A physiologically based pharmacokinetic model to predict disposition of CYP2D6 and CYP1A2 metabolized drugs in pregnant women

Optimal Dosing Recommendations of Clonidine in Pediatrics Using Physiologically Based Pharmacokinetic Modeling

OBJECTIVE

Clonidine has been widely used in the pediatric population to treat neonatal abstinence syndrome (NAS), attention deficit hyperactivity disorder (ADHD), sedation, and Tourette's syndrome; however, there is no consensus on dosing. This research aims to recommend optimal dosing of clonidine in the pediatric population using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The pediatric PBPK model was developed from an adult model by scaling the clearance processes from adults to pediatrics using ontogeny equations. The adult and pediatric models were verified using clinical PK data, and the model performance was evaluated based on visual predictive checks and absolute fold error (AFE). The final pediatric PBPK model was used to simulate clonidine PK in the virtual pediatric population. The optimal dose was recommended based on a target concentration representing clonidine's α-2 central agonist activity (EC50 = 40.5 nM).

RESULTS

The adult and pediatric models predicted well, with more than 90% of observed data captured within the 95% prediction interval of simulated data. The AFE values were within 2-fold for clonidine plasma concentrations from observed and predicted data. The pediatric simulations showed that 30 µg/kg dose orally for neonates and 0.9 mg/day orally for children (6-17 years) are optimal for achieving target concentrations for maximal α-2 adrenergic activity.

CONCLUSIONS

The pediatric PBPK model of clonidine scaled from the adult PBPK model provided optimal dosing recommendations for clonidine in different pediatric age groups. The pediatric PBPK model described in this study can be extended to other pediatric age groups and routes of administration.

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

The cytochrome P450 2D6 (CYP2D6) is a key xenobiotic-metabolizing enzyme involved in the clearance of many drugs. Genetic polymorphisms in CYP2D6 contribute to the large inter-individual variability in drug metabolism and could affect metabolic phenotyping of CYP2D6 probe substances such as dextromethorphan (DXM). To study this question, we (i) established an extensive pharmacokinetics dataset for DXM; and (ii) developed and validated a physiologically based pharmacokinetic (PBPK) model of DXM and its metabolites dextrorphan (DXO) and dextrorphan O-glucuronide (DXO-Glu) based on the data. Drug-gene interactions (DGI) were introduced by accounting for changes in CYP2D6 enzyme kinetics depending on activity score (AS), which in combination with AS for individual polymorphisms allowed us to model CYP2D6 gene variants. Variability in CYP3A4 and CYP2D6 activity was modeled based on in vitro data from human liver microsomes. Model predictions are in very good agreement with pharmacokinetics data for CYP2D6 polymorphisms, CYP2D6 activity as described by the AS system, and CYP2D6 metabolic phenotypes (UM, EM, IM, PM). The model was applied to investigate the genotype-phenotype association and the role of CYP2D6 polymorphisms for metabolic phenotyping using the urinary cumulative metabolic ratio (UCMR), DXM/(DXO + DXO-Glu). The effect of parameters on UCMR was studied via sensitivity analysis. Model predictions indicate very good robustness against the intervention protocol (i.e. application form, dosing amount, dissolution rate, and sampling time) and good robustness against physiological variation. The model is capable of estimating the UCMR dispersion within and across populations depending on activity scores. Moreover, the distribution of UCMR and the risk of genotype-phenotype mismatch could be estimated for populations with known CYP2D6 genotype frequencies. The model can be applied for individual prediction of UCMR and metabolic phenotype based on CYP2D6 genotype. Both, model and database are freely available for reuse.

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.

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.

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim^®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUC_last ratios and 9/12, 5/5 and 7/7 of DGI C_max ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUC_ss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Opioid exposure during pregnancy and the risk of congenital malformation: a meta-analysis of cohort studies

BACKGROUND

Opioid exposure during pregnancy has increased alarmingly in recent decades. However, the association between prenatal opioid exposure and congenital malformation risk has still been controversial. We aim to assess the association between opioid exposure during pregnancy and the risk of congenital malformations.

METHOD

PubMed, Embase, and Cochrane library of clinical trials were systematically searched to September 13th, 2021. Cohort studies reporting risk of congenital malformation after opioid exposure compared with non-exposure during pregnancy were included. Risk of studies was appraised with the ROBINS-I tool. Meta-analysis was conducted using the random-effects model. Subgroup analyses were conducted for the primary outcome based on indication, exposed period, whether adjusted data was used, and risk of bias assessment. Meta-regression was performed to evaluate the relation of publication year.

MAIN RESULTS

Eighteen cohort studies with 7,077,709 patients were included. The results showed a significant increase in the risk of overall congenital malformation (RR = 1.30, 95%CI: 1.11-1.53), major malformation (RR = 1.57, 95%CI:1.11-2.22), central nervous system malformation (RR = 1.36, 95% CI:1.19-1.55), and limb malformation (RR = 2.27, 95%CI:1.29-4.02) with opioid exposure during pregnancy. However, the predictive interval conveyed a different result on overall congenital malformation (95%PI: 0.82-2.09) and major malformation (95%PI: 0.82-2.09). No association between opioid exposure and overall congenital malformation in the first trimester (RR = 1.12, 95%CI:0.97-1.31) and prescribed for analgesic or antitussive treatment (RR = 1.03, 95%CI:0.94-1.13) were observed. In subgroups that study provided data adjusted for confounders (RR = 1.06, 95%CI:0.93-1.20) or identified moderate or serious risk of bias (RR = 1.00, 95%Cl: 0.85-1.16; RR = 1.21, 95%Cl: 1.60-2.68), no association was found.

CONCLUSION

Opioid exposed in the first trimester or prescribed for analgesic or antitussive treatment did not increase the risk of overall congenital malformation. The findings should be discussed in caution considering the situation of individual patients and weigh out its potential risk of congenital malformation.

TRIAL REGISTRATION

Registration number: CRD42021279445 .

