What does it mean to say a Phase 1 deal?
Phase 1 deals are relatively rare.
Deals are often done in a preclinical stage or in Phase 2. In the graph here, showing 2019 licensing deals where at least US rights were included, there are few Phase 1 deals.
If we look at what would be publicly available before the date of the deal announcement, we can see most of the "Phase 1 deals" had truly started Phase 1 before the date of the deal. Note, this is a very small number of deals as I removed generics, clinical trial collaborations (not really licenses), deals for a formulation only, and focused on deals where I could clearly identify the lead Phase 1 asset.
For 31% of the 2019 Phase 1 deals, Phase 1 results had been presented before the deal announcement date.
What data was available when results had been presented?
For those deals that had results, 1 was for preliminary activity, 1 had reported Phase 1b results, and 1 was started Phase 2 in a couple of months. 1 trial had reported safety data and 1 trial had been going for over a year, using up most of the expected duration of the trial. Others may have had results but there were not publicly released. So it seems many of the Phase 1 deals were likely to have some efficacy signal.
Value by stage and the problem with deal averages
We often get asked if we should wait for the next stage to partner. It is a great question without an easy answer. We often use deal averages as a piece of that answer (along with a discussion of risks and company strategy).
Here is a set of data from BioSci Advisors who do great work with deal terms.
But even with the access to deal terms from press releases and SEC filings and with BioSci Advisors, Freedom of Information Act requests, the averages should be looked at with a definite squint.
The deals within an average are not all the same!
Here Mark Edwards has highlighted one variable, the inclusion of co-development or no co-development. Co-development is probably generally not a driver of value but rather a term that bigger partners will put up with if the asset is exciting enough. With a really exciting Phase 1 asset, partner will accept the complexity that comes with cost and profit shares, or other forms, of co-development. (Note: even for the phrase co-development, not all co-development mean the same thing - some mean the right to contribute for a step up in royalties.)
Deal averages include a vast array of things.
Deal averages typically include all deals with assets whose highest stage is the one identified. (Caution: In GlobalData if you pull preclinical deals and don't filter them yourself, a search for preclinical deals returns all deals that include a preclinical asset but the selected deals might also include a phase 2 or a marketed asset whose value might really be the driver for deal value!). The deal averages include deals for generics at the same time as including very new novel molecules. The deal averages might include deals that include multiple assets or maybe only for a tiny territory versus global rights. Often the lists include things that are only a component of the "drug" such as formulation, a bit of IP, a manufacturing method, or in complex products such as CAR T immune cellular therapies - the scFv that forms the antigen-binding domain in a highly engineered cell with other expensive parts. And we know that deals with big pharma typically have higher values than with small biotechs or deals from universities. It is hard to compare your asset to the deal averages as the averages are a mix of so many different things. It is hard to know if you should continue to get the value of a deal at later stage. Is the average for Phase 2 deals reflective of the same mix as included in the average for Phase 1 deals?
So what can we do?
We can prune the lists to be a narrower set, generating averages that work better for our assets.
We should read deal descriptions and try to find meaningful comparable deals. Of course, that is fraught with difficulties too. Is it really comparable? Will partners see it the same way.
We can use medians versus averages to reduce the impact of the extreme deals.
We can report the minimum and maximum terms and the logic of inclusion and the numbers of deals included.
Clearly, we all still use deal averages but we should not be glib about what they mean.
2019 Licensing deals
In the January article https://app.robly.com/email/reports/1486810 about BiTEs and CAR-Ts, I may have disappointed some of our readers when I described NK cells as “accessory cells.” As an olive branch, I offer a little dive into the therapeutic potential of Natural Killer (NK) Cells. Other than having the "Coolest. Name. Ever"… why are there a growing number of therapeutic products designed to use them or target them? Are they evil villains, accessory sidekicks, or unsung heroes?
What does it mean to be a natural killer?
Natural killer cells are the snipers of the immune system. They are the cells designed to detect charlatans such as virally infected cells or tumor cells; cells that have mechanisms for evading detection by an immune response (T-cells). Natural Killer cells are innate effector lymphocytes meaning that they can kill indiscriminately*, as opposed to T-cells which can only kill cells they have been trained to recognize via their surface receptors (T-cell receptor, TCR). Rather, they kill by sensing abnormalities in cell surface expression - most notably the lack of HLA-1 on nucleated cells; a trick used by both viral-infected cells and tumor cells. Direct killing by NK cells is mediated by the release of cytolytic molecules such as perforins and granzymes, typically triggered by antibody-dependent cytotoxicity (ADCC) which involves the engagement of CD16A (Fc𝛾RIIIA) on the surface of NK cells. Furthermore, they can also induce target cells to initiate apoptosis.
*Some say NK cells should be called “serial” killers because they can kill different target cells. Cool.
NK cells in Immunotherapy
Beneficial activities of NK cells
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