Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease characterized by worsening dyspnea and lung function and has a median survival of 2–3 years. Forced vital capacity (FVC) is the primary endpoint used most commonly in IPF clinical trials as it is the best surrogate for mortality. This study assessed quantitative scores from high-resolution computed tomography (HRCT) developed by machine learning as a secondary efficacy endpoint in a 26-week phase II study of BMS-986020 – an LPA1 receptor antagonist – in patients with IPF. Methods: HRCT scans from 96% (137/142) of randomized subjects were utilized. Quantitative lung fibrosis (QLF) scores were calculated from the HRCT images. QLF improvement was defined as ⩾2% reduction in QLF score from baseline to week 26. Results: In the placebo arm, 5% of patients demonstrated an improvement in QLF score at week 26 compared with 15% and 27% of patients in the BMS-986020 600 mg once daily (QD) and twice daily (BID) arms, respectively [ versus placebo: p = 0.08 (600 mg QD); p = 0.0098 (600 mg BID)]. Significant correlations were found between changes in QLF and changes in percent predicted FVC, diffusing capacity for carbon monoxide (DLCO), and shortness of breath at week 26 ( ρ = −0.41, ρ = −0.22, and ρ = 0.27, respectively; all p < 0.01). Conclusions: This study demonstrated the utility of quantitative HRCT as an efficacy endpoint for IPF in a double-blind, placebo-controlled clinical trial setting. The reviews of this paper are available via the supplemental material section.
Breathe, breathe in the air: the anti-CCN2 antibody pamrevlumab (FG-3019) completes a successful phase II clinical trial for idiopathic pulmonary fibrosis
