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A Model-Based Approach to Predict Individual Weight Loss With Semaglutide in People With Overweight or Obesity
TAKE-HOME MESSAGE
- This study was designed to develop a model to characterize the relationship between semaglutide exposure levels and weight-loss trajectories following semaglutide treatment and evaluate how accurately this model-based method can predict individual 1-year weight-loss outcomes. The authors developed an exposure–response model to predict weight-loss trajectories using baseline characteristics, HbA1c, and pharmacokinetic data. Overall, the model adequately described the observed mean on-treatment weight-loss trajectories across treatment arms and the investigated exposure range. The authors concluded that an exposure–response model quantitatively describes the relationship between systemic semaglutide exposure and weight loss and predicts individual weight-loss trajectories for people with overweight or obesity receiving semaglutide doses up to 2.4 mg once weekly.
- Predicting an individual's expected weight loss during semaglutide treatment would be valuable to enable clinically relevant guidance and monitoring of treatment targets. By using an exposure–response model, physicians could create a more individual approach to weight loss with treatment-matching expectations.
abstract
This abstract is available on the publisher's site.
Access this abstract nowAIMS
To determine the relationship between exposure and weight-loss trajectories for the glucagon-like peptide-1 analogue semaglutide for weight management.
MATERIALS AND METHODS
Data from one 52-week, phase 2, dose-ranging trial (once-daily subcutaneous semaglutide 0.05-0.4 mg) and two 68-week phase 3 trials (once-weekly subcutaneous semaglutide 2.4 mg) for weight management in people with overweight or obesity with or without type 2 diabetes were used to develop a population pharmacokinetic (PK) model describing semaglutide exposure. An exposure-response model describing weight change was then developed using baseline demographics, glycated haemoglobin and PK data during treatment. The ability of the exposure-response model to predict 1-year weight loss based on weight data collected at baseline and after up to 28 weeks of treatment, was assessed using three independent phase 3 trials.
RESULTS
Based on population PK, exposure levels over time consistently explained the weight-loss trajectories across trials and dosing regimens. The exposure-response model had high precision and limited bias for predicting body weight loss at 1 year in independent datasets, with increased precision when data from later time points were included in the prediction.
CONCLUSION
An exposure-response model has been established that quantitatively describes the relationship between systemic semaglutide exposure and weight loss and predicts weight-loss trajectories for people with overweight or obesity who are receiving semaglutide doses up to 2.4 mg once weekly.
Additional Info
Disclosure statements are available on the authors' profiles:
A model-based approach to predict individual weight loss with semaglutide in people with overweight or obesity
Diabetes Obes Metab 2023 Jul 09;[EPub Ahead of Print], A Strathe, DB Horn, MS Larsen, D Rubino, R Sørrig, MTD Tran, S Wharton, RV OvergaardFrom MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
Semaglutide is a long-lasting, selective GLP-1 agonist administered once-weekly.1At the dosage approved as an adjunct to a reduced calorie diet and increased physical activity for chronic weight management (2.4 mg weekly), semaglutide treatment results in twice the magnitude of weight loss compared with a first-generation fatty-acylated peptide liraglutide, which is administered once daily.2 The mechanism for the extended duration of action pertains to the diacid nature of the fatty acylation that increases the reversible, noncovalent association with plasma proteins, most notably albumin.1 The greater efficacy is purported to result from increased concentration and retention at appetite control centers in the brain.3 The most notable adverse side effect is a pronounced impact on gut motility, which can produce gastrointestinal discomfort, nausea, and vomiting. Gradual dose escalation over a period of weeks is required to achieve steady state concentrations in a manner tolerable to patients. The weight lowering and gastrointestinal side effects are dose-dependent and correlate with plasma concentration of the active drug.4 Semaglutide initiation and dose escalation therefore represent a therapeutic challenge, because a balance must be struck to extract the weight loss benefit without undue discomfort.
