Pharmacokinetic changes along the developmental trajectory: A view of a clinician
Bibliographic record
Abstract
Pharmacokinetics (PK) has been the heart and soul of the discipline of clinical pharmacology. One of the reasons is because PK parameters provide key information necessary to design dosing schedules. Although the clinical relevance of PK information is evident, basic understanding of PK principles among clinicians remains poor, which highlights an important education role clinical pharmacologists need to play. The lack of the general understanding compromises appropriate clinical application of PK data to clinical practice particularly in special populations including infants and children. Difficulties in conducting conventional PK studies in children further intensify their therapeutic orphan status, causing rampant off-label/off-evidence uses of drug. Although the value of population PK allowing opportunistic sampling schedules to circumvent the conventional tight- scheduled sampling-intensive PK methods is widely recognized and advocated for infants and children, consistent efforts need to be made, so that PK information becomes available in a timely manner for this vulnerable population. The pediatric population is characterized by its remarkable transformation from a cluster of cells in the embryonic stage to a mature adult. The developmental trajectory is the hallmark of infants and children, which also poses challenges in PK data acquisition and interpretation. In the neonatal period, drug clearance per body weight (BW) or body surface area (BSA) is generally lower than in the older age groups. Over the following infantile period, drug clearance/BW consistently increases with variable rates among individuals and drugs in question. Specifically, drug-metabolizing enzymes such as CYP3A show relatively developed-expression patterns in the neonatal period, but CYP1A2 takes one year or more to reach an adult level of expression and function per BW or BSA. Due to increased water compartments per BW in the neonatal period, volume of distribution per BW of hydrophilic drugs is larger in this age group than in older age groups. Although the average pictures of these developmental profiles of PK can be described clearly, variations among drugs and individuals along the developmental trajectory are poorly understood. Application of physiologically-based PK modeling may be a first step to deepen our understanding of PK developmental profiles and its variations in the paediatric population.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".