Applied Concepts in PBPK Modeling: How to Extend an Open Systems Pharmacology Model to the Special Population of Pregnant Women

Physiologically based pharmacokinetic modeling to predict maternal pharmacokinetics and fetal carbamazepine exposure during pregnancy

Carbamazepine is an antiepileptic drug commonly used in pregnant women, during which the physiological changes may affect its efficacy. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) model of carbamazepine and its active metabolite carbamazepine-10,11-epoxide, and simulate maternal and fetal pharmacokinetic changes of carbamazepine and carbamazepine-10,11-epoxide in different trimesters and propose dose adjustment. We established pregnancy PBPK models for carbamazepine and carbamazepine-10,11-epoxide in PK-Sim® and Mobi® and validated the models with observed data from clinical reports. The placental transfer parameters obtained using different methods were also imported into the model and compared with the observed data to establish and validate fetal pharmacokinetic curves. The simulated results showed that mean steady-state trough plasma concentration of carbamazepine decreased by 27, 43.1, and 52 % during the first, second, and third trimesters, respectively. Therefore, to achieve an optimum therapeutic concentration, administering at least 1.4, 1.8, and 2.1 times the baseline dose of carbamazepine in the first, second, and third trimesters, respectively can be used as a dose reference. In conclusion, this study established and validated a pregnancy PBPK model of carbamazepine and carbamazepine-10,11-epoxide to assess exposure in pregnant women and fetuses, which provided a reference for the dosage adjustment of carbamazepine during pregnancy.

Physiologically Based Pharmacokinetic Modeling of Nilotinib for Drug-Drug Interactions, Pediatric Patients, and Pregnancy and Lactation

Nilotinib is a second-generation BCR-ABL tyrosine kinase inhibitor for the treatment of Philadelphia chromosome-positive chronic myeloid leukemia in both adult and pediatric patients. The pharmacokinetics (PK) of nilotinib in specific populations such as pregnant and lactating people remain poorly understood. Therefore, the objectives of the current study were to develop a physiologically based pharmacokinetic (PBPK) model to predict nilotinib PK in virtual drug-drug interaction (DDI) studies, as well as in pediatric, pregnant, and lactating populations. The nilotinib PBPK model was built in PK-Sim, which is part of the free and open-source software Open Systems Pharmacology. The observed clinical data for the validation of the nilotinib models were obtained from the literature. The model reasonably predicted nilotinib concentrations in the adult population; the DDIs between nilotinib and rifampin or ketoconazole in the adult population; and the PK in the pediatric, pregnant, and lactating populations, although in the latter 2 populations plasma concentrations were slightly underestimated. The ratio of predicted versus observed PK parameters for the adult model ranged from 0.71 to 1.11 for area under the concentration-time curve and 0.55 to 0.95 for maximum concentration. For the DDI, the predicted area under the concentration-time curve ratio and maximum concentration ratio fell within the Guest criterion. The current study demonstrated the utility of using PBPK modeling to understand the mechanistic basis of PK differences between adults and specific populations, such as pediatrics, and pregnant and lactating individuals, indicating that this technology can potentially inform or optimize dosing conditions in specific populations.

Salivary Therapeutic Monitoring of Buprenorphine in Neonates After Maternal Sublingual Dosing Guided by Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Opioid use disorder (OUD) during pregnancy is associated with high mortality rates and neonatal opioid withdrawal syndrome (NOWS). Buprenorphine, an opioid, is used to treat OUD and NOWS. Buprenorphine active metabolite (norbuprenorphine) can cross the placenta and cause neonatal respiratory depression (EC50 = 35 ng/mL) at high brain extracellular fluid (bECF) levels. Neonatal therapeutic drug monitoring using saliva decreases the likelihood of distress and infections associated with frequent blood sampling.

METHODS

An adult physiologically based pharmacokinetic model for buprenorphine and norbuprenorphine after intravenous and sublingual administration was constructed, vetted, and scaled to newborn and pregnant populations. The pregnancy model predicted that buprenorphine and norbuprenorphine doses would be transplacentally transferred to the newborns. The newborn physiologically based pharmacokinetic model was used to estimate the buprenorphine and norbuprenorphine levels in newborn plasma, bECF, and saliva after these doses.

