Obesity care is at a turning point. GLP-1s like Ozempic and Mounjaro have upended weight loss culture – and the research backs them up. For the first time, multiple randomized trials have found that anti-obesity medications can lead to an average 15-20% weight loss. Studies also show they can cut the risk of major heart-related events by about 20% in overweight adults with cardiovascular disease.
Biological drivers: Same BMI, different patient
But the field is also confronting a basic truth: obesity is a multifaceted, varied condition. Its risks and drivers, comorbidities, and treatment responses vary widely. We have known for a long time that cancer is not one disease, and big gains came when we stopped treating it like one. ‘Precision oncology’ did not happen because we found a magic drug; it happened because we accepted the differences.
Similarly, individuals with obesity – even those with similar physical presentations and BMI – can have different physiological and genetic drivers underlying their condition, which calls for different treatment pathways. However, this is also where obesity and oncology differ. Oncology personalization is often organized around biology, whereas obesity ‘sorting’ is messier. Choosing the right treatment means finding an option that fits a person’s overall health profile, how well they tolerate it, their mental health, food and activity environment, and ability to keep it up long-term.
The future of anti-obesity medications is growing more sophisticated with a focus on personalized targets, better formulations and easier administration scheduling. However, the next frontier in obesity care is not about just adding more medication options to the menu. No matter how ‘personalized’ they may be, we must create a better model that matches people with the right overall solution.
Ultimately, the systems needed to support anti-obesity medication’s personalised, safe, and durable use have not scaled as fast as their manufacture, access, and prescriptions. We’re still catching up on areas like ongoing monitoring, help with staying on treatment, insurance coverage rules, and tracking long-term outcomes. So what can pharma and life sciences do?
Understand the challenges
Behavioral change: Where the plan often breaks
First, it’s important to remember that if biology sets the baseline, behavior often determines what happens over time. Mental health, sleep, stress, pain, habits, and daily routines all matter. This is not willpower; it’s just how humans work. And it’s why two people with similar biological risk can end up in totally different places. One may manage to effectively deal with stress-eating, depression, or trauma-linked patterns, whereas another may have shift work, broken sleep, or caregiving demands that make consistency hard.
Anti-obesity medications can unlock significant early progress, but they do not rebuild routines. They do not resolve trauma. And they do not remove the many types of friction that drive failure in the real world: side effects, cost, coverage interruptions, life disruption, and when weight loss slows and motivation drops.
Personalization here does not mean that pharma has to fix all patient behaviors, but it does mean recognizing that one dosing plan and one follow-up schedule may not work for everyone. Behavioral patterns impact the odds of staying on therapy and the odds of relapse.
Environmental influence: Industrial scale hits a wall
Likewise, a patient’s environment often decides whether results stick, yet it’s the part of obesity care that nobody really owns. Work schedules, family load, food access, stress, and coverage rules shape what patients can actually do. The easiest example is the morning routine. Small requirements on paper turn into big barriers at scale: fasting rules, timing around other medicines, getting kids out the door, commuting, shift work, unpredictable caregiving. A regimen that fits a calm, stable morning can be a bad fit for a large share of real patients. That does not just hurt convenience. It raises drop-off and creates a ‘non-response’, which is really just a case of a patient not being able to take the medication as designed.
The same pattern shows up in the system around the drug. Coverage churn and admin burden can create stop and start therapy, raising the risk of weight regain. Provider capacity and short visit times can make risk-based follow-up hard, and cost, transportation, and supply issues hit the highest-need patients hardest.
This leads to a simple strategic choice for pharma. Sell the drug and let the market figure out solutions to the failure modes. Or help build the category so outcomes can hold up at scale. Category building does not mean becoming a provider – it means making it easier for the system adapt the drug to behavior and environment. So what does this look like in practice?
Recommendations for pharma: Industrialize personalization
Make real-world use part of development
As a priority, every anti-obesity medication program should produce clear, evidence-backed answers to four key questions.