Predicting Regional Respiratory Tissue and Systemic Concentrations of Orally Inhaled Drugs through a Novel PBPK Model

Oral inhalation (OI) of drugs is the route of choice to treat respiratory diseases or for recreational drug use (e.g., cannabis). After OI, the drug is deposited in and systemically absorbed from various regions of the respiratory tract. Measuring regional respiratory tissue drug concentrations at the site of action is important for evaluating the efficacy and safety of orally inhaled drugs (OIDs). Because such a measurement is routinely not possible in humans, the only alternative is to predict these concentrations, for example by physiologically based pharmacokinetic (PBPK) modeling. Therefore, we developed an OI-PBPK model to integrate the interplay between regional respiratory drug deposition and systemic absorption to predict regional respiratory tissue and systemic drug concentrations. We validated our OI-PBPK model by comparing the simulated and observed plasma concentration-time profiles of two OIDs, morphine and nicotine. Furthermore, we performed sensitivity analyses to quantitatively demonstrate the impact of key parameters on the extent and pattern of regional respiratory drug deposition, absorption, and the resulting regional respiratory tissue and systemic plasma concentrations. Our OI-PBPK model can be applied to predict regional respiratory tissue and systemic drug concentrations to optimize OID formulations, delivery systems, and dosing regimens. Furthermore, our model could be used to establish the bioequivalence of generic OIDs for which systemic plasma concentrations are not measurable or are not a good surrogate of the respiratory tissue drug concentrations. SIGNIFICANCE STATEMENT: Our OI-PBPK model is the first comprehensive model to predict regional respiratory deposition, as well as systemic and regional tissue concentrations of OIDs, especially at the drug's site of action, which is difficult to measure in humans. This model will help optimize OID formulations, delivery systems, dosing regimens, and bioequivalence assessment of generic OID. Furthermore, this model can be linked with organs-on-chips, pharmacodynamic and quantitative systems pharmacology models to predict and evaluate the safety and efficacy of OID.

A cross-sectional study of the relationship between CYP2D6 and CYP2C19 variations and depression symptoms, for women taking SSRIs during pregnancy

Depression during pregnancy affects 10-15% of women, and 5% of women take antidepressants during pregnancy. Clinical guidelines provide recommendations for selective serotonin reuptake inhibitor (SSRI) drug choice and dose based on CYP2D6 and CYP2C19 genotype; however, they are based on evidence from non-pregnant cohorts. This study aimed to test the hypothesis that women with function-altering variants (increased, decreased, or no function) in these pharmacogenes, taking SSRIs prenatally, would have more depression symptoms than women whose pharmacogenetic variants are associated with normal SSRI metabolism. Comprehensive CYP2D6 and CYP2C19 genotyping using a range of methods, including gene copy number analysis, was performed as secondary analyses on two longitudinal cohorts of pregnant women (N = 83) taking the SSRIs paroxetine, citalopram, escitalopram, or sertraline. The Kruskal-Wallis test compared mean depression scores across four predicted metabolizer groups: poor (n = 5), intermediate (n = 10), normal (n = 53), and ultrarapid (n = 15). There were no significant differences between mean depression scores across the four metabolizer groups (H(3) = .73, p = .87, eta-squared = .029, epsilon-squared = .0089). This is the first study of the relationship in pregnancy between CYP2C19 pharmacogenetic variations and depression symptoms in the context of SSRI use. Findings from this initial study do not support the clinical use of pharmacogenetic testing for SSRI use during the second or third trimesters of pregnancy, but these findings should be confirmed in larger cohorts. There is an urgent need for further research to clarify the utility of pharmacogenetic testing for pregnant women, especially as companies offering direct-to-consumer genetic testing expand their marketing efforts.

Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

This study provides a whole‐body physiologically‐based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O‐glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug‐gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent‐metabolite‐metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O‐glucuronide was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Plasma concentration‐time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration‐time profiles, area under the concentration‐time curve from the time of the first measurement to the time of the last measurement (AUC_last) and maximum concentration (C_max) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration‐time profiles and plasma concentration‐time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUC_last and seven of seven DGI C_max ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 k_cat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log‐normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.

The impact of pregnancy on antihypertensive drug metabolism and pharmacokinetics: current status and future directions

INTRODUCTION

Hypertensive disorders of pregnancy (HDP) are rising in prevalence, and increase risk of adverse maternal and fetal outcomes. Physiologic changes occur during pregnancy that alter drug pharmacokinetics. However, antihypertensive drugs lack pregnancy-specific dosing recommendations due to critical knowledge gaps surrounding the extent of gestational changes in antihypertensive drug pharmacokinetics and underlying mechanisms.

AREAS COVERED

This review (1) summarizes currently recommended medications and dosing strategies for non-emergent HDP treatment, (2) reviews and synthesizes existing literature identified via a comprehensive Pubmed search evaluating gestational changes in the maternal pharmacokinetics of commonly prescribed HDP drugs (notably labetalol and nifedipine), and (3) offers insight into the metabolism and clearance mechanisms underlying altered HDP drug pharmacokinetics during pregnancy. Remaining knowledge gaps and future research directions are summarized.

EXPERT OPINION

A series of small pharmacokinetic studies illustrate higher oral clearance of labetalol and nifedipine during pregnancy. Pharmacokinetic modeling and preclinical studies suggest these effects are likely due to pregnancy-associated increases in hepatic UGT1A1- and CYP3A4-mediated first-pass metabolism and lower bioavailability. Accordingly, higher and/or more frequent doses may be needed to lower blood pressure during pregnancy. Future research is needed to address various evidence gaps and inform the development of more precise antihypertensive drug dosing strategies.

