Understanding the Impact of Age-Related Changes in Pediatric GI Solubility by Multivariate Data Analysis
Bibliographic record
Abstract
The aim of this study was to understand drug solubilization as a function of age and identify drugs at risk of altered drug solubility in newborns and young infants in comparison to adults. Multivariate statistical analysis was used to understand drug solubilization as a function of drug's physicochemical properties and the composition of gastrointestinal fluids. The solubility of seven poorly soluble compounds was assessed in adult and age-specific fasted and fed state biorelevant media. Partial least squares regression (PLS-R) was used to assess the influence of (i) drug physicochemical properties and (ii) age-related changes in simulated GI fluids, as well as (iii) their interactions, on the pediatrics-to-adult solubility ratio (Sp/Sa (%)). For five out of seven of the compounds investigated, Sp/Sa (%) values fell outside of the 80-125% limits in at least one of the pediatric media. Lipophilicity was responsible for driving drug solubility differences between adults and children in all the biorelevant media investigated, while drug ionization was most relevant in the fed gastric media, and the fasted/fed intestinal media. The concentration of bile salts and lecithin in the fasted and fed intestinal media was critical in influencing drug solubility, while food composition (i.e., cow's milk formula vs. soy formula) was a critical parameter in the fed gastric state. Changes in GI fluid composition between younger pediatric patients and adults can significantly alter drug luminal solubility. The use of pediatric biorelevant media can be helpful to identify the risk of altered drug solubilization in younger patients during drug development.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".