PHARMACOGENOMIC INVESTIGATION OF ADVERSE DRUG REACTIONS (ADRS): THE ADR PRIORITIZATION TOOL, APT

Main Article Content

Kaitlyn Shaw
Ursula Amstutz
Lucila Castro-Pastrana
Tenneille T Loo
Colin J Ross
Shinya Ito
Michael J Rieder
Maurica Maher
Stuart MacLeod
Gideon Koren
Michael R Hayden
Bruce C Carleton

Keywords

Pharmacogenomics, adverse drug reactions, ADR study prioritization

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 abacavirinduced 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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