A pregnancy physiologically based pharmacokinetic (p-PBPK) model for disposition of drugs metabolized by CYP1A2, CYP2D6 and CYP3A4

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Simulated pharmacokinetics of inhaled caffeine and melatonin from existing products indicate the lack of dosimetric considerations

Numerous commercially available inhalable products claim to improve sleep-wake cycle-related target indications by delivering a wide variety of chemicals like caffeine and melatonin. The resulting exposure-responses from inhaling different doses are unknown and obtaining early understanding of resulting pharmacokinetics is beneficial. This study applied a physiologically based pharmacokinetic modeling approach to predict the inhalation pharmacokinetics of caffeine and melatonin for different target indications related to the sleep-wake cycle. The model predicted rapid systemic delivery of caffeine and melatonin based on airway regional deposition of inhaled aerosol. A low inhaled dose of 1 mg of caffeine resulted in a 72.3-times lower plasma maximal concentration and was predicted to not improve cognitive performance task outcomes compared to oral consumption of coffee containing 80 mg of caffeine. Conversely, 2-mg oral and inhaled doses of melatonin under recommended directions of use result in more than 25.1- and 645-times higher plasma concentrations compared to endogenous melatonin, respectively. The recommended doses for inhalation products for potential improvement in the target indications vary widely. Additional research is needed to evaluate the human pharmacokinetics, efficacy, and safety of inhaled products. Given the lack of assessments, inhaled caffeine and melatonin must be consumed with caution as the toxicological concerns are not known and could outweigh the potential beneficial effects.

A Comprehensive Physiologically Based Pharmacokinetic Model of Nadolol in Adults with Renal Disease and Pediatrics with Supraventricular Tachycardia

Nadolol is a long-acting non-selective β-adrenergic antagonist that helps treat angina and hypertension. The current study aimed to develop and validate the physiologically based pharmacokinetic model (PBPK) of nadolol in healthy adults, renal-compromised, and pediatric populations. A comprehensive PBPK model was established by utilizing a PK-Sim simulator. After establishing and validating the model in healthy adults, pathophysiological changes i.e., blood flow, hematocrit, and GFR that occur in renal failure were incorporated in the developed model, and the drug exposure was assessed through Box plots. The pediatric model was also developed and evaluated by considering the renal maturation process. The validation of the models was carried out by visual predictive checks, calculating predicted to observed (Rpre/obs) and the average fold error (AFE) of PK parameters i.e., the area under the concentration-time curve (AUC0-t), the maximum concentration in plasma (Cmax), and CL (clearance). The presented PBPK model successfully simulates the nadolol PK in healthy adults, renal-impaired, and pediatric populations, as the Rpre/obs values of all PK parameters fall within the acceptable range. The established PBPK model can be useful in nadolol dose optimization in patients with renal failure and children with supraventricular tachycardia.

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.

Physiologically Based Pharmacokinetics Modeling in the Neonatal Population-Current Advances, Challenges, and Opportunities

Physiologically based pharmacokinetic (PBPK) modeling is an approach to predicting drug pharmacokinetics, using knowledge of the human physiology involved and drug physiochemical properties. This approach is useful when predicting drug pharmacokinetics in under-studied populations, such as pediatrics. PBPK modeling is a particularly important tool for dose optimization for the neonatal population, given that clinical trials rarely include this patient population. However, important knowledge gaps exist for neonates, resulting in uncertainty with the model predictions. This review aims to outline the sources of variability that should be considered with developing a neonatal PBPK model, the data that are currently available for the neonatal ontogeny, and lastly to highlight the data gaps where further research would be needed.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

Total and Free Blood and Plasma Concentration Changes in Pregnancy for Medicines Highly Bound to Plasma Proteins: Application of Physiologically Based Pharmacokinetic Modelling to Understand the Impact on Efficacy

