Step Climb Dosing Guidance From Simulation Studies on Lamotrigine Concentration Changes During Pregnancy
Bibliographic record
Abstract
Empiric Dosing Strategies to Predict Lamotrigine Concentrations During Pregnancy Barry JM, French JA, Pennell PB, Karanam A, Harden CL, Birnbaum AK. Pharmacotherapy . 2023;43(10):998-1006. doi: 10.1002/phar.2856 . PMID: 37475496 Introduction: Maintaining seizure control with lamotrigine is complicated by altered pharmacokinetics and existence of subpopulations in whom clearance increases or remains constant during pregnancy. Objective: Our objective was to characterize the potential for particular dosing scenarios to lead to increased seizure risk or toxicity. Methods: Lamotrigine pharmacokinetic parameters obtained from our previous study were applied to a one-compartment model structure with subpopulations (75:25%) exhibiting different clearance changes. A single-patient simulation was conducted with typical pharmacokinetic parameter values from each subpopulation. Population-level simulations (N = 48,000) included six dosing scenarios and considered four preconception doses using the R package mrgsolve (Metrum Research Group). Thresholds for efficacy and toxicity were selected as drug concentration that are 65% lower than preconception concentrations and doubling of preconception concentrations, respectively. Results: Individual simulation results demonstrated that without dose increases, concentrations fell below 0.65 at 6-8 weeks in the high clearance change (HC) subpopulation, depending on preconception clearance. While no simulated dosing regimen allowed all women in both subpopulations to maintain preconception concentrations, some regimens provided a more balanced risk profile than others. Predicted concentrations suggested potential increased seizure risk for 7%-100% of women in the HC group depending on preconception dose and subpopulation. Additionally, in 63% of dosing scenarios for women with low clearance change (LC), there was an increased risk of toxicity (34%-100% of women). Significance: A substantial percentage of simulated individuals had concentrations low enough to potentially increase seizure risk or high enough to create toxicity. Early clearance changes indicate possible subpopulation categorization if therapeutic drug monitoring is conducted in the first trimester. An arbitrary “one-size-fits-all” philosophy may not work well for lamotrigine dosing adjustments during pregnancy and reinforces the need for therapeutic drug monitoring until a patient is determined to be in the LC or HC group.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".