Prediction of Pregnancy-Induced Changes in Secretory and Total Renal Clearance of Drugs Transported by Organic Anion Transporters

Pregnancy can significantly change the pharmacokinetics of drugs, including those renally secreted by organic anion transporters (OATs). Quantifying these changes in pregnant women is logistically and ethically challenging. Hence, predicting the in vivo plasma renal secretory clearance (CLsec) and renal CL (CLrenal) of OAT drugs in pregnancy is important to design correct dosing regimens of OAT drugs. Here, we first quantified the fold-change in renal OAT activity in pregnant versus nonpregnant individual using available selective OAT probe drug CLrenal data (training dataset; OAT1: tenofovir, OAT2: acyclovir, OAT3: oseltamivir carboxylate). The fold-change in OAT1 activity during the 2nd and 3rd trimester was 2.9 and 1.0 compared with nonpregnant individual, respectively. OAT2 activity increased 3.1-fold during the 3rd trimester. OAT3 activity increased 2.2, 1.7 and 1.3-fold during the 1st, 2nd, and 3rd trimester, respectively. Based on these data, we predicted the CLsec, CLrenal and total clearance ((CLtotal) of drugs in pregnancy, which are secreted by multiple OATs (verification dataset; amoxicillin, pravastatin, cefazolin and ketorolac, R-ketorolac, S-ketorolac). Then, the predicted clearances (CLs) were compared with the observed values. The predicted/observed CLsec, CLrenal, and CLtotal of drugs in pregnancy of all verification drugs were within 0.80-1.25 fold except for CLsec of amoxicillin in the 3rd trimester (0.76-fold) and cefazolin in the 2nd trimester (1.27-fold). Overall, we successfully predicted the CLsec, CLrenal, and CLtotal of drugs in pregnancy that are renally secreted by multiple OATs. This approach could be used in the future to adjust dosing regimens of renally secreted OAT drugs which are administered to pregnant women. SIGNIFICANCE STATEMENT: To the authors' knowledge, this is the first report to successfully predict renal secretory clearance and renal clearance of multiple OAT substrate drugs during pregnancy. The data presented here could be used in the future to adjust dosing regimens of renally secreted OAT drugs in pregnancy. In addition, the mechanistic approach used here could be extended to drugs transported by other renal transporters.

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.

Exploring the Use of Serum-Derived Small Extracellular Vesicles as Liquid Biopsy to Study the Induction of Hepatic Cytochromes P450 and Organic Anion Transporting Polypeptides

Liver-derived small extracellular vesicles (sEVs), prepared from small sets of banked serum samples using a novel two-step protocol, were deployed as liquid biopsy to study the induction of cytochromes P450 (CYP3A4, CYP3A5, and CYP2D6) and organic anion transporting polypeptides (OATP1B1 and OATP1B3) during pregnancy (nonpregnant (T0), first, second, and third (T3) trimester women; N = 3 each) and after administration of rifampicin (RIF) to healthy male subjects. Proteomic analysis revealed induction (mean fold-increase, 90% confidence interval) of sEV CYP3A4 after RIF 300 mg × 7 days (3.5, 95% CI = 2.5-4.5, N = 4, P = 0.029) and 600 mg × 14 days (3.7, 95% CI = 2.1-6.0, N = 5, P = 0.018) consistent with the mean oral midazolam area under the plasma concentration time curve (AUC) ratio in the same subjects (0.28, 95% CI = 0.22-0.34, P < 0.0001; and 0.17, 95% CI = 0.13-0.22, P < 0.0001). Compared with CYP3A4, liver sEV CYP3A5 protein (subjects genotyped CYP3A5*1/*3) was weakly induced (≤ 1.5-fold). It was also possible to measure liver sEV-catalyzed dextromethorphan (DEX) O-demethylation to dextrorphan (DXO), correlated with sEV CYP2D6 expression (r = 0.917, P = 0.0001; N = 10) and 3-hour plasma DXO-to-DEX concentration ratio (r = 0.843, P = 0.002, N = 10), and show that CYP2D6 was not induced by RIF. Nonparametric analysis of liver sEV revealed significantly higher CYP3A4 (3.2-fold, P = 0.003) and CYP2D6 (3.7-fold, P = 0.03) protein expression in T3 vs. T0 women. In contrast, expression of both OATPs in liver sEV was unaltered by RIF administration and pregnancy.

Drug development research in pregnant and lactating women

Pregnant and lactating women are considered "therapeutic orphans" because they generally have been excluded from clinical drug research and the drug development process owing to legal, ethical, and safety concerns. Most medications prescribed for pregnant and lactating women are used "off-label" because most of the clinical approved medications do not have appropriate drug labeling information for pregnant and lactating women. Medications that lack human safety data on use during pregnancy and lactation may pose potential risks for adverse effects in pregnant and lactating women as well as risks of teratogenic effects to their unborn and newborn babies. Federal policy requiring the inclusion of women in clinical research and trials led to considerable changes in research design and practice. Despite more women being included in clinical research and trials, the inclusion of pregnant and lactating women in drug research and clinical trials remains limited. A recent revision to the "Common Rule" that removed pregnant women from the classification as a "vulnerable" population may change the culture of drug research and drug development in pregnant and lactating women. This review article provides an overview of medications studied by the Obstetric-Fetal Pharmacology Research Units Network and Centers and describes the challenges in current obstetrical pharmacology research and alternative strategies for future research in precision therapeutics in pregnant and lactating women. Implementation of the recommendations of the Task Force on Research Specific to Pregnant Women and Lactating Women can provide legislative requirements and opportunities for research focused on pregnant and lactating women.

PBPK Modeling Approach to Predict the Behavior of Drugs Cleared by Kidney in Pregnant Subjects and Fetus

The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model predicting the pharmacokinetics (PK) of different compounds in pregnant subjects. This model considers the differences in tissue sizes, blood flow rates, enzyme expression levels, glomerular filtration rates, plasma protein binding, and other factors affected during pregnancy in both the maternal and fetal models. The PBPKPlus™ module in GastroPlus^® was used to model the PK of cefuroxime and cefazolin. For both compounds, the model was first validated against PK data in healthy non-pregnant volunteers and then applied to predict pregnant groups PK. The model accurately described the PK in both non-pregnant and pregnant groups and explained well differences in the plasma concentration due to pregnancy. The fetal plasma and amniotic fluid concentrations were also predicted reasonably well at different stages of pregnancy. This work describes 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 compounds excreted renally. The prediction for pregnant groups is also improved when the model is calibrated with postpartum or non-pregnant female group if such data are available.

