The arithmetic is easy to state and awkward to defend. Between now and 2030, over $300 billion in annual branded pharmaceutical revenue is set to expire as current blockbuster drugs (such as Keytruda, Eliquis, and Opdivo) lose their patents. Consequently, the largest pharmaceutical companies are eager to defend their revenue streams through acquiring other companies with promising assets.
Between $130 billion and $134 billion of biopharma M&A was announced in the first half of 2026, depending on the screen, and public targets closed at an average premium of 43%. Meanwhile, the ten-year Treasury yielded 4.91% on 2 November. The Federal Reserve raised rates in September while calling inflation elevated. Acquisitions are typically financed using loans (which interest rate rises will make more expensive) while most of the revenue being bought arrives after 2030.
Either the patent cliff has grown so large that buyer’s discipline is now a luxury, or the industry is capitalising a cost it has not learned to avoid. The narrower conclusion is more uncomfortable: the strategic logic is sound, the pricing is not, and the difference might be being financed by companies who will not see the test results for around 4 years.
A Gilmartin Group analysis of 14 public biotech M&A deals in 2026 indicated that the average acquisition premium and price were 43% and $5 billion respectively. The premium struck against the prior close rather than any estimate of intrinsic value. That matters more this year, because the base has moved. The SPDR S&P Biotech ETF (XBI) rose roughly 25% between January and July 2026. If a target simply tracked the sector, a 43% premium implies a price about 79% above its January level. The percentage looks stable across cycles. The cheque does not.
This Gilmartin-sourced statistic samples a narrow part of the market. BioPharma Dive counted 26 acquisitions of private biotechs this year with at least $50 million of guaranteed proceeds, against 21 for public companies. Private deals were found to generate no premium print and no disclosure of price against fair value, so the measured universe is the visible large-cap end of a market whose median deal is invisible.
Direction within the year is just as telling. RTW Investments, citing LifeSci and Jefferies, puts the average premium on the three transactions above $10 billion at 46%, above the broader cohort; Vertex’s $10 billion purchase of Crinetics in July carried a 102% premium. Volume discounts have not appeared. The largest cheques have drawn the largest mark-ups, which is what competition for a dozen de-risked assets looks like when every large buyer reaches the same conclusion at once.
The case for buying time
None of this makes the buyers irrational; the cliff is dated and documented. RBC’s Brian Abrahams put the ten-year revenue gap at $400 billion in January and noted that dealmaking had come nowhere near filling it. Evaluate’s forecasts put more than $400 billion of prescription sales at risk through 2030, with $158 billion exposed in 2031 alone. RTW’s version is starker: about $18 billion of large-pharma revenue lost exclusivity in 2025, against roughly $180 billion by 2030.
For a company facing that schedule, the relevant comparison is not the premium against the market price but the premium against the cost of not replacing the revenue. A $10 billion deal that adds $1.5 billion of peak sales is expensive at 43% and still cheaper than a $6 billion hole, because the hole is permanent and the asset has a patent. Strategic value of that kind does not appear in a discounted cash flow model.
The bull case rests on supply as much as demand. RTW notes that about 30% of US biotechs below $10 billion in market value traded under cash (i.e., total stock market valuation below cash plus cash equivalents) at the end of June, and that drugs launched between 2023 and 2025 beat pre-launch consensus by a median 151%, compared to 50% over the preceding eight years. On that reading, buyers are not overpaying for growth but correcting a market that still prices clinical risk as though the launch environment were 2019.
The base rate problem
The strongest counter-evidence concerns what happens after closing. L.E.K. examined 195 public biopharma acquisitions between 2010 and 2023 and found usable three-year post-launch data for 81 lead assets. Of those, 53% missed sell-side consensus (i.e., the combined average of financial forecasts for the standalone company) by a median of 35%. In five of seven transformational transactions, the acquirer’s total shareholder return trailed the VanEck Pharmaceutical ETF by an average of 5% after one year and 10% after three years.
That does not prove the 2026 cohort will repeat it. It establishes the base rate against which claims of strategic value must be measured, and nothing in this cycle yet shows the base rate has moved. What has moved is the price. Bloomberg data analysed by Bloomberg Law put average global pharma premiums at 31.9% in 2014 and 42.4% in 2023; for pharma-biotech deals the average rose from 46.7% in 2014 to 70.4% in 2022 before easing to 59.8%. Value the Markets drew the inference: as premium expectations rise among target boards and their advisers, the return available to the acquirer compresses. The seller collects the synergy before the buyer has built it.
Policy adds a second claim on the same revenue. The FTC’s posture toward serial acquisition, IRA negotiated prices and the prospect of most-favoured-nation pricing raise the cost of owning a franchise once it arrives.
Keytruda (pembrolizumab) is the world’s best-selling drug and generates roughly $31.7 billion in annual sales, constituting 56% of the company’s total annual revenue. Merck’s US patent for Keytruda expires in 2028.
San Diego’s biotechnology ecosystem is booming. At its heart, it consists of a mosaic of startup and mature companies working on the honorable task of discovering new therapies for patients, but it’s also peppered with organizations helping turn promising new drug candidates into clinical-stage products, and beyond.
