Pharmacogenomic investigation of adverse drug reactions(ADRs): the ADR prioritization tool, APT.
Bibliographic record
Abstract
BACKGROUND: The impact of genetic factors on the risk of adverse drug reactions (ADRs) is being increasingly recognized as clinically important. ADR Prioritization Tool (APT) was developed to facilitate the prioritization of drugs and their associated ADRs for future pharmacogenomic studies. OBJECTIVES: To describe a novel tool developed for the prioritization of pharmacogenomic investigation of ADRs and discuss the impact of specific scoring criteria. METHODS: APT scores were based on 25 key scientific and feasibility criteria relevant for clinical research evaluating the genetic basis of ADRs, with a maximum possible score of 60 points. The tool was independently applied to five ADRs (warfarin-induced bleeding/thrombosis, cisplatin-induced ototoxicity, methotrexate-induced neutropenia, carbamazepine-induced Stevens-Johnson syndrome, and abacavir-induced hypersensitivity) by two researchers. Scores were compared using the intraclass correlation coefficient (ICC) to determine level of agreement. RESULTS: Overall scores for target ADRs ranged from 19.5 to 44 points (33-73% of maximum possible score). Cisplatin-induced ototoxicity, a frequent and severe ADR, received the highest score (44). Lower scores were obtained for abacavir-induced hypersensitivity (19.5) and methotrexate-induced neutropenia (28). High agreement was observed between the scientific, feasibility, and total scores from two reviewers (ICC values = 0.895, 0.980, and 0.983, respectively). CONCLUSION: Application of APT enables simple and direct comparison of potential study targets for research groups embarking on pharmacogenomic investigation of ADRs. Research teams will be able to identify which study targets are best suited for their research environment and discern how to optimize resource allocation for successful discovery and replication of clinically relevant biomarkers.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".