Precision dosing of methadone during pregnancy: A pharmacokinetics virtual clinical trials study

BACKGROUND

Methadone use for the management of opioid dependency during pregnancy is commonplace. Methadone levels are altered during pregnancy due to changes in maternal physiology. Despite this, a paucity of data exist regarding the most appropriate optimal dosing regimens during pregnancy.

METHODS

This study applied a pharmacokinetic modeling approach to examine gestational changes in R- and S-methadone concentrations in maternal plasma and fetal (cord) blood. This study did so to derive a theoretical optimal dosing regimen during pregnancy, and to identify the impact of Cytochromes P450 (CYP) 2B6 and 2C19 polymorphisms on methadone maternal and fetal pharmacokinetics.

RESULTS

The study noted significant decreases in maternal R- and S-methadone plasma concentrations during gestation, with concomitant increases in fetal levels. At a dose of 90 mg once daily, 75% (R-) and 94% (S-) of maternal methadone trough levels were below the lower therapeutic window at term (week 40). The developed optimal dosing regimen escalated doses to 110 mg by week 5, followed by 10 mg increments every 5 weeks up to a maximum of 180 mg once daily near term. This increase resulted in 27% (R-) and 11% (S-) of subjects with trough levels below the lower therapeutic window at term. CYP2B6 poor metabolizers (PM) and either CYP2C19 extensive metabolizers (EM), PM, or ultra-rapid (UM) metabolizer phenotypes demonstrated statistically significant increases in concentrations when compared to their matched CYP2B6 EM counterparts.

CONCLUSIONS

Specific and gestation-dependent dose titrations are required during pregnancy to reduce the risks associated with illicit drug use and to maintain fetal safety.

When special populations intersect with drug-drug interactions: Application of physiologically-based pharmacokinetic modeling in pregnant populations

Pregnancy results in significant physiological changes that vary across trimesters and into the postpartum period, and may result in altered disposition of endogenous substances and drug pharmacokinetics. Pregnancy represents a unique special population where physiologically-based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms without subjecting pregnant women or their fetuses to extensive clinical studies. A critical review of applications of pregnancy PBPK models (pPBPK) was conducted to understand its current status for prediction of drug exposure in pregnant populations and to identify areas of further expansion. Evaluation of existing pPBPK modeling efforts highlighted improved understanding of cytochrome P450 (CYP)-mediated changes during pregnancy and identified knowledge gaps for non-CYP enzymes and the physiological changes of the postpartum period. Examples of the application of pPBPK beyond simple dose regimen recommendations are limited, particularly for prediction of drug-drug interactions (DDI) or differences between genotypes for polymorphic drug metabolizing enzymes. A raltegravir pPBPK model implementing UGT1A1 induction during the second and third trimesters of pregnancy was developed in the current work and verified against clinical data. Subsequently, the model was used to explore UGT1A1-related DDI risk with atazanavir and rifampicin along with the effect of enzyme genotype on raltegravir apparent clearance. Simulations of pregnancy-related induction of UGT1A1 either exacerbated UGT1A1 induction by rifampicin or negated atazanavir UGT1A1 inhibition. This example illustrated the advantages of pPBPK modeling for mechanistic evaluation of complex interplays of pregnancy- and drug-related effects in support of model-informed approaches in drug development.

Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure

Guidance regarding the effect of codeine and its metabolites on foetal development is limited by small studies and inconsistent findings. The primary objective was to use physiologically based pharmacokinetic modelling to investigate the impact of gestational stage and maternal CYP2D6 phenotype on foetal morphine exposure following codeine administration. Full body physiologically based pharmacokinetic models were developed and verified for codeine and morphine using Simcyp (version 19.1). The impact of gestational age and maternal CYP2D6 phenotype on foetal and maternal morphine and codeine exposure following oral codeine administration was modelled in a cohort of 250 pregnant females and foetuses at gestational weeks 0 (mothers only), 6, 12, 24 and 36. Consistent with the known effect on codeine metabolism, a clinically meaningful (> 1.65-fold) increase in foetal morphine AUC was observed in the CYP2D6 UM phenotype cohort compared to the CYP2D6 EM and PM phenotype cohorts. The mean (95% CI) foetal morphine AUC in the CYP2D6 UM cohort of 0.988 (0.902 to 1.073) ng/mL.h was 1.8-fold higher than the CYP2D6 EM cohort of 0.546 (0.492 to 0.600) ng/mL.h (p < 0.001). Despite a 2.8-fold increase in maternal CYP2D6 protein abundance between gestational weeks 6 and 36, the mean foetal morphine AUC in the CYP2D6 EM and UM phenotype cohorts reduced by 1.55- and 1.75-fold, respectively, over this period. Maternal CYP2D6 phenotype is a significant determinant of foetal morphine AUC. Simulations suggest that the greatest risk with respect to foetal morphine exposure is during the first trimester of pregnancy, particularly in CYP2D6 UM phenotype mothers.

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.

Characterization of Plasma Protein Alterations in Pregnant and Postpartum Individuals Living With HIV to Support Physiologically-Based Pharmacokinetic Model Development

Background: Alterations in plasma protein concentrations in pregnant and postpartum individuals can influence antiretroviral (ARV) pharmacokinetics. Physiologically-based pharmacokinetic (PBPK) models can serve to inform drug dosing decisions in understudied populations. However, development of such models requires quantitative physiological information (e.g., changes in plasma protein concentration) from the population of interest. Objective: To quantitatively describe the time-course of albumin and α1-acid glycoprotein (AAG) concentrations in pregnant and postpartum women living with HIV. Methods: Serum and plasma protein concentrations procured from the International Maternal Pediatric Adolescent AIDS Clinical Trial Protocol 1026s (P1026s) were analyzed using a generalized additive modeling approach. Separate non-parametric smoothing splines were fit to albumin and AAG concentrations as functions of gestational age or postpartum duration. Results: The analysis included 871 and 757 serum albumin concentrations collected from 380 pregnant (~20 to 42 wks gestation) and 354 postpartum (0 to 46 wks postpartum) women, respectively. Thirty-six and 32 plasma AAG concentrations from 31 pregnant (~24 to 38 wks gestation) and 30 postpartum women (~2-13 wks postpartum), respectively, were available for analysis. Estimated mean albumin concentrations remained stable from 20 wks gestation to term (33.4 to 34.3 g/L); whereas, concentrations rapidly increased postpartum until stabilizing at ~42.3 g/L 15 wk after delivery. Estimated AAG concentrations slightly decreased from 24 wks gestation to term (53.6 and 44.9 mg/dL) while postpartum levels were elevated at two wks after delivery (126.1 mg/dL) and subsequently declined thereafter. Computational functions were developed to quantitatively communicate study results in a form that can be readily utilized for PBPK model development. Conclusion: By characterizing the trajectory of plasma protein concentrations in pregnant and postpartum women living with HIV, our analysis can increase confidence in PBPK model predictions for HIV antiretrovirals and better inform drug dosing decisions in this understudied population.

Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUC_last (1.27) and C_max values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUC_last ratios and 18 out of 18 DGI C_max ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

Drug dosing during pregnancy-opportunities for physiologically based pharmacokinetic models

Drugs can have harmful effects on the embryo or the fetus at any point during pregnancy. Not all the damaging effects of intrauterine exposure to drugs are obvious at birth, some may only manifest later in life. Thus, drugs should be prescribed in pregnancy only if the expected benefit to the mother is thought to be greater than the risk to the fetus. Dosing of drugs during pregnancy is often empirically determined and based upon evidence from studies of non-pregnant subjects, which may lead to suboptimal dosing, particularly during the third trimester. This review collates examples of drugs with known recommendations for dose adjustment during pregnancy, in addition to providing an example of the potential use of PBPK models in dose adjustment recommendation during pregnancy within the context of drug-drug interactions. For many drugs, such as antidepressants and antiretroviral drugs, dose adjustment has been recommended based on pharmacokinetic studies demonstrating a reduction in drug concentrations. However, there is relatively limited (and sometimes inconsistent) information regarding the clinical impact of these pharmacokinetic changes during pregnancy and the effect of subsequent dose adjustments. Examples of using pregnancy PBPK models to predict feto-maternal drug exposures and their applications to facilitate and guide dose assessment throughout gestation are discussed.

Pharmacokinetics of HIV-Integrase Inhibitors During Pregnancy: Mechanisms, Clinical Implications and Knowledge Gaps

Prevention of mother-to-child transmission of HIV and optimal maternal treatment are the most important goals of antiretroviral therapy in pregnant women with HIV. These goals may be at risk due to possible reduced exposure during pregnancy caused by physiological changes. Limited information is available on the impact of these physiological changes. This is especially true for HIV-integrase inhibitors, a relatively new class of drugs, recommended first-line agents and hence used by a large proportion of HIV-infected patients. Therefore, the objective of this review is to provide a detailed overview of the pharmacokinetics of HIV-integrase inhibitors in pregnancy. Second, this review defines potential causes for the change in pharmacokinetics of HIV-integrase inhibitors during pregnancy. Despite increased clearance, for raltegravir 400 mg twice daily and dolutegravir 50 mg once daily, exposure during pregnancy seems adequate; however, for elvitegravir, the proposed minimal effective concentration is not reached during pregnancy. Lower exposure to these drugs may be caused by increased hormone levels and, subsequently, enhanced drug metabolism during pregnancy. The pharmacokinetics of bictegravir and cabotegravir, which are under development, have not yet been evaluated in pregnant women. New studies need to prospectively assess whether adequate exposure is reached in pregnant women using these new HIV-integrase inhibitors. To further optimize antiretroviral treatment in pregnant women, studies need to unravel the underlying mechanisms behind the changes in the pharmacokinetics of HIV-integrase inhibitors during pregnancy. More knowledge on altered pharmacokinetics during pregnancy and the underlying mechanisms contribute to the development of effective and safe antiretroviral therapy for HIV-infected pregnant women.

Methodological Approaches to Evaluate Fetal Drug Exposure

BACKGROUND

Drug prescriptions are usual during pregnancy, however, women and their fetuses still remain an orphan population with regard to drugs efficacy and safety. Most xenobiotics diffuse through the placenta and some of them can alter fetus development resulting in structural abnormalities, growth or functional deficiencies.

METHODS

To summarize the different methodologies developed towards the prediction of fetal drug exposure.

RESULTS

Neonatal cord blood concentration is the most specific measurement of the transplacental drug transfer at the end of pregnancy. Using the cord blood and mother drug concentrations altogether, drug exchanges between the mother and fetus can be modeled and quantified via a population pharmacokinetic analysis. Thereafter, it is possible to estimate the fetus exposure and the fetus-to-mother exposure ratio. However, the prediction of placental transfer before any administration to pregnant women is desirable. Animal studies remain difficult to interpret due to structural and functional inter-species placenta differences. The ex-vivo perfusion of the human placental cotyledon is the method of reference to study the human placental transfer of drugs because it is thought to mimic the functional placental tissue. However, extrapolation of data to in vivo situation remains difficult. Some research groups have extensively worked on physiologically based models (PBPK) to predict fetal drug exposure and showed very encouraging results.

CONCLUSION

PBPK models appeared to be a very promising tool in order to predict fetal drug exposure in-silico. However, these models mainly picture the end of pregnancy and knowledge regarding both, development of the placental permeability and transporters is strongly needed.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways

BACKGROUND

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes.

METHODS

Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature.

RESULTS

The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range.

CONCLUSION

The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

Prediction of Fetal Darunavir Exposure by Integrating Human Ex-Vivo Placental Transfer and Physiologically Based Pharmacokinetic Modeling

Background

Fetal antiretroviral exposure is usually derived from the cord-to-maternal concentration ratio. This static parameter does not provide information on the pharmacokinetics in utero, limiting the assessment of a fetal exposure–effect relationship.

Objective

The aim of this study was to incorporate placental transfer into a pregnancy physiologically based pharmacokinetic model to simulate and evaluate fetal darunavir exposure at term.

