Expansion of a PBPK model to predict disposition in pregnant women of drugs cleared via multiple CYP enzymes, including CYP2B6, CYP2C9 and CYP2C19

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

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

Physiologically-based pharmacokinetic modelling of long-acting injectable cabotegravir and rilpivirine in pregnancy

AIMS

Long-acting cabotegravir and rilpivirine have been approved to manage HIV in adults, but data regarding safe use in pregnancy are limited. Physiologically-based pharmacokinetic (PBPK) modelling was used to simulate the approved dosing regimens in pregnancy and explore if Ctrough was maintained above cabotegravir and rilpivirine target concentrations (664 and 50 ng/mL, respectively).

METHODS

An adult PBPK model was validated using clinical data of cabotegravir and rilpivirine in nonpregnant adults. This was modified by incorporating pregnancy-induced metabolic and physiological changes. The pregnancy PBPK model was validated with data on oral rilpivirine and raltegravir (UGT1A1 probe substrate) in pregnancy. Twelve weeks' disposition of monthly and bimonthly dosing of long-acting cabotegravir and rilpivirine was simulated at different trimesters and foetal exposure was also estimated.

RESULTS

Predicted Ctrough at week 12 for monthly long-acting cabotegravir was above 664 ng/mL throughout pregnancy, but below the target in 0.5% of the pregnant population in the third trimester with bimonthly long-acting cabotegravir. Predicted Ctrough at week 12 for monthly and bimonthly long-acting rilpivirine was below 50 ng/mL in at least 40% and over 90% of the pregnant population, respectively, throughout pregnancy. Predicted medians (range) of cord-to-maternal blood ratios were 1.71 (range, 1.55-1.79) for cabotegravir and 0.88 (0.78-0.93) for rilpivirine between weeks 38 and 40.

CONCLUSIONS

Model predictions suggest that monthly long-acting cabotegravir could maintain antiviral efficacy throughout pregnancy, but that bimonthly administration may require careful clinical evaluation. Both monthly and bimonthly long-acting rilpivirine may not adequately maintain antiviral efficacy in pregnancy.

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

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

The impact of pregnancy and associated hormones on the pharmacokinetics of Δ9-tetrahydrocannabinol

INTRODUCTION

(-)-Δ9-tetrahydrocannabinol (THC) is the main psychoactive component of cannabis. Cannabis is the most widely used drug of abuse by pregnant individuals, but its maternal-fetal safety is still unclear. The changes in THC disposition during pregnancy may affect THC safety and pharmacology.

AREAS COVERED

This review summarizes the current literature on THC metabolism and pharmacokinetics in humans. It provides an analysis of how hormonal changes during pregnancy may alter the expression of cannabinoid metabolizing enzymes and THC and its metabolite pharmacokinetics. THC is predominately (>70%) cleared by hepatic metabolism to its psychoactive active metabolite, 11-OH-THC by cytochrome P450 (CYP) 2C9 and to other metabolites (<30%) by CYP3A4. Other physiological processes that change during pregnancy and may alter cannabinoid disposition are also reviewed.

EXPERT OPINION

THC and its metabolites disposition likely change during pregnancy. Hepatic CYP2C9 and CYP3A4 are induced in pregnant individuals and in vitro by pregnancy hormones. This induction of CYP2C9 and CYP3A4 is predicted to lead to altered THC and 11-OH-THC disposition and pharmacodynamic effects. More in vitro studies of THC metabolism and induction of the enzymes metabolizing cannabinoids are necessary to improve the prediction of THC pharmacokinetics in pregnant individuals.

Application of a physiologically based pharmacokinetic model to predict isoniazid disposition during pregnancy

