Effect of Age, Weight, and CYP2C19 Genotype on Escitalopram Exposure
Bibliographic record
Abstract
The purpose of this study was to characterize escitalopram population pharmacokinetics (PK) in patients treated for major depression in a cross-national, US-Italian clinical trial. Data from the 2 sites participating in this trial, conducted at Pittsburgh (United States) and Pisa (Italy), were used. Patients received 5, 10, 15, or 20 mg of escitalopram daily for a minimum of 32 weeks. Nonlinear mixed effects modeling was used to model the PK characteristics of escitalopram. One- and 2-compartment models with various random effect implementations were evaluated during model development. Objective function values and goodness-of-fit plots were used as model selection criteria. CYP2C19 genotype, age, weight, body mass index, sex, race, and clinical site were evaluated as possible covariates. In total, 320 plasma concentrations from 105 Pittsburgh patients and 153 plasma concentrations from 67 Pisa patients were available for the PK model development. A 1-compartmental model with linear elimination and proportional error best described the data. Apparent clearance (CL/F) and volume of distribution (V/F) for escitalopram without including any covariates in the patient population were 23.5 L/h and 884 L, respectively. CYP2C19 genotype, weight, and age had a significant effect on CL/F, and patient body mass index affected estimated V/F. Patients from Pisa, Italy, had significantly lower clearances than patients from Pittsburgh that disappeared after controlling for patient CYP2C19 genotype, age, and weight. Postprocessed individual empirical Bayes estimates on clearance for the 172 patients show that patients without allele CYP2C19(*)2 or (*)3 (n = 82) cleared escitalopram 33.7% faster than patients with heterogeneous or homogeneous (*)2 or (*)3 ((*)17/(*)2, (*)17/(*)3, (*)1/(*)2, (*)1/(*)3, (*)2/(*)2, (*)2/(*)3, and (*)3/(*)3, n = 46). CL/F significantly decreased with increasing patient age. Patients younger than 30 years (n = 45) cleared escitalopram 20.7% and 42.7% faster than patients aged 30 to 50 years (n = 84) and older than 50 years of age (n = 43), respectively. CYP2C19 genotype, age, and weight strongly influenced the CL/F of escitalopram. These variables may affect patient tolerance of this antidepressant and may provide important information in the effort to tailor treatments to patients' individual needs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".