Estrogen profile- and pharmacogenetics-based lamotrigine dosing regimen optimization: Recommendations for pregnant women with epilepsy.

During pregnancy, various physiological changes occur that can alter the pharmacokinetics of antiepileptic drugs, such as lamotrigine (LTG). Anticipating the change in LTG dose required to achieve a pre-pregnancy target concentration is challenging. This study aimed to develop a refined population pharmacokinetic (PopPK) model of LTG in pregnant women with epilepsy (WWE) to identify factors explaining the variability in pharmacokinetics and to establish a model-informed individualized dosing regimen. On that basis, a coarsened model containing only clinical variables was also developed to examine its predictive performance compared to the refined model. In total, 322 concentration-time points from 51 pregnant WWE treated with LTG were employed to establish a refined PopPK model that included endogenous estrogen profiles, variants of candidate genes encoding LTG-metabolizing enzymes and -transporter proteins, and other clinical variables and a coarsened model that included only clinical variables, respectively. Data from an additional 11 patients were used for external validation of these two models. A nonlinear mixed-effect modeling approach was used for PopPK analysis of LTG. The standard goodness-of-fit method, bootstrap, normalized prediction distribution errors and external evaluation were adopted to estimate the stability and predictive performance of the candidate models. Akaike information criterion (AIC) was used to compare the goodness of fit between these two models. A lower AIC indicates a better fit of the data and the preferred model. Recommended dosing regimens for pregnant WWE were selected using Monte Carlo simulation based on the established optimal model. In the refined PopPK model, the population mean of apparent LTG clearance (CL/F) in pregnant WWE was estimated to be 2.82 L/h, with an inter-individual variability of 23.6%. PopPK analysis indicated that changes in estrogen profile during pregnancy were the predominant reason for the significant variations in LTG-CL/F. Up to the 3rd trimester, the concentration accumulation effect of E2 increased LTG-CL/F by 5.109 L/h from baseline levels. Contrary to effect of E2, E3 as the main circulating estrogen in pregnancy with a peak value of 34.41 ng/mL is 1000-fold higher than that in non-pregnancy reduced LTG-CL/F by 1.413 L/h. In addition, the UGT2B7 rs4356975 C > T and ABCB1 rs1128503 A > G variants may contribute to a better understanding of the inter-individual variability in LTG-CL/F. LTG-CL/F was 1.66-fold higher in UGT2B7 rs4356975 CT or TT genotype carriers than in CC genotype carriers. In contrast, ABCB1 rs1128503 GG genotype carriers had only 71.9% of the LTG-CL/F of AA or AG genotype carriers. In the coarsened PopPK model, the gestational age was a promising predictor of changes in LTG-CL/F. When comparing these two models, the refined PopPK model was favored over the coarsened PopPK model (AIC = -30.899 vs. -20.017). Monte Carlo simulation based on optimal PopPK model revealed that the LTG dosage administered to carriers of the UGT2B7 rs4356975 CT or TT genotype required a 33-50% increase to reach the pre-pregnancy target concentration, and carriers of the ABCB1 rs1128503 GG genotype required a 33-66% lower dose of LTG than carriers of the ABCB1 rs1128503 AA or AG genotype. Changes in estrogen profile during pregnancy was a better predictor of variations in LTG-CL/F than gestational age. The developed model based on estrogen profile and pharmacogenetics can serve as a foundation for further optimization of dosing regimens of LTG in pregnant WWE.

Effects of Postoperative Day and NR1I2 on Tacrolimus Clearance in Chinese Liver Transplant Recipients-A Population Model Approach.

We aimed to explore the new biomarkers influencing tacrolimus in vivo behavior in Chinese liver transplant recipients. A total of 418 drug concentration samples of 41 liver transplant patients were collected for modeling. A population pharmacokinetic model was developed using the nonlinear mixed-effects modeling approach. The potential covariates, such as postoperative day (POD), age, body weight, hepatic and renal function, and recipient genetic polymorphisms (ABCB1, CYP3A4, CYP3A5, NR1I2) were evaluated using forward-inclusion and backward-elimination methods. A 1-compartment model was used describing the in vivo behavior of tacrolimus in liver transplant patients. The estimates of CL/F and V/F were 8.88 L/h and 495.82 L, respectively. Two covariates, POD and NR1I2 rs2276707 genotypes, were incorporated into the final population pharmacokinetic model, and they could significantly impact the CL/F: CL/F (L/h) = 8.88 × (POD/16)0.18 × e0.91 × NR1I2 × eηCL . The model evaluation and validation indicated a stable and precise performance of the final model. The functional annotation using ENCODE data indicated that rs2276707 was located on the higher peak of the H3K4Me1 and H3K4Me3 histone marker. To our knowledge, this is the first report indicating NR1I2 rs2276707 genotypes is another biomarker impacting tacrolimus clearance in liver transplant recipients. The NR1I2 gene polymorphism may affect the in vivo behavior of tacrolimus by regulating gene expression.

Pharmacogenomics in pregnancy.

Pregnant women frequently take prescription and over the counter medications. The efficacy of medications is affected by the many physiological changes during pregnancy, and these events may be further impacted by genetic factors. Research on pharmacogenomic and pharmacokinetic influences on drug disposition during pregnancy has lagged behind other fields. Clinical investigators have demonstrated altered activity of several drug metabolizing enzymes during pregnancy. Emerging evidence also supports the influence of pharmacogenomic variability in drug response for many important classes of drugs commonly used in pregnancy. Prescribing medications during pregnancy requires an understanding of the substantial dynamic physiologic and metabolic changes that occur during gestation. Pharmacogenomics also contributes to the inter-individual variability in response to many medications, and more research is needed to understand how best to manage drug therapy in pregnant women.