- Who should start the medication? If BMI is not the only measure, consider what are the next best measures.
- Who is likely to struggle? It’s worth checking whether the patient is a 9 to 5 desk worker or part-time with overnight shifts.
- What support is needed to make outcomes last? Are simple reminders helpful, or is access to a community of patients working through similar struggles more beneficial?
- What is the plan when reality hits, like side effects or coverage breaks?
Pharma needs to set out clear support options for these common failures. As an example, side effects may require clinical support, whereas problems sticking to a routine may best solved through coaching. When these questions are addressed during a clinical trial program, the product can launch with a real-world pathway that primary care and payers can actually use. But if they’re left until after launch, the market will quickly end up discovering – at scale – the ways in which the system can fail.
Design for common failures
Next, most obesity treatment is still designed around the average result in controlled clinical settings. But that’s not where the battle is. The pharmaceutical industry should instead design for the predictable ways that obesity therapy can break down at scale – where people are not responding as expected, dropping off the medication, or regaining the weight over time.
A good example of this is dosing friction. Some oral therapies require strict timing around meals and other meds, whereas others fit into normal life with fewer restrictions. That difference matters because it changes who can take the drug reliably and how often people quit, which should in turn affect prescription decisions. Small daily frictions can turn into big population-level failure rates.
For every anti-obesity medication, it’s important to define early checkpoints and next steps for non-response, as well as enhancing tolerability and titration plans to reduce drop-off. Companies should also study maintenance and what happens when therapy stops or gets interrupted, and measure how real routines affect persistence and outcomes.
This is one of the best ways to scale personalization. It is not custom medicine for everyone. These are simply standard pathways that handle the common problems outside the ‘average patient’.
Start shipping a pathway, not a product
If obesity care is going to scale effectively, the market needs more than a label. It needs a simple operating model that primary care can run. At a minimum, the sector should launch medications with an evidence-backed pathway. That includes a short start profile beyond BMI, focused on risk, which should consider what combination of patient attributes correlate with non-response, regaining weight, and other issues. It should also provide rules for when to add support like coaching or behavioral health, based on these segments. Early checkpoints for whether it is working are key. The healthcare professional sees BMI, but segment-based signals may give them some more useful insight.
Meanwhile, adding a side-effect playbook to the pathway can bring validated clinical research to the patient and cut through the online noise they may be consuming. It should also have a plan for stopping and responding to regaining weight. For example, checking what the patient’s expectations are when their weight loss goal is met, and how these routines can be maintained.
This is not pharma becoming healthcare professionals. Rather, it is pharma solving both healthcare professional and patient-friction at scale.
Success beyond weight loss
Finally, percentage weight loss is not the full story. The system should care about outcomes that match health and durability, including the patient’s wider health risk, quality of life, and ability to maintain the loss over time.
For example, it’s important to consider not just whether a person’s BMI has dropped, but the impact on HBA1c, which measures their glucose control levels. Likewise, a change in the patient’s activity levels or social interactions can indicate that the behavioral and environmental factors of obesity have also improved.
If we measure success only by the lowest number on the weight scales, the system will continue short-term wins for the ‘average patient’ while overlooking what really matters: how these treatments perform over the long haul. True progress won’t come from simply launching the next molecule. It will come from building an infrastructure as sophisticated as oncology – one that can personalise treatment pathways using not just biology, but behaviour and environment as meaningful markers of risk and response.
The supply of anti-obesity medication is now being scaled at breakneck speed to meet demand. The next step is just as urgent: integrating the behavioural and environmental factors that drive an individual’s obesity into how these medicines are developed, prescribed and supported. When we design for the real-world contexts that people live in, treatment becomes genuinely personalised – and that’s how we unlock better outcomes at scale.
Author Bio
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- Laura Carlin
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- Charlotte Ledger
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- Chris Plance
Laura Carlin, Charlotte Ledger and Chris Plance are health and life sciences experts at PA Consulting, the global innovation consultancy.
