Five Key Decision Points That Set You Up for Commercial Success

The commercial success of a drug hinges mostly on five key decision points made during the development life cycle. They include choosing the appropriate 1) indication, 2) dose, 3) patient population, 4) study design and 5) total evidence package. Mel Formica, global head, Evidera value & development consulting, discusses how making those right decisions — while understanding the trade-offs involved in each — can translate to a product that meets unmet needs, that payers are willing to pay a price for in line with its value, that is willingly prescribed by doctors and wanted by patients, and that will, in turn, reach its full potential.

I’m often asked the question: Why do drugs fail to reach their potential, or why do drugs just plain fail when they reach the market? There are, in fact, lots of reasons, but I can pinpoint five interconnected decision points that account for approximately 75% of a product’s success (or lack thereof) when it hits the market.

Indication

Even at the earliest phase in the development life cycle, when narrowing down the indication, commercial considerations should be thought about during the decision process. The reality is, particularly for rare diseases, gene and cell therapies, and oncology, your development strategy is increasingly becoming your commercial strategy.

For example, an asset may have the potential for multiple therapeutic applications. Choosing the right indication involves being very thoughtful about the trade-offs in order to position and differentiate a product. It’s important, of course, to consider the regulatory pathway as you analyze options, but it’s equally important to consider other questions: What will the clinical development program look like? How will payers perceive it? Will you be able to access the patients you think you want to access?

There are now sophisticated methodologies that can help get more data — and better quality data — faster to inform decision making. For example, there are study designs that can address multiple research questions simultaneously, offering the potential to model for both toxicity and efficacy. In some cases, adaptive designs can also enable the study of indications and dosages simultaneously, helping to narrow down to those indications in which the desired signal and optimum dose are achieved. Many times, these methods can achieve results from a smaller number of patients, which may lead to faster answers along with cost savings. 

Dose

Failure to define the optimal dose in clinical development is a common problem. Phase I oncology studies, for example, have a correct maximum tolerated dose (MTD) estimation rate of only about 40%,¹ which may result in patients being treated at subtherapeutic doses or doses that are too toxic. Either of these issues may disrupt the outcomes of all subsequent phases of a clinical study, and potentially — without a correction — may lead to the failure of the entire development program at a later stage. In oncology trials, adaptive designs such as the continual reassessment method (CRM), a Bayesian model-based approach, carry a much lower risk of overestimating or underestimating the MTD.

Adaptive designs may have more historical use in oncology studies, but they have also proven their value in other therapeutic areas, including most recently and notably, COVID-19 trials. I believe adaptive designs will become more commonplace as pharmaceutical companies get more comfortable with them. Our experience with regulators is that they are accepting of these designs and often encourage them.

Patient population

The right patient population is a decision point that focuses on several questions in tandem: Where in the treatment paradigm does the product fit, and therefore, for whom is the product best suited?

This is a critical and nuanced decision point, as there can often be a disconnect between what regulators look at in a patient population versus what payers will look at. That’s why it’s critical to define who the patient population is, where the product is placed in the therapeutic algorithm, what the comparator should be, and what outcomes should be measured.

These questions build on each other but essentially boil down to helping stakeholders understand precisely who is going to benefit the most from this intervention. This leads to a critical question that is seen not through a regulatory lens, but primarily through a payer or physician’s perspective: Why should I pay for/prescribe this new drug? Why is it better for the patient than what I am already using?

These questions are particularly important in crowded disease areas. Segmenting distinct patient subpopulations who would benefit disproportionately from a new intervention is a strategy designed to create incremental value. By addressing the disease at a patient level — not a line-of-therapy level — there may be opportunities to carve out distinct patient subpopulations. Asking which patients could benefit more from a new intervention for a variety of reasons (be they clinical, biometric, genetic, patient preference, etc.) opens up new possibilities to position a product for commercial success.

Study design

Understanding the patient population that will benefit the most from the therapy, and where those patients sit within the treatment paradigm, should define the study design. Based on that specific patient population, a comparator accepted by multiple stakeholders as representing standard of care should be selected, followed by the right patient-relevant outcomes, which should lead to the optimum study design for those stakeholders.

The key driver of study design should be the anticipated positioning for the product. Focusing on specific patient populations helps bring clarity around design choices.  Some of the decisions that are involved in clarifying study design may be clear; others, less so. However, novel protocol optimization tools can help model different scenarios including study endpoints, target population, sample size and power determination to better understand the impact of different decisions.

Total evidence package

The end goal is a product that is perceived as valuable by regulators, that payers are willing to pay a price for in line with that value, and that is requested by patients and prescribed by physicians. To achieve a total evidence package that meets all those different stakeholders is required when a product comes to market.

It is not uncommon during a sprint to approval that as drug developers start to engage with different stakeholders, they realize that they initially neglected various evidence needs, which can lead to significant loss of commercial potential. As payers and health technology authorities (HTAs) grow more stringent in their data reviews and selective about the products they will cover, developers are increasingly being denied reimbursement for approved products.

Pressure is mounting to acquire evidence for a range of stakeholders alongside development programs, and for some products, different evidence standards might be required. For example, for an advanced gene therapy that needs a natural comparator, real-world data (RWD) may be used creatively to bridge evidence gaps for approval.

Additionally, a holistic evidence package must be of sufficient quality to address both key regulatory and HTA/payer needs. Addressing methodological issues and preparing for increasing standards for the use of RWD will serve manufacturers well as they navigate the disparate needs of different markets and health agencies. Early engagement via integrated scientific advice can be a valuable strategy to refine and evaluate evidence generation plans and align them with regulator and HTA/payer requirements.

The point is that these considerations need to be explored early in drug development programs to ensure sponsors have sufficient time to complete the studies necessary to develop a compelling evidence narrative for all the stakeholders who will influence the success of the product’s launch.

Conclusion

Having reflected on each of these five decision points, it’s important to recognize that there is no perfect model that can optimize the commercial value for every product in every organization. But factors that impact the quality of the decisions made will remain the same: Drug developers must be able to make key decisions early in the development cycle, and they need to have the right voices at the table. It is important to leverage advanced technologies and methodologies (e.g., simulation tools) when appropriate to generate more and better information. And you need to be agile and adapt as new information becomes available. Most importantly, you need to make sure that you consider the needs of all the stakeholders in the health care ecosystem. Lastly, remember that all five key decision points involve trade-offs, and that the people who will be facing the consequences of those decisions — from the development teams to the regulatory teams to the commercialization teams — will want to understand the rationale behind those decisions. Transparency is key, along with accountability, as an asset moves through the development life cycle, to set it up for commercial success.