Influence of cigarette smoking on drugs’ metabolism and effects: a systematic review
Bibliographic record
Abstract
PURPOSE: Cigarette smoke continues to be widely used around the world and it contains several substances that can affect the pharmacokinetics and/or pharmacodynamics of medications, altering their safety and effectiveness. The aim of this systematic review was to summarize the scientific evidence regarding possible changes in the pharmacokinetics and/or pharmacodynamics of drugs induced by cigarette smoking, possible mechanisms of action and related effects. METHODS: The systematic review was performed according to the PRISMA Statement and the protocol was registered on the PROSPERO platform (CRD42023477784). Pubmed, Scopus, Web of Science databases were used. We considered observational, semi-experimental or experimental studies written in English and published between January 1, 2000, and November 13, 2024, focused on smoking subjects (healthy volunteers or patients) receiving any kind of medication. Data regarding possible modifications in drugs' pharmacokinetics and/or pharmacodynamics induced by cigarette smoking were assessed. The quality of observational studies and experimental studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale and the Jadad Scale, respectively. RESULTS: In total, 37 studies were included, and 31 of them showed relevant modifications in the pharmacokinetics or effects of the drugs in smokers compared to non-smokers. Most of the included studies (n = 20) investigated drugs for psychiatric or neurological disorders, showing a reduction in plasma concentration or an increase in drug clearance in smokers as well as antibiotics metronidazole and cycloserine. Besides, seven articles focused on anticancer drugs indicating an increase in drug metabolism. The remaining articles reported effects of smoking on the metabolism of other drugs, such as cardiovascular drugs, phosphodiesterase 5 inhibitors, local anesthetics and medications for musculoskeletal or chronic obstructive pulmonary diseases. Induction of the cytochrome enzyme CYP1A2 is the most common mechanism mediating the reduction of drug concentrations by cigarette smoking. CONCLUSION: The results indicate an increased risk of therapeutic failure for smokers and represent further motivation to encourage smoking cessation or attention in formulating personalized therapy.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".