RESULTS

After maternal sublingual administration of buprenorphine (4 mg/d), the estimated plasma concentrations of buprenorphine and norbuprenorphine in newborns exceeded the toxicity thresholds for 8 and 24 hours, respectively. However, the norbuprenorphine bECF levels were lower than the respiratory depression threshold. Furthermore, the salivary buprenorphine threshold levels in newborns for buprenorphine analgesia, norbuprenorphine analgesia, and norbuprenorphine hypoventilation were observed to be 22, 2, and 162 ng/mL.

CONCLUSIONS

Using neonatal saliva for buprenorphine therapeutic drug monitoring can facilitate newborn safety during the maternal treatment of OUD using sublingual buprenorphine. Nevertheless, the suitability of using adult values of respiratory depression EC50 for newborns must be confirmed.

PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy

OBJECTIVE

This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants.

METHODS

A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model.

RESULTS

The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction.

CONCLUSION

Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.

Evaluating the Pharmacokinetics of Fentanyl in the Brain Extracellular Fluid, Saliva, Urine, and Plasma of Newborns from Transplacental Exposure from Parturient Mothers Dosed with Epidural Fentanyl Utilizing PBPK Modeling

BACKGROUND AND OBJECTIVE

Fentanyl can mitigate the mother and newborn complications resulting from labor pain. However, fentanyl shows a narrow therapeutic index between its respiratory depressive and analgesic effects. Thus, prenatally acquired high fentanyl levels in the newborn brain extracellular fluid (bECF) may induce respiratory depression which requires therapeutic drug monitoring (TDM). TDM using saliva and urine in newborns can reduce the possibility of infections and distress associated with TDM using blood. The objective of this study was to develop  a physiologically based pharmacokinetic (PBPK) model to predict fentanyl concentrations in different newborn tissues due to intrauterine exposure.

METHODS

A fentanyl PBPK model in adults after intravenous and epidural administration was built, validated, and scaled to pregnancy and newborn populations. The dose that the newborn received transplacentally at birth was calculated using the pregnancy model. Then, the newborn bECF, saliva, plasma, and urine concentrations after such a dose were predicted using the newborn PBPK model.

RESULTS

After a maternal epidural dose of fentanyl 245 µg, the predicted newborn plasma and bECF levels were below the toxicity thresholds. Furthermore, the salivary threshold levels in newborns for fentanyl analgesic and respiratory depression effects were estimated to be 0.39 and 14.7-18.2 ng/ml, respectively.

CONCLUSION

The salivary TDM of fentanyl in newborns can be useful in newborns exposed to intrauterine exposure from parturient females dosed with epidural fentanyl. However, newborn-specific values of µ-opioid receptors IC50 for respiratory depression are needed.

Physiologically Based Pharmacokinetic Modeling for Multiple Oral Administration Labetalol in Pregnant Women

BACKGROUND

Labetalol has an irreplaceable role in treating Hypertensive disorders of pregnancy (HDP), a common disease during pregnancy with a prevalence of 5.2-8.2%. However, there were big differences in dosage regimens between various guidelines.

PURPOSE

A physiologically-based pharmacokinetics (PBPK) model was established and validated to evaluate the existing oral dosage regimens, and to compare the difference in plasma concentration between pregnant and non-pregnant women.

METHODS

First, non-pregnant woman models with specific plasma clearance or enzymatic metabolism (UGT1A1, UGT2B7, CYP2C19) were established and validated. For CYP2C19, slow, intermediate, and rapid metabolic phenotypes were considered. Then, a pregnant model with proper structure and parameters adjustment was established and validated against the multiple oral administration data.

RESULTS

The predicted labetalol exposure captured the experimental data well. The following simulations with criteria lowering 15 mmHg blood pressure (corresponding to around 108 ng/ml plasma labetalol) found that the maximum daily dosage in the Chinese guideline may be insufficient for some severe HDP patients. Moreover, similar predicted steady-state trough plasma concentration was found between the maximum daily dosage in the American College of Obstetricians and Gynecologists (ACOG) guideline, 800 mg Q8h and a regimen of 200 mg Q6h. Simulations comparing non-pregnant and pregnant women found that the difference in labetalol exposure highly depended on the CYP2C19 metabolic phenotype.

CONCLUSIONS

In summary, this work initially established a PBPK model for multiple oral administration of labetalol for pregnant women. This PBPK model may lead to personalized labetalol medication in the future.

