Optimizing safety of cisplatin treatment: Utilizing pharmacogenomics to prevent nephrotoxicity
Bibliographic record
Abstract
Personalized cisplatin therapy could be of great potential to minimize the risk of side effects, especially nephrotoxicity without compromising its antineoplastic effect. It remains unclear if genetic factors can predict the risk of nephrotoxicity among cisplatin users. The primary objective of this thesis is to identify and validate genetic variants that could assist the prevention and management of cisplatin-induced nephrotoxicity through candidate gene studies and GWAS. The secondary objective is to identify strategies to minimize cisplatin-induced nephrotoxicity through modification of cisplatin dosing and optimization of co-medication selection. Part 1 of the thesis addresses the impact of genetic variations on cisplatin nephrotoxicity. The studies demonstrate that genetic predisposition could be important in the development of cisplatin-induced nephrotoxicity. These results warrant further replication effort, functional and pharmacokinetic/dynamic validation to reveal the mechanistic basis on how genetic variants may involve in cisplatin-induced nephrotoxicity. In the future, comprehensive assessment on cisplatin toxicities combined with an integration of cisplatin’s systems pharmacology and multi-omics cancer analysis could pave promising opportunities for individualized platinum selection and targeted organ protection including kidney. Part 2 explores the possibility of stratification and modification of cisplatin therapy to minimize nephrotoxicity without compromising its effectiveness. Modification of cisplatin dose and intensity, as well as optimization of standard co-medication such as 5-HT3 receptor antagonist antiemetics, could potentially minimize the risk of cisplatin-induced kidney injury without waiving its effectiveness. Further evidence from controlled trials and observational studies will strengthen the body of evidence to assess such potentially affordable prevention of cisplatin-induced nephrotoxicity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".