(This blog is the first of several about building a commercial capability as a company develops from pre-IND to Launch)
Every new biopharma startup needs a strong commercial perspective to inform its clinical development plan, valuation and market strategy. Some firms believe that all the competitive information they need can come from a couple of Decision Resources studies, or a one-off report from a commercial research vendor. Or perhaps, more forward-looking (and cash-heavy) companies will make an early full-time commercial hire.
An undervalued building block of any commercial capability is a foundational set of competitive data readily available for analysis. Most people think of commercial data as being generated by expensive primary market research - which it is, eventually… But a dataset built from secondary sources is difficult to develop and undervalued, and even harder to maintain.
This secondary source dataset is undervalued for several reasons:
Executives with a big pharma background (very common today) are used to automatic feeds from well-funded Business Analytics departments or plentiful third-party data sources that are cost prohibitive even for well-healed startups.
There is a sense that a lot of information is readily available from clinicaltrials.gov and is easy to access. But searching the site by line of therapy and MOA are hard and there is a paucity of outcomes information.
Science-oriented executives often significantly underestimate the people-hours behind relevant secondary market research.
Off-the-shelf reports (Decision Resources, EvaluatePharma, GlobalData, Cantor, etc.) are terrific, but always too general to apply directly to a specific situation. They are a starting point only, but some think this is “good enough” for pre-POC assets.
Too often, early-stage firms are overwhelmed by critical path issues such as IND filing, manufacturing challenges, clinical protocols, etc. Therefore, they only consider commercialization when they need to appease investors, such as at JP Morgan, or to show their board a plan. But the real value lays across the organization. For example, the data can inform the clinical development plan early enough to avoid obvious and not-so-obvious pitfalls (dosing, half-life, delivery, efficacy/safety tradeoff, patient selection, line and indication of entry, etc.).
As we note many times at First Principles, funding dollars for biopharma are precious, even when nine figures are raised. Every dollar not spent in the clinic is questioned. Therefore, whenever competitive intelligence is required, it is narrowly spec’d out and often ends up needing to be redone to support later work.
As an example, let us consider a hypothetical OncPlatformCo. OncPlatformCo has a technology that can be applied across several solid tumors at the very least, and maybe hematology as well. Management believes this technology works effectively in combination with immunotherapies. Therefore, the initial need is to track immunotherapy therapies across a spectrum of indications - which is does, hiring an outside firm. Eight months later, a decision is made to focus on NSCLC for value and a rare cancer for the lead. Now, with the indications in mind, line of therapy becomes important, particularly with lung cancer because of the complexity of the indication and additions to the standard of care. Before it wasn’t, therefore all the work will now be redone to add that detail (as that takes a lot of work). As the firm expands into lung, the management team realizes that other MOA’s are major competitive threats in this malignancy, and the search has to be expanded to include all MOA’s. In the meantime there has been some personnel changes and a new vendor was hired. A large bill comes in and the CEO wonders why all this money is spent for competitive intelligence as just a few months ago there was a six figure tab. It is not obvious the two datasets have little in common. And with the new vendor, the old data set is basically tossed out.
Now, OncPlatformCo has an inkling about its TPP and really wants to focus on a certain segment in third-line lung for its lead (with a second shot on goal for the rare cancer). Earlier research covered hundreds of trials, and now we are drilling into specifics such as trial inclusion/exclusion criteria and specific outcomes. Going to conferences and photographing posters is deemed a must. An even bigger bill comes in. And we have not even started primary market research on the TPP. The CBO requests an assessment of another indication in solid tumor, figuring there was earlier research across tumors, he thinks this is an easy ask. But of course it isn’t.
We can go on with the story, but it should be clear that we are only collecting information as we need it, and each time it is asked, it comes in a little differently. With different employees and different vendors coming and going, it feels like the same work is being redone over and over again. The same problem occurs at Big Pharma of course, but there are departments dedicated to it and there is scale.
A Potential Solution: Building a Competitive Intelligence Capability:
At First Principles, we think it is useful to think of the early stage commercial group a bit differently. The research and clinical sides of the house are constantly thinking about building capabilities, creating an evidence plan, validating models, establishing KOL relationships, developing channels to academia and institutions, etc. Even at the early stage, we should think of the commercial group as a capability, not as an input.
Part of that capability is a competitive data set. For a competitive data set, that means putting in a single source, one touch system. At First Principles, we have developed our own internal software for this purpose, but it can be done with discipline with Google Sheets. Structuring a dataset, and sourcing data carefully, a firm can build its capabilities with the idea that work will be re-examined from time to time. An alert system can be put into place to allow for easy updates to this single source of data, and a structured reporting structure can be put into place to allow for flexible access to data as needed. We will follow this blog with others about other capabilities that need to be developed from pre-IND to Launch.
