Whitepapers

Therapies designed to target cancers with specific molecular signatures have reshaped the landscape of oncological drug development.1 As the next generation of tumour-agnostic therapies make their way through clinical trials, we should be asking ourselves what we can do to prepare for the upcoming challenge of reimbursement in this space, and what lessons can be learnt from the experience of existing therapies. Over the coming months, we will provide recommendations from our own experience and a review of the experiences of the tropomyosin receptor kinase (TRK) inhibitors (larotrectinib and entrectinib) on how to overcome the common challenges in this space in five short papers. Here, in the third of our five-paper series, we focus on the difficulties of estimating the comparative effectiveness of tumour-agnostic treatments.

Drug development is changing, and guidance around the evaluation and approval of new treatments must change with it. The drive to develop medicines for conditions with the most severe unmet need – often rare and with no established or effective standard of care – frequently requires manufacturers to launch technologies based on evidence characterized by non-randomized trial designs with small sample sizes and limited follow-up. Of course, such evidence leads to regulatory and payer decision makers facing a high level of uncertainty when assessing the magnitude of the novel technology’s relative treatment benefit. If we are to accelerate access to transformative therapies, then we must acknowledge that greater flexibility in assessment and payment models is needed to address this uncertainty.

You will have likely seen recently that inappropriate use of Microsoft Excel® has led to a major issue with the UK’s Test and Trace system, with the reporting of almost 16,000 positive COVID-19 cases being delayed for over a week due to CSV row number limitations.1 The heart of the issue is the use of Excel to transfer data between one system and another – and, more generally, continuing to use outdated software versions and antiquated methods to transfer data, rather than creating systems that truly talk to each other and operate within the same framework (i.e. that are capable of record storage as well as the powerful analytical analysis required to perform country-wide pandemic modelling).