Synthetic Control Arms in Respiratory Trials: A Smarter Path to Faster Drug Development
Developing therapies for serious respiratory diseases is entering an exciting phase. Advances in fibrosis research, inhaled therapeutics, and precision medicine are creating new scientific opportunities for biopharma sponsors. However, one of the biggest obstacles when reaching the clinical trial stage is the control arm.
Traditional randomised controlled trials require a comparator cohort receiving placebo or standard-of-care therapy. In respiratory medicine, this model can be problematic as many diseases are rare, rapidly progressive, or diagnosed late in their course. Eligible patient populations are therefore limited and often already heavily treated, making it impractical to recruit large numbers of control patients, and unethical when limited treatment options are available. This leads to slower enrolment, longer timelines, higher trial costs, and increased development risk.
Synthetic control arms (SCAs) are emerging as a practical tool to complement conventional trial designs in selected respiratory programmes. By combining real-world data with advanced analytics and imaging AI, SCAs can provide biologically relevant external comparator groups, augmenting reliance on conventional controls.
Real-world applications in fibrotic lung disease
Qureight is at the forefront of applying synthetic control methodologies in fibrotic lung disease programmes, combining multimodal real-world datasets with AI-driven imaging analytics to support more efficient and biologically informed clinical development.
We recently worked with Avalyn to create an SCA to evaluate inhaled pirfenidone (AP01) for idiopathic pulmonary fibrosis (IPF). Working from a large repository of historical IPF clinical trial and high-resolution computed tomography (CT) imaging data, we developed tightly matched external comparator cohorts using a combination of demographic variables, lung function metrics, and AI-derived quantitative imaging biomarkers. By incorporating imaging-based measures of fibrosis extent and disease burden, we were able to deliver a biologically aligned comparison between treated patients and treatment-naïve controls. The resulting analysis, presented at the American Thoracic Society in 2025, demonstrated statistically significant treatment effects over 48 weeks while reducing reliance on a conventional placebo arm1.
We also supported Vicore’s Phase 2a AIR trial evaluating buloxibutid in IPF. Using real-world patient datasets and advanced cohort matching methodologies, we generated and analysed thousands of potential external control populations to identify highly comparable treatment-naïve patients. From this process, we constructed a rigorously matched SCA that enabled robust comparison against the active treatment arm. The study demonstrated a statistically significant difference in forced vital capacity decline at 36 weeks, further highlighting the potential of AI-enabled external controls to strengthen efficacy assessment in rare and progressive lung diseases2.
When sponsors should consider an SCA
For sponsors, the value of SCAs extends beyond ethics alone. Reducing placebo recruitment requirements can materially accelerate enrolment timelines, particularly in respiratory disease settings where every patient is critical. Smaller control cohorts may also lower study costs and improve site engagement. Importantly, synthetic controls can support earlier development decisions. By comparing investigational therapies against well-characterised treatment-naïve populations, sponsors may gain clearer insight into efficacy signals before committing to large pivotal programmes. This can reduce late-stage uncertainty and improve portfolio prioritisation.
SCAs may also enhance patient and investigator willingness to participate in trials. When participants know they are more likely to receive active therapy, enrolment and retention can improve, offering an increasingly valuable advantage in competitive therapeutic areas.
Methodological rigour
The value of an SCA depends on the quality, relevance, and traceability of the underlying data. Cohorts must be matched against trial eligibility criteria, baseline disease severity, background therapy, endpoint definitions, imaging protocols, and follow-up windows. Sensitivity analyses and prespecified statistical analysis plans are essential to demonstrate that observed differences are not driven by data-source bias.
Conclusion
For biopharma companies developing therapies in fibrotic and rare respiratory diseases, such as IPF and progressive pulmonary fibrosis (PPF), SCAs offer a practical way to improve trial efficiency, strengthen early efficacy assessment, and reduce unnecessary reliance on placebo recruitment where scientifically and regulatorily appropriate. By combining high-quality real-world clinical data with quantitative imaging biomarkers and rigorous cohort-matching methodology, Qureight can help sponsors design biologically aligned external comparators for IPF and PPF programmes, with applications expanding across selected respiratory indications as data coverage and validation mature.
To learn how Qureight can support synthetic control arm development for your respiratory clinical trial, please contact our Commercial team at [email protected].
Bussell et al (2025). Dose-Dependent Change of Inhaled Pirfenidone Seen in Lung Volume and Fibrosis Quantification in Patients With IPF: A Deep Learning Image-Based Analysis of Data From the ATLAS Phase 1b Trial. Am J Respir Crit Care Med, 211: A5330. doi: 10.1164/ajrccm.2025.211.Abstracts.A5330
Kirov K, Thillai M and Maher T (2025). Late Breaking Abstract - Synthetic Arm Generation Utilizing Real-World Patient Data Demonstrates Treatment Effect in the Phase 2a AIR trial of Buloxibutid in IPF. Eur Respir J, 66(suppl 69): PA3043. doi: 10.1183/13993003.congress-2025.PA3043