Generic Workflow to Predict Medicine Concentrations in Human Milk Using Physiologically-Based Pharmacokinetic (PBPK) Modelling-A Contribution from the ConcePTION Project

Women commonly take medication during lactation. Currently, there is little information about the exposure-related safety of maternal medicines for breastfed infants. The aim was to explore the performance of a generic physiologically-based pharmacokinetic (PBPK) model to predict concentrations in human milk for ten physiochemically diverse medicines. First, PBPK models were developed for "non-lactating" adult individuals in PK-Sim/MoBi v9.1 (Open Systems Pharmacology). The PBPK models predicted the area-under-the-curve (AUC) and maximum concentrations (C_max) in plasma within a two-fold error. Next, the PBPK models were extended to include lactation physiology. Plasma and human milk concentrations were simulated for a three-months postpartum population, and the corresponding AUC-based milk-to-plasma (M/P) ratios and relative infant doses were calculated. The lactation PBPK models resulted in reasonable predictions for eight medicines, while an overprediction of human milk concentrations and M/P ratios (>2-fold) was observed for two medicines. From a safety perspective, none of the models resulted in underpredictions of observed human milk concentrations. The present effort resulted in a generic workflow to predict medicine concentrations in human milk. This generic PBPK model represents an important step towards an evidence-based safety assessment of maternal medication during lactation, applicable in an early drug development stage.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

The Analysis of Pethidine Pharmacokinetics in Newborn Saliva, Plasma, and Brain Extracellular Fluid After Prenatal Intrauterine Exposure from Pregnant Mothers Receiving Intramuscular Dose Using PBPK Modeling

BACKGROUND AND OBJECTIVE

Pethidine (meperidine) can decrease labor pain-associated mother's hyperventilation and high cortisol-induced newborn complications. However, prenatal transplacentally acquired pethidine can cause side effects in newborns. High pethidine concentrations in the newborn brain extracellular fluid (bECF) can cause a serotonin crisis. Therapeutic drug monitoring (TDM) in newborns' blood distresses them and increases infection incidence, which can be overcome by using salivary TDM. Physiologically based pharmacokinetic (PBPK) modeling can predict drug concentrations in newborn plasma, saliva, and bECF after intrauterine pethidine exposure.

METHODS

A healthy adult PBPK model was constructed, verified, and scaled to newborn and pregnant populations after intravenous and intramuscular pethidine administration. The pregnancy PBPK model was used to predict the newborn dose received transplacentally at birth, which was used as input to the newborn PBPK model to predict newborn plasma, saliva, and bECF pethidine concentrations and set correlation equations between them.

RESULTS

Pethidine can be classified as a Salivary Excretion Classification System class II drug. The developed PBPK model predicted that, after maternal pethidine intramuscular doses of 100 mg and 150 mg, the newborn plasma and bECF concentrations were below the toxicity thresholds. Moreover, it was estimated that newborn saliva concentrations of 4.7 µM, 11.4 µM, and 57.7 µM can be used as salivary threshold concentrations for pethidine analgesic effects, side effects, and the risk for serotonin crisis, respectively, in newborns.

CONCLUSION

It was shown that saliva can be used for pethidine TDM in newborns during the first few days after delivery to mothers receiving pethidine.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Physiologically-based pharmacokinetic modeling of remdesivir and its metabolites in pregnant women with COVID-19

Pregnant individuals are at high risk for severe illness from COVID-19, and there is an urgent need to identify safe and effective therapeutics for this population. Remdesivir (RDV) is a SARS-CoV-2 nucleotide analog RNA polymerase inhibitor. Limited RDV pharmacokinetic (PK) and safety data are available for pregnant women receiving RDV. The aims of this study were to translate a previously published nonpregnant adult physiologically based PK (PBPK) model for RDV to pregnancy and evaluate model performance with emerging clinical PK data in pregnant women with COVID-19. The pregnancy model was built in the Open Systems Pharmacology software suite (Version 10) including PK-Sim® and MoBi® with pregnancy-related changes of relevant enzymes applied. PK were predicted in a virtual population of 1000 pregnant subjects, and prediction results were compared with in vivo PK data from the International Maternal, Pediatric, Adolescent AIDS Clinical Trials (IMPAACT) Network  2032 study. The developed PBPK model successfully captured RDV and its metabolites' plasma concentrations during pregnancy. The ratios of prediction versus observation for RDV area under the curve from time 0 to infinity (AUC0-∞ ) and maximum concentration (Cmax ) were 1.61 and 1.17, respectively. For GS-704277, the ratios of predicted versus observed were 0.94 for AUC0-∞ and 1.20 for Cmax . For GS-441524, the ratios of predicted versus observed were 1.03 for AUC0-24 , 1.05 for Cmax , and 1.07 for concentrations at 24 h. All predictions of AUC and Cmax for RDV and its metabolites were within a twofold error range, and about 60% of predictions were within a 10% error range. These findings demonstrate the feasibility of translating PBPK models to pregnant women to potentially guide trial design, clinical decision making, and drug development.

