Megafund versus Megalosaurus: Funding Drug Development

This new 10-minute TEDMED talk is getting quite a bit of attention:


 (if embedded video does not work, try the TED site itself.)

In it, Roger Stein claims to have created an approach to advancing drugs through clinical trials that will "fundamentally change the way research for cancer and lots of other things gets done".

Because the costs of bringing a drug to market are so high, time from discovery to marketing is so long, and the chances of success of any individual drug are so grim, betting on any individual drug is foolish, according to Stein. Instead, risks for a large number of potential assets should be pooled, with the eventual winners paying for the losers.

To do this, Stein proposes what he calls a "megafund" - a large collection of assets (candidate therapies). Through some modeling and simulations, Stein suggests some of the qualities of an ideal megafund: it would need in the neighborhood of $3-15 billion to acquire and manage 80-150 drugs. A fund of this size and with these assets would be able to provide an equity yield of about 12%, which would be "right in the investment sweet spot of pension funds and 401(k) plans".

Here's what I find striking about those numbers: let's compare Stein's Megafund to everyone's favorite Megalosaurus, the old-fashioned Big Pharma dinosaur sometimes known as Pfizer:

	Megafund (Stein)	Megalosaurus (Pfizer)
Funding	$3-15 billion	$9 billion estimated 2013 R&D spend
Assets	80-150	81 (in pipeline, plus many more in preclinical)
Return on Equity	12% (estimated)	9.2% (last 10 years) to 13.2% (last 5)

Since Pfizer's a dinosaur, it can't possibly compete with
the sleek, modern Megafund, right? Right?

These numbers look remarkably similar. Pfizer - and a number of its peers - are spending Megafund-sized budget each year to shepherd through a Megafund-sized number of compounds. (Note many of Pfizer's peers have substantially fewer drugs in their published pipelines, but they own many times more compounds - the pipeline is just the drugs what they've elected to file an IND on.)

What am I missing here? I understand that a fund is not a company, and there may be some benefits to decoupling asset management decisions from actual operations, but this won't be a tremendous gain, and would presumably be at least partially offset by increased transaction costs (Megafund has to source, contract, manage, and audit vendors to design and run all its trials, after all, and I don't know why I'd think it could do that any more cheaply than Big Pharma can). And having a giant drug pipeline's go/no go decisions made by "financial engineers" rather than pharma industry folks would seem like a scenario that's only really seen as an upgrade by the financial engineers themselves.

A tweet from V.S. Schulz pointed me to a post on Derek Lowe's In the Pipeline blog. which lead to a link to this paper by Stein and 2 others in Nature Biotechnology from a year and a half ago. The authors spend most of their time differentiating themselves from other structures in the technical, financial details rather than explaining why megafund would work better at finding new drugs. However, they definitely think this is qualitatively different from existing pharma companies, and offer a couple reasons. First,

[D]ebt financing can be structured to be more “patient” than private or public equity by specifying longer maturities; 10- to 20-year maturities are not atypical for corporate bonds. ... Such long horizons contrast sharply with the considerably shorter horizons of venture capitalists, and the even shorter quarterly earnings cycle and intra-daily price fluctuations faced by public companies.

I'm not sure where this line of though is coming from. Certainly all big pharma companies' plans extend decades into the future - there may be quarterly earnings reports to file, but that's a force exerted far more on sales and marketing teams than on drug development. The financing of pharmaceutical development is already extremely long term.

Even in the venture-backed world, Stein and team are wrong if they believe there is pervasive pressure to magically deliver drugs in record time. Investors and biotech management are both keenly aware of the tradeoffs between speed and regulatory success. Even this week's came-from-nowhere Cinderella story, Intercept Pharmaceuticals, was founded with venture money over a decade ago - these "longer maturities" are standard issue in biotech. We aren't making iPhone apps here, guys.

Second,

Although big pharma companies are central to the later stages of drug development and the marketing and distributing of approved drugs, they do not currently play as active a role at the riskier preclinical and early stages of development

Again, I'm unsure why this is supposed to be so. Of Pfizer's 81 pipeline compounds, 55 are in Phase 1 or 2 - a ratio that's pretty heavy on early, risky project, and that's not too different from industry as a whole. Pfizer does not publish data on the number of compounds it currently has undergoing preclinical testing, but there's no clear reason I can think of to assume it's a small number.

So, is Megafund truly a revolutionary idea, or is it basically a mathematical deck-chair-rearrangement for the "efficiencies of scale" behemoths we've already got?

[Image: the world's first known dino, Megalosaurus, via Wikipedia.]

The Coming of the MOOCT?

Big online studies, in search of millions of participants.

Back in September, I enrolled in the Heath eHeart Study - an entirely online research study tracking cardiac health. (Think Framingham Heart, cast wider and shallower - less intensive follow-up, but spread out to the entire country.)

[In the spirit of full disclosure, I should note that I haven’t completed any follow-up activities on the Heath eHeart website yet. Yes, I am officially part of the research adherence problem…]

Yesterday, I learned of the Quantified Diet Project, an entirely online/mobile app-supported randomized trial of 10 different weight loss regimens. The intervention is short - only 4 weeks - but that’s probably substantially longer than most New Year diets manage to last, and should be just long enough to detect some early differences among the approaches.