Pregnancy can increase the risk of latent tuberculosis infection (LTBI) progression to tuberculosis (TB) disease. Isoniazid (INH) is the preferred preventative treatment for LTBI in pregnancy. INH is mainly cleared by N-acetyltransferase 2 (NAT2) but the pharmacokinetics (PK) of INH in different NAT2 phenotypes during pregnancy is not well characterized. To address this knowledge gap, we used physiologically based pharmacokinetic (PBPK) modeling to evaluate NAT2 phenotype-specific effects of pregnancy on INH disposition. A whole-body PBPK model for INH was developed and verified for non-pregnant NAT2 fast (FA), intermediate (IA), and slow (SA) acetylators. Model predictive performance was assessed using a drug-specific model acceptance criterion for mean plasma area under the curve (AUC) and peak plasma concentration (Cmax ), and the absolute average fold error (AAFE) for individual plasma concentrations. The verified model was extended to simulate INH disposition during pregnancy in NAT2 SA, IA, and FA populations. A sensitivity analysis was conducted using the verified PBPK model and known changes in INH disposition during pregnancy to determine whether NAT2 activity changes during pregnancy or other INH clearance pathways are altered. This analysis suggested that NAT2 activity is unchanged while other INH clearance pathways increase by ~80% during pregnancy. The model was applied to explore the effect of pregnancy on INH disposition in two ethnic populations with different NAT2 phenotype distributions and with high TB burden. Our PBPK model can be used to predict INH disposition during pregnancy in diverse populations and expanded to other drugs cleared by NAT2 during pregnancy.

Mechanistic Modeling of the Drug-Drug Interaction Between Efavirenz and Dolutegravir: Is This Interaction Clinically Relevant When Switching From Efavirenz to Dolutegravir During Pregnancy?

Following the 2021 World Health Organization's updated recommendations on the management of HIV infection, millions of people living with HIV are currently switched from efavirenz-based antiretroviral therapy to dolutegravir-based antiretroviral therapy. Pregnant individuals transitioning from efavirenz to dolutegravir might be at increased risk of insufficient viral suppression in the immediate postswitch period because both efavirenz- and pregnancy-related increases in hormone levels induce enzymes involved in dolutegravir metabolism, namely, cytochrome P450 3A4 and uridine 5'-diphospho-glucuronosyltransferase 1A1. This study aimed at developing physiologically based pharmacokinetic models to simulate the switch from efavirenz to dolutegravir in the late second and third trimester. To this end, the drug-drug interaction between efavirenz and the uridine 5'-diphospho-glucuronosyltransferase 1A1 substrates dolutegravir and raltegravir was first simulated in nonpregnant subjects. After successful validation, the physiologically based pharmacokinetic models were translated to pregnancy and dolutegravir pharmacokinetics following efavirenz discontinuation were predicted. Modeling results indicated that, at the end of the second trimester, both efavirenz concentrations and dolutegravir trough concentrations fell below respective pharmacokinetic target thresholds (defined as reported thresholds producing 90%-95% of the maximum effect) during the time interval from 9.75 to 11 days after dolutegravir initiation. At the end of the third trimester, this time interval spanned from 10.3 days to >4 weeks after dolutegravir initiation. These findings suggest that dolutegravir exposure in the immediate post-efavirenz switch period during pregnancy may be suboptimal, leading to HIV viremia and, potentially, resistance. The clinical implications of these findings remain to be substantiated by future studies.

P-Glycoprotein and Organic Anion Transporter Polypeptide 1B/Breast Cancer Resistance Protein Drug Transporter Activity in Pregnant Women Living With HIV

This study evaluates the influence of pregnancy and HIV infection in conjunction with the use of raltegravir, lamivudine, and tenofovir disoproxil fumarate (combined antiretroviral therapy [cART]) on intestinal P-glycoprotein (P-gp) and hepatic organic anion transporter polypeptide (OATP) 1B1/1B3 and/or breast cancer resistance protein (BCRP) drug transporter activity using rosuvastatin (OATP1B/BCRP) and fexofenadine (P-gp) probes. Single oral doses of 5-mg rosuvastatin and 60-mg fexofenadine were administered to women living with HIV under cART in the third trimester of gestation (n = 15) and postpartum period (n = 10). A control group of 12 healthy nonpregnant women also was investigated. Pharmacokinetic parameters were estimated by using a noncompartmental method and evaluated by t test (P < .05). The rosuvastatin area under the plasma concentration-time curve from time 0 to the last quantifiable concentration (AUC_0-last ) value was higher in the third trimester of pregnancy (19.5 [95%CI, 16.8-22.3] ng • h/mL] when compared to postpartum (13.3 [95%CI, 9.3-17.5] ng • h/mL), while the fexofenadine AUC_0-last values did not differ between the third trimester of pregnancy (738.0 [95%CI, 611.4-864.6] ng • h/mL) and postpartum period (874.9 [95%CI, 408.2-1342.0] ng• h/mL). The rosuvastatin AUC_0-last values did not differ between healthy nonpregnant women (13.8 [95%CI, 10.0-17.6] ng • h/mL) and women living with HIV in the postpartum period (13.3 [95%CI, 9.3-17.5] ng • h/mL), and the fexofenadine AUC_0-last values did not differ between the 2 investigated groups (603.6 [95%CI, 467.5-739.7] ng • h/mL vs 874.9 [95%CI, 408.2-1342.0] ng • h/mL). It is suggested that gestation inhibits the hepatic OATP1B1/1B3 and/or BCRP activity but does not alter intestinal P-gp activity. The influence of HIV infection in conjunction with use of cART on OATP1B/BCRP and intestinal P-gp activity was not observed.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