Free drug concentrations are generally considered the pharmacologically active moiety and are important for cellular diffusion and distribution. Pregnancy-related changes in plasma protein binding and blood partitioning are due to decreases in plasma albumin, alpha-1-acid glycoprotein, and haematocrit; this may lead to increased free concentrations, tissue distribution, and clearance during pregnancy. In this paper we highlight the importance and challenges of considering changes in total and free concentrations during pregnancy. For medicines highly bound to plasma proteins, such as tacrolimus, efavirenz, clindamycin, phenytoin, and carbamazepine, differential changes in concentrations of free drug during pregnancy may be clinically significant and have important implications for dose adjustment. Therapeutic drug monitoring usually relies on the measurement of total concentrations; this can result in dose adjustments that are not necessary when changes in free concentrations are considered. We explore the potential of physiologically based pharmacokinetic (PBPK) models to support the understanding of the changes in plasma proteins binding, using tacrolimus and efavirenz as example drug models. The exposure to either drug was predicted to be reduced during pregnancy; however, the decrease in the exposure to the total tacrolimus and efavirenz were significantly larger than the reduction in the exposure to the free drug. These data show that PBPK modelling can support the impact of the changes in plasma protein binding and may be used for the simulation of free concentrations in pregnancy to support dosing decisions.

Physiologically Based Pharmacokinetic Model To Predict Metoprolol Disposition in Healthy and Disease Populations

The evolution in the development of drugs has increased the popularity of physiologically based pharmacokinetic (PBPK) models. This study seeks to assess the PK of metoprolol in populations with healthy, chronic kidney disease (CKD), and acute myocardial infarction (AMI) conditions by developing and evaluating PBPK models. An extensive literature review for identifying and selecting plasma concentration vs time profile data and other drug-related parameters was undergone for their integration into the PK-Sim program followed by the development of intravenous, oral, and diseased models. The developed PBPK model of metoprolol was then evaluated using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for all PK parameters, i.e., the area under the curve (AUC), maximal plasma concentration, and clearance. The model evaluation depicted that none of the PK parameters were out of the allowed range (2-fold error) in the case of the mean Robs/pre ratios. The model anticipations were executed to determine the influence of diseases on unbound and total AUC after the application of metoprolol in healthy, moderate, and severe CKD. The dosage reductions were also suggested based on differences in unbound and total AUC in different stages of CKD. The developed PBPK models have successfully elaborated the PK changes of metoprolol occurring in healthy individuals and those with renal and heart diseases (CKD & AMI), which may be fruitful for dose optimization among diseased patients.

Potential neonatal toxicity of new psychoactive substances

Cannabis, cocaine, 3,4-methylenedioxymethamphetamine, and lysergic acid diethylamide are psychoactive substances with a significant increase in consumption during the 21st century due to their popularity in medicinal and recreational use. New psychoactive substances (NPSs) mimic established psychoactive substances. NPSs are known as being natural and safe to consumers; however, they are neither natural nor safe, causing severe adverse reactions, including seizures, nephrotoxicity, and sometimes death. Synthetic cannabinoids, synthetic cathinones, phenethylamines, and piperazines are all examples of NPSs. As of January 2020, nearly 1000 NPSs have become documented. Due to their low cost, ease of availability, and difficulty of detection, misuse of NPSs has become a familiar and growing problem, especially in adolescents and young adults in the past decade. The use of NPSs is associated with higher risks of unplanned sexual intercourse and pregnancy. As many as 4 in 100 women seeking treatment for substance abuse are pregnant or nursing. Animal studies and human clinical case reports have shown that exposure to certain NPSs during lactation periods has toxic effects on neonates, increasing various risks, including brain damage. Nevertheless, neonatal toxicity effects of NPSs are usually unrecognized and overlooked by healthcare professionals. In this review article, we introduce and discuss the potential neonatal toxicity of NPSs, emphasizing synthetic cannabinoids. Utilizing the established prediction models, we identify synthetic cannabinoids and their highly accumulative metabolites in breast milk.