Prediction of in vivo prenatal chlorpyrifos exposure leading to developmental neurotoxicity in humans based on in vitro toxicity data by quantitative in vitro-in vivo extrapolation

Introduction: Epidemiological studies in children suggested that in utero exposure to chlorpyrifos (CPF), an organophosphate insecticide, may cause developmental neurotoxicity (DNT). We applied quantitative in vitro-in vivo extrapolation (QIVIVE) based on in vitro concentration and non-choline esterase-dependent effects data combined with Benchmark dose (BMD) modelling to predict oral maternal CPF exposure during pregnancy leading to fetal brain effect concentration. By comparing the results with data from epidemiological studies, we evaluated the contribution of the in vitro endpoints to the mode of action (MoA) for CPF-induced DNT. Methods: A maternal-fetal PBK model built in PK-Sim^® was used to perform QIVIVE predicting CPF concentrations in a pregnant women population at 15 weeks of gestation from cell lysate concentrations obtained in human induced pluripotent stem cell-derived neural stem cells undergoing differentiation towards neurons and glia exposed to CPF for 14 days. The in vitro concentration and effect data were used to perform BMD modelling. Results: The upper BMD was converted into maternal doses which ranged from 3.21 to 271 mg/kg bw/day. Maternal CPF blood levels from epidemiological studies reporting DNT findings in their children were used to estimate oral CPF exposure during pregnancy using the PBK model. It ranged from 0.11 to 140 μg/kg bw/day. Discussion: The effective daily intake doses predicted from the in vitro model were several orders of magnitude higher than exposures estimated from epidemiological studies to induce developmental non-cholinergic neurotoxic responses, which were captured by the analyzed in vitro test battery. These were also higher than the in vivo LOEC for cholinergic effects. Therefore, the quantitative predictive value of the investigated non-choline esterase-dependent effects, although possibly relevant for other chemicals, may not adequately represent potential key events in the MoA for CPF-associated DNT.

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.

Physiologically based pharmacokinetic modeling of daptomycin dose optimization in pediatric patients with renal impairment

Background and Objective: Daptomycin is used to treat Gram-positive infections in adults and children and its dosing varies among different age groups. We focused on the pharmacokinetics of daptomycin in children with renal impairment, which has not been evaluated.

Methods: A physiologically based pharmacokinetic (PBPK) model of daptomycin was established and validated to simulate its disposition in healthy populations and adults with renal impairment, along with a daptomycin exposure simulated in pediatric patients with renal impairment.

Results: The simulated PBPK modeling results for various regimens of intravenously administered daptomycin were consistent with observed data according to the fold error below the threshold of 2. The Cmax and AUC of daptomycin did not differ significantly between children with mild-to-moderate renal impairment and healthy children. The AUC increased by an average of 1.55-fold and 1.85-fold in severe renal impairment and end-stage renal disease, respectively. The changes were more significant in younger children and could reach a more than 2-fold change. This scenario necessitates further daptomycin dose adjustments.

Conclusion: Dose adjustments take into account the efficacy and safety of the drug; however, the steady-state Cmin of daptomycin may be above 24.3 mg/L in a few instances. We recommend monitoring creatine phosphokinase more than once a week when using daptomycin in children with renal impairment.

Non-clinical considerations for supporting accelerated inclusion of pregnant women in pre-licensure clinical trials with anti-HIV agents

Introduction

To allow the continued participation of women enrolled in pre‐licensure clinical trials who become pregnant, and to potentially enrol pregnant women in clinical trials, non‐clinical developmental and reproductive toxicology studies (DART) are essential. Generally during pharmaceutical development, DART studies are conducted late during clinical development, leading to the exclusion of pregnant women from enrolment and withdrawal of women becoming pregnant during pre‐licensure trials.

