Leveraging Genomic Data in Smoking Cessation Trials in the Era of Precision Medicine: Why and How
Bibliographic record
Abstract
In an era of Precision Medicine, it is vital to collect biological data within clinical trials and to integrate their analysis within the outcomes of the trial. The identification of genomic biomarkers that affect treatment response to smoking cessation treatment, both pharmacological and behavioral, or susceptibility to medication-related adverse reactions, holds real promise to improve treatment efficacy and to tailor the treatment approach to the individual. However, a clear challenge in identifying reliable biomarkers is in obtaining adequate sample sizes. Consortium-based approaches will likely be necessary to yield real successes. Thus, meta-analyses of data from individual smoking cessation trials will become crucial and will be facilitated by standardized trial designs, assessments, and outcomes and harmonizable measures. To foster increased collection of high-quality genetics data in clinical trials, we discuss (1) genetically informed trial design, (2) biological samples (collection requirements, storage, and analysis with a focus on genomic data) and genetics consortia, (3) participant consent and data sharing requirements for Institutional Review Board (IRB) approvals, and (4) information on phenotype characterization and meta-analysis. This work aligns with the objectives of the Precision Medicine Initiative and offers guidance for integrating treatment research and genetics/genomics within the nicotine and tobacco research community. It is intended to promote the collection and genotyping of biosamples in existing subject samples as well as the integration of genetic research elements into future study designs. This article cross-references a companion paper in this issue that reviews current evidence on genetic and epigenetic markers in cessation trials. This article outlines a framework for the consistent integration of biological data/samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence.
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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 | 0.387 | 0.581 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".