Model‐Based Approach to Predict Adherence to Protocol During Antiobesity Trials

Abstract

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect‐response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose‐ and time‐dependent pharmacodynamic (DTPD) model. Additionally, a population‐pharmacokinetic parameter‐ and data (PPPD)‐driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD‐MM and PPPD‐MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body‐weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model‐driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic‐driven approach.

Impact of anti‐obesity medication initiation and duration on weight loss in a comprehensive weight loss programme

“…In the real world, many patients discontinue AOM after reaching their desired weight loss goal. In the clinical trials for naltrexone–bupropion (Contrave®), 42% to 49% of patients dropped out due to medication nonadherence or other reasons related to weight gain . In the 2‐year SEQUEL extension study for controlled‐release phentermine/TPM, 84% (568/676) of patients completed the study, while 16% of patients discontinued due to either nonadherence or unrelated reasons .…”

A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study

“…Patient dropout scenarios, variations in time of medicine intake (dose time errors), and nonadherence in the presence of drug‐induced adverse effects can also be explored with the current framework for more granular CTS. A recent report elegantly considers the interaction between poor response and dropout rates in obesity trials in a pharmacometric model framework to predict the outcomes . In addition, the input model to describe adherence could be varied depending on the source and type of nonadherence of interest (variation in dose timing, longer holidays, and nonpersistence, etc.).…”