Clinical trial design, end-points, and emerging therapies in pulmonary arterial hypertension

Abstract

Clinical trials in pulmonary arterial hypertension (PAH) have led to the approval of several effective treatments that improve symptoms, exercise capacity and clinical outcomes. In phase 3 clinical trials, primary end-points must reflect how a patient "feels, functions or survives". In a rare disease like PAH, with an ever-growing number of treatment options and numerous candidate therapies being studied, future clinical trials are now faced with challenges related to sample size requirements, efficiency and demonstration of incremental benefit on traditional end-points in patients receiving background therapy with multiple drugs. Novel clinical trial end-points, innovative trial designs and statistical approaches and new technologies may be potential solutions to tackle the challenges facing future PAH trials, but these must be acceptable to patients and regulatory bodies while preserving methodological rigour. In this World Symposium on Pulmonary Hypertension task force article, we address emerging trial end-points and designs, biomarkers and surrogate end-point validation, the concept of disease modification, challenges and opportunities to address diversity and representativeness, and the use of new technologies such as artificial intelligence in PAH clinical trials.

FIGURE 1

Win statistics for composite end-points. In a hypothetical trial of 200 patients with pulmonary arterial hypertension randomised to active treatment (n=100) or control (n=100), there are 10 000 pairs. Each pair is compared for each outcome in the hierarchy sequentially. Starting with death, pairs in which the patient on active treatment lives (wins) and the patient in the control arm dies (loss) are considered first. If neither patient in a pair dies, the next outcome (transplantation) is considered, then next component (hospitalisation), and so on. The total wins, ties and losses are tallied and win statistics such as the win ratio, win odds and net benefit are calculated. Created with BioRender.com.

FIGURE 2

Example of a platform trial for pulmonary arterial hypertension (PAH). New therapies can be added to the platform at any time. At pre-specified interim analyses, statistical models are updated based on accumulated outcome data, statistical boundaries are assessed and randomisation probabilities may be updated. Based on interim analyses, recruitment may continue, stop for futility or safety, or stop due to efficacy for a given arm. Created with BioRender.com.

FIGURE 3

Trial designs to evaluate disease modification. a) Randomised withdrawal trial: following a placebo-controlled parallel-group trial, patients initially on placebo remain on placebo while patients on experimental treatment are randomised to continue or withdrawal to placebo. If the outcome measures in the withdrawal group deteriorate or regress to the initial placebo group, a disease-modifying effect is not present. Improvements in the outcome measures maintained beyond the washout of the direct vasodilatory effect of the intervention support a disease-modifying effect. b) Delayed-start trial: in the first period, patients randomly receive the experimental treatment or placebo in a double-blinded manner. In the second period, patients initially on placebo start the experimental treatment and outcomes are reassessed. Failure of the initial placebo group to “catch up” to the initial experimental group suggests a disease-modifying effect. 6MWD: 6-min walk distance; RV: right ventricle. Created with BioRender.com.