Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine

Physiologically Based Pharmacokinetic modelling of drugs in pregnancy: A mini-review on availability and limitations

Physiologically based pharmacokinetic (PBPK) modelling in pregnancy is a relatively new approach that is increasingly being used to assess drug systemic exposure in pregnant women to potentially inform dosing adjustments. Physiological changes throughout pregnancy are incorporated into mathematical models to simulate drug disposition in the maternal and fetal compartments as well as the transfer of drugs across the placenta. This mini-review gathers currently available pregnancy PBPK models for drugs commonly used during pregnancy. In addition, information about the main PBPK modelling platforms used, metabolism pathways, drug transporters, data availability and drug labels were collected. The aim of this mini-review is to provide a concise overview, demonstrate trends in the field, highlight understudied areas and identify current gaps of PBPK modelling in pregnancy. Possible future applications of this PBPK approach are discussed from a clinical, regulatory and industry perspective.

Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

PBPK Modeling Approach to Predict the Behavior of Drugs Cleared by Metabolism in Pregnant Subjects and Fetuses

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model that simulates metabolically cleared compounds' pharmacokinetics (PK) in pregnant subjects and fetuses. This model accounts for the differences in tissue sizes, blood flow rates, enzyme expression levels, plasma protein binding, and other physiological factors affecting the drugs' PK in both the pregnant woman and the fetus. The PBPKPlus™ module in GastroPlus® was used to model the PK of metoprolol, midazolam, and metronidazole for both non-pregnant and pregnant groups. For each of the three compounds, the model was first developed and validated against PK data in healthy non-pregnant volunteers and then applied to predict the PK in the pregnant groups. The model accurately described the PK in both the non-pregnant and pregnant groups and explained well the differences in the plasma concentration due to pregnancy. When available, the fetal plasma concentration, placenta, and fetal tissue concentrations were also predicted reasonably well at different stages of pregnancy. The work described the use of a PBPK approach for drug development and demonstrates the ability to predict differences in PK in pregnant subjects and fetal exposure for metabolically cleared compounds.

Olanzapine Pharmacokinetics: A Clinical Review of Current Insights and Remaining Questions

Olanzapine is one of the most widely used antipsychotics since its initial approval by the US Food and Drug Administration in 1996 and has undergone extensive pharmacokinetic study. Despite being utilized in clinical psychiatry for decades, there remain questions regarding the variety of available formulations, the utility of therapeutic drug monitoring, altered kinetic properties in special populations/medical illnesses, the use of high-dose olanzapine, and drug interactions, among many others. We performed a narrative literature review of olanzapine pharmacokinetics in June 2023 using the US National Library of Medicine's PubMed.gov resource (https://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar. Herein, we review clinically relevant aspects of olanzapine pharmacokinetic data while highlighting knowledge gaps and potential areas of future study.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Pharmacokinetic Evaluation in Pregnancy-Current Status and Future Considerations: Workshop Summary

As pregnant individuals have traditionally been excluded from clinical trials, there is a gap in knowledge at the time of drug approval regarding safety, efficacy, and appropriate dosing for most prescription medications used during pregnancy. Physiologic changes in pregnancy can result in changes in pharmacokinetics that can impact safety or efficacy. This highlights the need to foster further research and collection of pharmacokinetic data in pregnancy to ensure appropriate drug dosing in pregnant individuals. Therefore, the US Food and Drug Administration and the University of Maryland Center of Excellence in Regulatory Science and Innovation hosted a workshop on May 16 and 17, 2022, titled "Pharmacokinetic Evaluation in Pregnancy." This is a summary of the workshop proceedings.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Maternal Pharmacokinetics and Fetal Exposure to Oxcarbazepine

Pregnancy is associated with physiological changes that may affect drug pharmacokinetics (PKs). The aim of this study was to establish a maternal-fetal physiologically based pharmacokinetic (PBPK) model of oxcarbazepine (OXC) and its active metabolite, 10,11-dihydro-10-hydroxy-carbazepine (MHD), to (1) assess differences in pregnancy, (2) predict changes in PK target parameters of these molecules following the current dosing regimen, (3) assess predicted concentrations of these molecules in the umbilical vein at delivery, and (4) compare different methods for estimating drug placental penetration. Predictions using the pregnancy PBPK model of OXC resulted in maternal concentrations within a 2-fold error, and extrapolation of the model to early-stage pregnancies indicated that changes in median PK parameters remained above target thresholds, requiring increased frequency of monitoring. The dosing simulation results suggested dose adjustment in the last two trimesters. We generally recommend that women administer ≥ 1.5× their baseline dose of OXC during their second and third trimesters. Test methods for predicting placental transfer showed varying performance, with the in vitro method showing the highest predictive accuracy. Exposure to MHD in maternal and fetal venous blood was similar. Overall, the above-mentioned models can enhance understanding of the maternal-fetal PK behavior of drugs, ultimately informing drug-treatment decisions for pregnant women and their fetuses.

Antidepressants and Antipsychotics in Human Pregnancy: Transfer Across the Placenta and Opportunities for Modeling Studies

There is limited information about the transfer of antidepressants and antipsychotics across the human placenta. The objective of the current review was to systematically screen the scientific literature using relevant keywords to collect quantitative data on placental transfer of these drugs in humans and to give an overview of current modeling approaches used in this context. The collected data encompassed clinically measured fetal:maternal (F:M) concentration ratios (ie, the ratio between drug concentrations measured in the umbilical cord and drug concentrations measured in the mother) and transfer data obtained from ex vivo cotyledon perfusion experiments. These data were found for 18 antidepressants and some of their pharmacologically active metabolites, and for 10 antipsychotics and the metabolites thereof. Based on the collected data, similar maternal and fetal exposure could be observed for only a few compounds (eg, norfluoxetine and desvenlafaxine), whereas for most drugs (eg, paroxetine, sertraline, and quetiapine), fetal exposure appeared to be on average lower than maternal exposure. Venlafaxine appeared to be an exception in that the data indicated equivalent or higher concentrations in the umbilical cord than in the mother. Physiologically based pharmacokinetic (PBPK) models were sporadically used to investigate maternal pharmacokinetics of antidepressants or antipsychotics (eg, for sertraline, aripiprazole, and olanzapine), although without explicitly addressing fetal drug exposure. It is recommended that PBPK modeling is applied more frequently to these drugs. Although no substitute for clinical studies, these tools can help to better understand pregnancy-induced pharmacokinetic changes and ultimately contribute to a more evidence-based pharmacotherapy of depression and psychosis in pregnant subjects.