Week 4: Negative EBITDA and Model Enhancement

Welcome back readers for Week 4 of my blog! After looking over the results of last week’s work, I noticed a glaring flaw. Biotech companies such as Arcus produce extremely low revenue in their clinical stages while they are still in drug development. This is due to a higher focus placed on technological development instead of sales. Their primary focus is on researching, developing, and testing new drugs. This involves significant investment in research and development (R&D) activities, clinical trials, and regulatory processes. These activities consume a large portion of their resources and don’t generate immediate revenue. Since the drugs are still in the clinical trial phase, they haven’t received regulatory approval and can’t be commercially sold. Revenue generation only begins after a drug gets approved and reaches the market. The drug development process is lengthy and can take 10-15 years on average. There’s also a high degree of uncertainty associated with it. Many drugs fail to make it through clinical trials, meaning the investments in development may not translate into future revenue.

Essentially the two takeaways are low revenues and extremely high R&D costs and we can clearly see this in the numbers that Arcus has reported. Arcus generates revenue only from licensing their research findings (patents etc…) and burns an extremely high amount of cash through R&D. While this is normally fine, in our case, the financial model we are building relies on positive EBITDA numbers that can be scaled. Thus, we must find a remedy, and the easiest solution is to factor “hidden revenues” that can be unveiled once their products pass drug approval stages. 

Arcus Bio has multiple clinical candidates, including anti-TIGIT, adenosine axis, anti-PD-1, HIF-2α inhibitor, and AXL inhibitor. The success of just one of these products will result in massive increases in revenue, and is common with many other successful biotech companies. By analyzing the clinical stages of their products, industry averages, and past revenue info from Gilead, we can project a new “hidden revenue” (around a 430% increase) and use this in our model. Which will fix our negative EBITDA issues and allow us to scale other dependent values such as net income. New values and projections shown below: 

With this problem fixed, we can now continue with the model. Thanks for joining me this week, see you in the next installment.