In our inaugural San Diego spotlight feature, we’re taking a closer look at Catalent’s San Diego operations. From early development through clinical supply, Catalent’s San Diego facilities provide a connected pathway designed to help biotech companies move candidates into the clinic faster and with less complexity.
A 68,000-square-foot development facility supports early-phase small-molecule and peptide drug candidates through preformulation testing, formulation and analytical development, GMP manufacturing, stability testing, clinical packaging, with strategies for compounds with poor solubility.
What makes Catalent San Diego unique is the seamless transition from development to clinical supply. As programs progress, sponsors can leverage two closely connected facilities to support development, packaging, distribution, and clinical trial execution through a single integrated network.
Catalent’s purpose-built clinical supply facility extends that support through clinical supply management, packaging, complex labeling, cold-chain services, storage and global distribution. Together, the two facilities can help biotechnology companies move candidates from early development through clinical supply, helping sponsors reduce complexity and maintain momentum as programs advance towards clinical trials, all within San Diego’s growing life sciences community.
Q3 was not a buyers’ strike
The third quarter is usually read as the market’s verdict, and the reading is usually too simple. DealForma counted 100 R&D partnerships in Q3 against 97 in Q2, while announced value fell 39.8% to $50.3 billion. Upfront cash and equity fell only 12.5%, to $3.5 billion, and the median upfront rose 33% to $40 million. Deals above $100 million in value dropped from ten to eight, their combined worth from $35.8 billion to $16.8 billion.
That is not withdrawal – it is a refusal to underwrite very large, very early platforms combined with a willingness to pay more per asset for the ones buyers can see. J.P. Morgan’s tally, using DealForma data, puts disclosed upfronts at 6% of $221 billion of licensing value through September. Most of the headline is contingent. When committed cash falls far less than announced value, the headline is measuring optimism rather than capital at risk.
The shift is rational for the buyer and corrosive for the venture model that produced the assets, because fund returns depend on upfront cash rather than milestones that may land in 2033.
The discount rate does the work
The macro backdrop makes the timing harder to defend. The XBI returned about 29% through September, then stalled: it peaked near $170 in August and closed at $155.98 on 30 October, with the third quarter slightly negative. Clinical setbacks did part of the damage. Ultragenyx lost more than 40% of its value twice on trial failures, and Novartis and Ionis saw pelacarsen fail an endpoint William Blair had valued at $6 billion of peak US sales. Higher rates did the rest, because a basis point of discount rate takes more from a 2031 cash flow than from a 2027 one. The assets that justify the premium are the assets most damaged by the rate path.
Strategic buyers are less rate-sensitive than financial ones, since their alternative is permanent revenue loss. They are not immune. Balance-sheet capacity is finite and buybacks compete for the same dollars. Tolerance for dilution has limits the midterm elections will test.
The question for 2031 may not be whether these deals made sense when they were signed. Most did, given that the alternative was far worse. It is whether the industry has converted a one-time patent cliff into a permanent acquisition premium, a structural cost of replacing revenue that never reverts. If it holds, the 2026 vintage will be remembered less for its prices than for setting the clearing level at which innovation changes hands. The cliff, on that reading, is not an event the industry passes through. It is a rate it now pays.
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Exclusive: Accessible Science Isn’t Always Understood Science
Short-form media has changed how people engage with online content. While most of the content is benign, these formats have become a vehicle for spreading scientific information. In this way, science has become more accessible; people no longer need to seek out technically complex and jargon-filled articles. They can simply open an app and have the information explained clearly for a non-specialist.
However, those disseminating this information are often, too, non-specialists. We are now frequently seeing the proliferation of scientific “facts” that are in actuality half-truths or outright false. That’s not to say that a non-specialist cannot comprehend scientific research; they absolutely can. However, while a neuroscience graduate may naturally critique elements of a scientific article, such as study design, translation to humans, funding sources, or chosen experimental controls, such peer review may not come naturally to those without a scientific background.
Take fenbendazole as an example. These veterinary anti-parasitic drugs, i.e., dog worming tablets, have become popular in online discussions, where people claim the drugs can treat cancer. When we look into the science, there are in vitro and in vivo studies that show fenbendazole can disrupt cancer cell growth1. So, the use of fenbendazole does have a scientific basis. But this is where scientific literacy is crucial; there is no clinical evidence in humans that this drug works for cancer.
As a scientist, I know that only 5% of therapeutic interventions tested in animals successfully achieve final regulatory approval for humans2. This isn’t a fact that may be known to the general public, who may easily read beneficial results in animals as positive. From such a position of incomplete information, scientific disinformation can spread.
Accessibility in science is important. Non-specialists deserve to understand and engage with science just as much as those with PhDs. But making science accessible is not the same as making it understandable. We must equip people with the tools to not just explore science, but understand its context, critically evaluate its design, and appropriately interpret its significance.
References
1. Nguyen J, Nguyen TQ, Han B, Hoang BX. Oral Fenbendazole for Cancer Therapy in Humans and Animals. Anticancer Res. 2024;44(9):3725-3735. doi:10.21873/anticanres.17197
2. Ineichen BV, Furrer E, Grüninger SL, Zürrer WE, Macleod MR. Analysis of animal-to-human translation shows that only 5% of animal-tested therapeutic interventions obtain regulatory approval for human applications. PLOS Biol. 2024;22(6):e3002667. doi:10.1371/journal.pbio.3002667