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.

In vitro-in silico-based prediction of inter-individual and inter-ethnic variations in the dose-dependent cardiotoxicity of R- and S-methadone in humans

New approach methodologies predicting human cardiotoxicity are of interest to support or even replace in vivo-based drug safety testing. The present study presents an in vitro–in silico approach to predict the effect of inter-individual and inter-ethnic kinetic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either individual human liver microsomes or recombinant cytochrome P450 enzymes (rCYPs), were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations to predict inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. Chemical specific adjustment factors were defined and used to derive dose–response curves for the sensitive individuals. Our simulations indicated that Chinese are more sensitive towards methadone-induced cardiotoxicity with Margin of Safety values being generally two-fold lower than those for Caucasians for both methadone enantiomers. Individual PBK models using microsomes and PBK models using rCYPs combined with Monte Carlo simulations predicted similar inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. The present study illustrates how inter-individual and inter-ethnic variations in cardiotoxicity can be predicted by combining in vitro toxicity and metabolic data, PBK modelling and Monte Carlo simulations. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.

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.

Selective Serotonin Reuptake Inhibitor Pharmacokinetics During Pregnancy: Clinical and Research Implications

Pregnancy and associated physiologic changes affect the pharmacokinetics of many medications, including selective serotonin reuptake inhibitors—the first-line pharmacologic interventions for depressive and anxiety disorders. During pregnancy, SSRIs exhibit extensive pharmacokinetic variability that may influence their tolerability and efficacy. Specifically, compared to non-pregnant women, the activity of cytochrome P450 (CYP) enzymes that metabolize SSRIs drastically changes (e.g., decreased CYP2C19 activity and increased CYP2D6 activity). This perspective examines the impact of pharmacokinetic genes—related to CYP activity on SSRI pharmacokinetics during pregnancy. Through a simulation-based approach, plasma concentrations for SSRIs metabolized primarily by CYP2C19 (e.g., escitalopram) and CYP2D6 (e.g., fluoxetine) are examined and the implications for dosing and future research are discussed.

Application of proteomic data in the translation of in vitro observations to associated clinical outcomes

Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.

A review of pregnancy-induced changes in opioid pharmacokinetics, placental transfer, and fetal exposure: Towards fetomaternal physiologically-based pharmacokinetic modeling to improve the treatment of neonatal opioid withdrawal syndrome

Physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to study pharmacokinetics (PK) in special populations, such as pregnant women, fetuses, and newborns, where practical hurdles severely limit the study of drug behavior. PK in pregnant women is variable and everchanging, differing greatly from that in their nonpregnant female and male counterparts typically enrolled in clinical trials. PBPK models can accommodate pregnancy-induced physiological and metabolic changes, thereby providing mechanistic insights into maternal drug disposition and fetal exposure. Fueled by the soaring opioid epidemic in the United States, opioid use during pregnancy continues to rise, leading to an increased incidence of neonatal opioid withdrawal syndrome (NOWS). The severity of NOWS is influenced by a complex interplay of extrinsic and intrinsic factors, and varies substantially between newborns, but the extent of prenatal opioid exposure is likely the primary driver. Fetomaternal PBPK modeling is an attractive approach to predict in utero opioid exposure. To facilitate the development of fetomaternal PBPK models of opioids, this review provides a detailed overview of pregnancy-induced changes affecting the PK of commonly used opioids during gestation. Moreover, the placental transfer of these opioids is described, along with their disposition in the fetus. Lastly, the implementation of these factors into PBPK models is discussed. Fetomaternal PBPK modeling of opioids is expected to provide improved insights in fetal opioid exposure, which allows for prediction of postnatal NOWS severity, thereby opening the way for precision postnatal treatment of these vulnerable infants.

