance´rologie de Marseille UMR Inserm U1068, Aix Marseille University, Marseille, France; (4) Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France; (5) Mayo Clinic, Rochester, MS, U.S.A; (6) Team MONC, Inria Bordeaux Sud-Ouest, Institut de Mathématiques de Bordeaux, France. (*) co-last authors.
Introduction: Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) combination therapy has been shown to be an effective first line therapy for non-small cell lung cancer (NSCLC) in Phase III clinical trial [1]. PEM and CIS disrupt DNA synthesis in rapidly dividing cells – eventually leading to cell death [2], [3]. BEV is an antiangiogenic that disrupts neovascular growth in rapidly growing tissues such as tumors. Counter-intuitively, in the process of disrupting neovascular growth, BEV induces a transient period where perfusion, and consequently drug delivery, is improved. Currently, BEV is administered concomitantly with PEM and CIS. However, previous studies have observed that sequential scheduling of BEV-PEM/CIS, i.e. administering BEV several days before PEM/CIS, improves the efficacy of BEV-PEM/CIS combination therapy in NSCLC [4]. This is thought to be an effect of aligning BEV peak efficacy with PEM and CIS peak exposure.
Objectives: In this study, we used a large dataset generated from xenograft NSCLC tumor-bearing mice in Imbs et al. 2017 to validate and subsequently fit a previously published semi-mechanistic PKPD model of tumor growth vs. BEV-PEM/CIS exposure [5]. We then used relevant literature values to scale the model fit to describe tumor growth vs. BEV-PEM/CIS pharmacokinetics in humans. Lastly, we used Monte Carlo (MC) simulations to derive the optimal scheduling of BEV-PEM/CIS sequential dosing in humans.
Methods: PK models and parameter estimates for BEV, PEM and CIS were adapted from literature values [6]–[8]. Competing PKPD models were written as NLME and parameter estimates were obtained using the SAEM algorithm as implemented in Monolix 2018R2. Competing structural models were evaluated using Bayesian information criteria, precision of parameter estimates (as defined by RSE%), inspection of search stability, and visual predictive checks. Correlation between random effects were evaluated using correlation plots of the full posterior distribution of random effects, as well as Pearson correlation tests with a threshold of P < 0.01. Scaling of PK model parameters to humans was done by substituting mouse PK parameter estimates with values from the literature [9-11]. The PD portion of the model was then scaled by using literature estimates of human NSCLC tumor growth parameters [12]. After adapting the model to make predictions in humans, 1000 MC simulations were performed in R 3.4.4 using the mlxR package to estimate the optimal scheduling of sequential BEV-PEM/CIS in humans [13].
Results: Using the final semi-mechanistic model, we predicted that the optimal scheduling gap in mice is 2.0 days, which is consistent with findings in previous preclinical studies [4]. We observed little to no inter-individual variability in the estimated optimal gap. Based on simulations from the PKPD model, the optimal scheduling gap in BEV-PEM/CIS was estimated at 1.2 days in humans. Administrating BEV-PEM/CIS at a 1.2 day gap rather than concomitantly improved therapy efficacy (defined as relative tumor volume reduction) by 106% over 67 days of treatment. Finally, our results suggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.
Conclusion: These findings support a growing body of evidence suggesting that the efficacy of BEV-PEM/CIS would greatly improve if scheduling was optimized. Using mathematical modeling to explore a range of practical scheduling regimens allowed us to estimate the optimal scheduling gap in sequential BEV-PEM/CIS in both humans and mice without the considerable time and resource investment required to conduct a suite of in vivo experiments. The developed structural model can be used in future systems pharmacology modeling of tumor growth and response vs. antiangiogenic-antiproliferative combination therapy.
References:
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[12] M. Bilous et al., “Computational modeling reveals dynamics of brain metastasis in non-small cell lung cancer and provides a tool for personalized therapy,” bioRxiv, Oct. 2018.
[13] M. Lavielle, mlxR: Simulation of Longitudinal Data. 2018.
Reference: PAGE 28 (2019) Abstr 9135 [www.page-meeting.org/?abstract=9135]
Poster: Drug/Disease Modelling - Oncology