"p" or "β": How Clinical Success is Affected by the Macroeconomy

In the footnotes to my last blog post “Cost of Biotech Capital: Incorporating Development Risk into VC’s Target Return,” I contemplated which biotech start-up risks are uncorrelated with the general market and hence “diversifiable”. Many assume that a given clinical trial’s binary probability of technical or regulatory success (“p”) is one such risk.¹ As described in the last post, short-term oriented financial analysts (many at hedge & PE funds trying to gauge the outcome of binary events) assign a probability of success ("p") to a given clinical trial and do not expect that value to fluctuate with the general market (i.e. S&P 500). In the short run (event to occur in < 2 years), they are likely correct. In the long run (event to occur in >2 years), however, “p” is often influenced by general political and economic conditions. This is the timeframe considered by VCs and BioPharma companies. Below are three of the ways in which “p” can find its way into “β” in the long run and, ultimately, drive up the long term cost of capital for biotech.

(1)    FDA

FDA requirements affect clinical trial design and the choice of primary and secondary endpoints. All things equal, a stricter FDA will lead to a lower “p” or probability of what the FDA considers technical success. The FDA is in turn affected by the general political environment and hence the macroeconomy.

(2)    Availability of Capital

The free availability of capital can have dynamically opposed effects on “p”. On the one hand, more cash means better and bigger clinical trials which boosts “p”. On the other hand, too much capital leads to poor investment discipline and many companies getting funded that shouldn’t. This phenomenon is similar to what occurred in mortgages, where the free availability of low interest rate IO mortgages with no credit checks manifested in historical default rates 3-5 years later.   

(3)    Clinical Trial Recruitment

The status of the economy likely influences who and how many people agree to participate in clinical trials. One theory argues that in good times, more people can pay for experimental treatment or top-notch medical care themselves and decide not to participate in clinical trials. In bad times, the reverse may be true. Better recruitment may lead to better clinical trial results and a higher “p”. Likely (2) Availability of Capital and the existence of many competing trials is another factor in recruitment success.   

There are likely many other ways in which general economic conditions impact biopharma’s probability of technical success over time. In the long run, “p” shows up in “β”. Counter to my last blog post, this is an argument for adjusting biotech’s long-run cost of capital for binary technical risk. While 58.7% IRR still seems too high, perhaps the industry standard ~30% IRR is a reasonable happy medium.  

1.       “p” is not to be confused with the statistical term “p-value” which determines how likely a clinical trial result is due to chance.