Use of Physiologically Based Pharmacokinetic Modeling for Hepatically Cleared Drugs in Pregnancy: Regulatory Perspective

Physiologically based pharmacokinetic modeling could be used to predict changes in exposure during pregnancy and possibly inform medicine use in pregnancy in situations in which there is currently limited or no available clinical PK data. The Medicines and Healthcare Product Regulatory Agency has been evaluating the available models for a number of medicines cleared by hepatic clearance mechanisms. Models were evaluated for metoprolol, tacrolimus, clindamycin, ondansetron, phenytoin, caffeine, fluoxetine, clozapine, carbamazepine, metronidazole, and paracetamol. The hepatic metabolism through cytochrome P450 (CYP) contributes significantly to the elimination of these drugs, and available knowledge of CYP changes during pregnancy has been implemented in the existing pregnancy physiology models. In general, models were able to capture trends in exposure changes in pregnancy to some extent, but the magnitude of pharmacokinetic change for these hepatically cleared drugs was not captured in each case, nor were models always able to capture overall exposure in the populations. A thorough evaluation was hampered by the lack of clinical data for drugs cleared by a specific clearance pathway. The limited clinical data, as well as complex elimination pathways involving CYPs, uridine 5'-diphospho-glucuronosyltransferase and active transporter for many drugs, currently limit the confidence in the prospective use of the models. Pregnancy-related changes in uridine 5'-diphospho-glucuronosyltransferase and transport functions are emerging, and incorporation of such changes in current physiologically based pharmacokinetic modeling software is in progress. Filling this gap is expected to further enhance predictive performance of models and increase the confidence in predicting PK changes in pregnant women for hepatically cleared drugs.

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

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

Optimising Fluvoxamine Maternal/Fetal Exposure during Gestation: A Pharmacokinetic Virtual Clinical Trials Study

Fluvoxamine plasma concentrations have been shown to decrease throughout pregnancy. CYP2D6 polymorphisms significantly influence these changes. However, knowledge of an optimum dose adjustment according to the CYP2D6 phenotype is still limited. This study implemented a physiologically based pharmacokinetic modelling approach to assess the gestational changes in fluvoxamine maternal and umbilical cord concentrations. The optimal dosing strategies during pregnancy were simulated, and the impact of CYP2D6 phenotypes on fluvoxamine maternal and fetal concentrations was considered. A significant decrease in fluvoxamine maternal plasma concentrations was noted during gestation. As for the fetal concentration, a substantial increase was noted for the poor metabolisers (PM), with a constant level in the ultrarapid (UM) and extensive (EM) metabolisers commencing from gestation week 20 to term. The optimum dosing regimen suggested for UM and EM reached a maximum dose of 300 mg daily at gestational weeks (GW) 15 and 35, respectively. In contrast, a stable dose of 100 mg daily throughout gestation for the PM is sufficient to maintain the fluvoxamine plasma concentration within the therapeutic window (60-230 ng/mL). Dose adjustment during pregnancy is required for fluvoxamine, particularly for UM and EM, to maintain efficacy throughout the gestational period.

Pregnancy Increases CYP3A Enzymes Activity as Measured by the 4β-Hydroxycholesterol/Cholesterol Ratio

Changes in cortisol and other hormones during pregnancy may alter CYP3A enzymes activity, but data from sub-Saharan Africa are sparse. We investigated the effect of pregnancy and CYP3A5 genotypes on CYP3A enzymes activity using the plasma 4β-hydroxycholesterol (4β-OHC)/cholesterol (Chol) ratio, a known endogenous biomarker. Tanzanian pregnant women (n = 110) and non-pregnant women (n = 59) controls were enrolled. Plasma 4β-OHC and Chol were determined in the second and third trimesters for pregnant women and once for non-pregnant women using gas chromatography−mass spectrometry. Genotyping for CYP3A5 (*3, *6, *7) was performed. Wilcoxon Signed-Rank Test and Mann−Whitney U test were used to compare the median 4β-OHC/Chol ratio between trimesters in pregnant women and between pregnant and non-pregnant women. Repeated-measure ANOVA was used to evaluate the effect of the CYP3A5 genotypes on the 4β-OHC/Chol ratio in pregnant women. No significant effect of the pregnancy status or the CYP3A5 genotype on the cholesterol level was observed. The plasma 4β-OHC/Chol ratio significantly increased by 7.3% from the second trimester to the third trimester (p = 0.02). Pregnant women had a significantly higher mean 4β-OHC/Chol ratio than non-pregnant women, (p < 0.001). In non-pregnant women, the mean 4β-OHC/Chol ratio was significantly lower in carriers of defective CYP3A5 alleles (*3, *6 or *7) as compared to women with the CYP3A5*1/*1 genotypes (p = 0.002). Pregnancy increases CYP3A enzymes activity in a gestational-stage manner. The CYP3A5 genotype predicts CYP3A enzymes activity in the black Tanzanian population, but not during pregnancy-mediated CYP3A enzyme induction.

