Identification of Drug Candidates to Suppress Cigarette Smoke-induced Inflammation via Connectivity Map Analyses.
Gilles VanderstockenAnna Dvorkin-GhevaPamela ShenCorry-Anke BrandsmaMa'en ObeidatYohan BosseJohn A HassellMartin R StampfliPublished in: American journal of respiratory cell and molecular biology (2019)
Cigarette smoking is the main risk factor for chronic obstructive pulmonary disease, and to date, existing pharmacologic interventions have been ineffective at controlling inflammatory processes associated with the disease. To address this issue, we used the Connectivity Map (cMap) database to identify drug candidates with the potential to attenuate cigarette smoke-induced inflammation. We queried cMap using three independent in-house cohorts of healthy nonsmokers and smokers. Potential drug candidates were validated against four publicly available human datasets, as well as six independent datasets from cigarette smoke-exposed mice. Overall, these analyses yielded two potential drug candidates: kaempferol and bethanechol. Subsequently, the efficacy of each drug was validated in vivo in a model of cigarette smoke-induced inflammation. BALB/c mice were exposed to room air or cigarette smoke and treated with each of the two candidate drugs either prophylactically or therapeutically. We found that kaempferol, but not bethanechol, was able to reduce cigarette smoke-induced neutrophilia, both when administered prophylactically and when administered therapeutically. Mechanistically, kaempferol decreased expression of IL-1α and CXCL5 concentrations in the lung. Our data suggest that cMap analyses may serve as a useful tool to identify novel drug candidates against cigarette smoke-induced inflammation.
Keyphrases
- drug induced
- oxidative stress
- high glucose
- diabetic rats
- chronic obstructive pulmonary disease
- endothelial cells
- adverse drug
- emergency department
- type diabetes
- physical activity
- multiple sclerosis
- adipose tissue
- metabolic syndrome
- white matter
- resting state
- risk assessment
- deep learning
- climate change
- insulin resistance
- machine learning
- air pollution
- artificial intelligence