Combes FP (1), Vakilynejad M (2), Lahu G (3), Lesko LJ (1), Trame MN (1)
(1) Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA; (2) Takeda Pharmaceuticals Research Division, Pharmacometrics, Deerfield, IL, USA; (3) Takeda Pharmaceuticals Research Divsion, Pharmacometrics, Zurich, Switzerland
Objectives: Currently, drugs approved for obesity treatment have shown a wide range of response in clinical trials, even though the dropout rate remains high [1]. The mechanistic reason for non-response or dropout during the treatment period remains unclear. The objective of this study was (i) to develop a Population Pharmacodynamic (PopPD) model describing BodyWeight (BW) time-courses in obese subjects on placebo and LifeStyle Interventions (LSI), (ii) to evaluate the transition probabilities between Responder (R), Non-Responder (NR) and Dropout (D) states using a Markov Model (MM), and (iii) to simulate the outcome of anti-obesity trials using the developed PopPD/MM with regard to R, NR and D rates.
Methods: 1102 obese patients (BMI>30) on placebo, in Phase III naltrexone-bupropion trials, were included in this analysis with up to 60 weeks of BW information. Subjects were instructed on dietary changes (≈-500kcal/d) and encouraged to exercise 3 times/week. To analyze the placebo patient level data, an indirect-response model with 0 and 1st-order rate constants for BW gain and loss, respectively [2], for the purpose of predicting longitudinal BW loss under different LSI, was used.
MM was developed to predict time courses of transition probabilities between R, NR, and D states [3-4]. Longitudinal BW, predicted using the PopPD model, was examined as a covariate on the transition probabilities to explain the D or R pattern.
Results: The PopPD model described the BW change over time adequately for all subjects included in this analysis. The estimated MM probabilities of transitions between R to NR (0.162), R to D (0.048), NR to R (0.195), and NR to D (0.070), concluded that NR are more likely to dropout than R. The predicted longitudinal BW over time when used as a covariate was shown to significantly influence the transition probabilities between all R, NR, and D states. The developed PopPD/MM model was enabled to simulate R, NR, and D rates for anti-obesity clinical trials.
Conclusions: The joint PopPD/MM described longitudinal BW loss and transition probabilities between states in anti-obesity clinical trials well, concluding that BW is a major factor influencing the probabilities. Further research is required to explore other covariate effects such as treatments, and side effects. The developed framework can be utilized for clinical trial simulations to inform go/no-go decisions in anti-obesity drug development [5].
References:
[1] Gadde KM,Current pharmacotherapy for obesity: extrapolation of clinical trials data to practice. Expert Opin Pharmacother 2014;15(6):809-22.
[2] Van Wart S, et al, Modeling the time-course of body weight for subjects given placebo in an obesity trial, ACOP (American Conference on Pharmacometrics), 2011,San Diego, USA.
[3] Lacroix BD, et al, A pharmacodynamic Markov mixed-effects model for determining the effect of exposure to certolizumab pegol on the ACR20 score in patients with rheumatoid arthritis. Clin Pharmacol Ther 2009;86(4):387-95
[4] Karlsson MO, Introduction to Markov modelling, PAGE meeting (Population Approach Group in Europe), 2012, Venice, Italy. [5] Sonnenberg FA, et al, Markov models in medical decision making: a practical guide, Med Decis Making 1993;13:322-338.
Reference: PAGE 24 (2015) Abstr 3424 [www.page-meeting.org/?abstract=3424]
Poster: Drug/Disease modeling - Other topics