Methods of valuing pharmaceutical development-stage assets have been a perennial talking point for decades, and the advantages and disadvantages of each have been covered in numerous publications. Indeed, previous Alacrita whitepapers have discussed the merits and mechanics of different valuation approaches and advocated the use of MonteCarlo approaches to determining risk-adjusted Net Present Value (rNPV)1, 2. Asset valuation has become a progressively more salient issue with the dramatic increase in the number of licensing deals, which demand a value to be placed on the transacted assets.

Inevitably, no matter how solid the model, the result can only be as good as the quality of the inputs. And it is in determining the input variables where we find the most frequent sources of error. In this blog post we are going to focus on the most common mistakes we see in determining inputs in building valuation models.

Obviously the earlier in development the asset, the greater the uncertainty that exists. The target product profile (TPP) for a mid-clinical stage asset will be much more precisely defined, on the basis of actual (indicative) performance in the clinic, than for a preclinical asset where the TPP is largely aspirational. As a result, we can also expect, for example, details of the specific indication and the size and length of the associated Phase III clinical trials to be much better identified for a candidate in Phase IIb than one just approaching IND. So there is little excuse for not having thought through some of the key valuation inputs, especially for later stage projects.

Nevertheless, we not infrequently see the same basic mistakes cropping up in projects at all stages of maturity. These are the most common:

“Leave no stone unturned”

While it is true that for relatively early stage projects there may not have been the level of planning completed to provide detailed valuation model inputs, this isn’t an excuse to use unvalidated assumptions. There is often an abundance of data in the form of historical precedents or surrogates which can inform a modeling exercise. For example, when estimating indicative clinical trial sizes and duration, much can be gleaned from similar studies on Pricing is another area which often receives surprisingly little attention even as late as mid-stage clinical trials. Ideally by this stage payer studies should be informing pricing assumptions, especially in Europe, but even without this, pricing surrogates can be developed using relevant historic comparisons with US costs.

“The triumph of optimism”

It is surprising how often, having assiduously done all the right things in terms of collecting relevant surrogate data and historical precedents, performing the analysis and formulating plans, that planning assumptions err towards the optimistic end of the range - and sometimes cross the boundary in to over-optimistic. This is a natural human bias to the optimistic. Trials will be recruited faster, fewer centres will be needed and patients enrolled faster, multiple trials initiated in parallel and the upside/stretch TPP becomes the base case. In many cases this is in spite of the available evidence pointing the other way. In this situation it is essential to resist those Panglossian urges and leave the upside scenario as precisely that.

“Defying the odds”

A recurring argument we hear from time to time from innovators goes along the lines of “our project is inherently lower risk and therefore our probability of technical success should be higher than average”. This expectation of defying the odds is like that of the gambler…. and we know the real winner in that business is the casino. There are several well documented studies on clinical trial success rates over many years and while these show variations by indication area, what is most remarkable is how little the rates change over time. To think otherwise is to fool only oneself, as investors and licensing partners will of course assume no deviation from the average.

“Meeting those milestones”

A less common variant to the above we sometimes see is to assume that because a project has advanced to a new clinical phase, that all the risk is discharged from the previous phase. This is not always the case; for example, some key preclinical issues can remain outstanding even though a Phase I trial is underway. This overestimates the probability of technical success from the starting point and therefore results in an over-valuation of the project.

“Home (in) on the range”

Accepting that there is considerable uncertainty in many, if not all, of the inputs going into a valuation means that due consideration must be given to realistic ranges around the base case, whether these are used in MonteCarlo modeling or in standard upside and downside sensitivity analyses for standard rNPV. The ranges used around each input must be used intelligently – the more narrowly they can be defined the more meaningful.  For example, we have seen cases where the bottom and top ends of ranges for market penetration represented two completely different scenarios. In this situation the valuation results are totally meaningless.


In summary, developing robust and supportable asset valuations requires a focus on realistic input assumptions. We would hasten to add that the majority of valuation work we see does just that, but a significant number fall foul of at least one of these errors. Our key recommendations are:

  • Do make use of all available benchmarks and surrogates;
  • Do your homework – commensurate with the stage of development of the asset;
  • Resist the temptation to err on the optimistic side;
  • Don’t succumb to the belief that your project will defy the odds of success. There is very rarely any evidence to support that assumption and in any event, prospective investors or partners will not believe or appreciate it;
  • Build sensitivity analyses around credible and realistic ranges for your inputs.

And finally, remember, no matter how sophisticated the model, “garbage in equals garbage out”.



  1. What's wrong with NPV valuations?,
  2. Valuing Pharmaceutical Assets: When to Use NPV vs rNPV,


About the author

Simon has almost three decades of experience managing and advising on strategic and product development issues in life sciences, and over the past 15 years has focused on commercialization of technologies in early-stage life science companies, as entrepreneur, executive, venture capital investor and advisor.

His management consulting experience includes business and organizational strategy, technology evaluation and market appraisal, financial planning & capital raising, and economic impact assessment, much of this leveraging his earlier experiences both as VC investor and as start-up CEO.

Additional details on his expertise, as well as his contact details, can be found here.

Subscribe to mailing list