Interstitial Lung Disease clinical trials at University of California Health

Interstitial lung disease (ILD) is a lung condition resulting in inflammation and stiffening of the lung, often associated with connective tissue diseases (CTDs). ILD causes reduction in lung volume, shortness of breath, cough and fatigue therefore has high impact on quality of life and is also the leading cause of death in participants with these conditions. The study will assess whether treatment of CTD-ILD participants with belimumab in addition to standard therapy will result in the stabilization and/or improvement of lung function and improve symptoms associated with ILD with an acceptable safety profile.

This study investigates the efficacy and safety of belimumab compared to placebo, in addition to standard therapy, for the treatment of participants with systemic sclerosis associated interstitial lung disease (SSc-ILD). The study will evaluate the effect of belimumab treatment on lung function as well as on extra-pulmonary disease manifestations, including skin thickening and general symptoms, such as fatigue, that impact quality of life (QoL).

This is a prospective exploratory biodistribution study in patients with interstitial lung disease (ILD). The purpose of this research study is to determine where and to which degree the FAPI tracer (68Ga-FAPI-46) accumulates in normal and fibrotic lung tissues of patients with interstitial lung disease. The study will include patients with interstitial lung disease who have or will initiate a new ILD medication OR will undergo tissue biopsy or surgery of the lung. The study will include 30 patients, the upper limit for PET imaging studies conducted under the Radioactive Drug Research Committee (RDRC) purview. Participants will be injected with up to 7 mCi of 68-GaFAPi and will undergo one PET/CT scan and one High Resolution CT of the lungs. The study is sponsored by Ahmanson Translational Theranostic Division at UCLA.

This is a 2-Part study with Part A, a double-blind, randomized, placebo-controlled, PoC study to evaluate the efficacy, safety, and tolerability of efzofitimod in patients with SSc-ILD. The primary objective of the study is to evaluate the PoC for efficacy in a population with SSc-ILD. While improvement of ILD is the outcome of interest, the study will also evaluate changes in the skin. After initial screening (up to 4 weeks), approximately 25 eligible participants will be randomized 2:2:1 to 1 of 2 active (experimental) dose arms or placebo, administered every 4 weeks up to and including Week 20. Part B is an optional open-label extension to Part A in which participants can receive 450 mg efzofitimod every 4 weeks for 6 doses.

The Idiopathic Pulmonary Fibrosis Prospective Outcomes (IPF-PRO) Registry started recruiting in 2014 with the objective of studying Idiopathic Pulmonary Fibrosis. In 2018, the registry expanded to include recruitment of participants with other chronic fibrosing interstitial lung diseases (ILDs) with progressive phenotype also referred to as progressive fibrosing interstitial lung diseases in the Chronic Fibrosis Interstitial Lung Disease with Progressive Phenotype (ILD-PRO) Registry. When the third phase of the registry begins, the IPF-PRO registry will enroll additional patients with idiopathic pulmonary fibrosis. This IPF-PRO registry is a prospective registry that will collect information regarding the natural history, health care interactions, participant reported questionnaire data to assess quality of life, and the methods of treatment of participants with a diagnosis of idiopathic pulmonary fibrosis (IPF) or of another chronic fibrosing interstitial lung disease (ILD) with progressive phenotype established at the enrolling centers. In addition, blood samples and chest image studies will be collected and banked for future research projects.

This study is a prospective observational study for subjects with idiopathic pulmonary fibrosis (IPF) or non-IPF interstitial lung diseases (ILD). The purpose of this study is to compare whether imaging patterns from high-resolution computed tomography (HRCT) at baseline can predict worsening. Single Time point Prediction (STP) is a score derived from an artificial intelligenc/ machine learning (AI/ML) using the radiomic features from a HRCT scan that quantifies the imaging patterns of short-term predictive worsening.