June 9, 2016
By Mark Terry, BioSpace.com Breaking News Staff
SAN FRANCISCO -- At BIO 2016, Ben Bonifant, a partner with Triangle Insights and David Thomas, senior director of industry research and analysis at industry trade group BIO, joined together to present three recent studies on biotech industry innovation and clinical trial success rates.
Thomas presented “Clinical Development Success Rates 2006-2016” and “Emerging Therapeutic Company Investment and Deal Trends.”
Bonifant looked at “Closer to the Mark: Focusing on the Market Environment to Improve Pharmaceutical Revenue Forecasting.”
Both Bonifant and Thomas took some time to speak exclusively with BioSpace to give an analysis of how the three studies relate.
Improving Insight
Anyone who has tried to predict an industry trend—or been on the receiving end of a projection that missed its mark—will appreciate Bonifant’s study.
“What we did,” Bonifant says, “was look at the forecasts that existed one year ahead of the launch for 84 different products. We looked at what the forecasts would be for Year Three forecast sales in the U.S., then compared that to actual Year Three U.S. sales. And what we saw was that overall forecast accuracy was quite poor.”
Not at all unexpected. But what Bonifant and Triangle Insights Group did next was drill down on a product-by-product basis to better understand what drove those mistaken forecasts. What they found fell into eight categories:
• Unexpected changes in launch dates.
• Underestimates of the pricing potential of oncology assets.
• Specialty markets behave differently than general product launches.
• The Clash of the Titans—factors to consider when large companies launch similar products.
• Each new wave of innovation changes the standard of care, requiring taking into consideration those factors when making projections.
• Increasingly, specific market segments play a major role.
• When evaluating high-prevalence conditions treated by primary care physicians, order-of-entry plays a significant and complicated role, and
• Payers have power, so forecasters need to consider a payers’ appreciation of a product’s differentiation.
“The reality, the big headline,” says Bonifant, “is that actual product-level forecasts have not been good, but the portfolio forecast is pretty good—it’s within 2 percent. What I think is satisfying about that for people that have portfolios is, they had a range of scenarios that could play out for this product. But the reality isn’t that they didn’t have nuance in the input, but that they mistakenly chose the wrong variable. On balance, if you build a big enough portfolio, the forecasting is pretty good.”
For individuals who make financial investments or who shape a portfolio for large pharma, this is good news, say Bonifant. “But it can be devastating for people who do licensing or who are choosing to pay a royalty for an individual product.”
Drilling Down
Thomas’ two papers, rather than focusing on forecasts and projections, take a hard look at the nuts-and-bolts of investment trends, licensing deals and public offerings, as well as granularity regarding what types of drugs and drug categories are being approved or failing, and at what phase of clinical development.
The clinical trial study, says Thomas, “goes back 10 years and includes 10,000 clinical trial program transitions. It looks at three new ways to slice the success rate data.”
Seven primary points raised in terms of clinical development:
• The likelihood of approval (LOA) from Phase I for all candidates was 9.6 percent, which skewed to 11.9 percent for everything outside oncology.
• Rare disease programs that used specific biomarkers had a higher success rate.
• Chronic diseases with high patient populations didn’t have as high an LOA from Phase I as opposed to the overall dataset.
• Hematology had the highest LOA and oncology had the lowest.
• Within oncology, hematological cancers had twice the LOA than the LOA from Phase I in solid tumors.
• Oncology drugs had twice the success rate of first cycle approval than psychiatric drugs.
• The lowest success rate occurs in Phase II.
“The emerging companies’ paper focuses on small therapeutic-focused companies,” Thomas says. “In the biotech industry, over 95 percent of the companies are small, based on our definition of under a billion dollars in sales. The idea is to gain an understanding of where money is flowing in terms of investment into small companies, as well as licensing deal flow for R&D-stage programs across 14 major disease areas.”
The primary points of the “Emerging Therapeutic Company Investment and Deal Trends” report include:
• 2015 was the best year on record for U.S. biotech venture capital investments, with just under $7 billion raised.
• Series A financing doubled from 2014 to 2015.
• In 2015, 39 emerging U.S. therapeutic companies were listed on public exchanges.
• Follow-n public offerings by U.S. emerging companies set a record high last year, with $16.1 billion raised.
• Last year was also a record-breaking year for biotech licensing deals at the research-and-development stage.
• And not surprisingly, given 2015’s merger-and-acquisition activity, it was a record high for acquisition at research-and-development-stage companies, which produced $26.3 billion in upfront payments.
Coming Together
Thomas notes that 2015 was very unusual in the biopharma industry. “Last year was an incredible year for biotech. We set records on a number of different fronts and that’s very positive. The current year is showing signs it might be different. We’re coming off a year that was really exceptional and record-breaking on any of the four or five areas we break out.”
Still, even with all the information available to scientists and biopharma executives, it’s extremely difficult to bring a drug to market. Thomas says, “Only one of 10 makes it, which are very tough odds. Not only does only one out of 10 make it, but it’s a billion-plus dollars—a recent Tufts study says $2.6 billion—and over a decade to get a drug out there. Yet we have data showing the more you can target the patient and stratify your clinical trial and narrow where that drug is going to be effective, the more effective that company is going to be.”
The way these three topics come together is fairly clear—two of the papers present large amounts of historical data and analysis, while the third paper looked at trends that emerged from another set of historical data in the life sciences industry, and how they may apply to future forecasts.
“I think they match up wonderfully,” says Bonifant. “Part of it is David’s papers, one’s about probability of success and that’s a measure of providing information for the quantification of uncertainty. My paper provides some information about the quantification of uncertainty of revenue. When we put them together with the paper with transaction characteristics that David provides, that’s where everything comes together. It’s where people have to decide on those areas of uncertainty, revenue and the probability of success. It all blends together very well.”
