Brainomix, a leading medtech company specialising in AI-powered imaging tools for lung disease and stroke; and Endeavor BioMedicines, a clinical-stage biotechnology company, presented positive results from the Brainomix AI-driven quantitative analysis of HRCT imaging data in Endeavor’s phase 2a trial of taladegib in idiopathic pulmonary fibrosis (IPF) at the European Respiratory Society (ERS) Conference in Barcelona.
The randomised, double-blind, placebo-controlled 12-week study evaluated taladegib, a hedgehog pathway inhibitor in patients with IPF.
The trial previously demonstrated that treatment with taladegib improved lung function from baseline as measured by forced vital capacity (FVC), increased total lung capacity and reversed measures of interstitial lung disease (ILD) from baseline at week 12.
Brainomix 360 e-Lung is an FDA-cleared, CE-marked AI-powered image analysis tool that provides automatic quantifications and visualisations of radiographic features in lung CT scans.
Clinicians can use these outputs to aid in their diagnostic assessment of lung disease.
Brainomix and Endeavor entered into a collaboration to conduct a post-hoc analysis evaluating the potential of Brainomix AI-powered quantitative imaging outputs to support clinical trials.
Trained on diverse datasets, the platform has already been employed in several phase 2 and phase 3 trials and became the first quantitative CT imaging platform selected as a co-primary endpoint in a phase 3b pulmonary fibrosis trial.
In a post-hoc analysis of the 12-week phase 2a ENV-IPF-101 trial, e-Lung quantitative CT imaging was used. Analysis by the authors detected significant positive differences in favour of taladegib in lung volume, overall ILD burden and total fibrosis extent.
These findings highlight the potential of AI-powered quantitative imaging outputs to support the detection of meaningful treatment effects in small, relatively short clinical studies, supporting more efficient and informed clinical development.
Importantly, these results highlight the value of reliable, objective AI-endpoints in clinical development programmes for pulmonary fibrosis.
While forced vital capacity (FVC) remains an established clinical endpoint, it may not fully capture meaningful biological changes or provide mechanistic insights into treatment response.
