The business of science
An interview with Gary Pisano, Harry E. Figgie, Jr. Professor of Business Administration, and Senior Associate Dean for Faculty Development
In the time since the publication of Science Business, the Precision Medicine and Moon Shot Initiatives were funded to address the clinical observation that many cancer drugs are effective only in a percentage of the general patient population. Two paths emerged from these Initiatives: one path was to identify those who respond to drugs and determine why (responders); the other path was to support programs that develop drugs for targeted patient populations in a therapeutic area.
Also during this time, drug companies were staring at two realities: first, they could no longer depend on the now-fading blockbuster model (predicated on the “generic” patient) and, second, they were edging toward the so-called patent cliff (loss of exclusivity of blockbusters). Facing these prospects squarely, and with largely dry pipelines, many pharma turned to precision medicine as a viable drug development option.
We wanted to catch up with Professor Pisano with questions as to whether pharma and biotech applied the learnings from twenty years ago to precision medicine today.
Q1. Given that only a percent of any population responds to a drug in general, pharma is developing strategies to optimize its returns in smaller but targeted markets (e.g., a certain type of lung cancer). What are your thoughts on viable business models for pharma in such a stratified marketplace, especially one in which several companies may each have distinct slices of the same market?
A. As long as pharma companies can command high prices for more targeted drugs, not a lot changes in the business model. I think there used to be a view that targeted therapies which addressed smaller markets would be a death knell for big pharma. But, that assumed prices per patient per treatment would stay the same. That hasn’t happened as we all know. Now whether those prices are sustainable politically and economically is another matter.
Q2. Innovation is key for biotech and pharma to develop a patented franchise. Most large pharma companies have turned to biotechs for licensing or outright acquisition deals. Pharma also now partners with many other companies for goods and services, but also with innovative diagnostics companies for tests to identify responders. What are your thoughts on this model to partner for innovation as a primary source from which to develop their own branded drug products?
A. Partner and in-licensing is a great complement to a strong internal capability for discovery and development, it’s not a substitute. You can’t buy innovation or at least you can’t buy it and create value. As good drug candidates become scarcer, the price of those assets just increases. They are like free agents in sports. Sure, you can land the deal, but you paid an amount where all the value was captured by the seller.
Q.3 As noted in Q2, diagnostics companies have focused on discovering biomarkers to identify responders. In this sense, diagnostics companies have yoked their products and pipelines to pharma’s existing and emerging precision medicines, which also ties diagnostics companies to the success of these drugs. What strategies do you foresee for these companies to maximize return and mitigate risks (e.g., late-stage failures, failure to capture market share)?
A. The only way this strategy is viable is if you can charge a price premium for the diagnostic that reflects the new risk profile. This will depend on the number of competitors in the space. If you have multiple diagnostic companies capable of developing a biomarker for the same drug, then competition will drive the price premium down on the diagnostic. If a diagnostic company has a strong IP portfolio, or some other barrier to innovation, it can possibly do a deal with the pharma company that pays them up front to compensate for the risk.
Q4. Compared with traditional blockbusters, precision medicines and diagnostics are priced higher, reflecting more costly development programs and addressing (relatively) smaller markets to recoup their costs and drive profits. Since not all these drugs have coverage from CMS and, despite insurance coverage or direct support from private foundations, many patients simply cannot afford these drugs.
- What coverage models would you recommend (that do not bankrupt the system)?
- For drugs with $1M plus valuations, could you imagine new pricing models – e.g., each patient pays based on means to a funding pool for a given drug? Or otherwise?
- Can you comment on orphan drug models for rare diseases – large development costs for small populations leading to extremely costly therapies?
A. A few thoughts. Long-term, the only viable economic solution is to find ways to reduce the costs of development. Presumably, all the technologies that allow us to target patients should also allow smaller and maybe even faster (and thus cheaper) clinical trials. But, prices will only fall if there is also competition among players as well. I do like the idea of paying for the performance of a drug, but this can be tough to figure out. One idea I have kicked around is whether you can create some investment funds that essentially pay-out based on long-term clinical performance of a drug. So rather than paying (as we do now) for the promise that a drug will work well, we pay after the fact for how it’s done. But this ex post payment scheme is complex because it means you have risks to manage as well as cash flow. That’s where possibly we could get third-party investors involved who want to take those risks and possibly benefit from the returns. One might even be able to securitize the investment pools.
Q5. Given the parallel advances in biomarkers and diagnostics, the notion of preventive medicine by screening early and often in at-risk populations is now feasible. Still, most results are likely to be negative. A relatively small number of positives, however, may be found, some potentially life-saving (e.g., early detection of pancreatic or ovarian cancers). Are you aware of any precedence for business models that could justify such a program?
A. We presumably save lots of people’s lives by investing in maintenance of aircraft. We forecast the weather for lots of reasons, but good weather forecasts also save lives. Cars are loaded with safety devices to prevent accidents and injury. So, yes, I would say we figured out this problem.
Q6. Another area of innovation is using AI in pharma (e.g., see respective releases for Novartis and Microsoft). AI benefits from the deep, rich data sets generated from a large cohort of data sources, from research to post-marketing surveillance. Several areas for applying AI in medicine have been considered, from basic research to managing large- scale clinical trials to providing options to doctors and patients. In this regard, will AI prove to have an impact in precision medicine? If so, where might we see AI having the biggest impact in this space?
A. AI will have an impact everywhere, of course, but I think it will fundamentally change the way we do biology research, drug discovery, and drug development. Yes, there will be an impact. It might, as these things tend to go, take longer to see the impact, but we will see it.
Q7. Final question – going back to the beginning of these questions, have pharma and biotech companies learned their lessons from twenty years ago (the life cycle of the industry)?
A. I think so. Industries evolve through trial-and- error. All newer industries go through periods of heavy learning. As I wrote more than 20 years ago, biotech was a bit of an “experiment” in the fusion of science and business. That was all pretty new and we had to figure out a lot about the right funding arrangements, business models, management approaches, etc. I think a massive amount has been learned since then.
Gary Pisano is the Harry E. Figgie Professor of Business Administration and Senior Associate Dean of Faculty Development at the Harvard Business School. He has been on the faculty since 1988. Pisano is an expert in the fields of technology and operations strategy, the management of innovation, and competitive strategy. His research and consulting experience span a range of industries including aerospace, biotechnology and pharmaceuticals, specialty chemicals, health care, nutrition, computers, software, telecommunications, and semiconductors. He is the author of several books, including “Science Business: The Promise, the Reality, and the Future of Biotech” on lessons learned from failed biotech launches in the 1990’s-2000’s.