John, Paul and Eroom

Catching up on “In the Pipeline” over spring break, I followed Derek Lowe’s pointer to yet another article in Nature Drug Discovery attempting to explain the declining productivity of drug R&D.

Rather than start from scratch, the team from Sanford C. Bernstein (an investment research company) summarize many of the previous diagnoses, noting the lack of incommensurability of the earlier proscriptions:

They include: the FDA’s ‘Critical Path Initiative’; a series of prescient papers by Horrobin, arguing that bottom-up science has been a disappointing distraction; an article by Ruffolo focused mainly on regulatory and organizational barriers; a history of the rise and fall of medical innovation in the twentieth century by Le Fanu; an analysis of the organizational challenges in biotechnology innovation by Pisano†; critiques by Young and by Hopkins et al., of the view that high-affinity binding of a single target by a lead compound is the best place from which to start the R&D process; an analysis by Pammolli et al., looking at changes in the mix of projects in ‘easy’ versus ‘difficult’ therapeutic areas; some broad-ranging work by Munos; as well as a handful of other publications.

There is also a problem of scope. If we compare the analyses from the FDA, Garnier, Horrobin, Ruffolo, Le Fanu, Pisano, Young and Pammolli et al., there is limited overlap. In many cases, the different sources blame none of the same countervailing forces. This suggests that a more integrated explanation is required.

† The book by Gary Pisano (2006) of Harvard Business School is a favorite here at KGI — I think I’ve seen it on more shelves than any other.

The main point of the article is to introduce a so-called “Eroom’s Law”, in which the number of new FDA-approved therapies (per $ million R&D) fell by half every nine years. It is intended to be the inverse of the Moore’s Law that has driven IT improvements or more than 40 years, in which the number of transistors per chip doubles every two years.

The Eroom’s Law was the major new gimmick of the Scannell et al article, and Lowe devoted an entire posting to it. I’m far from convinced: unlike Gordon Moore, there’s no real explanation of the causal mechanisms — even post hoc. Instead, it seems more like Eroom’s Syndrome — a collection of symptoms with multiple root causes.

The four (possibly five) causes that the authors identified:

• “the ‘better than the Beatles’ problem;
• the ‘cautious regulator’ problem;
• the ‘throw money at it’ tendency; and the
• ‘basic research–brute force’ bias.

There may also be some contribution from a fifth factor, termed ‘the low-hanging fruit’ problem.”

Some of these are dog-bites-man stories. The excess of caution by the FDA is like the weather — everyone complains about it but nobody does anything about it. Even though they mostly dismiss it, the “low-hanging fruit” argument is also an old one.

Interestingly, they argue that “throw money” is easily reversed: if, in effect, big pharma R&D shops had become fat, dumb and happy (my term not theirs), a prescription of lean and mean can be applied with little impact on output. If true, this would certainly help reverse the trend.

It’s the two remaining ideas that to me seemed the novel ones.

Better than the Beatles

This was I thought the most clever argument — both in its identification, and in its framing:

Imagine how hard it would be to achieve commercial success with new pop songs if any new song had to be better than the Beatles, if the entire Beatles catalogue was available for free, and if people did not get bored with old Beatles records. We suggest something similar applies to the discovery and development of new drugs. Yesterday's blockbuster is today's generic. An ever-improving back catalogue of approved medicines increases the complexity of the development process for new drugs, and raises the evidential hurdles for approval, adoption and reimbursement. It deters R&D in some areas, crowds R&D activity into hard-to-treat diseases and reduces the economic value of as-yet-undiscovered drugs. The problem is progressive and intractable.

As they point out, content-based IP loses its novelty and so people seek out new things. After everyone saw Gone with the Wind, there was room for Casablanca, On The Waterfront, The Sound of Music, and a couple of Godfather movies. In resource based industries, the consumption of the old content (e.g. coal from a mine) makes the remaining holdings more valuable.

They point to a specific area — anti-ulcerants — as an example where drug discovery is held back by the back catalog. Two families of (now generic) solutions are already available, and while the third approach “would probably be safe and effective,” it is unlikely that any healthcare reimbursement system would pay for a new class of patented medicines, except for those rare cases not treated by the first two.

Their conclusion is even more depressing: “This general problem applies in diabetes, hypertension, cholesterol management and many other indications.” They also note that some of the decline of R&D productivity may be because pharma companies shifted from crowded (but high approval rate) therapeutic areas to less crowded (but lower approval rate) areas.