Intrauterine growth restriction alters the activity of drug metabolising enzymes in the maternal-placental-fetal unit

PURPOSE

Ten percent of pregnancies are affected by intrauterine growth restriction (IUGR), and evidence suggests that affected neonates have reduced activity of hepatic cytochrome P450 (CYP) drug metabolising enzymes. Given that almost all pregnant individuals take medications and additional medications are often required during an IUGR pregnancy, we aimed to determine the impact of IUGR on hepatic CYP activity in sheep fetuses and pregnant ewes.

METHODS

Specific probes were used to determine the impact of IUGR on the activity of several CYP isoenzymes (CYP1A2, CYP2C19, CYP2D6 and CYP3A) in sheep fetuses and pregnant ewes. Probes were administered intravenously to the ewe at 132 days (d) gestation (term 150 d), followed by blood sampling from the maternal and fetal circulation over 24 h. Maternal and fetal liver tissue was collected at 139-140 d gestation, from which microsomes were isolated and incubated with probes. Metabolite and maternal plasma cortisol concentrations were measured using Liquid Chromatography - tandem mass spectrometry (LC-MS/MS).

RESULTS

Maternal plasma cortisol concentration and maternal hepatic CYP1A2 and CYP3A activity was significantly higher in IUGR pregnancies. Maternal hepatic CYP activity was higher than fetal hepatic CYP activity for all CYPs tested, and there was minimal CYP1A2 or CYP3A activity in the late gestation fetus when assessed using in vitro methods.

CONCLUSIONS

The physiological changes to the maternal-placental-fetal unit in an IUGR pregnancy have significant effects on maternal drug metabolism, suggesting changes in medications and/or doses may be required to optimise maternal and fetal health.

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.

Successful Prediction of Human Fetal Exposure to P-Glycoprotein Substrate Drugs Using the Proteomics-Informed Relative Expression Factor Approach and PBPK Modeling and Simulation

Many women take drugs during their pregnancy to treat a variety of clinical conditions. To optimize drug efficacy and reduce fetal toxicity, it is important to determine or predict fetal drug exposure throughout pregnancy. Previously, we developed and verified a maternal-fetal physiologically based pharmacokinetic (m-f PBPK) model to predict fetal K_p,uu (unbound fetal plasma AUC/unbound maternal plasma AUC) of drugs that passively cross the placenta. Here, we used in vitro transport studies in Transwell, in combination with our m-f PBPK model, to predict fetal K_p,uu of drugs that are effluxed by placental P-glycoprotein (P-gp)-namely, dexamethasone, betamethasone, darunavir, and lopinavir. Using Transwell, we determined the efflux ratio of these drugs in hMDR1-MDCK^cP-gpKO cells, in which human P-gp was overexpressed and the endogenous P-gp was knocked out. Then, using the proteomics-informed efflux ratio-relative expressive factor approach, we predicted the fetal K_p,uu of these drugs at term. Finally, to verify our predictions, we compared them with the observed in vivo fetal K_p,uu at term. The latter was estimated using our m-f PBPK model and published fetal [umbilical vein (UV)]/maternal plasma drug concentrations obtained at term (UV/maternal plasma). Fetal K_p,uu predictions for dexamethasone (0.63), betamethasone (0.59), darunavir (0.17), and lopinavir (0.08) were successful, as they fell within the 90% confidence interval of the corresponding in vivo fetal K_p,uu (0.30-0.66, 0.29-0.71, 0.11-0.22, 0.04-0.19, respectively). This is the first demonstration of successful prediction of fetal K_p,uu of P-gp drug substrates from in vitro studies. SIGNIFICANCE STATEMENT: For the first time, using in vitro studies in cells, this study successfully predicted human fetal K_p,uu of P-gp substrate drugs. This success confirms that the m-f PBPK model, combined with the ER-REF approach, can successfully predict fetal drug exposure to P-gp substrates. This success provides increased confidence in the use of the ER-REF approach, combined with the m-f PBPK model, to predict fetal K_p,uu of drugs (transported by P-gp or other transporters), both at term and at earlier gestational ages.

