Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. This presentation reviews selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.

Background: Hybrid designs with a randomised and external control arm preserve key features of randomization and utilize patient-level information from registries and electronic health records to augment clinical trials. Integration of external control data, however, can distort study results if the external cohort is not interchangeable with the randomised controls. In this study, we propose to leverage high-quality, patient-level concurrent registries for augmenting clinical trials and illustrate its impact on trial design for Amyotrophic Lateral Sclerosis (ALS).

Methods: The proposed methodology was evaluated in a completed, randomised, placebo-controlled clinical trial. We used patient-level information from a well-defined, population-based registry that ran parallel to the clinical trial to reconstruct the eligible trial population, identify concurrently non-participating eligible patients who matched with trial participants, and integrate them into the statistical analysis. We assessed the impact of the matched external data on the treatment effect estimate, precision, and time to reach a conclusion.

Findings: During the runtime of the trial, a total of 1,141 patients were alive in the registry, of whom 473 (41·5%) fulfilled the eligibility criteria and 133 (11·7%) participated. Trial participants were younger and in a better health compared to non-participating eligible patients. A matched control population could be identified among the non-participating patients. Integration of external controls into a hybrid design improved precision and increased statistical power from 58·1% to 77·3%. Augmenting the randomised controls with matched external controls could prevent randomization of an additional 17 patients (12·8%) and reduce the trial duration by 25·0% to terminate a futile trial. Matching eligible external controls from a different calendar period did not lead to improvement.

Interpretation: Hybrid trial designs utilizing a concurrent patient-level registry with rigorous matching may minimise bias due to a mismatch in calendar time and differences in care pathways, and can accelerate the development of new treatments.

The EMA Guideline on Clinical Trials in Small Populations dates back to 2006 and has been “on the list” for revision for at least five years now. The general message that the guideline sets is: On the one hand what could increase efficiency of (randomised) clinical trials in small populations would be equally applicable to large populations. On the other hand, it acknowledges that advancing new therapies is more challenging for small population and thus less common or less tried approaches can be considered, if usual standards are difficult or impossible to implement. From a methodological perspective, several new promising designs have been proposed to improve clinical development – as illustrated in this session. In this presentation I aim to explore how we could address more systematically the potential added uncertainty, trade-offs and also benefits versus the well-known paths with randomised clinical trials. Of the novel designs, I will draw on examples for hybrid designs (leveraging available data) and basket trials. I aim to enhance the discussion on regulatory assessment as well as health economic assessment, to ensure novel designs can be instrumental to new treatments reaching patients with rare diseases.