In recent years, a considerable amount of industry discussion has suggested that the pharmaceutical industry should move away from its traditional “blockbuster” drug model and lean towards one with a more personalized approach, driven by advances in genomics and the success of precision oncology and rare disease therapies. Yet today, we’re witnessing a remarkable counter-trend. The pharmaceutical industry is scaling up rather than narrowing down, with analysts projecting the obesity market alone to reach $100 billion by 2030.
While precision medicine continues to grow in certain areas, in particular with the advent of gene therapies targeting monogenic diseases, current market dynamics and advances in generative AI are moving biotech and pharma companies to prioritize therapies that can treat larger patient segments. Drugs that have the potential to treat multiple tumor types, diverse autoimmune indications, and/or other highly prevalent diseases are of increasing focus for drug developers. As a result, biotech is entering a new era of “mega blockbusters”: which we define as drugs with the potential to benefit broad swaths of the population and generate greater than $10 billion in peak sales.
High Costs, Low Success: A Big Challenge Facing Drug Developers
Developing a new drug is both an expensive and a risky endeavor. The cost to develop each drug is significant, with estimates ranging from $800 million to $2.3 billion for a single approved drug; and these costs have been growing at the rapid rate of 8.5% per year.1 This means that drug development costs will double every 8 to 9 years. While many factors account for the increasing costs of drug development, much of the increase has been driven by increasing clinical trial expenses and the increasing challenges of patient recruitment. This helps explain the intense focus of pharmaceutical companies on new technologies (including AI/ML-enabled platforms) that can streamline the design and execution of clinical trials.
Moreover, this large investment of time and money does not always pan out. The success rate of drugs that enter the clinic are alarmingly low, with less than 15% making it through to approval.3 Given these high costs and minimal success rates, pharmaceutical companies are under pressure to maximize the commercial value of each approved drug by developing therapies that can treat as many patients as possible, bringing about a greater return on investment via mega blockbusters.
Industry Focus on Obesity Therapeutics Leads the Charge
Interestingly, the industry’s aggressive investment into weight loss drugs is helping to usher in this new era of “mega blockbusters.” Obesity is well on its way to becoming the largest therapeutic area in the history of the pharmaceutical industry, as the appetite for treatments is growing the market rapidly. In 2022, over 35% of adults in the U.S. were obese and IQVIA estimates that nearly 25% of the global population will be obese by 2035, almost 2 billion people.4,5 In addition, the expansion of these highly-effective drugs into weight-related comorbidities (e.g., MASH, cardiovascular disease, obstructive sleep apnea), further enhances the impact that they will have.
The numbers tell a compelling story: GLP-1 drugs like Wegovy and Zepbound have seen unprecedented demand, with waiting lists in multiple countries. Novo Nordisk’s recent $6 billion investment in manufacturing expansion underscores the scale of this opportunity.
To capture this unprecedented commercial opportunity, pharma companies are not just developing new drugs—they are also heavily investing in building up key infrastructure that can be used as groundwork for future mass market indications. This involves direct-to-consumer platforms, expanding supply chain capacity, rebuilding primary care channels, and more.
From One Disease to Many: Identification of Common Pathways
Further, advancements in scientific techniques and methods are enabling biologists to more effectively identify common pathways across different disease indications, enhancing the likelihood of treatments that can be used for multiple diseases, instead of just one. Biological targets and pathways are commonly implicated across various clinical indications, especially within the field of immunology and inflammation, but can be challenging to validate. Versatile tools such as single cell and spatial sequencing, CRISPR screening, and the deployment of knowledge graphs, and machine learning models are providing a deeper interrogation of the underlying drivers of many diseases.
The idea of developing a single molecule that can treat multiple diseases is something that is highly attractive to biotech companies (Table 1). For example, incretin-targeting drugs (e.g., GLP1 agonists) are showing promise in treating not only obesity, but also substance use disorders, fibrosis, and cardiovascular conditions. Within the field of immunology, Dupixent stands out as a drug that has evolved from “traditional blockbuster” status to “mega blockbuster” status. Initially approved in 2017 for the treatment of dermatitis (eczema), Dupixent has since received approvals for asthma (2018), chronic rhinosinusitis with nasal polyposis (2019), eosinophilic esophagitis (2022), and prurigo nodularis (2022). Most recently, it was also approved for chronic obstructive pulmonary disease (COPD) in 2024. Impressively, since its launch, it has generated $34 billion in sales, and analysts forecast that it will generate $25 billion annually by 2030.
Generative AI and the Shift Toward Multimodal Drug Design
The complexity of disease is another factor behind the shift towards “mega blockbusters”, as it limits what can be achieved with traditional drugs. Multiple pathological targets are likely to be involved in disease progression, and drugs designed to go after a single target are unlikely to maximize efficacy. In the past, the solution to this was to use combination therapies, such as Trikafta, a fixed dose combination of three separate agents which targets multiple pathways simultaneously—but these therapies can increase toxicity concerns and can be especially challenging to implement with biologics such as monoclonal antibodies.
Generative AI is revolutionizing how the industry is approaching this problem: both small molecules and biologics can be designed to have multimodal activity, modulating more than one pathway simultaneously. David Baker, winner of the 2024 Nobel Prize in Chemistry, has been a pioneer in the field of generative AI for de novo protein design, most notably with the release of RFDiffusion, a model that enables the in silico design of protein structure based on specific molecular inputs and, ultimately, a desired biological function.10These AI tools introduce the potential for designing molecules with increased dynamic functionality, such as context-dependent agonism and antagonism, which would be almost impossible to achieve with traditional drug discovery methods.
Racing Against Time to Capture Market Value
While the trend towards “mega blockbusters” is still growing, the impact of the Inflation Reduction Act (IRA) on this broader trend is still uncertain. The IRA shortens the time period that pharma companies have to capture the value of a new drug, which may reduce the incentive to pursue additional approvals for new indications after a certain point. However, the incentive is also in favor of pursuing larger indications sooner in development instead of the historical approach, which was to initially pursue a smaller indication before expanding into the larger opportunity. This has already led to a greater number of drugs in development for larger indications and will likely mean that we can expect to see a higher number of approvals for drugs that target these big-market opportunities.
Looking ahead, the mega blockbuster era presents both opportunities and challenges. While the potential for broader patient impact and stronger returns is clear, companies must navigate manufacturing scale-up challenges, pricing pressures, and complex regulatory requirements across multiple indications. Success will likely depend on strategic early-stage decisions about indication selection, innovative trial designs that can support multiple approvals, and robust manufacturing capabilities that can meet global demand. For smaller biotech companies, this may mean focusing on novel biological insights and AI-driven approaches that can be partnered with larger pharma companies equipped to handle mega blockbuster commercialization.
For now, the industry is still racing towards the new era of “mega blockbusters,” and we have yet to see just how transformative these advancements in drug development will be for patient populations. With pharma investment in large-scale frameworks, the identification of common pathways across diseases, and the use of AI to design complex drugs, its future is geared toward making the biggest impact on public health—and on the bottom line.