Drug launch curves in the modern era, a new article in the January 2017 issue of Nature Reviews: Drug Discovery, contains projections of relevance for forecasting pharmaceutical sales in the US market. Relying on unit sales for 61 innovative drugs receiving FDA approval between 2000 and 2002, the authors determined that the median product follows an S-shaped launch-to-peak penetration curve: achieving 11% of peak sales in Year 1, 31% in Year 2, 58% in Year 3, 76% in Year 4, 89% in Year 5, and 100% (i.e. peak sales) in Year 6. On an interquartile basis, time-to-peak (TTP) ranged from 4 to 9 years; the minimum TTP was 2 years and the maximum was 14.
Using data from the Supplemental Materials file we find that the average for TTP for first-in-class drugs is 5.65 years as opposed to 6.93 years for subsequent entrants. Unlike other research on the subject, however, the authors did not that there was a statistically significant relationship between entry order and TTP. But, as they note, TTP is distinct from factors such as market share and revenue potential that may be more indicative of first-mover advantage.
Not long ago, gloom and doom was predicted for the pharma industry as blockbuster drugs ran out of patent protection, taking their historic profits with them. The patent cliff, a several-year span in which Lipitor, Diovan, Singulair, Plavix, Lovenox, Nexium, and Cymbalta (just to name a few) faced generic competition with limited options for replacement in the pipeline, was seen as heralding a period of long-term difficulty for innovative drug makers.
Based on information from IMS Health’s newly published Medicine Use and Spending Shifts: A Review of the Use of Medicines in the U.S. in 2014, however, it may be time to change the tune. Domestic drug spending last year reached $374 billion, exceeding forecasts and achieving the highest annual increase (13.1%) since the early 2000s. While fewer patent expirations and an increase in brand prices get some of the credit for the upswing, the real growth driver was specialty drugs.
Of the $20.2 billion increase in spending on new brands, 78% was from this category, defined by IMS as “products that are often injectable, high-cost, biologics or require cold-chain distribution … are mostly used by specialists … and include treatment for cancer and other serious chronic conditions. “ More than half of the new brand sales growth ($11.3 billion) comes from recent launches of new hepatitis C drugs while new cancer and multiple sclerosis medicines were respectively responsible for $1.6 billion and $2.0 billion. Overall, according to IMS, the $54 billion in increased specialty medicine spending over the last five years makes up 73% of growth across all categories in that period.
These trends have given specialty drugs a firm foothold in the market, making up 33% of all drug spending last year. And it looks like this will continue for the next few years, as 42% of late-stage-pipeline drugs are currently in this category.
Bright and shiny topline growth numbers aren’t everything though: at some point, we’ll get around to posting an analysis of how these phenomenal revenue figures are really derived from a handful of extremely successful drugs rather than a broad-based industry turnaround. The plateau in drug utilization, which we evaluated in-depth in a 2013 whitepaper, provides further reason for skepticism regarding the sustainability of the current rally.
It has also been said that everything carries within itself the seeds of its own destruction – this was true in the late 1990s/early 2000s when the last period of outsized growth was eventually contained through Paragraph IV challenges, higher copays, aggressive formulary management, and pro-generic policies. Considering that specialty drug prices have already sparked backlash among physicians and pharmacy benefits managers and that we’ve just recently seen the advent of biosimilars which promise to exert pricing pressure on biologics, it is not too difficult to predict that counterforces will eventually curtail the latest round of growth as well.
VOI Consulting is a big proponent of applying probabilistic forecasting techniques such as Monte Carlo analysis (MCA) to pharmaceutical industry decision making. In the last year alone we’ve used MCA to evaluate pipeline opportunities, find an optimal price for a new drug with multiple segments and price sensitivities, develop a wholesale bidding strategy for a generic manufacturer, establish the likelihood of competitors reaching market by certain dates, select clinical trial sites, and determine distributions of Medicare DRG payments based on hospital and patient characteristics.
Unfortunately, probabilistic forecasting is not nearly as widely known (or used) as it should be. Most likely the name implies a headache-inducing level math that most busy pharmaceutical executives simply don’t want to tackle. It’s actually much easier than that mainly because we are only asking clients to be intelligent consumers of the analytical results and this requires little more than the ability to understand a probability distribution chart or statements like “there’s an 85% chance of generating positive ROI on this marketing initiative.” It’s so much easier than people expect and so much more powerful than standard spreadsheet modeling that after reviewing the results of a Monte Carlo analysis on a recent pipeline licensing opportunity, one of our C-level clients stated, “I wish someone had explained this approach to me 20 years ago.”
With this in mind, we’re always glad to see Monte Carlo analysis promoted for pharmaceutical industry applications. One such instance is a webinar from Palisade Software, the makers of the @Risk suite of probabilistic modeling tools who recently hosted “Using @RISK in Evaluating Full (late stage) Compound Development in the Pharmaceutical Industry” by Venkat Raman of VR Advisors LLC. An archived version is available here.