Strathe and colleagues5 describe the development and validation of an exposure–outcome model to estimate the trajectories of weight loss over one year of semaglutide treatment for people with overweight or obesity. The development of the model involved utilizing data from one phase II dose-ranging trial and two phase III trials, namely "Semaglutide Treatment Effect in People with Obesity 1 and 2" (STEP 1 and STEP 2). To assess its predictive performance, the model was evaluated using data from three independent phase III trials, namely STEP 3, STEP 4, and STEP 5. Predictor variables included semaglutide exposure based on population pharmacokinetics at each dosage studied, baseline demographic characteristics, glycemic control (HbA1c), and early weight loss data up to 28 weeks. The results showed that medication exposure (average plasma semaglutide concentration) strongly predicted weight loss trajectory. Predictors of lower exposure for a given dosage, and thus lower percentage weight loss, included male sex, Asian race, prediabetes or diabetes, and higher baseline body weight. Factors associated with higher exposure and greater percentage weight loss included mild to moderate renal impairment and Black/African American race. The model performed well and, as expected,6 precision for prediction of percentage weight loss at 1 year increased as more information was added for weight loss from earlier timepoints, ie, precision improved when week 16 weight loss was included, compared with week 8 alone, and improved further when weight loss at 28 weeks was also included.
The response (or perhaps more aptly outcome to avoid unsubstantiated implications of causation) model developed by Strathe et al has several potential clinical applications. Healthcare professionals (HCPs) can utilize it to set personalized weight loss goals for patients, communicate realistic expectations regarding the range for anticipated weight loss, and enhance motivation for adhering to the prescribed pharmaceutical and lifestyle regimen, especially considering the gastrointestinal side effects associated with Semaglutide, which can be a barrier for adherence and persistence with therapy. Additionally, the model can be used to track progress toward weight loss goals, providing feedback and facilitating discussions between patients and HCPs about possible adjustments to treatment strategies. For example, if the model-predicted weight loss declines from week 8 to a subsequent follow-up timepoint, the patient may be presumed to be at increased risk for not achieving the predicted weight loss goal. This can prompt a discussion about adherence and challenges the patient may be experiencing. The model also allows adjustment of predicted weight loss for interruptions in therapy, or longer ramp-up periods. Such reductions in exposure may require alteration of expectations regarding weight loss at 1 year.
Although the model developed is potentially useful, there are interesting points about clinical utility and interpretation that should be considered. The first is that these associations do not necessarily represent causation. That is, observing that some pattern or quantity of weight change early in a study predicts a pattern or quantity of weight change later, does not mean that this is a cause of the later weight change, or that differences in weight change between individual patients can be attributed to differences in semaglutide concentrations. This is not necessarily a problem if one is only doing "predictology." Only time will tell how helpful such predictology will be in clinical practice. Predictive models can be especially useful when there are finite resources that can be expended on treating patients, and one needs to optimize their use. In such situations, even modest predictive ability as to who will have the best and worst outcomes after receiving treatment can have substantial utility for targeted allocation of resources.7 The authors have committed to making data available from this project to others, which will be valuable for further refinement, evaluation, and extension of these findings.8 By fulfilling their commitment to sharing the raw data and code necessary to reproducing all the results reported to qualified scientists, the authors provide a valuable service to the scientific community.
Finally, the outcome merits further consideration. Is weight alone the most important dimension, or would incorporation of changes in biomarkers for risks of adiposity-associated diseases enhance the value of the model? How can we determine who is likely to benefit most and least over longer periods? Larger sample sizes, inclusion of biomarkers, and longer study periods may increase both predictive capacity and clinical utility. Even when assessing weight alone, we might ask whether specific elements pertaining to when, where, and how the weight loss curves differ across individuals may have relevance regarding longer-term outcomes. The investigators are to be commended for pioneering an informative path with the first breakthrough drug for long-term body weight management.
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