Discussion

Completing all DART studies prior to or early during the conduct of phase 3 trials (i.e. earlier than current common practice) can accelerate and facilitate the inclusion of women who become pregnant during pre‐licensure trials to remain on study drug and to potentially enrol pregnant women more rapidly. Promoting complementary strategies, such as alternative combinations of DART study designs and physiologically based pharmacokinetic modelling, could better inform drug dosing and safety in pregnancy at an earlier stage in drug development. The interpretation of the results of non‐clinical DART studies is often complex. Institutional review boards/ethics committees should have access to relevant expertise for interpretation and application of results of non‐clinical developmental and reproductive toxicity studies. Clear communication and thorough understanding of non‐clinical findings and the overall benefit–risk profile of the product are critical to review protocols and determine if women who become pregnant during a clinical trial could continue on study drug and/or to enrol pregnant women in the trial. The informed consent document should be well written so that participants can make an informed decision to stay on study drug or participate in a trial during pregnancy. Ultimately, the decision to allow women who become pregnant during pre‐licensure trials to remain on study will depend on the totality of the evidence and benefit–risk considerations.

Conclusions

We propose that industry completes non‐clinical reproductive toxicity studies prior to or early during the conduct of phase 3 trials in HIV drug development, especially for priority agents, and potentially uses alternative DART study design strategies to achieve this goal.

Development of a Generic Physiologically-Based Pharmacokinetic Model for Lactation and Prediction of Maternal and Infant Exposure to Ondansetron via Breast Milk

Ondansetron is commonly used in breastfeeding mothers to treat nausea and vomiting. There is limited information in humans regarding safety of ondansetron exposure to nursing infants and no adequate study looking at ondansetron pharmacokinetics during lactation. We developed a generic physiologically-based pharmacokinetic lactation model for small molecule drugs and applied this model to predict ondansetron transfer into breast milk and characterize infant exposure. Drug-specific model inputs were parameterized using data from the literature. Population-specific inputs were derived from a previously conducted systematic literature review of anatomic and physiologic changes in postpartum women. Model predictions were evaluated using ondansetron plasma and breast milk concentration data collected prospectively from 78 women in the Commonly Used Drugs During Lactation and infant Exposure (CUDDLE) study. The final model predicted breast milk and plasma exposures following a single 4 mg dose of intravenous ondansetron in 1,000 simulated women who were 2 days postpartum. Model predictions showed good agreement with observed data. Breast milk median prediction error (MPE) was 18.4% and median absolute prediction error (MAPE) was 53.0%. Plasma MPE was 32.5% and MAPE was 43.2%. The model-predicted daily and relative infant doses were 0.005 mg/kg/day and 3.0%, respectively. This model adequately predicted ondansetron passage into breast milk. The calculated low relative infant dose indicates that mothers receiving ondansetron can safely breastfeed. The model building blocks and population database are open-source and can be adapted to other drugs.

Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach

AIMS

The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates.

METHODS

A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database.

RESULTS

The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCR_max  = 2.23) and CYP2C19 (AUCR_max  = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp.

CONCLUSION

Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.

Model-Informed Dose Optimization in Pregnancy

Pregnancy is associated with several physiological changes that can alter the pharmacokinetics (PK) and pharmacodynamics of drugs. These may require dosing changes in pregnant women to achieve drug exposures comparable to the nonpregnant population. There is, however, limited information available on the PK and pharmacodynamics of drugs used during pregnancy. Practical difficulties in performing PK studies and potential liability issues are often the reasons for the availability of limited information. Over the past several years, there has been a rapid development in the application of various modeling strategies such as population PK and physiologically based PK modeling to provide guidance on drug dosing in this special patient population. Population PK models rely on measured PK data, whereas physiologically based PK models integrate physiological, preclinical, and clinical data to quantify changes in PK of drugs in various patient populations. These modeling strategies offer a promising approach to identify the drugs with PK changes during pregnancy and guide dose adjustment in pregnant women. This review focuses on PBPK modeling to guide drug therpay in pregnancy.

Physiologically Based Pharmacokinetic Modeling to Characterize Acetaminophen Pharmacokinetics and N-Acetyl-p-Benzoquinone Imine (NAPQI) Formation in Non-Pregnant and Pregnant Women

Background and Objective

Little is known about acetaminophen (paracetamol) pharmacokinetics during pregnancy. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict acetaminophen pharmacokinetics throughout pregnancy.

