The Impact of Pharmacogenetics on Pharmacokinetics and Pharmacodynamics in Neonates and Infants: A Systematic Review
Bibliographic record
Abstract
In neonates, pharmacogenetics has an additional layer of complexity. This is because in addition to genetic variability in genes that code for proteins relevant to clinical pharmacology, there are rapidly maturational changes in these proteins. Consequently, pharmacotherapy in neonates has unique challenges. To provide a contemporary overview on pharmacogenetics in neonates, we conducted a systematic review to identify, describe and quantify the impact of pharmacogenetics on pharmacokinetics and -dynamics in neonates and infants (PROSPERO, CRD42022302029). The search was performed in Medline, Embase, Web of Science and Cochrane, and was extended by a PubMed search on the 'top 100 Medicines' (medicine + newborn/infant + pharmacogen*) prescribed to neonates. Following study selection (including data in infants, PGx related) and quality assessment (Newcastle-Ottawa scale, Joanna Briggs Institute tool), 55/789 records were retained. Retained records relate to metabolizing enzymes involved in phase I [cytochrome P450 (CYP1A2, CYP2A6, CYP2B6, CYP2C8/C9/C18, CYP2C19, CYP2D6, CYP3A5, CYP2E1)], phase II [glutathione-S-transferases, N-acetyl transferases, UDP-glucuronosyl-transferase], transporters [ATP-binding cassette transporters, organic cation transporters], or receptor/post-receptor mechanisms [opioid related receptor and post-receptor mechanisms, tumor necrosis factor, mitogen-activated protein kinase 8, vitamin binding protein diplotypes, corticotrophin-releasing hormone receptor-1, nuclear receptor subfamily-1, vitamin K epoxide reductase complex-1, and angiotensin converting enzyme variants]. Based on the available overview, we conclude that the majority of reported pharmacogenetic studies explore and extrapolate observations already described in older populations. Researchers commonly try to quantify the impact of these polymorphisms in small datasets of neonates or infants. In a next step, pharmacogenetic studies in neonatal life should go beyond confirmation of these associations and explore the impact of pharmacogenetics as a covariate limited to maturation of neonatal life (ie, fetal malformations, breastfeeding or clinical syndromes). The challenge is to identify the specific factors, genetic and non-genetic, that contribute to the best benefit/risk balance.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".