Labetalol Dosing in Pregnancy: PBPK/PD and CYP2C19 Polymorphisms.

As detailed information on the pharmacokinetics (PK) of labetalol in pregnant people are lacking, the aims of this study were: (1) to build a physiologically based PK (PBPK) model of labetalol in non-pregnant individuals that incorporates different CYP2C19 genotypes (specifically, *1/*1, *1/*2 or *3, *2/*2, and *17/*17); (2) to translate this model to the second and third trimester of pregnancy; and (3) to combine the model with a previously published direct pharmacodynamic (PD) model to predict the blood pressure lowering effect of labetalol in the third trimester. Clinical data for model evaluation was obtained from the scientific literature. In non-pregnant populations, the mean ratios of simulated versus observed peak concentration (Cmax), time to reach Cmax (Tmax), and exposure (area under the plasma concentration-time curve, AUC) were 0.94, 0.82, and 1.16, respectively. The pregnancy PBPK model captured the observed PK adequately, but clearance was slightly underestimated with mean ratios of simulated versus observed Cmax, Tmax, and AUC of 1.28, 1.30, and 1.39, respectively. The results suggested that pregnant people with CYP2C19 *2/*2 alleles have similar labetalol exposure and trough levels compared to non-pregnant controls, whereas those with other alleles were found to have increased exposure and trough concentrations. Importantly, the pregnancy PBPK/PD model predicted that, despite increased exposure in some genotypes, the blood pressure lowering effect was broadly comparable across all genotypes. In view of the large inter-individual variability and the potentially increasing blood pressure during pregnancy, patients may need to be closely monitored for achieving optimal therapeutic effects and avoiding adverse events.

Development of a Generic Fetal Physiologically Based Pharmacokinetic Model and Prediction of Human Maternal and Fetal Organ Concentrations of Cefuroxime.

BACKGROUND AND OBJECTIVE: Physiologically based pharmacokinetic (PBPK) models for pregnant women have recently been successfully used to predict maternal and umbilical cord pharmacokinetics (PK). Because there is very limited opportunity for conducting clinical and PK investigations for fetal drug exposure, PBPK models may provide further insights. The objectives of this study were to extend a whole-body pregnancy PBPK model by multiple compartments representing fetal organs, and to predict the PK of cefuroxime in the maternal and fetal plasma, the amniotic fluid, and several fetal organs. METHODS: To this end, a previously developed pregnancy PBPK model for cefuroxime was updated using the open-source software Open Systems Pharmacology (PK-Sim®/MoBi®). Multiple compartments were implemented to represent fetal organs including brain, heart, liver, lungs, kidneys, the gastrointestinal tract (GI), muscles, and fat tissue, as well as another compartment lumping organs and tissues not explicitly represented. RESULTS: This novel PBPK model successfully predicted cefuroxime concentrations in maternal blood, umbilical cord, amniotic fluid, and several fetal organs including heart, liver, and lungs. Further model validation with additional clinical PK data is needed to build confidence in the model. CONCLUSIONS: Being developed with an open-source software, the presented generic model can be freely re-used and tailored to address specific questions at hand, e.g., to assist the design of clinical studies in the context of drug research or to predict fetal organ concentrations of chemicals in the context of fetal health risk assessment.

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.

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.

Mechanistic Modeling of Placental Drug Transfer in Humans: How Do Differences in Maternal/Fetal Fraction of Unbound Drug and Placental Influx/Efflux Transfer Rates Affect Fetal Pharmacokinetics?

Background: While physiologically based pharmacokinetic (PBPK) models generally predict pharmacokinetics in pregnant women successfully, the confidence in predicting fetal pharmacokinetics is limited because many parameters affecting placental drug transfer have not been mechanistically accounted for. Objectives: The objectives of this study were to implement different maternal and fetal unbound drug fractions in a PBPK framework; to predict fetal pharmacokinetics of eight drugs in the third trimester; and to quantitatively investigate how alterations in various model parameters affect predicted fetal pharmacokinetics. Methods: The ordinary differential equations of previously developed pregnancy PBPK models for eight drugs (acyclovir, cefuroxime, diazepam, dolutegravir, emtricitabine, metronidazole, ondansetron, and raltegravir) were amended to account for different unbound drug fractions in mother and fetus. Local sensitivity analyses were conducted for various parameters relevant to placental drug transfer, including influx/efflux transfer clearances across the apical and basolateral membrane of the trophoblasts. Results: For the highly-protein bound drugs diazepam, dolutegravir and ondansetron, the lower fraction unbound in the fetus vs. mother affected predicted pharmacokinetics in the umbilical vein by ≥10%. Metronidazole displayed blood flow-limited distribution across the placenta. For all drugs, umbilical vein concentrations were highly sensitive to changes in the apical influx/efflux transfer clearance ratio. Additionally, transfer clearance across the basolateral membrane was a critical parameter for cefuroxime and ondansetron. Conclusion: In healthy pregnancies, differential protein binding characteristics in mother and fetus give rise to minor differences in maternal-fetal drug exposure. Further studies are needed to differentiate passive and active transfer processes across the apical and basolateral trophoblast membrane.

