Pharmacogenomics of antibiotic-induced hypersensitivity reactions: current evidence and implications in clinical practice
Bibliographic record
Abstract
Adverse drug reactions (ADRs) are gradually becoming a concerning health threat worldwide in patients undergoing acute or chronic therapy. Antibiotics are the main drugs that cause immune-mediated ADRs, such as severe cutaneous adverse reactions (SCARs), allergic reactions, and organ-specific diseases, representing a significant threat to patient safety. In this review, we present the current genetic evidence available for antibiotic-related toxicities from a pharmacogenomics (PGx) perspective. We also explore the current state of PGx-based dosing recommendations and the factors limiting their widespread application in routine clinical practice. Through a systematic literature review, this study identified at least 12 antibiotic–gene pairs (amikacin–MT-RNR1, gentamicin–MT-RNR1, kanamycin–MT-RNR1, streptomycin–MT-RNR1, neomycin–MT-RNR1, tobramycin–MT-RNR1, isoniazid–NAT2, dapsone–HLA-B, co-trimoxazole–HLA-B, HLA-C, flucloxacillin–HLA-B, daunorubicin–SLC28A3, and doxorubicin–SLC28A3) with moderate to high Pharmacogenomics Knowledgebase (PharmGKB) evidence levels for toxicity. However, PGx-based dosing guidelines, as recommended by the Clinical Pharmacogenetics Implementation Consortium (CPIC), the Dutch Pharmacogenetics Working Group (DPWG), and the Canadian Pharmacogenomics Network for Drug Safety (CPNDS), are currently available only for the following antibiotic–gene pairs: amikacin, gentamicin, kanamycin, streptomycin, neomycin, and tobramycin–MT-RNR1; flucloxacillin–HLA-B; dapsone–G6PD; nitrofurantoin–G6PD; and daunorubicin and doxorubicin–RARG, SLC28A3, and UGT1A6. Despite the established and growing genetic evidence for toxicity, particularly for Co-trimoxazole-induced SCARs by HLA-B and HLA-C, dapsone-induced SCARs by the HLA-B, and isoniazid-induced liver injury by the NAT2, insufficient approaches are being undertaken to translate these findings into routine clinical practice. The lack of validation of preliminary genetic associations, due to the scarcity of proper follow-up and large-scale replication, remains a key setback for PGx-based implementation of antibiotic therapy in clinical settings. More focused clinical studies, cost-effectiveness analyses, and polygenic risk score development are required to enable the PGx-based clinical use of antibiotics and optimize both safety and effectiveness in achieving precision medicine.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".