Methods

An existing and validated pregnancy physiologically based pharmacokinetic model of maternal darunavir/ritonavir exposure was extended with a feto-placental unit. To parameterize the model, we determined maternal-to-fetal and fetal-to-maternal darunavir/ritonavir placental clearance with an ex-vivo human cotyledon perfusion model. Simulated maternal and fetal pharmacokinetic profiles were compared with observed clinical data to qualify the model for simulation. Next, population fetal pharmacokinetic profiles were simulated for different maternal darunavir/ritonavir dosing regimens.

Results

An average (±standard deviation) maternal-to-fetal cotyledon clearance of 0.91 ± 0.11 mL/min and fetal-to-maternal clearance of 1.6 ± 0.3 mL/min was determined (n = 6 perfusions). Scaled placental transfer was integrated into the pregnancy physiologically based pharmacokinetic model. For darunavir 600/100 mg twice a day, the predicted fetal maximum plasma concentration, trough concentration, time to maximum plasma concentration, and half-life were 1.1, 0.57 mg/L, 3, and 21 h, respectively. This indicates that the fetal population trough concentration is higher or around the half-maximal effective darunavir concentration for a resistant virus (0.55 mg/L).

Conclusions

The results indicate that the population fetal exposure after oral maternal darunavir dosing is therapeutic and this may provide benefits to the prevention of mother-to-child transmission of human immunodeficiency virus. Moreover, this integrated approach provides a tool to prevent fetal toxicity or enhance the development of more selectively targeted fetal drug treatments.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-017-0583-8) contains supplementary material, which is available to authorized users.

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.

Drug-Drug Interactions of Infectious Disease Treatments in Low-Income Countries: A Neglected Topic?

Despite recent advances in recognizing and reducing the risk of drug-drug interactions (DDIs) in developed countries, there are still significant challenges in managing DDIs in low-income countries (LICs) worldwide. In the treatment of major infectious diseases in these regions, multiple factors contribute to ineffective management of DDIs that lead to loss of efficacy or increased risk of adverse events to patients. Some of these difficulties, however, can be overcome. This review aims to evaluate the inherent complexities of DDI management in LICs from pharmacological standpoints and illustrate the unique barriers to effective management of DDIs, such as the challenges of co-infection and treatment settings. A better understanding of comprehensive drug-related properties, population-specific attributes, such as physiological changes associated with infectious diseases, and the use of modeling and simulation techniques are discussed, as they can facilitate the implementation of optimal treatments for infectious diseases at the individual patient level.

Sex-Gender Variable: Methodological Recommendations for Increasing Scientific Value of Clinical Studies

There is a clear sex-gender gap in the prevention and occurrence of diseases, and in the outcomes and treatments, which is relevant to women in the majority of cases. Attitudes concerning the enrollment of women in randomized clinical trials have changed over recent years. Despite this change, a gap still exists. This gap is linked to biological factors (sex) and psycho-social, cultural, and environmental factors (gender). These multidimensional, entangled, and interactive factors may influence the pharmacological response. Despite the fact that regulatory authorities recognize the importance of sex and gender, there is a paucity of research focusing on the racial/ethnic, socio-economic, psycho-social, and environmental factors that perpetuate disparities. Research and clinical practice must incorporate all of these factors to arrive at an intersectional and system-scenario perspective. We advocate for scientifically rigorous evaluations of the interplay between sex and gender as key factors in performing clinical trials, which are more adherent to real-life. This review proposes a set of 12 rules to improve clinical research for integrating sex-gender into clinical trials.

SSRIs and SNRIs (SRI) in Pregnancy: Effects on the Course of Pregnancy and the Offspring: How Far Are We from Having All the Answers?

Serotonin has important roles in the development of the brain and other organs. Manipulations of synaptic serotonin by drugs such as serotonin reuptake inhibitors (SRI) or serotonin norepinephrine reuptake inhibitors (SNRI) might alter their development and function. Of interest, most studies on the outcome of prenatal exposure to SRI in human have not found significant embryonic or fetal damage, except for a possible, slight increase in cardiac malformations. In up to a third of newborns exposed to SRI, exposure may induce transient neonatal behavioral changes (poor neonatal adaptation) and increased rate of persistent pulmonary hypertension. Prenatal SRI may also cause slight motor delay and language impairment but these are transient. The data on the possible association of prenatal SRIs with autism spectrum disorder (ASD) are inconsistent, and seem to be related to pre-pregnancy treatment or to maternal depression. Prenatal SRIs also appear to affect the hypothalamic hypophyseal adrenal (HPA) axis inducing epigenetic changes, but the long-term consequences of these effects on humans are as yet unknown. SRIs are metabolized in the liver by several cytochrome P450 (CYP) enzymes. Faster metabolism of most SRIs in late pregnancy leads to lower maternal concentrations, and thus potentially to decreased efficacy which is more prominent in women that are rapid metabolizers. Studies suggest that the serotonin transporter SLC6A4 promoter is associated with adverse neonatal outcomes after SRI exposure. Since maternal depression may adversely affect the child's development, one has to consider the risk of SRI discontinuation on the fetus and the child. As with any drug treatment in pregnancy, the benefits to the mother should be considered versus the possible hazards to the developing embryo/fetus.

Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

This study provides whole‐body physiologically‐based pharmacokinetic models of the strong index cytochrome P450 (CYP)1A2 inhibitor and moderate CYP3A4 inhibitor fluvoxamine and of the sensitive CYP1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug–drug interaction (DDI) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP1A2 substrate), rifampicin (moderate CYP1A2 inducer), and midazolam (sensitive index CYP3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve (AUC) ratios (AUC during DDI/AUC control) and seven of seven of the predicted DDI peak plasma concentration (C_max) ratios (C_max during DDI/C_max control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community.

