Bibliographic record
Abstract
Personalized medicine is an emerging field with a goal of applying genomic information as a predictor of disease risk as well as individualization of drug therapy. For optimization of drug therapy, significant progress has been made in the past decade in linking genetic variation in genes associated with drug disposition to prediction of drug response and adverse reactions. For most drugs in clinical use, the interplay of many factors, including genetics, demographics, drug-drug interactions, disease states and the environment, result in the interindividual variability observed during drug therapy. Broadly speaking, such determinants of drug response are mediated through modulation of drug concentrations reflective of pharmacokinetic factors, as well as drug targets, often referred to as pharmacodynamics. It is clear that for personalized medicine to become clinically meaningful, genomic as well as clinical and environmental influences must be considered together. We show, for a number of drugs in clinical use, that genomics-guided treatment options not only are becoming feasible but are also on the cusp of showing superiority in terms of clinical outcomes as well as cost-benefit. One of the most widely studied drugs with regard to genomics-guided dosing options is the oral anticoagulant, warfarin. Genetic polymorphisms in the gene encoding cytochrome P450 2C9 (CYP2C9) and those in the target gene responsible for the warfarin anticoagulant effect, vitamin K epoxide reductase (VKORC1), account for much of the variability in the warfarin maintenance dose; however, routine genotyping in warfarin therapy remains controversial. We will outline the importance of understanding all of the variables that mediate warfarin response as the prerequisite to successful utilization of genotype-guided warfarin therapy. Similarly, HMG Co-A reductase inhibitors, commonly known as statins, also display wide interindividual variability in plasma concentration, response and toxicity due in part to polymorphisms in transporter genes, including SLCO1B1 and ABCG2. Genetic factors are also important considerations in treatment with other therapeutic agents discussed, including clopidogrel and tamoxifen. Implementation of personalized medicine-based treatment options for these and other drugs, the pharmacokinetics or pharmacodynamics of which are impacted by functional genetic variations, will require overcoming a number of challenges, including cost, turnaround time, and demonstration of clinical benefit, as well as better training of health care professionals about genomics in general, and pharmacogenomics in particular.
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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 | 0.042 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.017 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".