Bibliographic record
Abstract
Over 220 million people worldwide are diagnosed with diabetes and rising prevalence is reported in nearly all surveyed populations. Accordingly, the percentage of pregnancies affected by pre-existing type 1 or 2 diabetes or by diabetes that develops during pregnancy, called gestational diabetes mellitus (GDM), is also on the rise. Today, approximately 8% of all pregnancies are complicated by diabetes. Diabetes alters drug disposition mechanisms in non-pregnant subjects but the impact of diabetes on drug disposition in pregnancy has not been properly evaluated. Atypical drug disposition in pregnancy has implications for maternal and fetal health. Because liver tissue from pregnant women is not readily available, this thesis investigated drug disposition mechanisms primarily in a rat model of experimental GDM. This model consisted of administering streptozotocin, a diabetogenic toxin, to pregnant rats on gestational day 6. One key finding was that elevated circulating lipids in GDM rats competed with drugs (e.g., glyburide and saquinavir) for plasma protein binding so as to increase free drug concentrations. Another key finding was that important hepatic drug efflux transporters (e.g., Mdr1a/b) and metabolic enzymes (e.g., Cyp3a2 and Ugt1a1) were upregulated in GDM as a consequence of, most likely, enhanced nuclear receptor activity (e.g., pregnane X receptor upregulation). Upregulation of hepatic drug efflux transporters and metabolic enzymes, coupled with larger unbound drug fractions, would be expected to increase the hepatic clearance of many drugs. Consistent with this, in GDM, maternal and fetal exposure to the Mdr1 and Cyp3a2 substrate lopinavir was substantially lower than controls post-administration and data supporting enhanced lopinavir metabolite formation were obtained. Placental drug efflux transporters were also examined in this lopinavir study. Elevated placental Mdr1b and Bcrp expression was observed in GDM, which was associated with decreased fetal exposure to lopinavir (even after correcting for maternal unbound concentrations). Taken together, this thesis demonstrates that experimental GDM can significantly impact drug disposition by altering key drug disposition mechanisms. If confirmed in humans, this drug-disease interaction would need to be considered when atypical therapeutic outcomes occur in diabetic pregnancies. Data from experiments with human placentas, obtained from pregnancies complicated by insulin-managed diabetes, is included/discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".