Scientists are well versed in experimental bias, which is why we address it by using experimental controls, masking our clinical trials, and using the scientific method to approach questions. However, how do we control for bias within our own minds? Cognitive bias refers to any number of ways that our brain prevents us from making entirely objective decisions. In an article that Harvard Business Review published in June 2011, “Before You Make That Big Decision…”, several types of cognitive bias are defined and discussed along with case studies, and a 12-step checklist to root out bias is defined.
Major decisions in pharmaceuticals are impacted by cognitive bias. When developing a product, there are a million decisions that can have a significant impact on the cost, timescale, clinical success, and eventual marketability of your product. Many of these decisions are originally made at the bench level, and may not be able to be changed without considerable additional time or expense as the project progresses through later stages of development.
For example, a formulator may demonstrate a bias for a particular type of formulation process because of previous experience and comfort, or the wish for high visibility through the use of trendy new technology, or convenience according to what equipment is on site and available. Decision makers should recognize the potential for this bias and make sure the best formulation is chosen regardless of the above factors. Once this formulation makes it into human studies, there is considerable inertia that makes change difficult, since the project team doesn’t want to delay timelines by having to repeat animal studies or bridge with additional human pharmacokinetic studies.
Bias can be very costly to big pharma companies, but attempts to avoid bias are not without cost. Multiple layers of peer review, involving Marketing early in development where most compounds fail for other reasons, and execution of checklists also take time, but could save billions for that one “blockbuster in the rough.”
According to the article, it is nearly impossible to detect your own bias, but through learning about bias, we can better detect it in our peers and use this knowledge to better challenge decisions. For example, when performing due diligence, you must be alert for bias from the company under scrutiny, the fellow members of your team, and in how your team prioritizes and reports the findings.
Here are some types of bias from the article and how they could come up in pharma:
This type of bias is hard to avoid. Almost every person on a project team is heavily vested in the success of their project. Part of this is due to corporate culture, which tends to reward those people who happen to be on successful projects. This bias can be minimized by shifting the focus from project success, which can be largely due to the luck of being assigned to a safe and effective compound, to excellence in contributing to the project. Another similar bias is loss aversion, a fancy business term for “fear of failure.” Pharma is understandably already risk-averse, but it is also disadvantageous to have people avoiding difficult projects, or killing projects that are a deviation from the norm without sufficient basis. If people on failing projects are rewarded for swiftly contributing to clinical evaluation and cost-effectively killing their project, there is less motivation to “succeed at all costs” or “run for the hills.”
In a similar vein, even when project members’ fates are not tied to a project outcome, a project team can fall in love with a concept after expending a lot of hard effort, which also makes an objective analysis of the product’s value difficult. In this case, it is up to the peer reviewers or due diligence team to make sure that they are getting a clear picture and not an overly positive projection based on the best subset of data.
Groupthink is the result of insufficient diversity on the team or strong dominant members that quash all dissent before it can be fully explored. If you have a group of scientists from similar backgrounds, who have been working together in the same field for a long time, groupthink can occur. Most Big Pharma companies indirectly solve groupthink by aggressively promoting diversity and reorganizing fairly often, so you aren’t working with the same people for more than a few years. Groupthink can be challenged head-on in peer review by considering the people making up the team- was there enough varied expertise? Were all voices heard?
There is a whole book devoted to this type of bias. Where does it come up for pharma? In audits of suppliers and due diligence for in-sourcing, this bias can be difficult to avoid. A related bias is the saliency bias, where a previous success casts a rosy glow on a new, similar project. The halo effect can come up in decisions regarding outsourcing. If you have a company that you love and frequently use for analytical capability, that positive association may bias you to choose them for formulation work, even though it may turn out that their capabilities for formulation are insufficient. As common as this bias is, at least it is easier to spot than some other types of bias. Auditing and due-diligence teams will benefit from reminding themselves of this potential bias before visiting a favorite supplier, as tempting as a shortened visit would be.
This bias may be the most insidious for pharma. In confirmation bias, the team generates one path forward and seeks only data to support the chosen path, disregarding all else. In drug development, each decision builds over a thousand smaller previous decisions. A common pitfall in oral formulation development is dose. Early in development, a high dose is required, so you develop a melt granulation. Later in development, when the dose has dropped to 10 mg, did the project team scale down the melt granulation, or evaluate a cheaper dry blend process?
There is much scientific information to evaluate in the early stages of product development. Even still, many times you have to move forward with less info than you would like. Analytical testing is a bit like exploring a cave with a flashlight, where the light cast by the flashlight is the capability of your test. Is there anything lurking in the shadows? It’s important to do a risk assessment based on what data is missing at the time of the decision and evaluate “what ifs.” What if the drug substance supply was not an issue? What if you had another month to develop? How would the decision change? Should a contingency plan be in place in case a critical factor does change?
For example, many times your first formulation is developed while your salt program is ongoing. For now, you are assuming your compound is insoluble, but what if a soluble salt is found? How will this change your approach? Do you have a workable backup plan?
Sunk Cost Fallacy
Pharma is very susceptible to the sunk cost fallacy because it is just so expensive to develop a drug. The sunk cost fallacy is when you, for better or worse, factor in past cost/resource into a decision for the future.
Consider the simplistic hypothetical case where you have a drug that you have already spent $500 million developing. The Food and Drug Administration (FDA) then restricts your patient population, driving the market forecast from blockbuster level to only $5 million a year over a projected remaining patent life of 7 years. You have $5 million in expected future costs prior to launch. If you consider the sunk costs, this project is a loser, and you may be tempted to cut your losses and save $5 million. However, if you ignore the past money spent and focus only on the future, the return on investment is pretty good.
The sunk cost fallacy can also work in the opposite way and be a powerful companion to the self-interest bias and related biases above, also known as the “We Have to Make This Work Because We Have Already Spent Ungodly Sums on It” bias.
Considering the impact and cost of bias to Big Pharma, an organizational assessment to determine how susceptible you are to bias may be in order:
– How aware are your project teams of cognitive bias and how to recognize it? Is this awareness only at the executive level, or does it reach to your bench-level decision makers?
– How are your decisions controlled? Is there peer review? Are the peer groups involved sufficiently diverse?
– Is your corporate or departmental culture breeding bias? Are people rewarded based on only project success? Have you ever rewarded a “positive failure”? Are dissenting opinions welcomed?
– Are there physical or process factors that could create bias in your decisions? For example, scientists may have a bias toward equipment housed in the same building as their office. If ordering a new excipient requires multiple forms and a six-month auditing process, there will be a strong preference for what’s already in the warehouse.
Pharmaceutical employees weather a perfect storm of conditions that promote bias: high financial stakes, a strong scientific drive to produce successful results, considerable time pressure, and a highly regulated environment resistant to change. A pharma company that promotes awareness of bias and implements effective counter-measures at all levels of the organization can sail through this storm toward better outcomes.
- Kahneman D, Lovallo D, Sibony O. The Big Idea: Before You Make That Big Decision… https://hbr.org/2011/06/the-big-idea-before-you-make-that-big-decision. Accessed January 19, 2019.
- Rosenzweig, Phil. The Halo Effect: . . . and the Eight Other Business Delusions That Deceive Managers. Free Press, 2014.