Bringing a product to market requires overcoming several separate hurdles. The first is to gain regulatory approval. The next is to convince two key stakeholders about the fundamental value of their innovation: first the payer, who will decide the conditions and price to make it available to the clinicians, and second the clinician, who will decide whether or not to use it.
As life sciences companies define their development plans to successfully meet these milestones, they need to strategically consider the expectations of each stakeholder. More often than not, payer value and evidence to support reimbursement discussions tend to be relegated as companies focus on building data to support the regulatory process and to gain physician trust for the product.
In order to improve the chances of successful reimbursement and anticipated pricing levels, companies should implement an early phase modelling (EPM) process. This process should include key evidence from early in development to support and inform decisions around pricing and reimbursement and market positioning.
EPM can provide awareness about gaps as well as product potential sooner, ensuring evidence is gathered to support claims or respond to regulatory and health technology assessment (HTA) questions and concerns. Early phase modelling can also help companies to demonstrate a product’s potential to investors, which is particularly valuable for developers of more complex therapies and medical devices. This is important, given investor caution over ATMP investment in light of a number of product withdrawals in Europe, largely because they lacked commercial viability.
A key benefit of early phase modelling is to enable the product developer to understand, from the outset, the type of information needed to describe and support the value of the technology, and ultimately achieve business objectives. During pre-clinical studies, for example, the modelling can help to identify additional endpoints for a future trial that could improve the chances for reimbursement or achieving price expectations.
The modelling will make use of relevant available data at the time of assessment, such as efficacy target expectations, interim analysis from a study or, in the case of a rare disease, it might be published data from proxy conditions. The goal is to extract parameters that are as relevant as possible to carry out the modelling according to stated objectives. That might be to identify costs, comparators or quality-of-life data. The modelling might be a simple exercise or a highly complex project. For example, in oncology, there might be a lot of comparators and a decision tree model to bring together to help a company determine the potential impact of their product. These early insights can offer information on a product’s potential cost-effectiveness when comparing overall cost and quality of life data with current treatment options.
Scenarios for early phase modelling
Early phase modelling will produce a base-case scenario to demonstrate likely cost-effectiveness where all uncertainties are balanced. In addition, sensitivity analyses, where all variables are assessed separately will identify the parameter(s) with highest impact on the result, and thus, also those that stand out and should be further substantiated.
As an example, one company had developed a technology to support early diagnosis of a relatively common condition in patients with diabetes. The company had based their price strategy on an assumption that treatment for the condition was very costly. However, when the cost of early diagnosis and expected impact on the condition was modelled, indications were that it would not result in a significant enough decrease in the incidence of the condition to warrant use of the product across a broad patient population. The findings led the company to drop the program, which, while a disappointing outcome, meant large sums of money weren’t invested in a solution that likely would not have achieved reimbursement goals.
In another example, this time with a drug developed for an ultra-rare disease, early phase modelling allowed a small company to gain insights to help it determine outcomes not covered in the current trial program that would be important for later reimbursement discussions. Since there was no previous data published on the condition, the modelling meant matching the disease with a proxy condition – that is one with similar symptoms and progression. The modelling helped to demonstrate the importance of collecting quantitative data on healthcare, patient and caregiver burden. It also helped to describe pricing potential with regards to expected efficacy and intended positioning.
Armed with that information, the company is now conducting several studies to generate this data in parallel with its clinical trial program. When the product is ready for regulatory submission, the company will also have the data required to support adoption and reimbursement.
Unfortunately, it is not unusual for the clinical development process to fail to capture key evidence to support reimbursement. For example, there are often no or few active comparators, noninferiority studies aren’t conducted, there are indications of selection bias in the inclusion criteria, outcome variables for the payer are unusual or unattractive or the follow-up and clinical practice criteria are not in keeping with the reality of care.
Adapting clinical development to all these different endpoints, especially during phase 3, may entail a significant increase in costs and outcome risk. However, companies should weigh these costs with the risk of not being reimbursed or having the product reimbursed and used under unfavorable access conditions because of payer uncertainty about the benefit of the product from a clinical, budget and efficiency perspective.
Early phase modelling would provide valuable data early on to better manage patient access expectations. At the very least, it will ensure understanding and early consideration of how to mitigate potential limitations to patients’ ability to access the product. These limitations may stem from the product itself, including available evidence, from the therapeutic area at which it is targeted, from its level of social and political priority, or from the competitors already in the market.
Companies armed with good data early on are best placed to shorten the route to patient access.
About the authors:
Annabelle Forsmark, Ph.D., is Senior Manager, Health Economics & Outcomes Research at PharmaLex. Ida Sjöberg is Market Access Director at PharmaLex. Alberto Rubio is Senior Director Market Access at PharmaLex.
