Financial modelling for biotech start-ups (part 1)

As someone with a business background, I am often approached by technical folks to assist with preparing or reviewing financial models. Most are under the impression there is some magical touch required to prepare a model. Although my Excel skills are pretty advanced, and I can churn through a set of financials with ease, the most important thing to be known about preparing a model is within the grasp of anyone, regardless of their business experience. Simply put, a model is only as good as its underlying assumptions, so considerable time and effort must be invested to identify and question those assumptions. This article (part 1) will discuss this idea further, and the next article (part 2) will provide some handy tips and tricks to prepare a presentable model.

Suppose you are a venture capitalist in the life sciences space, and you meet a company with a very promising technology. As conversations proceed, you ask to see their financial model. Do you accept it at face value? The answer is obviously ‘no’! Rather, you seek independent, objective and corroborative evidence as validation. If the model says that $100,000 has to be spent on regulatory consultants in year one, you ask to see a quote. What do those services encompass? Is this a fixed fee or a per hour arrangement? Has the consultant had a similar engagement in the past, and if so, were there cost or time over-runs? What have previous clients said about the consultant, etc. etc. etc.? As an entrepreneur, you can impress and help a potential investor by asking these questions, and collecting answers in advance. As equally important, you can illuminate some aspects of the venture that you might not have been aware of or incorrectly assumed. Ultimately, every financial model is wrong because it is impossible to predict the future with certainty. However, the degree to which you are wrong can you mitigated by doing your own due diligence on your own venture.

With that being said, the choice and cost of a regulatory consultant is not a “priority” assumption. I believe there are three critical over-arching assumptions that have to be answered as part of any financial model:

(i) What are your inflection points?
(ii) How much time and money will it take to reach those inflection points?
(iii) What is the value of reaching an inflection point?

For those that are unaware, an inflection point (or milestone) is an event or happening with a significant impact on the value of a business. Intelligent investors make investments with the intent of achieving specific milestones, and therefore, it is important to have these clearly articulated. In my opinion, there are five types of inflection points in life sciences:

(i) Technological – major advancement in the development of drug, device or diagnostic. For example, in the case of a medical device, a technological inflection point would be the creation of a bench prototype

(ii) Intellectual property – securing a patent, patents or freedom to operate for a technology

(iii) Regulatory – meeting FDA or other national licensing requirements

(iv) Reimbursement – receiving a billing or current procedural terminology (CPT) code for a device or diagnostic, or being included on a formulary for a drug

(v) Market adoption – this goes beyond just final use by a physician or patient, and includes obtaining the support of key opinion leaders or early customers

Once you have identified your milestones, it is necessary to determine how much time and money is necessary to reach them. Most often, entrepreneurs focus more of the cost than the time aspect. If you recall from an earlier article, time is just as important as multiple in calculating return, so, a delay of one or two years could have a significant adverse impact. It is also important to note that venture capital funds have a 10-12 year life cycle, and therefore, time is of the essence.

Finally, and perhaps most importantly, it is necessary to have an understanding of the value created by reaching an inflection point. For example, although being granted a patent is a time-consuming and relatively expensive process, it is of little value if the technology has not been reduced to practice. As another example, regulatory approval for a medical device is significantly less valuable than a drug, as very few out-licensing deals happen for approved devices, whereas it is common for big pharma or biotech companies to in-license from smaller companies with compounds that have achieved phase II or III approval. In my opinion, the minimum inflection points that a life science venture must have achieved in order to be considered for substantial private investment include technological validation (i.e. proof-of-concept) and patent approval.

At this point, I have not really provided much practical advice involved in building a financial model. Again, that will be the purpose of the part 2. However, that is truly secondary. Financial modelling has much less to do with income statements and balance sheets, and much more to do with questioning, testing and validating critical assumptions that must be translated in quantities of money and time.

http://twitter.com/sciencescanner David Grainger

Good advice.

Actually, the principles are much more important than the practice. The actual model is not as useful as the validated datapoints from which it is constructed.

The reason is that the big unknown for an entrepreneur starting out in biotech is the business model of their investors. How much risk do they want to assume? In other words, would they like the plan to be ultra-risky but cheap, or have more money spent early to de-risk the programme?

There are no right or wrong answers to this, because it depends on the investors business model, and not on yours!

So dont spend too long creating just one model, because as you talk to different investors, they will want different things.

The key take home message from this blog is simple: what you need to do is define accurately the playing cards in your hand, rather than come up with the strategy for playing them. What you don’t want to do, when in discussion with investors, is suddenly discover the card you thought was an ace is actually a deuce. Thats what doing due diligence on your own business model is really about.

You can then assemble the hand into a viable strategy for playing poker – thats what building the actual model involves – but remember its only one possible model based on the same input data and you cant know whether its the model the investors will actually want to see. But if you have certainty about the cards in your hand, the investor can shuffle them and come up with their favoured strategy for playing your cards.

Stefano Picone

David – thanks for the comment. I really like the poker analogy (i.e.
validate the cards in your hand, and then play based on the investor’s
preference.) Great to see that we’re starting to generate some dialogue
from many viewpoints, which is the point of this website/community!