This post is in response to a prospective client, who asked us about the required depth and complexity required in a financial model(s) when out-licensing.
It’s an interesting question. So in this post, we’ll take a step back to examine this issue.
The folks at Bain and Company recently published a short white paper entitled How to Make Your Drug Launch a Success.
The paper discusses the three key activities which successful companies engage in after a new product launch to drive sales.
But what caught our eye was this opening paragraph (emphasis added):
The pain is becoming all too common for many pharmaceutical executives. After a decade or more of investment in drug development and clinical trials, a company launches a promising new product only to see sales fall far short of expectations. Our research shows that nearly 50% of launches over the past eight years have underperformed analyst expectations, and more than 25% have failed to reach even 50% of external revenue forecasts.
Now we fully appreciate what Bain is trying to say and do.
They are using analyst / external forecasts to argue that corporate / internal forecasting is overly optimistic and highly flawed.
Hence, companies need to engage in various strategies and tactics (which, coincidentally, Bain has identified) in order to maximize launch success.
We believe this is a weak argument.
Our experience with pharma is that the folks who conduct market research, due diligence, forecasting, and valuation are usually top-notch folks, with the expertise and resources to provide realistic revenue and profitability forecasts to Senior Management.
Note we say realistic, which is likely the key difference between internal/corporate and external/analyst forecasts and valuations.
However, let’s set this issue aside and look at it from a small pharma/small biotech perspective.
Small companies typically do not have the internal resources and/or the expertise to conduct detailed forecasts and valuations.
Fortunately, there are plenty of companies who can perform product-level valuations on an out-sourced basis. So access to this expertise should not be an issue, unless the capital is simply not available to pay for this important activity.
If this capital is not there for this, then the company has other, more pressing issues.
So the question before us is a slightly different one…how detailed should a forecast/valuation be for a small company, especially one seeking development partners?
To put this another way…should a highly detailed revenue and NPV model be prepared for every single pipeline opportunity being developed and/or out-licensed?
Should a company in out-licensing mode even bother, especially as the prospective partner will prepare a far better financial model anyway?
Is there a financial mode/framework which is “good enough” for out-licensing purposes, but not good enough for development planning and commercialization purposes?
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Generally speaking, the more detail in the financial models, the better positioned a company will be for pipeline prioritization, investor negotiations, Board presentations, and partnering discussions.
But what do we mean by “detail?”
To answer this question, let’s look at three general approaches to valuation.
Comparables – Companies – In this approach, we gather information on listed companies which have the same profile as the company being analyzed. Assuming you can find enough of these, you can use their financial data, correct it for pipeline size and stage of development, and then apply that correction to the company being analyzed.
It’s a relatively simple way to value a company, and it can be developed as part of a broader understanding of the competitive space. But finding enough companies can be a challenge, especially outside of major therapeutic areas like oncology.
And, there is no way to adjust the results for the inherent liquidity premium which is baked into a listed company’s valuation.
Comparables – Products & Transactions – If a company is out-licensing, then clearly gathering information on similar products and transactions is important. But can this data be used for valuation purposes?
We’re not big fans of using product revenues and licensing transactions as a means of deriving a valuation. Both can serve as good guidance and “gut checks” for more sophisticated modeling.
But it can be a conundrum for companies to make a case that their product will achieve PYS of over a billion simply because the current market leader is over a billion. How will the market change and evolve in the interim? How will reimbursement evolve over time?
Comparable Transactions are also a good way to “gut check” assumptions in a Term Sheet, or to help build a case for the components there in. But, again, finding a sufficient amount of data on transactions is challenging.
And, press releases rarely supply enough detail to get a good handle on the precise present value / timing of potential payments.
Net Present Value – Multi-day classes are available which discuss the method and details surrounding NPV analyses. So we will not delve into the details here. But there are several significant advantages using this approach over the others discussed earlier:
Apples to Apples – By using drug candidate-appropriate assumptions, NPV (or, more correctly, risk-adjusted NPV, or rNPV), is an excellent method to compare and prioritize drug candidates in a pipeline. It is frequently assumed that the most advanced asset is the most valuable one from an out-licensing perspective.
And, this is generally a solid and very reasonable assumption. However, if a company has m assets with similar valuations (expressed as rNPV), it may make sense to eave an out-licensing story around both opportunities, and not just one, especially if both are in the same therapeutic area.
Negotiations – rNPV analyses are typically very sensitive to key assumptions. Slight changes in assumptions regarding disease prevalence or drug pricing can cause wild swings in the final rNPV values. However, understanding the key assumptions can result in more fruitful, detailed discussions and negotiations.
Scenario modeling becomes possible, making the overall financial picture more granular. rNPV also enables the development of “pie sharing” models, which are critical for valuing licensing transactions.
Conclusion
It may sound fairly obvious to most readers of this blog, but rNPV is the way to go. However, the discussion we had, coupled with the Bain article, reminded us that not all companies realize this.
Nor do many companies have the expertise to conduct these kinds of analysis, i.e., if the management team is overly biased towards scientific and technical aspects of the business.
Ultimately, however, the objective of a revenue forecast and an rNPV analysis is not to precisely peg the revenue and expenses for a drug that’s 5-10 years away from the market.
That understanding will evolve over time, and the partner will have to take the lead on forecasting after they have licensed the asset.
However, those of us who are out-licensing simply have to perform the most robust analysis we can, if at minimum, so that we are speaking in the same language with our potential licensing partners.