I have been excited about the potential for online medical research for quite some time. For me, the real beginning was when PatientsLikeMe published the results of their online lithium for ALS research study - as I wrote at the time, I have never been so enthused about a negative trial before or since.

That was two and a half years ago, and there hasn't been a ton of activity since then outside of PatientsLikeMe (who have expanded and formalized their activities in the Open Research Exchange). So I’m eager to hear how these two new studies go. There are some interesting similarities and differences:

• Both are university/private collaborations, and both (perhaps unsurprisingly) are rooted in California: Heath eHeart is jointly run by UCSF and the American Heart Association, while Quantified Diet is run by app developer Lift with scientific support from a (unidentified?) team at Berkeley.
• Both are pushing for a million or more participants, dwarfing even very large traditional studies by orders of magnitude.
• Health eHeart is entirely observational, and researchers will have the ability to request its data to test their own hypotheses, whereas Quantified Diet is a controlled, randomized trial.

Data entry screen on Health eHeart

I really like the user interface for Heath eHeart - it’s extremely simple, with a logical flow to the sections. It clearly appears to be designed for older participants, and the extensive data intake is subdivided into a large number of subsections, each of which can typically be completed in 2-4 minutes.

I have not enrolled into the Quantified Diet, but it appears to have a strong social media presence. You can follow the Twitter conversation through the #quantdiet hashtag. The semantic web and linked data guru Kerstin Forsberg has already posted about joining, and I hope to hear more from her and from clinical trial social media expert Rahlyn Gossen, who’s also joined.

To me, probably the most intriguing technical feature of the QuantDiet study is its “voluntary randomization” design. Participants can self-select into the diet of their choice, or can choose to be randomly assigned by the application. It will be interesting to see whether any differences emerge between the participants who chose a particular arm and those who were randomized into that arm - how much does a person’s preference matter?

In an earlier tweet I asked, “is this a MOOCT?” - short for Massive Open Online Clinical Trial. I don’t know if that’s the best name for it, and l’d love to hear other suggestions. By any other name, however, these are still great initiatives and I look forward to seeing them thrive in the coming years.

The implications for pharmaceutical and medical device companies is still unclear. Pfizer's jump into world of "virtual trials" was a major bust, and widely second-guessed. I believe there is definitely a role and a path forward here, and these big efforts may teach us a lot about how patients want to be engaged online.

Pfizer Shocker: Patient Recruitment is Hard

In what appears to be, oddly enough, an exclusive announcement to Pharmalot, Pfizer will be discontinuing its much-discussed “Trial in a box”—a clinical study run entirely from a patient’s home. Study drug and other supplies would be shipped directly to each patient, with consent, communication, and data collection happening entirely via the internet.

The trial piloted a number of innovations, including some novel and intriguing Patient Reported Outcome (PRO) tools.  Unfortunately, most of these will likely not have been given the benefit of a full test, as the trial was killed due to low patient enrollment.

The fact that a trial designed to enroll less than 300 patients couldn’t meet its enrollment goal is sobering enough, but in this case the pain is even greater due to the fact that the study was not limited to site databases and/or catchment areas.  In theory, anyone with overactive bladder in the entire United States was a potential participant. 

And yet, it didn’t work.  In a previous interview with Pharmalot, Pfizer’s Craig Lipset mentions a number of recruitment channels – he specifically cites Facebook, Google, Patients Like Me, and Inspire, along with other unspecified “online outreach” – that drove “thousands” of impressions and “many” registrations, but these did not amount to, apparently, even close to the required number of consented patients. 

Two major questions come to mind:

1.    How were patients “converted” into the study?  One of the more challenging aspects of patient recruitment is often getting research sites engaged in the process.  Many – perhaps most – patients are understandably on the fence about being in a trial, and the investigator and study coordinator play the single most critical role in helping each patient make their decision. You cannot simply replace their skill and experience with a website (or “multi-media informed consent module”). 

2.    Did they understand the patient funnel?  I am puzzled by the mention of “thousands of hits” to the website.  That may seem like a lot, if you’re not used to engaging patients online, but it’s actually not necessarily so. 

Jakob Nielsen's famous "Lurker Funnel"
seems worth mentioning here...

Despite some of the claims made by patient communities, it is perfectly reasonable to expect that less than 1% of visitors (even somewhat pre-qualified visitors) will end up consenting into the study.  If you’re going to rely on the internet as your sole means of recruitment, you should plan on needing closer to 100,000 visitors (and, critically: negotiate your spending accordingly). 

In the prior interview, Lipset says:

I think some of the staunch advocates for using online and social media for recruitment are still reticent to claim silver bullet status and not use conventional channels in parallel. Even the most aggressive and bullish social media advocates, generally, still acknowledge you’re going to do this in addition to, and not instead of more conventional channels.

This makes Pfizer’s exclusive reliance on these channels all the more puzzling.  If no one is advocating disintermediating the sites and using only social media, then why was this the strategy?

I am confident that someone will try again with this type of trial in the near future.  Hopefully, the Pfizer experience will spur them to invest in building a more rigorous recruitment strategy before they start.

[Update 6/20: Lipset weighed in via the comments section of the Pharmalot article above to clarify that other DTP aspects of the trial were tested and "worked VERY well".  I am not sure how to evaluate that clarification, given the fact that those aspects couldn't have been tested on a very large number of patients, but it is encouraging to hear that more positive experiences may have come out of the study.]