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.

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

Background and Objective

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

Methods

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

Results

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

Conclusion

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

Electronic supplementary material

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

Development and Verification of a Linked Δ 9-THC/11-OH-THC Physiologically Based Pharmacokinetic Model in Healthy, Nonpregnant Population and Extrapolation to Pregnant Women

Conducting clinical trials to understand the exposure risk/benefit relationship of cannabis use is not always feasible. Alternatively, physiologically based pharmacokinetic (PBPK) models can be used to predict exposure of the psychoactive cannabinoid (-)-Δ⁹-tetrahydrocannabinol (THC) and its active metabolite 11-hydroxy-Δ⁹-tetrahydrocannabinol (11-OH-THC). Here, we first extrapolated in vitro mechanistic pharmacokinetic information previously quantified to build a linked THC/11-OH-THC PBPK model and verified the model with observed data after intravenous and inhalation administration of THC in a healthy, nonpregnant population. The in vitro to in vivo extrapolation of both THC and 11-OH-THC disposition was successful. The inhalation bioavailability (F_inh) of THC after inhalation was higher in chronic versus casual cannabis users (F_inh = 0.35 and 0.19, respectively). Sensitivity analysis demonstrated that 11-OH-THC but not THC exposure was sensitive to alterations in hepatic intrinsic clearance of the respective compound. Next, we extrapolated the linked THC/11-OH-THC PBPK model to pregnant women. Simulations showed that THC plasma area under the curve (AUC) does not change during pregnancy, but 11-OH-THC plasma AUC decreases by up to 41%. Using a maternal-fetal PBPK model, maternal and fetal THC serum concentrations were simulated and compared with the observed THC serum concentrations in pregnant women at term. To recapitulate the observed THC fetal serum concentrations, active placental efflux of THC needed to be invoked. In conclusion, we built and verified a linked THC/11-OH-THC PBPK model in healthy nonpregnant population and demonstrated how this mechanistic physiologic and pharmacokinetic platform can be extrapolated to a special population, such as pregnant women. SIGNIFICANCE STATEMENT: Although the pharmacokinetics of cannabinoids have been extensively studied clinically, limited mechanistic pharmacokinetic models exist. Here, we developed and verified a physiologically based pharmacokinetic (PBPK) model for (-)-Δ⁹-tetrahydrocannabinol (THC) and its active metabolite, 11-hydroxy-Δ⁹-tetrahydrocannabinol (11-OH-THC). The PBPK model was verified in healthy, nonpregnant population after intravenous and inhalation administration of THC, and then extrapolated to pregnant women. The THC/11-OH-THC PBPK model can be used to predict exposure in special populations, predict drug-drug interactions, or impact of genetic polymorphism.

In Vivo-to-In Vitro Extrapolation of Transporter-Mediated Renal Clearance: Relative Expression Factor Versus Relative Activity Factor Approach

About 30% of approved drugs are cleared predominantly by renal clearance (CL_r). Of these, many are secreted by transporters. For these drugs, in vitro-to-in vivo extrapolation of transporter-mediated renal secretory clearance (CL_sec,plasma) is important to prospectively predict their renal clearance and to assess the impact of drug-drug interactions and pharmacogenetics on their pharmacokinetics. Here we compared the ability of the relative expression factor (REF) and the relative activity factor (RAF) approaches to quantitatively predict the in vivo CL_sec,plasma of 26 organic anion transporter (OAT) substrates assuming that OAT-mediated uptake is the rate-determining step in the CL_sec,plasma of the drugs. The REF approach requires protein quantification of each transporter in the tissue (e.g., kidney) and transporter-expressing cells, whereas the RAF approach requires the use of a transporter-selective probe substrate (both in vitro and in vivo) for each transporter of interest. For the REF approach, 50% and 69% of the CL_sec,plasma predictions were within 2- and 3-fold of the observed values, respectively; the corresponding values for the RAF approach were 65% and 81%. We found no significant difference between the two approaches in their predictive capability (as measured by accuracy and bias) of the CL_sec,plasma or CL_r of OAT drugs. We recommend that the REF and RAF approaches can be used interchangeably to predict OAT-mediated CL_sec,plasma Further research is warranted to evaluate the ability of the REF or RAF approach to predict CL_sec,plasma of drugs when uptake is not the rate-determining step. SIGNIFICANCE STATEMENT: This is the first direct comparison of the relative expression factor (REF) and relative activity factor (RAF) approaches to predict transporter-mediated renal clearance (CL_r). The RAF, but not REF, approach requires transporter-selective probes and that the basolateral uptake is the rate-determining step in the CL_r of drugs. Given that there is no difference in predictive capability of the REF and RAF approach for organic anion transporter-mediated CL_r, the REF approach should be explored further to assess its ability to predict CL_r when basolateral uptake is not the sole rate-determining step.