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.

Simulations of radioiodine exposure and protective thyroid blocking in a new biokinetic model of the mother-fetus unit at different pregnancy ages

In the case of nuclear incidents, radioiodine may be released. After incorporation, it accumulates in the thyroid and enhances the risk of thyroidal dysfunctions and cancer occurrence by internal irradiation. Pregnant women and children are particularly vulnerable. Therefore, thyroidal protection by administering a large dose of stable (non-radioactive) iodine, blocking radioiodide uptake into the gland, is essential in these subpopulations. However, a quantitative estimation of the protection conferred to the maternal and fetal thyroids in the different stages of pregnancy is difficult. We departed from an established biokinetic model for radioiodine in pregnancy using first-order kinetics. As the uptake of iodide into the thyroid and several other tissues is mediated by a saturable active transport, we integrated an uptake mechanism described by a Michaelis–Menten kinetic. This permits simulating the competition between stable and radioactive iodide at the membrane carrier site, one of the protective mechanisms. The Wollf–Chaikoff effect, as the other protective mechanism, was simulated by adding a total net uptake block for iodide into the thyroid, becoming active when the gland is saturated with iodine. The model’s validity was confirmed by comparing predicted values with results from other models and sparse empirical data. According to our model, in the case of radioiodine exposure without thyroid blocking, the thyroid equivalent dose in the maternal gland increases about 45% within the first weeks of pregnancy to remain in the same range until term. Beginning in the 12th pregnancy week, the equivalent dose in the fetal thyroid disproportionately increases over time and amounts to three times the dose of the maternal gland at term. The maternal and fetal glands’ protection increases concomitantly with the amount of stable iodine administered to the mother simultaneously with acute radioiodine exposure. The dose–effect curves reflecting the combined thyroidal protection by the competition at the membrane carrier site and the Wolff–Chaikoff effect in the mother are characterized by a mean effective dose (ED₅₀) of roughly 1.5 mg all over pregnancy. In the case of the fetal thyroid, the mean effective doses for thyroid blocking, taking into account only the competition at the carrier site are numerically lower than in the mother. Taking into account additionally the Wolff–Chaikoff effect, the dose–effect curves for thyroidal protection in the fetus show a shift to the left over time, with a mean effective dose of 12.9 mg in the 12th week of pregnancy decreasing to 0.5 mg at term. In any case, according to our model, the usually recommended dose of 100 mg stable iodine given at the time of acute radioiodine exposure confers a very high level of thyroidal protection to the maternal and fetal glands over pregnancy. For ethical reasons, the possibilities of experimental studies on thyroid blocking in pregnant women are extremely limited. Furthermore, results from animal studies are associated with the uncertainties related to the translation of the data to humans. Thus model-based simulations may be a valuable tool for better insight into the efficacy of thyroidal protection and improve preparedness planning for uncommon nuclear or radiological emergencies.

Improving Development of Drug Treatments for Pregnant Women and the Fetus

The exclusion of pregnant populations, women of reproductive age, and the fetus from clinical trials of therapeutics is a major global public health issue. It is also a problem of inequity in medicines development, as pregnancy is a protected characteristic. The current regulatory requirements for drugs in pregnancy are being analyzed by a number of agencies worldwide. There has been considerable investment in developing expertise in pregnancy clinical trials (for the pregnant person and the fetus) such as the Obstetric-Fetal Pharmacology Research Centers funded by the National Institute of Child Health and Human Development. Progress has also been made in how to define and grade clinical trial safety in pregnant women, the fetus, and neonate. Innovative methods to model human pregnancy physiology and pharmacology using computer simulations are also gaining interest. Novel ways to assess fetal well-being and placental function using magnetic resonance imaging, computerized cardiotocography, serum circulating fetoplacental proteins, and mRNA may permit better assessment of the safety and efficacy of interventions in the mother and fetus. The core outcomes in women’s and newborn health initiative is facilitating the consistent reporting of data from pregnancy trials. Electronic medical records integrated with pharmacy services should improve the strength of pharmacoepidemiologic and pharmacovigilance studies. Incentives such as investigational plans and orphan disease designation have been taken up for obstetric, fetal, and neonatal diseases. This review describes the progress that is being made to better understand the extent of the problem and to develop applicable solutions.

