Estimating fetal exposure to the P-gp substrates, corticosteroids, by PBPK modeling to inform prevention of neonatal respiratory distress syndrome

Systematic Quantitative Analysis of Fetal Dexamethasone Exposure and Fetal Lung Maturation in Pregnant Animals: Model Informed Dexamethasone Precision Dose Study

Dexamethasone (DEX) was applied in neonatal respiratory distress syndrome treatment of pregnant women. We established a pharmacokinetics (PK)/pharmacodynamics(PD)/end point model of pregnant animals based on published data and then extrapolated to simulate fetal exposure and lung maturation in pregnant women. We first established the PK/PD/end point model for DEX in pregnant sheep. We considered the competitive effect of cortisol (Cort) and DEX binding with glucocorticoid receptor and then used the indirect response model to describe disaturated-phosphatidylcholine (DSPC) dynamics. Based on that, we established a regression relationship between DSPC and fetal lung volume (V40). We then extrapolated the PD/end point model of pregnant sheep to pregnant monkeys by corrected stages of morphologic lung maturation in two species. Finally, we utilized the interspecies extrapolation strategy to simulate fetal exposure (AUC0-48h) and V40 relationship in pregnant women. The current model could well describe the maternal-fetal PK of DEX in pregnant animals. Simulated DEX AUC0-24h values of the umbilical venous to maternal plasma ratio in pregnant sheep and monkeys were 0.31 and 0.27, respectively. The simulated Cort curve and V40 in pregnant sheep closely matched the observed data within a 2-fold range. For pregnant monkeys, model-simulated V40 were well fitted with external verification data, which showed good interspecies extrapolation performance. Finally, we simulated fetal exposure-response relationship in pregnant women, which indicated that the fetal AUC0-48h of DEX should not be less than 300 and 100 ng/mL·hr at GW28 and GW34 to ensure fetal lung maturity. The current model preliminarily provided support for clinical DEX dose optimization.

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.

Pharmacokinetic and toxicological considerations affecting antiretroviral drug dosing in pregnant women

INTRODUCTION

To prevent mother-to-child transmission (PMTCT) of the human immunodeficiency virus (HIV) during pregnancy, the appropriate dosing regimens of antiretroviral (ARV) drugs need to be determined. Reliable data about pharmacokinetic (PK) characteristics of ARVs from randomized clinical trials (RCTs) are lacking, and post-marketing observational studies may offer valuable, but sometimes insufficient data, especially in pregnant people living with HIV (PLWHIV). This review article is focused PK and toxicological considerations affecting ARV dosing in pregnant PLWHIV.

AREAS COVERED

In our search, we included studies focused on PKs of ARVs in pregnancy available on PubMed, abstracts from recent global conferences and data from modeling studies. There are no significant changes in PKs of nucleoside/nucleotide reverse transcriptase inhibitors and non-nucleoside reverse transcriptase inhibitors throughout pregnancy. In contrast, the PKs of PIs and INSTIs are more variable, especially in the second and third trimesters.

EXPERT OPINION

Pregnant women are left out of RCTs. To the greatest extent possible, future research should include pregnant persons in RCTs, including PK studies, strictly considering maternal and fetal safety. Alternative innovative approaches/models need to be developed to obtain reliable data about rational pharmacotherapy of ARVs in the effective PMTCT of HIV, with maximum safety.

Successful Prediction of Fetal Exposure to Dual BCRP/P-gp Drug Substrates Using the Efflux Ratio-Relative Expression Factor Approach and PBPK M&S

To inform fetal drug safety, it is important to determine or predict fetal drug exposure throughout pregnancy. The former is not possible in the first or second trimester. In contrast, at the time of birth, fetal drug exposure, relative to maternal exposure, can be estimated as Kp,uu (unbound fetal umbilical venous (UV) plasma area under the curve (AUC)/unbound maternal plasma (MP) AUC), provided the observed UV/MP values, spanning the dosing interval, are available from multiple maternal-fetal dyads. However, this fetal Kp,uu cannot be extrapolated to other drugs. To overcome the above limitations, we have used an efflux ratio-relative expression factor (ER-REF) approach to successfully predict the fetal Kp,uu of P-gp substrates. Because many drugs taken by pregnant people are also BCRP substrates, here, we extend this approach to drugs that are effluxed by both placental BCRP and P-gp or P-gp alone. To verify our predictions, we chose drugs for which UV/MP data were available at term: glyburide and imatinib (both BCRP and P-gp substrates) and nelfinavir (only P-gp substrate). First, the ER of the drugs was determined using Transwells and MDCKII cells expressing either BCRP or P-gp. Then, the ER was scaled using the proteomics-informed REF value to predict the fetal Kp,uu of the drug at term. The ER-REF predicted fetal Kp,uu of glyburide (0.43), imatinib (0.42), and nelfinavir (0.40) fell within two-fold of the corresponding in vivo fetal Kp,uu (0.44, 0.37, and 0.46, respectively). These data confirm that the ER-REF approach can successfully predict fetal drug exposure to BCRP/P-gp and P-gp substrates, at term.