Pharmacokinetics of HIV-Integrase Inhibitors During Pregnancy: Mechanisms, Clinical Implications and Knowledge Gaps

Prevention of mother-to-child transmission of HIV and optimal maternal treatment are the most important goals of antiretroviral therapy in pregnant women with HIV. These goals may be at risk due to possible reduced exposure during pregnancy caused by physiological changes. Limited information is available on the impact of these physiological changes. This is especially true for HIV-integrase inhibitors, a relatively new class of drugs, recommended first-line agents and hence used by a large proportion of HIV-infected patients. Therefore, the objective of this review is to provide a detailed overview of the pharmacokinetics of HIV-integrase inhibitors in pregnancy. Second, this review defines potential causes for the change in pharmacokinetics of HIV-integrase inhibitors during pregnancy. Despite increased clearance, for raltegravir 400 mg twice daily and dolutegravir 50 mg once daily, exposure during pregnancy seems adequate; however, for elvitegravir, the proposed minimal effective concentration is not reached during pregnancy. Lower exposure to these drugs may be caused by increased hormone levels and, subsequently, enhanced drug metabolism during pregnancy. The pharmacokinetics of bictegravir and cabotegravir, which are under development, have not yet been evaluated in pregnant women. New studies need to prospectively assess whether adequate exposure is reached in pregnant women using these new HIV-integrase inhibitors. To further optimize antiretroviral treatment in pregnant women, studies need to unravel the underlying mechanisms behind the changes in the pharmacokinetics of HIV-integrase inhibitors during pregnancy. More knowledge on altered pharmacokinetics during pregnancy and the underlying mechanisms contribute to the development of effective and safe antiretroviral therapy for HIV-infected pregnant women.

Physiologically Based Pharmacokinetic/Pharmacodynamic Model for Caffeine Disposition in Pregnancy

Caffeine is the most consumed active stimulant. About 80% of pregnant women consume caffeine orally on a daily basis. Many reports indicated consumption of >200 mg caffeine during pregnancy could increase the likelihood of miscarriage. In this article, we developed a pregnancy physiological-based pharmacokinetic/pharmacodynamic (PBPK/PD) model for caffeine to examine association between maternal caffeine consumption during pregnancy and caffeine plasma levels at doses lower and higher than 200 mg to predict changes in caffeine concentrations across the 3 trimesters, and to predict associated changes in caffeine PD parameters. Two models were successfully developed using GastroPlus software, a nonpregnant model for validation purposes and a pregnant model for validation and prediction of maternal caffeine plasma concentrations following single and multiple dosing. Using observed and predicted data, we were able to validate and simulate PK changes of caffeine in nonpregnant women and the PD effect of caffeine on certain enzymes and catecholamines associated with caffeine intake. Furthermore, the pregnancy PBPK model successfully predicted changes in caffeine PK across the three trimesters. Caffeine increased exposure during pregnancy was related to reduced activity of caffeine metabolizing enzyme CYP1A2. The model also predicted increased levels of caffeine in the fetoplacental compartment (FPC) due to increased maternal caffeine plasma concentrations. Increased caffeine levels in maternal blood was accompanied by greater inhibition of the phosphodiesterase enzyme, higher cyclic adenosine monophosphate, and greater increase of epinephrine levels, which could increase the risk of pregnancy loss. The application of the developed PBPK model to predict the PD effect could provide a useful tool to help define potential cut-offs for caffeine intake in various stages of pregnancy.

Dose Optimization of Chloroquine by Pharmacokinetic Modeling During Pregnancy for the Treatment of Zika Virus Infection

The insidious nature of Zika virus (ZIKV) infections can have a devastating consequence for fetal development. Recent reports have highlighted that chloroquine (CQ) is capable of inhibiting ZIKV endocytosis in brain cells. We applied pharmacokinetic modeling to develop a predictive model for CQ exposure to identify an optimal maternal/fetal dosing regimen to prevent ZIKV endocytosis in brain cells. Model validation used 13 nonpregnancy and 3 pregnancy clinical studies, and a therapeutic CQ plasma window of 0.3-2 μM was derived. Dosing regimens used in rheumatoid arthritis, systemic lupus erythematosus, and malaria were assessed for their ability to target this window. Dosing regimen identified that weekly doses used in malaria were not sufficient to reach the lower therapeutic window; however, daily doses of 150 mg achieved this therapeutic window. The impact of gestational age was further assessed and culminated in a final proposed regimen of 600 mg on day 1, 300 mg on day 2 and 3, and 150 mg thereafter until the end of trimester 2, which resulted in maintaining 65% and 94% of subjects with a trough plasma concentration above the lower therapeutic window on day 6 and at term, respectively.

Pharmacokinetics of Increased Nelfinavir Plasma Concentrations in Women During Pregnancy and Postpartum

This study aims to evaluate the safety, acceptability, and pharmacokinetics (PK) of an increased dose of nelfinavir (NFV) during the third trimester of pregnancy. The study was registered as part of the International Maternal Pediatric Adolescent AIDS Clinical Trials network (IMPAACT-P1026s), an ongoing multicenter prospective cohort study of antiretroviral PK during pregnancy (NCT00042289). NFV intensive PK evaluations were performed at steady state during the third trimester of pregnancy and 2-3 weeks postpartum. Plasma concentrations of NFV and its active metabolite, hydroxyl-tert-butylamide (M8) were measured using high-performance liquid chromatography with ultraviolet detection. A total of 18 women are included in the analysis. NFV area under the concentration-time curve (AUC) with the increased dose during the third trimester was nearly identical to the standard dose postpartum, with a geometric mean ratio for third trimester to postpartum AUC of 0.98 (90%CI 0.71-1.35). Despite the increased dose, M8 AUC was lower during the third trimester compared to postpartum (0.53, IQR [0.38-0.75]), as was the M8/NFV AUC ratio (0.51, IQR [0.42-0.63]). NFV AUC_0-12 was above target in 15 of 18 (83%) of participants during the third trimester compared to 14 of 16 (88%) postpartum. No major safety concerns were noted. Increasing the NFV dose to 1875 mg twice daily during the third trimester achieved similar concentrations postpartum compared to standard dosing (1250 mg twice daily). Increased NFV dose regimens may still have some benefit to human immunodeficiency virus (HIV)-positive pregnant women living in countries where novel protease inhibitors are currently unavailable or in individuals who are intolerant to ritonavir-boosted HIV medications.