Basic Research/Brute Force

In this argument, the authors contend that the whole attempt to solve health problems through basic research and a molecular understanding of disease has been a disappointment, if not an outright failure. In addition to the molecular basis — and brute force analysis attempting to find new molecules — is also the assumption that the best way to solve a disease is to make a single molecule that binds to a single target.

Based on his industry experience, Lowe shows great sympathy for this argument

This gets us back to a topic that's come up around here several times: whether the entire target-based molecular-biology-driven style of drug discovery (which has been the norm since roughly the early 1980s) has been a dead end. Personally, I tend to think of it in terms of hubris and nemesis. We convinced ourselves that were were smarter than we really were.

I lack both the science and the industry experience to assess the validity of this complaint, except to note that this critique is obviously not universally shared. It would be nice to think that this article would start an honest conversation, but my guess is that too much money (private, government, university) has been invested in molecular approaches for this idea to gain traction until the evidence is undeniable.

Conclusions

The issue of declining R&D efficacy is a huge one for the industry, and also for investors in pharma companies big and small. The authors list a number of reasons why they are optimistic in the next five years:

Flat to declining R&D costs, as well as a bolus of oncology drugs, more orphan drugs and 'biosimilars as BLAs', might put an end to Eroom's Law at an industry level. Whether this improves things enough to provide decent financial returns on the industry's R&D investment is a different question. Financial markets don't think so. Industry executives do.

Their final idea is for each big pharma company to create a Chief Dead Drug Officer (CDDO), who is incentivized to analyze the failure of R&D investments and report the board and the public about the reasons beyond Eroom’s Law. A provocative idea — but I’m not holding my breath.

References

Gary Pisano, Science Business: The Promise, the Reality, and the Future of Biotech, Boston: Harvard Business School Press, 2006.

Jack W. Scannell, Alex Blanckley, Helen Boldon & Brian Warrington, “Diagnosing the decline in pharmaceutical R&D efficiency,” Nature Reviews Drug Discovery 11 (March 2012): 191-200. Doi: 10.1038/nrd3681

How "Perfecting Medicine" will reshape the life science industry

At his talk this morning at KGI, Dr. Paul Billings argued that the vision for 21st century medicine needs to go beyond personalized medicine to “perfected medicine.”

The chief medical officer of Life Technologies, Billings started by reviewing where medicine was for centuries, and the progress that was made in the 20th century to improve upon that. He illustrated the 19th century state of the art with “The Doctor,” pained by Sir Luke Fildes after his son died in 1877 of tuberculosis. As an MD working for a life science company, Billings argued for a world that both keeps the personal attention of the caring physician while providing doctors with the best science for preventing and curing illness.

Unfortunately, while the 20th century brought many lifesaving therapeutics, the approach towards selecting and dispensing these therapies is remains one of “trial and error.” He showed a chart that failure rates for post-heart attack medications and mammographs of are more than 10%, while airline flights have a fatality rate of less than 1 PPM. Billings lamented that having hospitals "more like bagging handling is not what I'd like to see about healthcare delivery".

From the perspective of systems biology, a solution proposed for this existing paradigm has been proposed through approach of “4P medicine”: predictive, personalized, preventive, participatory.

However, Billings argues that we need to go further: "We want 1P medicine: perfected medicine.” Such an approach would rebuild healthcare from the ground up, to tailor solutions for individuals make choices based on scientific understanding. To support this vision, he cited the 2008 report of a presidential advisory board, “Priorities for Personalized Medicine.”

Billings wants sequencing to be more mainstream in healthcare, the way that imaging and other modalities are: "I don't want to be in the basement of hospitals any more — I want it to be on the ground floor”. (Of course, Billings works for one of the major vendors of sequencing products.)

Based on the research of Jonathan Rothberg (of Ion Torrent), DNA sequencing has shifted optical-based sequencing to semiconductor-based sequencing. For the audience, Billings waved around a DNA sequencer chip the size of a postage stamp. A $50k sequencer that in 2013 will soon do a sequence in 2 hours for a marginal cost of $1,000.

Billing predicted that the performance improvements by this CMOS-based sequencing will continue indefinitely — as did microprocessors — and allowing sequencing to shift from sequencing specific genes to whole genome sequencing. (Of course, there is the nagging problem of the cost of the data analysis to make sense of that data).

