Comparative assessment of incretin therapies on weight loss through a model-based meta-analysis approach
Akshita Chawla, Peter Chang, Li Qin, Matthew L. Zierhut, Maria Trujillo
Merck & Co., Inc., Rahway, NJ, USA ; Certara, Inc., Princeton, NJ, USA
Objectives:
This Model-Based Meta-Analysis (MBMA) aimed to develop a comparator model that provides a quantitative framework for the comparison of treatment effects of incretin therapies on body weight in overweight/obese patients (with or without type 2 diabetes [T2D]). This framework is being used to support strategic and tactical decisions for the development of new drugs for weight loss. Percent body weight change from baseline was considered as the primary endpoint for the analysis.
Methods:
A systematic literature review was conducted to construct a database that consists of publicly available summary level safety and efficacy data from 343 trials of incretin-based therapies. This database included 275 studies with a GLP1 agonist arm (17 oral), 18 with a GIP/GLP1 agonist arm, and 12 with a GCGR/GLP1 agonist arm (updated Oct 2023). The primary source of information for this database is PubMed, clinicaltrials.gov, company registries, conference abstracts/posters/presentations, and regulatory reviews from FDA websites. This database was filtered down to 118 studies (76% placebo controlled) to focus on GLP1-based therapies including dual and triple agonists, combinations that include a GLP1 agonist, and amylin analogues. A model-based meta-analysis resulted in a model that captured the various titration regimens as well as the time course (onset) of dose response relationships of these drugs. The response in the MBMA was described as the sum of a trial specific non-parametric (unstructured) placebo effect and a parametric drug effect depending on dose, titration scheme, time, model parameters and covariates. Dose response was estimated where possible, using drug specific Emax and potency (ED50) parameters. Additionally, a differential curve superpositioning method was used to capture the influence of different titration schemes. The observations were weighted based on reported measures of precision. Covariates such as mean baseline body weight, indication (T2D or non-T2D), patient domicile status, background therapy, and analyzed population (ITT vs completers) were graphically explored and tested for statistical significance.
Model development, evaluation, and simulations were performed in R 3.5.2. Model appropriateness was assessed using numerical and graphical diagnostics.
Results: Exploratory analysis indicates that efficacy for oral and injectable GLP agonists is comparable, and that weight loss is greater in the non-diabetic vs diabetic population. It also shows that dual and triple incretin agonists show greater weight loss compared to GLP-1 agonist monotherapy. In the MBMA, a dose response relationship with an Emax model was identified for 14 drugs. Emax for the drugs belonging to GCGR/GLP1 and GIP/GLP1 classes ranged from approximately -26% to -60% which was higher than those estimated for the GLP1 class (ranging from -3% to -20%). Drug-specific sigmoidal onset (ET50) was estimated for 10 drugs, while a class specific ET50 for the GCGR/GLP1 drug class was estimated for the remaining drugs. The GLP1 agonists had a shorter onset with ET50 ranging from 3 to 12 weeks as compared to drugs from other classes. Indication (T2D vs non-T2D) was selected as a covariate on Emax and hence is a strong driver of treatment effect. Diagnostics indicated that the model captured both dose response, titration, and time course trends in the data well. This model allows us to simulate predicted treatment effects for drugs in late-stage development and compare drugs differing in the duration of treatment. Results show that the mean body weight change difference from baseline and placebo for marketed drugs range from 4 to 30 % for obesity indication while changes in T2D are expected to be somewhat smaller.
Conclusions: The MBMA modeling framework integrates data across clinical trials varying in treatment, design, and patient population to draw novel insights about the competitive landscape. It provides a quantitative framework for cross-study comparisons and benchmarking new investigational compounds to existing drugs for weight loss, while also accounting for the influence of dose, titration, and patient populations, thus facilitating decision-making in the development of optimal therapies for obese patients.