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.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

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.

Pregnancy-Related Hormones Increase Nifedipine Metabolism in Human Hepatocytes by Inducing CYP3A4 Expression

Pregnancy-related hormones (PRH) have emerged as key regulators of hepatic cytochrome P450 (CYP) enzyme expression and function. The impact of PRH on protein levels of CYP3A4 and other key CYP enzymes, and the metabolism of nifedipine (a CYP3A4 substrate commonly prescribed during pregnancy), was evaluated in primary human hepatocytes. Sandwich-cultured human hepatocytes (SCHH) from female donors were exposed to PRH (estradiol, estriol, estetrol, progesterone, and cortisol), individually or in combination as a cocktail. Absolute protein concentrations of twelve CYP isoforms in SCHH membrane fractions were quantified by nanoLC-MS/MS, and metabolism of nifedipine to dehydronifedipine in SCHH was evaluated. PRH significantly increased CYP3A4 protein concentrations and nifedipine metabolism to dehydronifedipine in a concentration-dependent manner. CYP3A4 mRNA levels in hepatocyte-derived exosomes positively correlated with CYP3A4 protein levels and dehydronifedipine formation in SCHH. PRH also increased CYP2B6, CYP2C8 and CYP2A6 levels. Our findings demonstrate that PRH increase nifedipine metabolism in SCHH by inducing CYP3A4 expression and alter expression of other key CYP proteins in an isoform-specific manner, and suggest that hepatocyte-derived exosomes warrant further investigation as biomarkers of hepatic CYP3A4 metabolism. Together, these results offer mechanistic insight into the increases in nifedipine metabolism and clearance observed in pregnant women.

The impact of intrauterine growth restriction on cytochrome P450 enzyme expression and activity

With the increased prevalence of non-communicable disease and availability of medications to treat these and other conditions, a pregnancy free from prescribed medication exposure is rare. Up to 99% of women take at least one medication during pregnancy. These medications can be divided into those used to improve maternal health and wellbeing (e.g., analgesics, antidepressants, antidiabetics, antiasthmatics), and those used to promote the baby's wellbeing in either fetal (e.g., anti-arrhythmics) or postnatal life (e.g., antenatal glucocorticoids). These medications are needed for pre-existing or coincidental illnesses in the mother, maternal conditions induced by the pregnancy itself through to conditions that arise in the fetus or that will be encountered by the newborn. Thus, medications administered to the mother may be used to treat the mother, the fetus or both. Metabolism of medications is regulated by a range of physiological processes that change during pregnancy. Other pathological processes such as placental insufficiency can in turn have both immediate and lifelong adverse health consequences for babies. Individuals born growth restricted are more likely to require medications but may also have an altered ability to metabolise these medications in fetal and postnatal life. This review aims to determine the effect of suboptimal fetal growth on the fetal expression of the drug metabolising enzymes (DMEs) that convert medications into active or inactive metabolites, and the transporters that remove both these medications and their metabolites from the fetal compartment.

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.

Integrating in vitro data and physiologically based kinetic modeling-facilitated reverse dosimetry to predict human cardiotoxicity of methadone

Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.

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.