But, Billings argues, the benefits are not just limited to price. Instead of sequencing in batches and taking weeks, the results will come back in hours. Various conditions — such as a heart attack, TV or sepsis — need answers much much sooner than weeks to make the appropriate clinical intervention. Cheap sequencing will also allow applying therapeutics — especially expensive biologic compounds — only for those patients who will benefit.

In an era of limited resources for healthcare, there’s a need to also demonstrate this technology can (as in the case of Sir Luke’s personal tragedy) to deliver "outcomes that matter". He provided the example of the May 2011 fatal E. coli outbreak in Germany, in which a DNA sequencer was used to characterize the bacteria and develop a scalable screening process in less than a week. (See the PLOSone article for more info.)

This is going to be highly disruptive to existing business models. For example, when Billings put up a slide showing how a series of existing tests might eventually be replaced by a singled sequencing test, I immediately thought about all the buggy whip makers that are going to put out of business.

I raised this question during the subsequent panel discussion, a discussion that included Billings, Board of Trustees chairman (and health care VC) Bob Curry, Advisory Council member Russ Teagarden of Medco, and Professor Jim Osborne (formerly of Beckman Coulter).

To Billings, I suggested that if firm can’t sell diagnostic tests, they’ll try to extract patent royalties from their gene patents from companies that are selling the all-in-one tests. I’ve lived this world in my previous research, as it’s S.O.P. for cellphones and other parts of the telecom industry. Diagnosing human disease with a single whole genome test will bring patent stacking that will go far beyond any pocket-sized device (at least until the patents expire).

Billings noted that companies who have such gene patents are aware of the problem (although he doesn’t know or wouldn’t say what they think the solution is). As an example, he mentioned the controversial diagnostics company Myriad Genetics, whose sequencing of cancer genes has done so much to improve our understanding and treatment of breast cancer. (At lunchtime, we discussed whether the gene patenting industry needed an Apple to impose an iTunes-like pricing model, or whether it could organize an ASCAP or BMI to collect and allocate royalties among rightsholders).

Ready or not, the existing businesses are going to be disrupted in a classic Schumpeterian example of “creative destruction.“

On the other hand, based on past experience it seems unlikely that the US regulatory bureaucracy — or the rest of the healthcare payment or delivery system — are ready to be disrupted. As Advisory Council member Weaver Gaines put it, it takes 20 years for a new medical approach to be adopted — or even a generation for the old guard to die off.

Bob Curry was a little more optimistic about FDA cooperation due to the pressure it will face from regulatory arbitrage: if the US won’t approve new approaches, then other countries — like Denmark or Switzerland — will.

However, adoption of a new paradigm also requires acceptance by healthcare providers and also healthcare payers. Both Teagarden and Curry noted the delays (and often unrealistic evidentiary expectations) required to get payers to agree to reimburse for diagnostics. The panel agreed that adoption will increasingly require that new approaches not only save lives, but also save money.

The panel also discussed the challenge of getting new practice accurately explained and adopted by frontline health providers. Curry pointed to the ongoing trend for doctors to leave their individual practices and affiliate with regional hospital groups, which he predicted would make it easier both to reach and to incentivize these M.Ds. to adopt the new scientific paradigm.

Photo credit: “The Doctor” (1891) by Sir Luke Fildes, courtesy of Wikimedia Commons.

KGI's related diversification: PharmD in pharmacogenomics

Like corporations, universities (and other nonprofits) often pursue related diversification to leverage existing assets and achieve economies of scope. Today the Keck Graduate Institute of Applied Life Sciences announced its planned partnership with another LA-area private college, Chapman University, to launch a new graduate pharmacy school in Fall 2014.

I don’t know as much about Chapman, but at KGI it’s a chance to leverage our understanding of human healthcare, business, our labs, our researcher and our industry connections (among other things). It’s an exciting opportunity for KGI to do more to fulfill our mission to translate into practice the power and potential of the life sciences for the benefit of society.

Here is the official announcement:

KGI Partners with Chapman University for New School of BioPharmacy
Innovative School of BioPharmacy, set to open in 2014 in collaboration with Chapman University, rethinks the role of the 21st century pharmacist.