Physiologically-based pharmacokinetic models to predict drug exposure during pregnancy

As pregnant women are constantly exposed to drugs during pregnancy, either to treat long-term conditions or acute illnesses, drug safety is a major concern for the fetus and the mother. Clinical trials are rarely made in this population due to strict regulation and ethical reasons. However, drug pharmacokinetic (PK) parameters vary during pregnancy with an increase in distribution volume, renal clearance and more. In addition, the fetal distribution should be evaluated with the importance of placental diffusion, both active and passive. Therefore, there is a recent interest in the use of physiologically-based pharmacokinetic (PBPK) modeling to characterize these changes and complete the sparse data available on drug PK during pregnancy. Indeed, PBPK models integrate drug physicochemical and physiological parameters corresponding to each compartment of the body to estimate drug concentrations. This review establishes an overview on the current use of PBPK models in drug dosage determination for the pregnant woman, fetal exposure and drug interactions in the fetal compartment.

A physiologically based toxicokinetic model of P-glycoprotein transporter-mediated placenta perfusion of dexamethasone in the pregnant rat

The present dosage of Dexamethasone (DEX) administered to pregnant women may pose a risk of toxicity to their unborn offspring. We aimed to develop a maternal-fetal physiologically based toxicokinetic (PBTK) model for DEX in pregnant rats, with a specific focus on the role of the P-glycoprotein (P-gp) transporter in placenta perfusion, and finally facilitate the optimization of clinical DEX dosage. We conducted animal experiments to determine DEX concentrations in various rat tissues, and constructed the PBTK model using MATLAB software. Sensitivity analysis was performed to assess input parameters and the model stability, with fold error (FE) values serving as evaluation indices. Our results indicate the successful construction of the PBTK model, with the fitting key parameters such as the absorption rate constant (Ka), intrinsic hepatic clearance (CLh,int) and intrinsic P-gp clearance (CLint,P-gp). The median concentration of DEX in maternal plasma, fetal plasma, fetal lung, and fetal brain were determined, which allowed us to fit the tissue-to-plasma partition coefficients for the fetal lung (Kp,lung,f) and fetal brain (Kp,brain,f). After making adjustments, all calculated FE values were found to be less than 2, demonstrating the acceptability and accuracy of our model's predictions. Our model integrated external literature data and internal animal experimentation to comprehensively evaluate the maternal-fetal PK characteristics of DEX. These findings provide valuable support for the optimization of clinical DEX dosing.

Drug-drug interactions between COVID-19 therapeutics and antiretroviral treatment: the evidence to date

INTRODUCTION

With new effective treatments for SARS-CoV-2, patient outcomes have greatly improved. However, new medications bring a risk of drug interactions with other medications. People living with HIV (PLWH) are at particular risk for these interactions due to heightened risk of immunosuppression, polypharmacy, and overlap in affected organs. It is critical to identify drug interactions are a significant barrier to care for PLWH. Establishing a better understanding of the pharmacologic relationships between COVID-19 therapies and antiretrovirals will improve patient-centered care in COVID-19.

AREAS COVERED

Potential drug-drug interactions between Human Immunodeficiency Virus (HIV) and COVID-19 treatments are detailed and reviewed here. The mechanisms seen in these interactions include alterations in metabolic enzymes, drug transporters, pharmacoenhancement, and organ toxicities. We also review the limitations and solutions that can be used to combat drug-drug interactions between these two disease states.

EXPERT OPINION

While current drug interactions are relatively mild between HIV and COVID-19 therapies, improvements in identifying these beforehand must take place as new therapies are approved. Antiretroviral therapy (ART) is essential in PLWH and must be maintained when treating COVID-19. As advancements in care occur, there is the possibility that newly approved drugs may have additional unknown interactions.

Predicting Human Fetal Drug Exposure Through Maternal-Fetal PBPK Modeling and In Vitro or Ex Vivo Studies

Medication (drug) use in human pregnancy is prevalent. Determining fetal safety and efficacy of drugs is logistically challenging. However, predicting (not measuring) fetal drug exposure (systemic and tissue) throughout pregnancy is possible through maternal-fetal physiologically based pharmacokinetic (PBPK) modeling and simulation. Such prediction can inform fetal drug safety and efficacy. Fetal drug exposure can be quantified in 2 complementary ways. First, the ratio of the steady-state unbound plasma concentration in the fetal plasma (or area under the plasma concentration-time curve) to the corresponding maternal plasma concentration (ie, Kp,uu ). Second, the maximum unbound peak (Cu,max,ss,f ) and trough (Cu,min,ss,f ) fetal steady-state plasma concentrations. We (and others) have developed a maternal-fetal PBPK model that can successfully predict maternal drug exposure. To predict fetal drug exposure, the model needs to be populated with drug specific parameters, of which transplacental clearances (active and/or passive) and placental/fetal metabolism of the drug are critical. Herein, we describe in vitro studies in cells/tissue fractions or the perfused human placenta that can be used to determine these drug-specific parameters. In addition, we provide examples whereby this approach has successfully predicted systemic fetal exposure to drugs that passively or actively cross the placenta. Apart from maternal-fetal PBPK models, animal studies also have the potential to estimate fetal drug exposure by allometric scaling. Whether such scaling will be successful is yet to be determined. Here, we review the above approaches to predict fetal drug exposure, outline gaps in our knowledge to make such predictions and map out future research directions that could fill these gaps.