Methods

PBPK models for acetaminophen and its metabolites were developed in non-pregnant and pregnant women. Physiological and enzymatic changes in pregnant women expected to impact acetaminophen pharmacokinetics were considered. Models were evaluated using goodness-of-fit plots and by comparing predicted pharmacokinetic profiles with in vivo pharmacokinetic data. Predictions were performed to illustrate the average concentration at steady state (C_ss,avg) values, used as an indicator for efficacy, of acetaminophen achieved following administration of 1000 mg every 6 h. Furthermore, as a measurement of potential hepatotoxicity, the molar dose fraction of acetaminophen converted to N-acetyl-p-benzoquinone imine (NAPQI) was estimated.

Results

PBPK models successfully predicted the pharmacokinetics of acetaminophen and its metabolites in non-pregnant and pregnant women. Predictions resulted in the lowest C_ss,avg in the third trimester (median [interquartile range]: 4.5 [3.8–5.1] mg/L), while C_ss,avg was 6.7 [5.9–7.4], 5.6 [4.7–6.3], and 4.9 [4.1–5.5] mg/L in non-pregnant, first trimester, and second trimester populations, respectively. Assuming a constant raised cytochrome P450 2E1 activity throughout pregnancy, the molar dose fraction of acetaminophen converted to NAPQI was highest during the first trimester (median [interquartile range]: 11.0% [9.1–13.4%]), followed by the second (9.0% [7.5–11.0%]) and third trimester (8.2% [6.8–10.1%]), compared with non-pregnant women (7.7% [6.4–9.4%]).

Conclusion

Acetaminophen exposure is lower in pregnant than in non-pregnant women, and is related to pregnancy duration. Despite these findings, higher dose adjustments cannot be advised yet as it is unknown whether pregnancy affects the toxicodynamics of NAPQI. Information on glutathione abundance during pregnancy and NAPQI in vivo data are required to further refine the presented model.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00799-5) contains supplementary material, which is available to authorized users.

Physiologically Based Pharmacokinetic Modeling Framework to Predict Neonatal Pharmacokinetics of Transplacentally Acquired Emtricitabine, Dolutegravir, and Raltegravir

BACKGROUND AND OBJECTIVE

Little is understood about neonatal pharmacokinetics immediately after delivery and during the first days of life following intrauterine exposure to maternal medications. Our objective was to develop and evaluate a novel, physiologically based pharmacokinetic modeling workflow for predicting perinatal and postnatal disposition of commonly used antiretroviral drugs administered prenatally to pregnant women living with human immunodeficiency virus.

METHODS

Using previously published, maternal-fetal, physiologically based pharmacokinetic models for emtricitabine, dolutegravir, and raltegravir built with PK-Sim/MoBi^®, placental drug transfer was predicted in late pregnancy. The total drug amount in fetal compartments at term delivery was estimated and subsequently integrated as initial conditions in different tissues of a whole-body, neonatal, physiologically based pharmacokinetic model to predict drug concentrations in the neonatal elimination phase after birth. Neonatal elimination processes were parameterized according to published data. Model performance was assessed by clinical data.

RESULTS

Neonatal physiologically based pharmacokinetic models generally captured the initial plasma concentrations after delivery but underestimated concentrations in the terminal phase. The mean percentage error for predicted plasma concentrations was - 71.5%, - 33.8%, and 76.7% for emtricitabine, dolutegravir, and raltegravir, respectively. A sensitivity analysis suggested that the activity of organic cation transporter 2 and uridine diphosphate glucuronosyltransferase 1A1 during the first postnatal days in term newborns is ~11% and ~30% of that in adults, respectively.

CONCLUSIONS

These findings demonstrate the general feasibility of applying physiologically based pharmacokinetic models to predict washout concentrations of transplacentally acquired drugs in newborns. These models can increase the understanding of pharmacokinetics during the first postnatal days and allow the prediction of drug exposure in this vulnerable population.

Maternal-Fetal Pharmacology of Drugs: A Review of Current Status of the Application of Physiologically Based Pharmacokinetic Models

Pregnancy and the postpartum period are associated with several physiological changes that can alter the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. For certain drugs, dosing changes may be required during pregnancy and postpartum to achieve drug exposures comparable to what is observed in non-pregnant subjects. There is very limited data on fetal exposure of drugs during pregnancy, and neonatal exposure through transfer of drugs via human milk during breastfeeding. Very few systematic clinical pharmacology studies have been conducted in pregnant and postpartum women due to ethical issues, concern for the fetus safety as well as potential legal ramifications. Over the past several years, there has been an increase in the application of modeling and simulation approaches such as population PK (PopPK) and physiologically based PK (PBPK) modeling to provide guidance on drug dosing in those special patient populations. Population PK models rely on measured PK data, whereas physiologically based PK models incorporate physiological, preclinical, and clinical data into the model to predict drug exposure during pregnancy. These modeling strategies offer a promising approach to identify the drugs with PK changes during pregnancy to guide dose optimization in pregnancy, when there is lack of clinical data. PBPK modeling is also utilized to predict the fetal exposure of drugs and drug transfer via human milk following maternal exposure. This review focuses on the current status of the application of PBPK modeling to predict maternal and fetal exposure of drugs and thereby guide drug therapy during pregnancy.