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

Introduction

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

Discussion

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

Conclusions

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

Application of a Physiologically Based Pharmacokinetic Model to Predict Cefazolin and Cefuroxime Disposition in Obese Pregnant Women Undergoing Caesarean Section

Intravenous (IV) cefuroxime and cefazolin are used prophylactically in caesarean sections (CS). Currently, there are concerns regarding sub-optimal dosing in obese pregnant women compared to lean pregnant women prior to CS. The current study used a physiologically based pharmacokinetic (PBPK) approach to predict cefazolin and cefuroxime pharmacokinetics in obese pregnant women at the time of CS as well as the duration that these drug concentrations remain above a target concentration (2, 4 or 8 µg/mL or µg/g) in plasma or adipose tissue. Cefazolin and cefuroxime PBPK models were first built using clinical data in lean and in obese non–pregnant populations. Models were then used to predict cefazolin and cefuroxime pharmacokinetics data in lean and obese pregnant populations. Both cefazolin and cefuroxime models sufficiently described their total and free levels in the plasma and in the adipose interstitial fluid (ISF) in non–pregnant and pregnant populations. The obese pregnant cefazolin model predicted adipose exposure adequately at different reference time points and indicated that an IV dose of 2000 mg can maintain unbound plasma and adipose ISF concentration above 8 µg/mL for 3.5 h post dose. Predictions indicated that an IV 1500 mg cefuroxime dose can achieve unbound plasma and unbound ISF cefuroxime concentration of ≥8 µg/mL up to 2 h post dose in obese pregnant women. Re-dosing should be considered if CS was not completed within 2 h post cefuroxime administration for both lean or obese pregnant if cefuroxime concentrations of ≥8 µg/mL is required. A clinical study to measure cefuroxime adipose concentration in pregnant and obese pregnant women is warranted.

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.

Model-Informed Dose Optimization in Pregnancy

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

Physiologically Based Pharmacokinetic Modelling for Nicotine and Cotinine Clearance in Pregnant Women

Introduction: Physiologically based pharmacokinetic (PBPK) models for the absorption, disposition, metabolism and excretion (ADME) of nicotine and its major metabolite cotinine in pregnant women (p-PBPK) are rare. The aim of this short research report is to present a p-PBPK model and its simulations for nicotine and cotinine clearance.

Methods: The maternal-placental-fetal compartments of the p-PBPK model contain a total of 16 compartments representing major maternal and fetal organs and tissue groups. Qualitative and quantitative data of nicotine and cotinine disposition and clearance have been incorporated into pharmacokinetic parameters.

Results: The p-PBPK model reproduced the higher clearance rates of nicotine and cotinine in pregnant women than non-pregnant women. Temporal profiles for their disposition in organs such as the brain were also simulated. Nicotine concentration reaches its maximum value within 2 min after an intravenous injection.

Conclusion: The proposed p-PBPK model produces results consistent with available data sources. Further pharmacokinetic experiments are required to calibrate clearance parameters for individual organs, and for the fetus.

Prediction of maternal and fetal pharmacokinetics of indomethacin in pregnancy

AIMS

Indomethacin is used for the treatment of preterm labour, short cervices and idiopathic polyhydramnios during pregnancy. Few studies have described the pharmacokinetics (PK) of indomethacin during pregnancy. This study aimed to determine maternal and fetal PK of indomethacin during different trimesters of pregnancy using physiologically based PK (PBPK) modelling and simulations.

METHODS

Full PBPK simulations were performed in nonpregnant subjects and pregnant subjects from each trimester of pregnancy at steady state using Simcyp's healthy volunteers and pregnancy PBPK model, respectively. The fetal exposures were predicted using a fetoplacental pregnancy PBPK model. The models were verified by comparing PBPK-based predictions with observed PK profiles.