February 28, 2012

The presidents of Chapman University and Keck Graduate Institute (KGI) have announced plans to establish a joint School of BioPharmacy, initially launching at the KGI campus in Claremont, California. The school will focus on preparing graduate pharmacists for professions in biotechnology and the pharmaceutical industry as well as for modern pharmacy practice. The school will open in fall 2014, pending national accreditation.

"This collaboration is a remarkable opportunity," said James L. Doti, president of Chapman University. "It builds on KGI's innovative professional master's and postdoctoral programs and close biotechnology and pharmaceutical industry ties, and takes advantage of Chapman's strengths in computational sciences and entrepreneurship."

The new Chapman-KGI School of Biopharmacy will reorient the Doctor of Pharmacy degree toward crucial developments in pharmacogenomics. The program will reflect rapidly developing changes in the field, including the growth of personalized medicine, the delivery of biomolecules, changes in drug and device development, and progress in clinical trials and team-based operating environments. The curriculum is planned to equip graduates to become licensed pharmacists with sophisticated knowledge of the applied life sciences for modern health care practice, and experience with therapeutics, therapy management, and regulatory affairs to shape discovery in biotechnology and pharmaceutical companies. The school's vision reflects the ways in which pharmacists are increasingly asked to shoulder a primary role in matching drugs and therapies to a particular patient or strain of illness, as well as translating their clinical expertise into drug discovery in industry.

"The Chapman-KGI School of BioPharmacy will embrace this change in the profession," said Sheldon Schuster, president and professor of biochemistry at Keck Graduate Institute. "Current advances in genomics and the growing convergence of therapeutics, diagnostics and medical devices are creating new opportunities for pharmacists in the life-sciences industry and modern clinical practice. This new school will help prepare highly qualified individuals to take advantage of those opportunities."

The two universities have begun a national search for an entrepreneurial founding dean with experience in pharmacy education and industry. The first classes for the Chapman-KGI School of BioPharmacy will be held on the KGI campus in Claremont, while Chapman pursues construction of a 120,000-square-foot Science Center in the City of Orange. The new Science Center will house Chapman's portion of the School of BioPharmacy and the university's Schmid College of Science and Technology. KGI is planning a major capital campaign that will expand facilities for ongoing teaching, research programs and the Chapman-KGI School of BioPharmacy, which is expected to grow to a full enrollment of 320 students in the four-year PharmD program.

Keck Graduate Institute of Applied Life Sciences is a member of The Claremont Colleges. Along with other highly regarded graduate programs, KGI offers an innovative professional master's program that integrates rigorous science with business, clinical trial management and regulatory affairs. KGI represents The Claremont Colleges' first entry into graduate-level, application-based scientific research and education. It remains the only graduate institute in the country with this sole focus.

Tracing its history to 1861, Chapman University is one of California's oldest and most respected private institutions. The university is known for its commitment to deeply engaged learning, providing its 7,000 students with a remarkable educational environment from the moment they step onto campus. Chapman offers bachelor degrees in 47 majors, and graduate or terminal degrees in law, education, film, business, economics, the humanities and science.

New markets, new models for old pharma

One of the most knowledgeable healthcare economists, Scott Gottlieb (M.D.) of AEI has weighed in on big pharma’s dying business models. While no Pollyanna, Dr. Gottlieb finds the glass surprisingly half full — while (as he often does) identifying areas where the FDA is a barrier to innovation.

Writing at the WSJ (and the AEI website) Dr. Gottlieb summarizes the success and limitations of the old business model. The former was represented by Lipitor, which earned $13b/year at its peak. The latter “mostly involved screening millions of random compounds against a molecular target.”

However, he argues that big pharma is learning the proper lessons from the end of their traditional models.

Lesson number one is to use a biological (rather than scattershot chemical) approach:

They took advantage of new science that allowed drug discovery to become a much more targeted endeavor—focused around precise information about the molecular fingerprints of disease.

In this kind of "rational" discovery process, size matters less than precision. Drugs are designed around the individual biological receptor they're targeting rather than being screened out of a library of random compounds. Deploying bigger tools no longer counts as much as having concentrated expertise in the particular pathway that causes a disease.

The next lesson has to do with the organization of innovation:

[B]ig bureaucracies were hostile to the kind of risk-taking and scientific focus needed to do good research. At most companies, the majority of new drugs are discovered by a handful of scientists with repeated successes. There's something unanticipated in drug research that can't be industrialized.

