Pharmacology and Pharmacogenetics of Chemotherapeutic Agents
Bibliographic record
Abstract
The last decade the field of oncology has seen the introduction of several efficacious chemotherapeutic agents. However the benefits achieved have been modest at best. The choice of chemotherapeutic agent is often empirical and geared to fit the average patient with the result that approximately 40% of patients may be receiving the wrong drug. With greater understanding of the mechanisms behind the heterogeneity observed across patient populations, both in terms of efficacy and toxicity of a variety of therapeutic agents, research has now focused on individualizing treatment strategies by incorporating a combination of physiological variables, genetic characteristics and environmental factors together with the traditional tumor characteristics that currently drives clinical decision making. This review focuses on defining some of the principle components of personalized medicine. In addition we will review the pharmacological and pharmacogenetic predictors of toxic effects of chemotherapeutic agents drawing on examples of commonly used agents in oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".