RESULTS

Predicted exposure (AUC_0-6h ) and clearance of indomethacin in nonpregnant women and pregnant women are similar to the clinical observations. AUC_0-6h of indomethacin is approximately 14, 24 and 32% lower, consistent with 18, 34 and 52% higher clearance in the first, second and third trimesters of pregnancy, respectively, compared to nonpregnant women. Predicted fetal plasma exposures increased by approximately 30% from the second trimester to the third trimester of pregnancy.

CONCLUSION

A mechanistic PBPK model adequately described the maternal and the fetal PK of indomethacin during pregnancy. As the pregnancy progresses, a modest decrease (≤32%) in systemic exposures in pregnant women and a 33% increase in fetal exposures to indomethacin were predicted. Higher fetal exposures in the third trimester of pregnancy may pose safety risks to the fetus. Additional studies are warranted to understand the exposure-response relationship and provide appropriate dosing recommendations during pregnancy that consider both safety and efficacy.

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.

Perampanel and pregnancy

Objective

The objective was to summarize pregnancy and fetal/postnatal outcomes following maternal perampanel exposure using preclinical and clinical data, and to use physiologically based pharmacokinetic (PBPK) modeling to improve understanding of perampanel pharmacokinetics (PK) during pregnancy.

Methods

Preclinical developmental studies with perampanel were conducted in pregnant rats and rabbits. Clinical data were collated from the Eisai global perampanel safety database, comprising reports of perampanel exposure during pregnancy from routine clinical settings, interventional studies, and non‐interventional post‐marketing studies, searched for events coded to Medical Dictionary for Regulatory Activities (MedDRA) high‐level group terms of Pregnancy, Labor, Delivery, and Postpartum Conditions and/or the Standardized MedDRA Query terms of Congenital, Familiar, and Genetic Disorders. A PBPK model was used to predict clinical perampanel PK throughout pregnancy.

Results

Preclinical studies indicated that perampanel may be linked with post‐implantation loss and/or some specific physical development delays but not fertility and early embryonic development. As of August 31, 2018, 96 pregnancies in 90 women receiving perampanel had been reported. No concomitant medications were reported in 26 (28.9%) women taking perampanel. Overall, 43 pregnancies reached full term (all normal live births), 28 did not reach term (induced abortion, n = 18; spontaneous miscarriage, n = 6; incomplete spontaneous miscarriage, n = 2; premature delivery, n = 1; stillbirth [Fallot’s tetralogy], n = 1), 18 were lost to follow‐up, and seven were ongoing at data cut‐off. Adverse events were reported in five full‐term neonates (low Apgar score, n = 2; fatal neonatal aspiration, n = 1; cystic fibrosis and congenital deafness, n = 1; poor sucking reflex and shallow breathing, n = 1). PK simulations predicted perampanel exposure decreases throughout pregnancy and is up to four‐ and three‐fold lower towards the end of pregnancy compared with non‐pregnant women for total and unbound perampanel, respectively.

Significance

These data provide preliminary information on perampanel use during pregnancy and should be interpreted with caution. Further outcome data are required to estimate the prevalence of adverse pregnancy outcomes with perampanel exposure.

Fetal Physiologically Based Pharmacokinetic Models: Systems Information on Fetal Cardiac Output and Its Distribution to Different Organs during Development

BACKGROUND AND OBJECTIVE

Fetal circulation is unique and the parameters describing hemodynamic status during development are critical for constructing a fetal physiologically based pharmacokinetic model. To date, a comprehensive review of circulatory changes during fetal development, with a specific focus on developing these models, has not been reported. The objective of this work was to collate, analyze, and mathematically describe physiological information on fetal cardiac output and tissue blood flows during development.

METHODS

A comprehensive literature search was carried out to collate and evaluate the changes to fetal cardiac output and fetal tissue blood flows during growth. The collated data were assessed, integrated, and analyzed to establish continuous mathematical functions describing the average parameter changes and variability during development.

RESULTS

Data were available for fetal cardiac output (14 Doppler studies), blood flow through the fetal umbilical vein (15 studies), ductus venosus (6 studies), liver veins (5 studies), brain (4 studies), lungs (5 studies), and kidneys (2 studies). Fetal cardiac output is described as either an age- or weight-dependent function. The latter is preferred as it generates an individualized cardiac output that is correlated to the fetal body weight. Blood flow as a proportion of fetal cardiac output to the liver, placenta, brain, kidneys, and lungs was age varying, whilst for the adipose, bone, heart, muscle, and skin the blood flow proportions were fixed. The pattern of change (with respect to direction and pace) for each of these parameters was different.

