A Study Protocol on the Overlooked Role of ABCB1 Pharmacogenetics in Atorvastatin Therapy: A Systematic Review of Clinical, Pharmacokinetics, and Population Evidence
Bibliographic record
Abstract
ABSTRACT Atorvastatin is widely used for dyslipidaemia and cardiovascular disease prevention, yet interindividual variability in efficacy and adverse effects, particularly statin-induced myopathy, limits its clinical use. Pharmacogenetic studies have largely focused on SLCO1B1 and CYP3A4/5; the role of ABCB1, which encodes P-glycoprotein, remains underexplored. This review aims to systematically evaluate the influence of ABCB1 polymorphisms on atorvastatin pharmacokinetics, efficacy, and safety outcomes. Following PRISMA guidelines, clinical studies involving adults on atorvastatin therapy will be identified from PubMed, Web of Science, and Scopus. Eligible studies must assess associations between common ABCB1 variants (e.g., C3435T, G2677T/A, and C1236T) and atorvastatin response outcomes. Data on study design, population, demographics, genotyping, and other key findings will be extracted. The risk of bias will be assessed using the Newcastle-Ottawa Scale for observational studies, and a meta-analysis will be performed. The review will clarify the contribution of ABCB1 polymorphisms to atorvastatin pharmacokinetics, lipidlowering efficacy, and adverse events. It will also explore ethnic differences in allele frequencies and treatment response. By addressing this overlooked aspect of statin pharmacogenetics, the insights gained may support the integration of ABCB1 genotyping into clinical decision-making and enhance personalised cardiovascular 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 | 0.071 | 0.104 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.009 |
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".