3 forecasting pitfalls to avoid

*Blair Gibson, executive director of planning and strategy at Merck, on how learning from past mistakes improves future forecasts* Failure is never fun.

Blair Gibson, executive director of planning and strategy at Merck, on how learning from past mistakes improves future forecasts

Failure is never fun.

Revisiting it can be even less so. Thats why businesses typically study and mold themselves after best practices rather than trudge through the ashes of past mistakes.

Analyzing failure, however, with the same rigor as we dissect success is key to ensuring that we do, in fact, succeed.

Everyones got this best-practice database in their company, Blair Gibson, executive director of planning and strategy at Merck, said at eyeforpharmas Forecasting USA summit in Boston.

A worst-practice database that highlights how not to launch a drug would be as valuable, Gibson said.

Ive always been of the opinion that we need to mitigate those failures and systemically examine them as we examine success in best practice.

This is especially true today, when the balance between blockbuster boom or bust tips ever more toward the latter.

According to IMS data, 45 new chemical entities launched in 1999. In 2009, that had plummeted by 50 percent to 27.

Also in 2009, 33 compounds were expected to be blockbusters; only 10 panned out.

The average time it took for a company to realize if their launch had been successful or not? Just six weeks.

So youve got a very short time to seize when things arent going well, Gibson said.

It should not be a surprise at six weeks when you dont see your inflection. We should know this before the thing launches, well before.

And thats where avoiding pitfalls in forecasting comes in.

Pitfall no. 1: Poor analogues

One common pitfall that can ruin otherwise sound forecasts is the use of poor analogues, said Gibson.

In hunting for insightful corollaries, we sometimes settle for an analogy thats out-of-date or not relevant enough to the drug at hand.

We all use analogies in our forecasting, he said, but is the analogy recent enough to be relevant? And is the analogy itself relevant to the drug that were forecasting?

He offered a cautionary tale from his past, when he was launching a diabetes drug in the European market.

The forecast used Avandia as an analogue but failed to consider that NICE had recently put a box around Avandia for several concerns.

We ended up using an analogy that wasnt analogous at all, Gibson said.

Thus, when forecasting pre-launch, its very important to make sure that the situation or environment around the analogy is analogous to your compound, and if its not, make allowances for that, Gibson said.

Likewise, when brand teams go back and use forecasts in-line, Gibson asks them to look at and refine the rigor around those forecasts.

Some forecasts get dusty, Gibson said, and I like to dust off the forecast and challenge the assumptions on them.

Pitfall no. 2: Ignoring payers

As the market for pharmaceuticals becomes more challenging, payers are more reluctant to adopt new drugs without clear proof of the products value and its ability to reduce medical costs or improve quality of life.

Its thus critical to accurately gauge payers interest in your product pre-launch and to integrate that level of interest into your forecast.

To the extent that you can, go beyond payer research and involve the payers almost as partners, if you can, in the development of your drug and the development of your forecasts, Gibson said.

That prevents a lot of consternation post launch.

He pointed to Pfizers famous flameout Exubera, an inhaled diabetes drug that the company walked away from in 2007, shuttering a manufacturing plant and waving goodbye to, according to some reports, close to $3 billion.

Exubera looked on the surface to be a very, very promising and good drug, Gibson said.

The problem was that the payers didnt see it that way, complaining that the manufacturer didnt have the data or documentation to prove that Exubera would reduce medical costs and improve quality of life for diabetics in the long run.

So there was a disconnect here between the forecasting and what the payers were saying, Gibson said.

Pitfall no. 3: Missing clinical connections

Failing to align with R&D and clinical trials is another pitfall that often trips up forecasts, Gibson said.

Specifically, he warned that forecasts must be connected to the clinical development plan.

Here he offered yet another example from his own experience.

When working on a compound for peripheral arterial disease (PAD), he asked his forecasting team to add another indicationcritical limb ischaemiainto the forecast.

That indication, although a typical progression for PAD, had not been included in the clinical development plan.

The resulting snafu led to a discrepancy of about $500 million in peak sales.

Im no longer working at that company, Gibson said wryly, but I learned a very, very valuable lesson, and that was to get with my brothers and sisters in R&D and get the clinical development plan and the forecast synched up.

Likewise, when studying clinical results, be sure to keep in mind that adherence statistics from clinical trials are going to be higher than what your compound experiences in the market.

Unless you have rock-solid evidence that compliance and adherence are improved by this technology, do not bake it into the forecast, Gibson said.

Weve got to be as ruthless in value add as we are in safety and efficacy when it comes to looking at the forecasts for these compounds, he added.

In the end, Gibson urged forecasters to get back to basics.

Pipelines are weaker, patent expiries are keeping CEOs up at night, and payers and the FDA are more demanding than ever that your product be better than whats on the market, he said.

If forecasters study past mistakes and work to overcome themby ensuring alignment with R&D and payers and rigor with analoguesitll go a long way toward putting their current drugs in the best-practice bin.

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