In recent years, pharmaceutical companies started to carve up their sprawling research enterprises into smaller, more focused teams. The right size of a research team is now said to be 20-40 people. To get at new science early, drug makers rely on collaborations with academic research teams and licensing deals with smaller biotech outfits.

(We call the latter process “open innovation”).

The final point is one that been often remarked over the past few years. Drug companies shouldn’t try for incremental improvements for conditions that have been treated in the past 30 years, but instead try to address other conditions (like Alzheimer’s) which lack an effective theraphy.

As a success story for the new, improved big pharma, Dr. Gottlieb cites Pfizer bringing Crizotinib (a lung cancer therapy) to market in 6 years instead of 10-15. Still, he points to the role of the FDA as increasing the costs and delaying the availability of therapies, by increasing the length of trials and the cost per patient.

While various regulatory reforms have been proposed, “none has been meaningfully advanced.” He concludes:

Drug makers are renewing themselves by realizing that their research success wasn't tied to the scale of those endeavors, but their precision. Today, the continuation of these scientific advances depends on their regulators reaching a similar understanding.

HIPAA vs. preventive medicine

This is finals week for the fall semester at KGI. Teams from our first year Medical Devices course (ALS 320 in KGI-speak) are presenting their technical and business analysis of why the world needs a specific new medical diagnostic test.

As an aside, one of the teams made a persuasive case the compliance and liability issues associated with HIPAA will deter (and possibly prevent) the adoption of new technologies for preventative medicine.

The specific context was monitoring of diabetics, of the most expensive chronic conditions in the United States. (I didn’t write down the statistics, but this article estimates diabetes directly accounts for 10% of healthcare spending, or more than $90 billion/year).

The students suggested that the health impacts of diabetes could be reduced through computerized monitoring of various symptoms — not just glucose, but hypertension and other effects as well. (Disclaimer: I am not an MD not do I play one on TV). One product they pointed to was the Withings blood pressure monitor, which uploads data to your iPhone, iPad or iPod Touch and can be manually emailed to your doctor.

The idea is that if data were gathered and stored in the EHR, then it would be possible to catch problems well before a regularly scheduled test. Getting these widely deployed would probably take a HMO (like Kaiser) or hospital group that is caring for diabetics on a long term basis.

However, the security implications of such as system are daunting. Yes, a firm or nonprofit needs to be diligent in avoiding security breaches that compromise patient privacy. However, a data breach (of the source that seem routine nowadays) could lead to government fines or even a lawsuit.

A regulatory barrier like this could be a dealbreaker for efforts such as San Diego’s wireless health initiatives. This chilling effect seems a perfect example of the law of unintended consequences.

How to solve the problem? One way is that the Federal government can’t be fined or sued under HIPAA. Does this mean that these approaches for data monitoring to support preventative health have to wait until the Feds are innovative enough to try this approach? (Or private insurance is out of business and we all are covered by the Feds anyway?)

Charging towards a bleak future

Many reporters, analysts and other observers over the past decade have remarked on how the traditional big pharma business model has been running out of steam. Proposed solutions have included buying biotech companies (as Roche did) and forming generic divisions (as has Sanofi). Still, from outside, the (in)actions of big pharma resemble the controlled flight into terrain of other IP companies.

Last week, two consultants from Booz & Company published their own analysis of the problems in the Booz house journal, strategy+business.

Alex Kandybin and Vessela Genova deduced the strategic choices of 10 major pharma companies (Abbott, AstraZeneca, Bayer, GlaxoSmithKline, Johnson & Johnson, Merck, Novartis, Pfizer, Roche, Sanofi) through their acquisitions and divestitures from 2004-2010. Nine of the 10 have bet on biologics, five on OTC, four on generics and one (Sanofi) on animal health.

They draw an analogy to the choices of the computer industry:

Most industries go through periods of both deterministic and stochastic development. For instance, the computer industry in the 1960s and ’70s had all the characteristics of a deterministic process. IBM, Burroughs, Cray, and others pursued similar strategies, selling giant data processing machines known as main- frames. The personal computer changed the dynamics of the industry, triggering a turbulent stochastic period. It became impossible to predict where the computer industry was going, and in the early 1980s the incumbent players’ strategies diverged significantly.

