Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

Area of Science:

• Pharmacometrics
• Clinical Pharmacology
• Drug Development

Background:

• Antiobesity drug development faces challenges with high clinical trial dropout rates.
• Understanding factors influencing patient adherence and dropout is crucial for successful trials.

Purpose of the Study:

• To develop a population pharmacodynamic model predicting temporal body weight changes.
• To characterize responder and nonresponder dynamics driving dropout rates using Markov modeling (MM).
• To support clinical trial simulations and predict trial adherence.

Main Methods:

• Applied Markov modeling (MM) to 4591 subjects from 6 Contrave® trials.
• Developed population dose- and time-dependent pharmacodynamic (DTPD) and pharmacokinetic (PPPD) models.
• Linked DTPD/PPPD models with MM to predict transition rates among responder, nonresponder, and dropout states.

Main Results:

• The linked DTPD-MM and PPPD-MM models accurately predicted transition rates.
• Body weight change was identified as a significant factor influencing dropout rates.
• Both DTPD and PPPD model-driven approaches provided similar predictions for clinical trial outcomes.

Conclusions:

• Population pharmacodynamic modeling, particularly with MM, can effectively predict antiobesity trial dropout rates.
• Body weight change is a key determinant of patient adherence and trial outcomes.
• Modeling approaches support better clinical trial design and outcome prediction.