Decision Making

One mystery of drug productivity at large pharma is the persistent low productivity despite the fact that there are tremendously talented scientists at every large pharma company. Their expertise is often encyclopedic, and their creativity is often very evident. Despite this, productivity at large companies have been less than impressive. The productivity appears to be low bin comparison to small biotechs, but even more shocking, the net return on investment seems to be negative. Studies by multiple entities, such as Ernst and Young, have demonstrated that the return on investment is lower than the cost of capital for the large companies.

One possible explanation is that higher productivity at small biotechs is illusory. There are some studies showing that on average small companies are no more productive than large pharma companies. This may be true – it may simply be that the variability is higher, that there are both very productive and very unproductive small companies, simply because smaller number of employees means that it is easier to be an outlier (See my post “Small Sample Paradox”).

But it probably goes deeper than that. One driver for this is likely to be the decision-making process at large companies.

At most large companies, the decisions are made by consensus. Every person on the team, and there can be twenty or more people on teams, have to agree before the team can move forward. And for each of them to agree, their functions (their bosses) have to agree. This means that the decisions default to the lowest common denominator, namely the least risky decision. In addition, because everyone is responsible for the decision, no one was.

The reason why consensus decision making doesn’t work is that in an innovation based industry where 90% – 95% of drugs fail, you must take calculated risks to be successful. There is an apocryphal story about a senior executive at Pfizer who would vote against every project because he knew that if he did so, he would  be right 95% of the time (because 95% of drugs fail), while if he voted for a project even 10% of the time, he would be wrong 50% of the time.

If you rely on consensus decisions, you usually end up with the decision that the most conservative person on the team is comfortable with. That is a problem. I can’t tell you where on the spectrum of risk any given decision should sit, but one thing I can tell you is that the least risky decision is almost never the right one (except when it comes to safety). In a risky industry such as drug development, the only way to have no failures is to have no successes.

In addition, in a consensus decision making system, what often happens is that the functions horse trade, so that the decision that is the least painful to every function, and pain of the decision is relatively equally distributed. The problem is that often, the right decision is one that is beneficial to one function at the cost of another one. A claim for the drug, for example, may be worth so much that the clinical group should conduct a difficult study. Or stability may be so fickle for a drug from a CMC standpoint that marketing needs to take a hit on the shelf life.

Left to consensus decision making, the decision often ends up such that it’s a compromise between two functions. Once again, I can’t tell you where on the spectrum the right decision is, but what I can tell you is that it’s rarely at the halfway point between what two functions want.

Of course, once a decision is made, all the functions need to line up behind the decision. In a healthy corporate culture, this happens. In an unhealthy one, there are a lot of foot dragging and passive aggressive behavior. This leads to consensus decision making solely to insure that foot dragging is avoided.

And it gets worse.

Not only do you need horizontal consensus across team members, but then you have to obtain vertical consensus. What I mean by that is that you have to go up through several layers of approval up the chain of command. And once again, I can’t tell you what the right decision is, but if six people up the chain of command all agree with the decision, it is almost never the right decision because once again, only the least risky decision will pass muster.

Genentech during the 2000’s had a run of successes. Its success rate in drug development was about 80%, which is more than an order of magnitude higher than the standard 5%-10% seen in industry.

One of the keys to Genentech’s success, in my opinion, was its decision making process.

Unlike many other companies, which make decisions by consensus, Genentech always had one final decision maker. One of its strengths was that decisions were never consensus driven. Usually, the team leader had that responsibility, but in some cases, the portfolio committee’s head, such as Sue Hellman or Myrtle Potter, had the final say. In some cases, the committee vote would be 20 to 1, but if the 1 was the committee head, that single vote carried the day. The team leader had the authority and the responsibility to make the final decision. The decision maker could delegate the decision, and he/she was responsible for making sure everyone had a chance to present their case. Of course, the decision maker had the responsibility of listening to and weighing all arguments and data, but the final decision was his/hers.

Of course, once a decision is made, all the functions need to line up behind the decision. In a healthy corporate culture, this happens. In an unhealthy one, there are a lot of foot dragging and passive aggressive behavior. This leads to consensus decision making solely to insure that foot dragging is avoided.

The second component of decision making that was done well at Genentech was that decisions were based on data. Opinion carried some, but not much, weight. The decisions were scientific and fact-based. In some cases, this might have been carried too far, since at a certain point, it becomes impossible to further de-risk projects with additional data (see my comment about complicated vs. complex decisions), but overall, the data-based decisions turned out to be high quality decisions.

The third component of decision making that Genentech excelled at was cutting losses. They didn’t fool themselves by performing secondary ad hoc analysis to data dredge for a positive signal. If the primary endpoint was missed, they moved on to the next molecule. Of course, this was easier to do for them than some other companies because they had 20 promising molecules waiting for every 1 that was dropped, but they avoided the futile efforts that many other companies exerted, chasing a phantom positive signal from post hoc analyses.

I’ve tried to incorporate that decision-making philosophy wherever I go. In addition, I tell the teams that I don’t have to agree with their decisions. I only have to understand it and be sure that it is thoughtful. The way I see it is, if we’ve hired the right team, and provided the right corporate context for the decision, then the likelihood that the team that spends every day thinking about the program would be right is much higher than the likelihood that I, spending a few hours a month thinking about the program, would be right. Of course, that means that senior management has to be very transparent and provide the corporate information the teams need to make the decisions.

The other important aspect of decision-making, which I learned from my mentor, Hal, is to distinguish between good/bad decisions vs. right/wrong decisions. A decision, if made thoughtfully and with all the appropriate input, is a good (high quality) decision. The decision may turn out to be wrong–for example the drug may not work in the end–but it remains a good decision. Some companies punish good decisions that turn out to be wrong. That fosters risk aversion and can paralyze the organization. With a 5% success rate across industry, most good decisions will turn out to be wrong decisions.

What we want to avoid is the urban legend sometimes related in pharmaceutical circles about the Senior VP at a large pharma who voted against every project because he knew he would be right 95% of the time if he did that. He knew that if he voted for a project even 10% of the time, he would be wrong 50% of the time. That, of course, is the exactly wrong attitude in drug development. You can’t run a pharmaceutical company with the goal of not failing, you have to run it with the goal of succeeding.

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