Evaluation of Placental Transfer and Tissue Distribution of cis- and Trans-Permethrin in Pregnant Rats and Fetuses Using a Physiological-Based Pharmacokinetic Model

Biomonitoring studies have highlighted the exposure of pregnant women to pyrethroids based on the measurement of their metabolites in urine. Pyrethroids can cross the placental barrier and be distributed in the fetus as some pyrethroids were also measured in the meconium of newborns. Prenatal exposure to pyrethroids is suspected to alter the neurodevelopment of children, and animal studies have shown that early life exposure to permethrin, one of the most commonly used pyrethroid in household applications, can alter the brain development. This study aimed to characterize the fetal permethrin exposure throughout gestation in rats. We developed a pregnancy physiologically based pharmacokinetic (pPBPK) model that describes the maternal and fetal kinetics of the cis- and trans- isomers of permethrin during the whole gestation period. Pregnant Sprague-Dawley rats were exposed daily to permethrin (50 mg/kg) by oral route from the start of gestation to day 20. Permethrin isomers were quantified in the feces, kidney, mammary gland, fat, and placenta in dams and in both maternal and fetal blood, brain, and liver. Cis- and trans-permethrin were quantified in fetal blood and tissues, with higher concentrations for the cis-isomer. The pPBPK model was fitted to the toxicokinetic maternal and fetal data in a Bayesian framework. Several parameters were adjusted, such as hepatic clearances, partition coefficients, and intestinal absorption. Our work allowed to estimate the prenatal exposure to permethrin in rats, especially in the fetal brain, and to quantitatively estimate the placental transfer. These transfers could be extrapolated to humans and be incorporated in a human pPBPK model to estimate the fetal exposure to permethrin from biomonitoring data.

Development of a Multifunctional Bioerodible Nanocomposite Containing Metronidazole and Curcumin to Apply on L-PRF Clot to Promote Tissue Regeneration in Dentistry

Teeth extractions are often followed by alveolar bone reabsorption, although an adequate level of bone is required for reliable rehabilitations by dental implants. Leukocyte and platelet-rich fibrin (L-PRF) has been widely applied in regenerative procedures and with antibiotic and antioxidant agents could play an essential role in hard and soft tissue healing. In this work, a nanocomposite (Sponge-C-MTR) consisting of a hyaluronate-based sponge loaded with metronidazole (MTR) and nanostructured lipid carriers containing curcumin (CUR-NLC) was designed to be wrapped in the L-PRF™ membrane in the post-extraction sockets and characterized. CUR-NLCs, obtained by homogenization followed by high-frequency sonication of the lipid mixture, showed loading capacity (5% w/w), drug recovery (95% w/w), spherical shape with an average particle size of 112.0 nm, and Zeta potential of -24 mV. Sponge-C-MTR was obtained by entrapping CUR-NLC in a hydrophilic matrix by a freeze-drying process, and physico-chemical and cytocompatibility properties were evaluated. Moreover, the aptitude of CUR and MTR to the penetrate and/or permeate both L-PRF™ and porcine buccal tissue was assessed, highlighting MTR penetration and CUR accumulation promoted by the system. The results positively support the action of nanocomposite in dental tissues regeneration when applied together with the L-PRF™.

Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice

Personalized, stratified, or precision medicine (PM) introduces a new era in healthcare that tries to identify and predict optimum treatment outcomes for a patient or a cohort. It also introduces new scientific terminologies regarding therapeutic approaches and the need of their adoption from healthcare providers. Till today, evidence-based practice (EBP) was focusing on population averages and their variances among cohorts for clinical values that are essential for optimizing healthcare outcome. It can be stated that EBP and PM are complementary approaches for a modern healthcare system. Healthcare providers through EBP often see the forest (population averages) but miss the trees (individual patients), whereas utilization of PM may not see the forest for the trees. Nursing personnel (NP) play an important role in modern healthcare since they are consulting, educating, and providing care to patients whose needs often needs to be individualized (personalized nursing care, PNC). Based on the clinical issues earlier addressed from clinical pharmacology, EBP, and now encompassed in PM, this review tries to describe the challenges that NP have to face in order to meet the requisites of the new era in healthcare. It presents the demands that should be met for upgrading the provided education and expertise of NP toward an updated role in a modern healthcare system.