Utilizing Pediatric Physiologically Based Pharmacokinetic Models to Examine Factors That Contribute to Methadone Pharmacokinetic Variability in Neonatal Abstinence Syndrome Patients

Chronic intrauterine exposure to psychoactive drugs often results in neonatal abstinence syndrome (NAS). NAS is the symptomatic drug withdrawal in newborns that generally occurs after in utero chronic opioid exposure. Methadone is an opioid analgesic commonly prescribed for pharmacologic management of NAS. It exhibits high pharmacokinetic (PK) variability. The current study used physiologically based PK modeling to predict the PK profile of methadone in 20 newborns treated for NAS. The physiologically based PK simulations adequately predicted the PK profile of the clinical data for 45% of the patients. Sensitivity analyses were conducted to explore contributing factors to methadone PK variability. The data suggest that P450 enzymatic activity impacts the clearance of methadone in virtual adults and neonates, while the contribution of cardiac output may be negligible. Understanding maturational and/or pharmacogenetic changes in cytochrome P450 enzymatic activity may further explain the large PK variability of methadone in newborns with NAS and will help individualized treatment.

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.

Effect of CYP2C9 Polymorphisms on the Pharmacokinetics of Indomethacin During Pregnancy

BACKGROUND AND OBJECTIVE

Cytochrome P450 (CYP) 2C9 catalyzes the biotransformation of indomethacin to its inactive metabolite O-desmethylindomethacin (DMI). The aim of this work was to determine the effect of CYP2C9 polymorphisms on indomethacin metabolism in pregnant women.

METHODS

Plasma concentrations of indomethacin and DMI at steady state were analyzed with a validated LC-MS/MS method. DNA was isolated from subject blood and buccal smear samples. Subjects were grouped by genotype for comparisons of pharmacokinetic parameters.

RESULTS

For subjects with the *1/*2 genotype, the mean steady-state apparent oral clearance (CL/F_ss) of indomethacin was 13.5 ± 7.7 L/h (n = 4) and the mean metabolic ratio (AUC_DMI/AUC_indomethacin) was 0.291 ± 0.133. For subjects with the *1/*1 genotype, these values were 12.4 ± 2.7 L/h and 0.221 ± 0.078, respectively (n = 14). Of note, we identified one subject who was a carrier of both the *3 and *4 alleles, resulting in an amino acid change (I359P) which has not been reported previously. This subject had a metabolic ratio of 0.390 and a CL/F_ss of indomethacin (24.3 L/h) that was nearly double the wild-type clearance.

CONCLUSION

Although our results are limited by sample size and are not statistically significant, these data suggest that certain genetic polymorphisms of CYP2C9 may lead to an increased metabolic ratio and an increase in the clearance of indomethacin. More data are needed to assess the impact of CYP2C9 genotype on the effectiveness of indomethacin as a tocolytic agent.

Physiologically-based pharmacokinetics of ziprasidone in pregnant women

AIMS

Pregnancy is associated with physiological changes that alter the pharmacokinetics (PK) of drugs. The aim of this study was to predict the PK of ziprasidone in pregnant women.

METHODS

A full physiologically-based pharmacokinetic (PBPK) model of ziprasidone was developed and validated for the non-pregnant population (healthy adults, paediatrics, geriatrics), and this was extended to the pregnant state to assess the change in PK profile of ziprasidone throughout pregnancy.

RESULTS

The PBPK model successfully predicted the ziprasidone disposition in healthy adult volunteers, wherein the predicted and observed AUC, C_max and t_max were within the fold-difference of 0.94-1.09, 0.89-1.40 and 0.80-1.08, respectively. The paediatric and geriatric population, also showed predicted AUC, C_max and t_max within a two-fold range of the observed values. The simulated exposure in pregnant women using a p-PBPK model showed no significant difference when compared to non-pregnant women.

CONCLUSIONS

The PBPK model predicted the impact of physiological changes during pregnancy on PK and exposure of ziprasidone, suggesting that dose adjustment is not necessary in this special population.

