A rare disease affects fewer than one in two thousand people. The Orphan Drug Act, passed in 1983, transformed R&D productivity for rare conditions by offering grants, and market exclusivity to incentivise sponsors working on these areas of high unmet need. The impact of these measures has been vast, and the number of clinical trials focused on rare diseases has grown substantially over the last decade. Nevertheless, sponsors working in the space continue to face a range of challenges, many of which can be mitigated by expert analytical input.
The Veramed team has supported various orphan disease developments from rare oncology indications such as multiple myeloma to ultra-orphan immunology indications such as Activated PI3K Delta Syndrome by providing trial design, statistical analysis and reporting activities.
Our work encompasses all phases of development for rare indications and our solutions span:
- Trial designs including innovative approaches
- Statistical analysis and reporting
- Interim analyses
- PK reporting
- Data Monitoring Committees
Statistical and reporting challenges in rare diseases
The critical issue facing drug developers working in rare disease is the scarcity of patients and clinical data. Further, there is often poor disease understanding, heterogeneous responses between subjects and a lack of established, well-defined endpoints.
Several statistical approaches can assist sponsors working in the rare disease space.
Consider innovative trial designs that maximise the use of accruing data
Adaptive designs, in particular, have been proposed as a means of gaining efficiency in studying rare diseases. Many adaptive methods are related to trials in rare diseases.
Incorporating information from real-world data
Traditional approaches are not always feasible in rare conditions, and increasingly, sponsors are looking to incorporate real-world data (RWD) from disease registries, medical records and literature, respectively, to complement regulatory-grade evidence for selected rare diseases.
Harnessing model-informed drug development
Quantitative modelling approaches may accelerate rare disease development by informing trial design, dose selection, and dosing regimens.
Carefully planning data collection to make the most of every data point
Rare disease trials are often subject to many protocol amendments, and sponsors should factor this in upfront. Further, Statisticians need to liaise carefully with data managers to simplify data collection and avoid burdening sites with capturing unnecessary information.
Enabling success for sponsors in rare disease
Veramed has supported rare disease programmes for some of the world’s most innovative biopharmaceutical companies. These sponsors often run a lean, virtual operation with limited in-house analytical resource and rely on Veramed to extend their internal team, delivering high-quality statistical solutions from consultancy to full study-based support.
Examples of our work in rare diseases
Supporting the development of a new gene therapy for an ultra-rare disease
Our client is an international biopharmaceutical company specialising in gene therapies for rare diseases. They came to Veramed to support the statistical analysis and reporting for several trials with fewer than ten subjects in an ultra-rare, life-limiting genetic condition. The client also required an integrated summary pooling the analysis datasets from these studies. The client produced the trial designs and statistical analysis plans in-house, but Veramed was on hand to review and provided additional input.
- Due to the severity of this severe illness, the trial used significant amounts of laboratory data for analysis.
- Gene therapies require long-term follow up of patients, which needed to be accounted for, and so the team needed to ensure the code was robust, durable, and ‘future-proofed’.
Veramed provided a strong team of Statisticians and Programmers and delivered high-quality outputs vital to enable the client’s success as they progress their program of studies in this area of high unmet patient need.