This is, alas, an inaccurate revisionist view of the industry: in the 1980s, it was quite clear that the PC was democratizing computers and that standardized microprocessors enabled market entry and reduced margins.

More importantly, the computer industry of the 1970s has significant a priori heterogeneity: it was not for nothing that people referred to IBM and the Seven Dwarfs (or IBM and the BUNCH). Cray was in a very narrow and dangerous niche diametrically opposed to commoditization trends and desperately dependent on Cold War spending.

One place where the authors clearly have it right is that big pharma is fleeing from the highest margins in the life sciences (and among the highest margins anywhere) towards average or sub-average margins. The operating margins of pharmaceuticals is 29%, vs. 12% for generics, 8% for services and 2% for drug wholesaling.

This is utterly consistent with the 15-year-old observations of Clay Christensen: lower cost solutions eventually supplant higher cost solutions, destroying margins. Or, as my former colleagues Jason Dedrick and Ken Kraemer showed in their 1998 book, IBM’s shift from hardware to services dramatically grew revenues but cut margins.

What to do? The authors offer fairly generic (i.e. undifferentiated) advice: firms should embrace change, consider multiple scenarios, and assess the firm’s unique capabilities.

In the end, the old model of one-size-fits-all drug is breaking down. What will replace it? One prediction is personalized, genomic-based medicine. But even if that’s true, many uncertainties remain, including how quickly that future will get here and which part of the value chain will be the most unique and thus valuable.

Saving lives through data mining

At the Open Science Summit (@OpenScienceSum) this weekend, a major theme was applying the lessons of open source software (and other means of social production) to human health and drug discovery. WIthin this, my interest was (as in OSS a decade ago) new firms were organizing to take advantage of these opportunities.

Several of the sessions focused on creating (or simulating) a data commons in which the collective findings of dozens (or thousands) of researchers could be aggregated to allow researchers to spot patterns that would be otherwise unobservable in a single trial.

One example of that was Marty Tenenbaum, founder of Cancer Commons (cancercommons.org), which as its name suggests is seeking to be the clearinghouse for cancer data. Some of this is published research, but the goal is gather and share its own data, as the mission statement articulates:

Cancer Commons is a new patient-centric paradigm for translational medicine, in which every patient receives personalized therapy based upon the best available science, and researchers continuously test and refine their models of cancer biology and therapeutics based on the resulting clinical responses.

Representatives of two for-profit companies talked about their efforts to aggregate the terrabytes (or petabytes) of medical data to allow researchers to mine existing data for new insights.

NextBio aggregates public genomic data and allows (paid) customers to combine that with proprietary data to identify possible relationships. The freemium model gets academic researchers started for free — presumably so that grad students get hooked as grad students and take that to their new employers.

DNAnexus is preparing for the shift from a world with one sequenced human genome to millions, and the vast amounts of storage (and computing power) that will be necessary in only a few years to handle this avalanche of data. Earlier this month it teamed with Google (one of its investors) to host the Sequence Read Archive on the cloud for all researchers to have access.

Meanwhile, the consumer genomics pioneer 23 and Me has both genotype and phenotype data for 125,000 people: while the scale is much smaller, the uniformity is greater. One audience member called on the company to use the data for medicinal research, given the notorious lack of comparability for most phenotype data.

Data mining for medical research reminds me of data mining for social science research. Spending money to gather data is normally an entry barrier in the social sciences, so if there’s a free source of data (patents, open source repositories) this attracts a flood of new researchers and studies.

So if researchers don’t have to gather their own data to hypothesize — or evaluate — potential treatments, this is going to drastically cut the cost of entry into pharmaceutical research. We’ve seen this story before — again in open source — there will be a few big winners that move quickly and decisively to exploit this new technology, while other incumbents will find their traditional business models fail as barriers to entry crumble.

Of course, there are other entry barriers for pharma besides research — trials, funding, distribution, brand. Pressure will come on these fronts too.

An executive of Celtic Therapeutics talked about approaches to cut the cost of clinical trials,by using crowdsourcing for the design of trials and telemedicine for more efficiently processing patients. He claimed the costs of trials could be cut from $15-20 million to $1-2 million. Normally I’d take such dramatic improvements with a pound of salt, but the company claims as an advisor Karim Lakhani — one of the world’s leading crowdsourcing experts (and a personal friend).