Hepatic Enzymes Relevant to the Disposition of (-)-∆9-Tetrahydrocannabinol (THC) and Its Psychoactive Metabolite, 11-OH-THC

Marijuana use by pregnant women is increasing. To predict developmental risk to the fetus/neonate from such use, in utero fetal exposure to (-)-∆9-tetrahydrocannabinol (THC), the main psychoactive cannabinoid in marijuana and its active psychoactive metabolite, 11-hydroxy-∆9-tetrahydrocannabinol (11-OH-THC), needs to be determined. Since such measurement is not possible, physiologically based pharmacokinetic (PBPK) modeling and simulation can provide an alternative method to estimate fetal exposure to cannabinoids. To do so, pharmacokinetic parameters for the disposition of THC and 11-OH-THC need to be elucidated. Here, we report a first step to estimate these parameters, namely, those related to maternal metabolism of THC/11-OH-THC in human liver microsomes (HLMs) at plasma concentrations observed after smoking marijuana. Using recombinant cytochrome P450 (P450) and UDP-glucuronosyltransferase (UGT) enzymes, CYP1A1, 1A2, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7, and UGT1A9 and UGT2B7 were found to be involved in the disposition of THC/11-OH-THC. Using pooled HLMs, the fraction metabolized (f m) by relevant enzymes was measured using selective enzyme inhibitors, and then adjusted for enzyme cross-inhibition. As previously reported, CYP2C9 was the major enzyme responsible for depletion of THC and formation of 11-OH-THC with f m values of 0.82 ± 0.08 and 0.99 ± 0.10, respectively (mean ± S.D.), while CYP2D6 and CYP2C19 were minor contributors. 11-OH-THC was depleted by UGT and P450 enzymes with f m values of 0.60 ± 0.05 and 0.40 ± 0.05, respectively (mean ± S.D.), with UGT2B7, UGT1A9, CYP2C9, and CYP3A4 as contributors. These mechanistic data represent the first set of drug-dependent parameters necessary to predict maternal-fetal cannabinoid exposure during pregnancy using PBPK modeling.

Dose adjustment in orphan disease populations: the quest to fulfill the requirements of physiologically based pharmacokinetics

INTRODUCTION

While the media is engaged and fascinated by the idea of 'Precision Medicine', the nuances related to 'Precision Dosing' seem to be largely ignored. Assuming the 'right drug' is selected, clinicians still need to decide on the 'right dose' for individuals. Ideally, optimal dosing should be studied in clinical trials; however, many drugs on the market lack evidence-based dosing recommendations, and small groups of patients (orphan disease populations) are dependent on local guidance and clinician experience to determine drug dosage adjustments. Areas Covered: This report explores the current understanding of dosing adjustment in special populations and examines the requirements for developing 'in silico' models for pediatric, elderly and pregnant patients. The report also highlights current use of modeling to provide evidence-based recommendations for drug labeling in the absence of complete clinical trials in orphan disease populations. Expert Opinion: Physiologically based pharmacokinetics (PBPK) is an attractive prospect for determining the best drug dosage adjustments in special populations. However, it is not sufficient for individualized, or even stratified dosing, unless the systems (drug-independent) data required to build robust PBPK models are obtained. Such models are not a substitute for clinical trials, but they are an alternative to undocumented and inconsistent guesswork.

Investigation on Metabolism of Di(2-Ethylhexyl) Phthalate in Different Trimesters of Pregnant Women

Di(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer but shows diverse toxicity. To investigate the time- and maternal age-differences in metabolism process of DEHP in pregnant women, three urine samples were collected from each pregnant woman ( n = 847) at the first (T1, mean 13.04 gestational weeks), the second (T2, mean 23.63 gestational weeks) and the third time point (T3, mean 35.91 gestational weeks), respectively. Four metabolites of DEHP were analyzed in 2541 urine samples (847 × 3) by using ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry. The level of urinary mono(2-ethylhexyl) phthalate (MEHP) had a decreasing trend across the pregnancy periods. The geometric mean concentrations of mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) and mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) were significantly decreased in T2 than T1, and recovered slightly in T3. The transformation rate of MEHP to MEHHP in T3 was significantly higher than those in other two time points. The transformation rate of MEHHP to MEOHP in T2 and T3 was significantly higher than that in T1, indicating the oxidation was more efficient in late pregnancy compared with early and middle pregnancy. The percentages of oxidation products MEHHP and MECPP were higher in the higher-age group compared with the lower-age group in the second trimester samples. It is therefore concluded that DEHP metabolism may be influenced by the pregnancy stage and maternal age. The findings may benefit the risk assessment and toxicity evaluation of DEHP.

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.

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.