CONCLUSIONS

Despite limitations in the availability of some values, the collected data provide a useful resource for fetal physiologically based pharmacokinetic modeling. Potential applications of these data include predicting xenobiotic exposure and risk assessment in the fetus following the administration of maternally dosed drugs or unintended exposure to environmental toxicants.

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.

Development of a physiologically based pharmacokinetic model of diisononyl phthalate (DiNP) in pregnant rat and human

A physiologically based pharmacokinetic (PBPK) model for di-isononyl phthalate (DiNP) was developed by adapting the existing models for di(2-ethylhexyl) phthalate (DEHP) and di-butylphthalate (DBP). Both pregnant rat and human time-course plasma and urine data were used to address the hydrolysis of DiNP in intestinal tract, plasma, and liver as well as hepatic oxidative metabolism and conjugation of the monoester and primary oxidative metabolites. Data in both rats and humans were available to inform the uptake and disposition of mono-isononyl phthalate (MiNP) as well as the three primary oxidative metabolites including hydroxy (7-OH)-, oxo (7-OXO)-, and carboxy (7-COX)-monoisononyl phthalate in plasma and urine. The DiNP model was reliable over a wide range of exposure levels in the pregnant rat as well as the two low exposure levels in humans including capturing the nonlinear behavior in the pregnant rat after repeated 750 mg/kg/day dosing. The presented DiNP PBPK model in pregnant rat and human, based upon an extensive kinetic dataset in both species, may provide a basis for assessing human equivalent exposures based upon either rodent or in vitro points of departure.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

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.

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.

Quetiapine dose optimisation during gestation: a pharmacokinetic modelling study

Objectives

The second-generation antipsychotic quetiapine has been demonstrated to undergo gestation-related changes in pharmacokinetics. This study applied pharmacokinetic modelling principles to investigate the mechanism of these changes and to propose new dosing strategies to counteract these changes.

Methods

A pharmacokinetic modelling approach was implemented using virtual population groups. Changes in quetiapine trough plasma concentration during gestation were quantified across all trimesters, and dose adjustment strategies were applied to counteract these changes by targeting a therapeutic range of 50–500 ng/ml throughout gestation.

Key findings

The application of the model during gestation predicted a decrease in trough concentration. A maximum decrease of 58% was predicted during trimester 2, and being associated with a statistically significant decrease in oral clearance at gestation week 25, 204 l/h ± 100.8 l/h compared with non-pregnant subjects, 121.9 l/h ± 51.8 l/h. A dosing optimisation strategy identified that dose increases to 500–700 mg twice daily would result in 32–55% of subjects possessing trough concentration in excess of 50 ng/ml.

Conclusions

Quetiapine doses in pregnancy should be increased to 500–700 mg twice daily to counteract a concomitant increase in metabolic clearance, increase in volume of distribution and decrease in plasma protein binding.

Fetal Physiologically Based Pharmacokinetic Models: Systems Information on Fetal Blood Components and Binding Proteins

BACKGROUND

Fetal blood and plasma volume and binding components are critical parameters in a fetal physiologically based pharmacokinetic model. To date, a comprehensive review of their changes during fetal development has not been reported.

OBJECTIVE

The objective of this work was to collate and analyze physiological information on fetal blood and plasma volume and binding component data during development and to provide a mathematical description of these parameters that can be integrated within a fetal physiologically based pharmacokinetic model.

METHODS

A comprehensive literature search was conducted on fetal blood and plasma volume and binding component parameters and their changes during growth from apparently healthy fetuses from uncomplicated pregnancies. Collated data were assessed, integrated, and analyzed to establish continuous mathematical functions describing their growth trends with fetal age and weight.

