Onyx Pharmaceuticals (NASDAQ:ONXX) is developing a cancer drug called BAY 43-9006 in collaboration with its partner, Bayer (NYSE:BAY). This drug is in clinical development for the treatment of various cancers including renal cell carcinoma (RCC), liver, melanoma, and pancreatic. In this article, I am going to use the drug as an example of how to forecast potential sales. I will focus solely on the cancer type where BAY 43-9006 is farthest along in development, and that is metastatic RCC.

A drug sales forecast depends upon making assumptions about the patient population that is likely to use the drug, market share, and the annual cost of therapy. To derive the values I use in my assumptions, I rely heavily on sources of medical information that I believe to be accurate and credible. All of the information sources I use will be referenced as links.

A topic such as this one, which is highly dependent upon making educated guesses based upon best-available information, can generate a very thoughtful discussion. Especially if someone wants to offer constructive criticism either of the methodology or of the results I'm going to present. Care to chat about my calculations? You can find me over at the biotech discussion board.

The patient population
The starting point in calculating a sales estimate is to identify the patient population. BAY 43-9006 is currently in a large phase 3 trial in patients with metastatic renal cell cancer. Details on the study can be found at ClinicalTrials.gov.

The American Cancer Society estimates that there will be 35,710 new cases of kidney cancer in 2004. In the organization's "Practice Guidelines in Oncology," the National Comprehensive Cancer Network cites a reference that approximately 90% of kidney cancers are renal cell carcinoma. Looking at the details of the BAY 43-9006 study, we see that patients with the other types of kidney cancer are excluded from the study.

Those patients with RCC that has not spread to other organs are likely going to have surgery to remove their tumor and are not going to receive a drug like BAY 43-9006. However, many patients who have surgery will relapse and develop metastatic disease. Since the phase 3 trial of BAY 43-9006 is in patients with metastatic RCC, an important question is: What is the percentage of all RCC patients who have metastatic cancer?

To address this crucial question, I ventured over to a great site called PubMed that searches medical and science journals. Over there I found an article abstract providing the answer I needed. The authors state that patients with RCC develop metastases in approximately 33% of cases. Additionally, 20% to 50% of patients who initially have surgery to remove a localized tumor will eventually relapse and develop metastatic disease. These figures are generally in agreement with another presentation I came across. Taking both of these pieces of information into account, I'll estimate that in a given year approximately 50% of patients with renal cell carcinoma will have metastatic cancer and are therefore potentially eligible to use a drug like BAY 43-9006.

Factoring in all of this information to narrow down the population of patients eligible to use BAY 43-9006, I come up with:

  • 35,710 patients with kidney cancer

* ~90% subset with renal cell carcinoma

  • 32,130 patients with RCC

* ~50% metastatic cancer

According to my calculations, 16,100 patients are potentially eligible to use BAY 43-9006 in a given year.

Market share
The treatment options for metastatic renal cell carcinoma are poor. Therefore, a new drug that demonstrates a survival improvement in a well-controlled trial is likely to see widespread use. I don't claim to have the foresight to be able to predict the exact market share that BAY 43-9006 will attain if approved.

For the purposes of this example, I will use 25% market share as that reflects significant usage, which would be likely for a drug that demonstrates an increase in patient survival. I am also using 25% market share as the only patients that will use BAY 43-9006 for an extended period of time are the one's that experience clinical benefit. Applying this market share estimate to the number of patients eligible to use the drug gives:

  • 16,100 patients potentially eligible to use BAY 43-9006

* 25% market share

This means about 4,000 patients could be treated with BAY 43-9006 annually.

Drug pricing
Drug pricing is rarely revealed prior to approval and launch, so we have to again use a best estimate. One way to arrive at the possible revenues is to estimate that the cost will be in line with comparable drugs. For this example, I will use Iressa and Gleevec as the comps, since they are both orally dosed, small molecule drugs that are inhibitors of signal transduction pathways like BAY 43-9006. From my handy June 2004 update of the Red Book, the average wholesale price (AWP) for a one-month supply of Iressa (30 pills of 250 mg dosage) is $1,945, and the AWP for Gleevec (30 pills of 400 mg dosage) is $2,623.

I'll go with the midpoint between those two drugs and estimate that the AWP for a one-month supply of BAY 43-9006 will be $2,300.

The annual revenue from BAY 43-9006 is going to be based upon the monthly cost times the average number of months that patients use the drug. Patients who respond well to treatment with BAY 43-9006 could very well use it for all 12 months of the year. On the other hand, there will be patients from whom the drug has no effect, and they will only use it for a very brief period. The trick is to try to estimate how long the "average" patient will use the drug. Ideally, this estimate will be based upon clinical trial data.

Data just reported at the American Society of Clinical Oncology states that patients who had their tumor shrink while using BAY 43-9006 had a median time until the tumor resumed growing of 48 weeks. Thus, for the purposes of this example, I will assume that the "average" patient who has their tumor shrink with BAY 43-9006 will use the drug for 11 months (48 weeks).

The annual revenue to the company per patient can now be calculated by using 11 months of treatment times a monthly AWP of $2,300. That results in an "average" of $25,300 in annual revenue per patient treated.

Putting it all together
Now, let's add together all of the above pieces to arrive at an annual revenue estimate for BAY 43-9006 in the treatment of metastatic renal cell carcinoma.

We have roughly 4,000 patients treated each year, multiplied by an average annual cost of treatment of $25,300 to give, drum roll, please, an annual revenue estimate of approximately $100 million.

Note that this revenue estimate is for U.S. sales only and only in the treatment of renal cell carcinoma. Of course, this assumes that the drug is approved for the treatment of this disease, which it may not be. A quick rule of thumb to arrive at a worldwide sales total is to take the U.S. figure and double it. Additionally, approval for the treatment of other cancers, such as melanoma, would increase the drug's sales. But the idea here was to lay out a process for how to forecast sales and not to give a comprehensive revenue projection for BAY 43-9006.

Final thoughts
The idea of this process is to get a ballpark figure for how well a drug will sell in a particular market. The primary goal of this method is to arrive at a general idea of the sales potential. You want to be able to distinguish between a drug that has maximum sales potential of $100 million versus a drug that has $500 million potential. The goal is not to try to be so precise as to differentiate between $300 million and $350 million in sales. Such precision is not really possible or even necessary.

The limitations of this approach are the reliance upon single assumptions such as the use of 25% market share instead of 20% or 30%. Any of those figures would be equally valid in my opinion. The same criticism applies to the drug AWP. Why $2,300 and not $2,000 or $2,500?

One way to get around the limitations of using single data-point assumptions is to employ a more sophisticated model that incorporates Monte Carlo simulation. The Monte Carlo approach allows for variation in the different estimates, leading to a forecast that incorporates a range of possible outcomes. I will discuss applying Monte Carlo analysis to drug forecasts in the next article.

To read more by Charly on the exciting prospects of the biotech industry, check out his recent articles:

Fool contributor Charly Travers is not a doctor and has never played one on TV. Charly does not own shares in any of the companies mentioned in this article. The Motley Fool has a disclosure policy.