Identification of Drug Candidates to Suppress Cigarette Smoke–induced Inflammation via Connectivity Map Analyses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".