Evaluation of Physiologically Based Pharmacokinetic Models to Predict the Absorption of BCS Class I Drugs in Different Pediatric Age Groups.

Age-related changes in many parameters affecting drug absorption remain poorly characterized. The objective of this study was to apply physiologically based pharmacokinetic (PBPK) models in pediatric patients to investigate the absorption and pharmacokinetics of 4 drugs belonging to the Biopharmaceutics Classification System (BCS) class I administered as oral liquid formulations. Pediatric PBPK models built with PK-Sim/MoBi were used to predict the pharmacokinetics of acetaminophen, emtricitabine, theophylline, and zolpidem in different pediatric populations. The model performance for predicting drug absorption and pharmacokinetics was assessed by comparing the predicted absorption profile with the deconvoluted dose fraction absorbed over time and predicted with observed plasma concentration-time profiles. Sensitivity analyses were performed to analyze the effects of changes in relevant input parameters on the model output. Overall, most pharmacokinetic parameters were predicted within a 2-fold error range. The absorption profiles were generally reasonably predicted, but relatively large differences were observed for acetaminophen. Sensitivity analyses showed that the predicted absorption profile was most sensitive to changes in the gastric emptying time (GET) and the specific intestinal permeability. The drug's solubility played only a minor role. These findings confirm that gastric emptying time, more than intestinal permeability or solubility, is a key factor affecting BCS class I drug absorption in children. As gastric emptying time is prolonged in the fed state, a better understanding of the interplay between food intake and gastric emptying time in children is needed, especially in the very young in whom the (semi)fed condition is the prevailing prandial state, and hence prolonged gastric emptying time seems more plausible than the fasting state.

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.

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.

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.

Drug Transporters Expressed in the Human Placenta and Models for Studying Maternal-Fetal Drug Transfer.

Tremendous efforts have been directed to investigate the ontogeny of drug transporters in fetuses, neonates, infants, and children based on their importance for understanding drug pharmacokinetics. During development (ie, in the fetus and newborn infant), there is special interest in transporters expressed in the placenta that modulate placental drug transfer. Many of these transporters can decrease or increase drug concentrations in the fetus and at birth, stressing the relevance of elucidating expression in the placenta and potential gestational age-dependent changes therein. Hence, the main objective of this review was to summarize the current knowledge about expression and ontogeny of transporters in the human placenta in healthy pregnant women. In addition, various in vitro, ex vivo, and in silico models that can be used to investigate placental drug transfer, namely, placental cancer cell lines, ex vivo cotyledon perfusion experiments, and physiologically based pharmacokinetic (PBPK) models, are discussed together with their advantages and shortcomings. A particular focus was placed on PBPK models because these models can integrate different types of information, such as expression data, ontogeny information, and observations obtained from the ex vivo cotyledon perfusion experiment. Such a mechanistic modeling framework may leverage the available information and ultimately help to improve knowledge about the adequacy and safety of pharmacotherapy in pregnant women and their fetuses.

Monoclonal Antibodies and Fc-Fusion Proteins for Pediatric Use: Dosing, Immunogenicity, and Modeling and Simulation in Data Submitted to the US Food and Drug Administration.

The experience with the use of monoclonal antibodies and Fc-fusion proteins (mAb/Fc) in the pediatric population is limited. The objective of this study is to review those factors impacting the clinical efficacy and product safety of mAb/Fc products in pediatric patients during drug development. We reviewed the list of biologic products in the US Food and Drug Administration's Purple Book as of March 2018 with a focus on mAb/Fc products that are indicated for use in both adults and pediatric patients. Of 68 mAb/Fc products in the Purple Book (excluding biosimilars), 20 products have approved indications in both adults and children. Thirteen products had concurrent approval for both adult and pediatric populations. The sample size of pediatric studies generally ranged from approximately 2% to 70% of the sample size of adult studies with the same indication. In general, pediatric dosing regimens were found to be more based on body weight and weight tiered than the regimens for adults. Modeling and simulation techniques comprised mainly population pharmacokinetic and pharmacodynamic models. A review of the immunogenicity incidence did not reveal any notable difference in the 5 products having data on both pediatric and adult patients. In conclusion, most of the mAb/Fc products have a different weight-based dosing regimen for pediatric patients versus adults. An understanding of the comparative experience in drug development for mAb/Fc products between adult and pediatric patients coupled with the application of advanced modeling and simulation methods should assist future development of new mAb/Fc products for pediatric patients.

A Comparison of Pediatric and Adult Safety Studies for Antipsychotic and Antidepressant Drugs Submitted to the United States Food and Drug Administration.