Prediction of Maternal and Fetal Pharmacokinetics of Dolutegravir and Raltegravir Using Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Predicting drug pharmacokinetics in pregnant women including placental drug transfer remains challenging. This study aimed to develop and evaluate maternal-fetal physiologically based pharmacokinetic models for two antiretroviral drugs, dolutegravir and raltegravir.

Clinical Pharmacokinetic Studies in Pregnant Women and the Relevance of Pharmacometric Tools

BACKGROUND

In clinical pharmacokinetic (PK) studies, pregnant women are significantly underrepresented because of ethical and legal reasons which lead to a paucity of information on potential PK changes in this population. As a consequence, pharmacometric tools became instrumental to explore and quantify the impact of PK changes during pregnancy.

METHODS

We explore and discuss the typical characteristics of population PK and physiologically based pharmacokinetic (PBPK) models with a specific focus on pregnancy and postpartum.

RESULTS

Population PK models enable the analysis of dense, sparse or unbalanced data to explore covariates in order to (partly) explain inter-individual variability (including pregnancy) and to individualize dosing. For population PK models, we subsequently used an illustrative approach with ketorolac data to highlight the relevance of enantiomer specific modeling for racemic drugs during pregnancy, while data on antibiotic prophylaxis (cefazolin) during surgery illustrate the specific characteristics of the fetal compartments in the presence of timeconcentration profiles. For PBPK models, an overview on the current status of reports and papers during pregnancy is followed by a PBPK cefuroxime model to illustrate the added benefit of PBPK in evaluating dosing regimens in pregnant women.

CONCLUSIONS

Pharmacometric tools became very instrumental to improve perinatal pharmacology. However, to reach their full potential, multidisciplinary collaboration and structured efforts are needed to generate more information from already available datasets, to share data and models, and to stimulate cross talk between clinicians and pharmacometricians to generate specific observations (pathophysiology during pregnancy, breastfeeding) needed to further develop the field.

Physiologically Based Pharmacokinetic Models to Predict Maternal Pharmacokinetics and Fetal Exposure to Emtricitabine and Acyclovir

Pregnancy is associated with physiological changes that may impact drug pharmacokinetics (PK). The goals of this study were to build maternal-fetal physiologically based pharmacokinetic (PBPK) models for acyclovir and emtricitabine, 2 anti(retro)viral drugs with active renal net secretion, and to (1) evaluate the predicted maternal PK at different stages of pregnancy; (2) predict the changes in PK target parameters following the current dosing regimen of these drugs throughout pregnancy; (3) evaluate the predicted concentrations of these drugs in the umbilical vein at delivery; (4) compare the model performance for predicting maternal PK of emtricitabine in the third trimester with that of previously published PBPK models; and (5) compare different previously published approaches for estimating the placental permeability of these 2 drugs. Results showed that the pregnancy PBPK model for acyclovir predicted all maternal concentrations within a 2-fold error range, whereas the model for emtricitabine predicted 79% of the maternal concentrations values within that range. Extrapolation of these models to earlier stages of pregnancy indicated that the change in the median PK target parameters remained well above the target threshold. Concentrations of acyclovir and emtricitabine in the umbilical vein were overall adequately predicted. The comparison of different emtricitabine PBPK models suggested an overall similar predictive performance in the third trimester, but the comparison of different approaches for estimating placental drug permeability revealed large differences. These models can enhance the understanding of the PK behavior of renally excreted drugs, which may ultimately inform pharmacotherapeutic decision making in pregnant women and their fetuses.

Physiologically Based Pharmacokinetic Modeling in Pregnancy: A Systematic Review of Published Models

During recent years there has been a surge in developing and applying physiologically based pharmacokinetic (PBPK) models in pregnant women to better understand and predict changes in drug pharmacokinetics throughout pregnancy. As a consequence, the number of publications focusing on pregnancy PBPK models has increased substantially. However, to date these models, especially across various platforms, have not been systematically evaluated. Hence, this review aims to assess published PBPK models in pregnancy used for therapeutic purposes.