Clinical implications of selective serotonin reuptake inhibitors-selective serotonin norepinephrine reuptake inhibitors pharmacogenetics during pregnancy and lactation

Depression occurs during pregnancy in 3.9–12.8% of the women. The different serotonin reuptake inhibitors (SRIs) are metabolized in the liver by CYP450 enzymes. CYP2D6 metabolizes paroxetine, fluoxetine, duloxetine and venlafaxine, while CYP2C19 deactivates citalopram and escitalopram. Polymorphisms in these enzymes change the metabolic clearance and levels of these drugs. Higher metabolism of most SRIs in late pregnancy results in lower maternal levels, which could result in decreased efficacy. Very few studies have addressed the potential interaction between pregnancy-induced increase in 2D6 metabolism and specific genotypes of the women, suggesting that ultra-rapid and extensive metabolizers exhibit lower serum concentrations than the other slower genotypes. Preliminary studies suggest that some genotypes of the serotonin transporter (SLC6A4) promoter are associated and are linked to adverse effects in infants with SRI exposure during pregnancy. Presently, there are no clear clinical implications of SRI pharmacogenetic status in pregnancy and lactation. In late pregnancy, women may exhibit lower steady state concentrations of these drugs, necessitating increased doses but these are presently guided clinically and not through genotyping. Much more work is needed to define whether SRI genotype has clinical implications and predictive value for either mother or offspring.

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.

Inclusion of pregnant and breastfeeding women in research - efforts and initiatives

Pregnant and breastfeeding women have been rendered therapeutic orphans as they have been historically excluded from clinical trials. Labelling for most approved drugs does not provide information about safety and efficacy during pregnancy. This lack of data is mainly due to ethico-legal challenges that have remained entrenched in the post-diethylstilbestrol and thalidomide era, and that have led to pregnancy being viewed in the clinical trial setting primarily through a pharmacovigilance lens. Policy considerations that encourage and/or require the inclusion of pregnant or lactating women in clinical trials may address the current lack of available information. However, there are additional pragmatic strategies, such the employment of pharmacometric tools and the introduction of innovative clinical trial designs, which could improve knowledge about the safety and efficacy of medication use during pregnancy and lactation. This paper provides a broad overview of the pharmacoepidemiology of drugs used during pregnancy and lactation, and offers recommendations for regulators and researchers in academia and industry to increase the available pharmacokinetic and -dynamic understanding of medication use in pregnancy.

Drug Dosing in Pregnant Women: Challenges and Opportunities in Using Physiologically Based Pharmacokinetic Modeling and Simulations

The unmet medical need of providing evidence‐based pharmacotherapy for pregnant women is recognized by the regulatory bodies. Physiologically based pharmacokinetic (PBPK) modeling offers an attractive platform to quantify anticipated changes in the pharmacokinetics (PKs) of drugs during pregnancy. Recent publications applying a pregnancy PBPK module to the prediction of maternal and fetal exposure of drugs are summarized. Future opportunities to use PBPK models to predict breast milk exposure and assess human fetotoxicity risks are presented.

A Physiologically-Based Pharmacokinetic Model to Predict Human Fetal Exposure for a Drug Metabolized by Several CYP450 Pathways

BACKGROUND

Pregnant women and their fetuses are exposed to numerous drugs; however, they are orphan populations with respect to the safety and efficacy of drugs. Therefore, the prediction of maternal and fetal drug exposure prior to administration would be highly useful.

METHODS

A physiologically-based pharmacokinetic (PBPK) model for nevirapine, which is metabolized by the cytochrome P450 (CYP) 3A4, 2B6 and 2D6 pathways, was developed to predict maternal and fetal pharmacokinetics (PK). The model was developed in both non-pregnant and pregnant women, and all physiological and enzymatic changes that could impact nevirapine PK were taken into account. Transplacental parameters estimated from ex vivo human placenta perfusion experiments were included in this PBPK model. To validate the model, observed maternal and cord blood concentrations were compared with predicted concentrations, and the impact of fetal clearance on fetal PK was investigated.

RESULTS

By implementing physiological changes, including CYP3A4, 2D6 and 2B6 inductions, we predicted a clearance increase of 21 % in late pregnancy. The PBPK model successfully predicted the disposition for both non-pregnant and pregnant populations. Parameters obtained from the ex vivo experiments allowed the prediction of nevirapine concentrations that matched observed cord blood concentrations. The fetal-to-maternal area under the curve ratio (0-24 h interval) was 0.77, and fetal metabolism had no significant effect on fetal PK.

CONCLUSIONS

The PBPK approach is a useful tool for quantifying a priori the drug exposure of metabolized drugs during pregnancy, and can be applied to evaluate alternative dosing regimens to optimize drug therapy. This approach, including ex vivo human placental perfusion parameters, is a promising approach for predicting human fetal exposure.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

Development of a Novel Maternal-Fetal Physiologically Based Pharmacokinetic Model I: Insights into Factors that Determine Fetal Drug Exposure through Simulations and Sensitivity Analyses

Determining fetal drug exposure (except at the time of birth) is not possible for both logistical and ethical reasons. Therefore, we developed a novel maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) model to predict fetal exposure to drugs and populated this model with gestational age-dependent changes in maternal-fetal physiology. Then, we used this m-f-PBPK to: 1) perform a series of sensitivity analyses to quantitatively demonstrate the impact of fetoplacental metabolism and placental transport on fetal drug exposure for various drug-dosing regimens administered to the mother; 2) predict the impact of gestational age on fetal drug exposure; and 3) demonstrate that a single umbilical venous (UV)/maternal plasma (MP) ratio (even after multiple-dose oral administration to steady state) does not necessarily reflect fetal drug exposure. In addition, we verified the implementation of this m-f-PBPK model by comparing the predicted UV/MP and fetal/MP AUC ratios with those predicted at steady state after an intravenous infusion. Our simulations yielded novel insights into the quantitative contribution of fetoplacental metabolism and/or placental transport on gestational age-dependent fetal drug exposure. Through sensitivity analyses, we demonstrated that the UV/MP ratio does not measure the extent of fetal drug exposure unless obtained at steady state after an intravenous infusion or when there is little or no fluctuation in MP drug concentrations after multiple-dose oral administration. The proposed m-f-PBPK model can be used to predict fetal exposure to drugs across gestational ages and therefore provide the necessary information to assess the risk of drug toxicity to the fetus.