OBJECTIVES

: Decision making around funding and access of rare disease products is a well-known challenge for healthcare systems worldwide. Generation of clinical evidence is not straightforward, due to inability to perform randomised controlled trials, lack of comparators and/or use of surrogate endpoints. From an economic perspective, high “price density” with high one-off upfront costs adds to the existing challenge. This research aims to explore potential solutions that can remove barriers to fund and make rare disease drugs available to patients.

METHODS

: A targeted literature review was first undertaken to identify HTAs of rare disease drugs assessed in the last 3 years by the EU3 HTA bodies (NICE, G-BA, HAS). Based on findings, interviews were conducted with selected payers across UK, Germany and France to gain further insights on the feasibility and requirements for any innovative sources of data to be considered.

RESULTS

: Non-comparative studies, uncontrolled studies, or even real-world evidence may provide the best available evidence to inform health care decision making, when RCTs are not appropriate. Data engineering will be required to properly integrate the data from multiple sources (e.g. claims data, digital health technologies), which is expected to require intensive time and investment resources. Modelling is highly useful to address the issue of lack of comparative data by building artificial comparator out of RWD. Financial engineering will allow payers to better predict required investments and explore/accept innovative reimbursement solutions.

CONCLUSIONS

: With innovative therapies becoming increasingly available, there is an urgent need to rethink or adapt HTA processes to address challenges around clinical uncertainty and affordability. Innovative source data and financial engineering are promising solutions to overcome those barriers.