OBJECTIVE: To examine the differences in the adverse drug reaction (ADR) profile of antipsychotic and antidepressant agents between pediatric and adult patients in studies submitted to the Food and Drug Administration (FDA) during the drug development process. STUDY DESIGN: Clinical trials in adult and pediatric patients were conducted by sponsors as part of the drug development programs for antipsychotic and antidepressant agents, and ADR information was collected as part of those trials and submitted to the FDA. Data collection was conducted by reviewing publicly available FDA-authored reviews and FDA-approved product labels for 10 drugs with an antipsychotic or an antidepressant indication from 2007 to 2017. RESULTS: There were 308 drug and ADR combinations for the 10 drugs and drug combinations with 113 (36.7%) having a significantly different incidence in pediatric patients compared with adults. Sixty-eight (60.2%) of these ADRs had a significantly higher incidence in pediatric patients than in adults. Sedation was higher in 6 of the 10 drugs and drug combinations with risk differences ranging from 9.6 to 36.6%. CONCLUSIONS: This analysis indicates that there were significant differences between the pediatric and adult safety profiles of antipsychotic and antidepressant drugs. Sedation was the major ADR associated with the use of atypical antipsychotic drugs in pediatric patients. Clinicians caring for children should consider the ADR profile when prescribing antipsychotics and antidepressants in pediatric patients.

Surrogate Endpoints in Pediatric Studies Submitted to the US FDA.

The 21st Century Cures Act was passed in December, 2016, and included a number of provisions to facilitate drug approval. Considerable discussion was generated related to some aspects of the Act, especially to the use of surrogate endpoints (SEs) as a means to shorten the time required prior to receiving US Food and Drug Administration (FDA) approval.1 The objective of this analysis was to identify the use and outcomes of SEs and clinical endpoints in pediatric drug development trials.

Primary Endpoints in Pediatric Efficacy Trials Submitted to the US FDA.

The selection of appropriate endpoints in pediatric drug development trials is a critical aspect of trial design. Given the high pediatric trial failure rate, selecting optimal trial design elements, such as the primary efficacy endpoint, is essential to ensuring increased potential for trial success. The objectives of this study were to identify the primary efficacy endpoints measured in pediatric drug development trials submitted to the US Food and Drug Administration and to relate endpoint attributes to trial and label outcome. The analysis included pediatric pivotal efficacy studies submitted from September 2007 to July 2016 for which there was a corresponding adult trial for the same indication. Two hundred and thirty-four efficacy trials on 138 unique products studied in pediatric patients were assessed. The adult and pediatric endpoints were the same in 141 of the 234 trials (60.3%), and these trials succeeded in meeting their primary endpoint more often (122 of 141 [86.5%]) than when the adult and pediatric endpoints differed (57 of 93 [61.3%]; odds ratio, 4.03; 95%CI, 2.10-7.80). Trials that included both pediatric and adult patients succeeded more frequently than those trials that did not combine pediatric and adult patients (85 of 95 versus 94 of 139, respectively; odds ratio, 4.05; 95%CI, 1.94-9.31). No differences were observed in pediatric trial success between those using subjective and objective endpoints. Using the same endpoint in the pediatric trial as was measured in the corresponding adult trial and enrolling pediatric and adult patients in the same trial were attributes associated with trial success.

Enrichment Strategies in Pediatric Drug Development: An Analysis of Trials Submitted to the US Food and Drug Administration.

Clinical trial enrichment involves prospectively incorporating trial design elements that increase the probability of detecting a treatment effect. The use of enrichment strategies in pediatric drug development has not been systematically assessed. We analyzed the use of enrichment strategies in pediatric trials submitted to the US Food and Drug Administration from 2012-2016. In all, 112 efficacy studies associated with 76 drug development programs were assessed and their overall success rates were 78% and 75%, respectively. Eighty-eight trials (76.8%) employed at least one enrichment strategy; of these, 66.3% employed multiple enrichment strategies. The highest trial success rates were achieved when all three enrichment strategies (practical, predictive, and prognostic) were used together within a single trial (87.5%), while the lowest success rate was observed when no enrichment strategy was used (65.4%). The use of enrichment strategies in pediatric trials was found to be associated with trial and program success in our analysis.

Pharmacogenetics in transplant patients: can it predict pharmacokinetics and pharmacodynamics?

Pharmacogenetics holds the potential to allow individualized dosing of immunosuppressive agents to optimize their therapeutic effect while minimizing adverse effects. As more pharmacogenetic information accumulates, the prospect of reducing or discontinuing the intensive therapeutic drug monitoring of immunosuppressants looks attractive. However, the long process of developing useful clinical information from basic information on the genes of interest is at a very early stage, and our present information does not supercede pharmacokinetic or blood concentration monitoring of immunosuppressants. The most extensive blood concentration/dose information available is on tacrolimus and its dosing related to CYP3A5 and ABCB1 gene polymorphisms. Although CYP3A5 genotype is definitely associated with tacrolimus dosing, the only recommendation presently published is for an arbitrary doubling of the starting tacrolimus dose in CYP3A5 expressors. For cyclosporine, sirolimus, and corticosteroids, the presently available pharmacogenetic information does not permit pharmacokinetic predictions. The pharmacodynamics of immunosuppressants, as evidenced by effects on acute rejection or adverse drug effects, have considerably more potential for prediction by pharmacogenetic models. Drug-resistant rejection, nephrotoxicity, steroid resistance and osteonecrosis, and even patient survival may ultimately be predicted by models incorporating multiple gene polymorphisms and other critical patient information. At this point, treatment algorithms can be developed that will allow us to individualize a transplant patient's immunosuppressive therapy.