RESULTS

Data were available from 14 studies for blood, ten studies for hematocrit, 12 studies for albumin, and four studies for alpha-1-acid glycoprotein, while plasma and red blood cell volumes were described based on blood and hematocrit data. Fetal physiologically based pharmacokinetic parameters, including blood, plasma and red blood cell volumes, hematocrit, serum albumin, and acid glycoprotein were quantified as a function of fetal age and weight. Variability around the mean parameters at different fetal ages was also investigated. The growth of each of these parameters was different (with respect to direction and monotonicity).

CONCLUSIONS

Despite the limitations identified in the availability of some values, the collected data presented in this article provide a useful resource for fetal physiologically based pharmacokinetic modeling. Potential applications include predicting xenobiotic exposure and risk assessment in the fetus following maternally administered drugs or unintended exposure to environmental toxicants.

Using mechanistic physiologically-based pharmacokinetic models to assess prenatal drug exposure: Thalidomide versus efavirenz as case studies

Maternofoetal physiologically-based pharmacokinetic models integrating multi-compartmental maternal and foetal units were developed using Simbiology® to estimate prenatal drug exposure. Processes governing drug disposition were described using differential equations with key system and drug-specific parameters. Transplacental drug transfer was modelled as bidirectional passive diffusion and benchmarked against those for thalidomide as a control. Model-predictions for pharmacokinetic parameters during pregnancy were within acceptable ranges for qualification (two-fold difference of clinically-observed values). Predicted foetal exposure to thalidomide was higher than efavirenz, with median (range) foetal-to-maternal plasma ratios of 4.55 (3.06–9.57) for 400 mg thalidomide versus 0.89 (0.73–1.05) for 400 mg efavirenz at third trimester. Model-predictions indicated foetal exposure consistently above 300% of maternal plasma concentration for thalidomide throughout pregnancy, while exposure to efavirenz increased from under 20% at second trimester to above 100% at third trimester. Further qualification of this approach as a tool in evaluating drug exposure and safety during pregnancy is warranted.

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.

Evaluation of Maternal Drug Exposure Following the Administration of Antenatal Corticosteroids During Late Pregnancy Using Physiologically-Based Pharmacokinetic Modeling

Betamethasone and dexamethasone are the most widely studied antenatal corticosteroids (ACS) administered to pregnant women, just prior to the birth of a preterm neonate, to accelerate fetal lung maturation. Although betamethasone, predominantly used in developed countries, has been shown to be an effective and safe intervention for reducing neonatal mortality, the choice of ACS and optimal dosing in low and middle income countries (LMICs) remains unclear. This is primarily because the exposure-response relationships have not been established for ACS despite the long history of use. As the first step toward the optimal use of ACS in LMICs, we developed physiologically-based pharmacokinetic (PBPK) models to describe the kinetics of ACS following i.v., p.o., or i.m. dosing. In vitro data describing the cytochrome P450 3A4 enzyme contribution were incorporated and this was refined using clinical data. The models can be applied prospectively to predict kinetics of ACS in pregnant women receiving various dosing regimens.

Empirical models for anatomical and physiological changes in a human mother and fetus during pregnancy and gestation

Many parameters treated as constants in traditional physiologically based pharmacokinetic models must be formulated as time-varying quantities when modeling pregnancy and gestation due to the dramatic physiological and anatomical changes that occur during this period. While several collections of empirical models for such parameters have been published, each has shortcomings. We sought to create a repository of empirical models for tissue volumes, blood flow rates, and other quantities that undergo substantial changes in a human mother and her fetus during the time between conception and birth, and to address deficiencies with similar, previously published repositories. We used maximum likelihood estimation to calibrate various models for the time-varying quantities of interest, and then used the Akaike information criterion to select an optimal model for each quantity. For quantities of interest for which time-course data were not available, we constructed composite models using percentages and/or models describing related quantities. In this way, we developed a comprehensive collection of formulae describing parameters essential for constructing a PBPK model of a human mother and her fetus throughout the approximately 40 weeks of pregnancy and gestation. We included models describing blood flow rates through various fetal blood routes that have no counterparts in adults. Our repository of mathematical models for anatomical and physiological quantities of interest provides a basis for PBPK models of human pregnancy and gestation, and as such, it can ultimately be used to support decision-making with respect to optimal pharmacological dosing and risk assessment for pregnant women and their developing fetuses. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Mathematical modeling in cancer nanomedicine: a review

Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

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.

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.