Dataset Information

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease.

ABSTRACT: Interstitial lung diseases (ILDs) are a group of respiratory disorders characterized by chronic inflammation and fibrosis of the pulmonary interstitial tissues. Although the etiology of ILD remains unclear, some drug treatments are among the primary causes of ILD. In the present study, we analyzed the FDA Adverse Event Reporting System and JMDC Inc. insurance claims to identify a coexisting drug that reduced the incidence of ILD associated with the use of an anti-arrhythmic agent, amiodarone, and found that the thrombin inhibitor dabigatran prevented the amiodarone-induced ILD in both clinical datasets. In an experimental validation of the hypothesis, long-term oral treatment of mice with amiodarone caused a gradual decrease in body weight caused by respiratory insufficiency. In the lungs of amiodarone-treated mice, infiltration of macrophages was observed in parallel with a delayed upregulation of the platelet-derived growth factor receptor α gene. In contrast, co-treatment with dabigatran significantly attenuated these amiodarone-induced changes indicative of ILD. These results suggest that dabigatran is effective in preventing drug-induced ILD. This combinatorial approach of drug repurposing based on clinical big data will pave the way for finding a new treatment with high clinical predictability and a well-defined molecular mechanism.

SUBMITTER: Siswanto S

PROVIDER: S-EPMC8490809 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease.

Siswanto Soni S Yamamoto Hiroki H Furuta Haruka H Kobayashi Mone M Nagashima Takuya T Kayanuma Gen G Nagayasu Kazuki K Imai Yumiko Y Kaneko Shuji S

Frontiers in pharmacology 20210921

Interstitial lung diseases (ILDs) are a group of respiratory disorders characterized by chronic inflammation and fibrosis of the pulmonary interstitial tissues. Although the etiology of ILD remains unclear, some drug treatments are among the primary causes of ILD. In the present study, we analyzed the FDA Adverse Event Reporting System and JMDC Inc. insurance claims to identify a coexisting drug that reduced the incidence of ILD associated with the use of an anti-arrhythmic agent, amiodarone, an ...[more]

PMID: 34621164

Similar Datasets

Project description:BackgroundInterstitial lung disease (ILD) results in high morbidity and health-care utilization. Diagnostic delays remain common and often occur in nonpulmonology settings. Screening for ILD in these settings has the potential to reduce diagnostic delays and improve patient outcomes.Research questionThis study sought to determine whether a pulmonary function test (PFT)-derived diagnostic prediction tool (ILD-Screen) could accurately identify incident ILD cases in patients undergoing PFT in nonpulmonology settings.Study design and methodsClinical and physiologic PFT variables predictive of ILD were identified by using iterative multivariable logistic regression models. ILD status was determined by using a multi-reader approach. An ILD-Screen score was generated by using final regression model coefficients, with a score ≥ 8 considered positive. ILD-Screen test performance was validated in an independent external cohort and applied prospectively to PFTs over 1 year to identify incident ILD cases at our institution.ResultsVariables comprising the ILD-Screen were age, height, total lung capacity, FEV1, diffusion capacity, and PFT indication. The ILD-Screen showed consistent test performance across cohorts, with a sensitivity of 0.79 and a specificity of 0.83 when applied prospectively. A positive ILD-Screen strongly predicted ILD (OR, 18.6; 95% CI, 9.4-36.9) and outperformed common ILD clinical features, including cough, dyspnea, lung crackles, and restrictive lung physiology. Prospective ILD-Screen application resulted in a higher proportion of patients undergoing chest CT imaging compared with a historical control cohort (74% vs 56%, respectively; P = .003), with a significantly shorter median time to chest CT imaging (5.6 vs 21.1 months; P < .001).InterpretationThe ILD-Screen showed good test performance in predicting ILD across diverse geographic settings and when applied prospectively. Systematic ILD-Screen application has the potential to reduce diagnostic delays and facilitate earlier intervention in patients with ILD.

Project description:BackgroundDrug-induced interstitial lung disease (DIILD) is a form of interstitial lung disease resulting from exposure to drugs causing inflammation and possibly interstitial fibrosis. Antineoplastic drugs are the primary cause of DIILD, accounting for 23%-51% of cases, with bleomycin, everolimus, erlotinib, trastuzumab-deruxtecan and immune checkpoint inhibitors being the most common causative agents. DIILD can be difficult to identify and manage, and there are currently no specific guidelines on the diagnosis and treatment of DIILD caused by anticancer drugs.ObjectiveTo develop recommendations for the diagnosis and management of DIILD in cancer patients.MethodsBased on the published literature and their clinical expertise, a multidisciplinary group of experts in Italy developed recommendations stratified by DIILD severity, based on the Common Terminology Criteria for Adverse Events.ResultsThe recommendations highlight the importance of multidisciplinary interaction in the diagnosis and management of DIILD. Important components of the diagnostic process are physical examination and careful patient history-taking, measurement of vital signs (particularly respiratory rate and arterial oxygen saturation), relevant laboratory tests, respiratory function testing with spirometry and diffusing capacity of the lung for carbon monoxide and computed tomography/imaging. Because the clinical and radiological signs of DIILD are often similar to those of pneumonias or interstitial lung diseases, differential diagnosis is important, including microbial and serological testing to exclude or confirm infectious causes. In most cases, management of DIILD requires the discontinuation of the antineoplastic agent and the administration of short-term steroids. Steroid tapering must be undertaken slowly to prevent reactivation of DIILD. Patients with severe and very severe (grade 3 and 4) DIILD will require hospitalisation and often need oxygen and non-invasive ventilation. Decisions about invasive ventilation should take into account the patient's cancer prognosis.ConclusionsThese recommendations provide a structured step-by-step diagnostic and therapeutic approach for each grade of suspected cancer-related DIILD.

Dataset Information

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease.

Publications

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets