A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy

Physiologically Based Pharmacokinetic Modeling in Pregnancy, during Lactation and in Neonates: Achievements, Challenges and Future Directions

Obstetric subjects represent a special population in pharmacology [...].

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.

Clinical trial simulation to evaluate tenofovir disoproxil fumarate/emtricitabine HIV pre-exposure prophylaxis dosing during pregnancy

Objective

To evaluate upward-adjustment of tenofovir disoproxil fumarate (TDF)/emtricitabine (FTC) pre-exposure prophylaxis (PrEP) dosing during pregnancy in order to maintain target plasma concentrations associated with HIV protection.

Design

Population pharmacokinetic (PK) modeling and clinical trial simulation (CTS).

Material and methods

We developed population pharmacokinetic models for TFV and FTC using data from the Partners Demonstration Project and a PK study of TDF/FTC among cisgender women by Coleman et al., and performed an in-silico simulation. Pregnancy-trimester was identified as a significant covariate on apparent clearance in the optimized final model. We simulated 1,000 pregnant individuals starting standard daily oral TDF/FTC (300 mg/200 mg) prior to pregnancy. Upon becoming pregnant, simulated patients were split into two study arms: one continuing standard-dose and the other receiving double standard-dose throughout pregnancy.

Results

Standard-dose trough TFV concentrations were significantly lower in pregnancy compared to pre-pregnancy, with 34.0%, 43.8%, and 65.1% of trough plasma concentrations below the lower bound of expected trough concentrations presumed to be the protective threshold in the 1st, 2nd, and 3rd trimesters, respectively. By comparison, in the simulated double-dose group, 10.7%, 14.4%, and 27.8% of trough concentrations fell below the estimated protective thresholds in the 1st, 2nd, and 3rd trimesters, respectively. The FTC trough plasma concentration during pregnancy was also lower than pre-pregnancy, with 45.2% of the steady-state trough concentrations below the estimated protective trough concentrations of FTC. In the pregnancy-adjusted double-dose group, 24.1% of trough plasma concentrations were lower than protective levels.

Conclusions

Our simulation shows >50% of research participants on standard dosing would have 3rd trimester trough plasma TFV concentrations below levels associated with protection. This simulation provides the quantitative basis for the design of prospective TDF/FTC studies during pregnancy to evaluate the safety and appropriateness of pregnancy-adjusted dosing.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Minimal physiologically-based hybrid model of pharmacokinetics in pregnant women: Application to antenatal corticosteroids

Minimal physiologically-based pharmacokinetic (mPBPK) models are an alternative to full physiologically-based pharmacokinetic (PBPK) models as they offer reduced complexity while maintaining the physiological interpretation of key model components. Full PBPK models have been developed for pregnancy, but a mPBPK model eases the ability to perform a "top-down" meta-analysis melding all available pharmacokinetic (PK) data in the mother and fetus. Our hybrid mPBPK model consists of mPBPK models for the mother and fetus with connection by the placenta. This model was applied to describe the rich PK data of antenatal corticosteroid betamethasone (BET) jointly with the limited data for dexamethasone (DEX) in the mother and fetus. Physiologic model parameters were obtained from the literature while drug-dependent parameters were estimated by the simultaneous fitting of all available data for DEX and BET. Maternal clearances of DEX and BET confirmed the literature values, and the expected fetal-to-maternal plasma ratios ranged from 0.3 to 0.4 for both drugs. Simulations of maternal plasma concentrations for the dosing regimens of BET and DEX recommended by the World Health Organization based on our findings revealed up to 60% lower exposures than found in nonpregnant women and offers a means of devising alternative dosing regimens. Our hybrid mPBPK model and meta-analysis approach could facilitate assessment of other classes of drugs indicated for the treatment of pregnant women.

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.

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.

Opportunities and Challenges for PBPK Model of mAbs in Paediatrics and Pregnancy

New drugs may in some cases need to be tested in paediatric and pregnant patients. However, it is difficult to recruit such patients and there are many ethical issues around their inclusion in clinical trials. Modelling and simulation can help to plan well-designed clinical trials with a reduced number of participants and to bridge gaps where recruitment is difficult. Physiologically based pharmacokinetic (PBPK) models for small molecule drugs have been used to aid study design and dose adjustments in paediatrics and pregnancy, with several publications in the literature. However, published PBPK models for monoclonal antibodies (mAb) in these populations are scarce. Here, the current status of mAb PBPK models in paediatrics and pregnancy is discussed. Seven mAb PBPK models published for paediatrics were found, which report good prediction accuracy across a wide age range. No mAb PBPK models for pregnant women have been published to date. Current challenges to the development of such PBPK models are discussed, including gaps in our knowledge of relevant physiological processes and availability of clinical data to verify models. As the availability of such data increases, it will help to improve our confidence in the PBPK model predictive ability. Advantages for using PBPK models to predict mAb PK in paediatrics and pregnancy are discussed. For example, the ability to incorporate ontogeny and gestational changes in physiology, prediction of maternal, placental and foetal exposure and the ability to make predictions from in vitro and preclinical data prior to clinical data being available.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background

Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor.

Objective

The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure.

Methods

Physiologically based pharmacokinetic models were developed in PK-Sim^® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development.

Results

The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species.

Conclusions

Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00896-w) contains supplementary material, which is available to authorized users.

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 in pregnancy: Model reproducibility and external validation

AIMS

Physiologically based pharmacokinetic (PBPK) models have been previously developed for betamethasone and buprenorphine for pregnant women. The goal of this work was to replicate and reassess these models using data from recently completed studies.

METHODS

Betamethasone and buprenorphine PBPK models were developed in Simcyp V19 based on prior publications using V17 and V15. Ability to replicate models was verified by comparing predictions in V19 to those previously published. Once replication was verified, models were reassessed by comparing predictions to observed data from additional studies in pregnant women. Model performance was based upon visual inspection of concentration vs. time profiles, and comparison of pharmacokinetic parameters. Models were deemed reproducible if parameter estimates were within 10% of previously reported values. External validations were considered acceptable if the predicted area under the concentration-time curve (AUC) and peak plasma concentration fell within 2-fold of the observed.

RESULTS

The betamethasone model was successfully replicated using Simcyp V19, with ratios of reported (V17) to reproduced (V19) peak plasma concentration of 0.98-1.04 and AUC of 0.95-1.07. The model-predicted AUC ratios ranged from 0.98-1.79 compared to external data. The previously published buprenorphine PBPK model was not reproducible, as we predicted intravenous clearance of 70% that reported previously (both in Simcyp V15).

CONCLUSION

While high interstudy variability was observed in the newly available clinical data, the PBPK model sufficiently predicted changes in betamethasone exposure across gestation. Model reproducibility and reassessment with external data are important for the advancement of the discipline. PBPK modelling publications should contain sufficient detail and clarity to enable reproducibility.

Estimating fetal exposure to the P-gp substrates, corticosteroids, by PBPK modeling to inform prevention of neonatal respiratory distress syndrome

We have previously developed a maternal-fetal physiologically-based pharmacokinetic (m-f PBPK) model to dynamically predict (and verify) fetal-maternal exposure to drugs that passively diffuse across the placenta. Here, we extended the application of this model to dynamically predict fetal exposure to drugs which are effluxed by placental P-glycoprotein, namely the antenatal corticosteroids (ACS; dexamethasone [DEX], and betamethasone [BET]). To do so, we estimated both the placental P-gp mediated efflux clearance (CL) and the passive diffusion CL of the ACS. The efficacy and toxicity of the currently used maternal ACS dosing regimens to prevent neonatal respiratory distress syndrome could be improved by altering their dosing regimens. Therefore, to illustrate the utility of our m-f PBPK model, we used it to design alternative dosing regimens of DEX and BET that could potentially improve their efficacy and reduce their toxicity. The redesigned dosing regimens are convenient to administer, maintain maternal-fetal exposure (area under the concentration-time curve [AUC]) or maximum plasma concentration (C_max ) or both (DEX and BET) or minimize maternal exposure while maintaining fetal drug plasma concentrations above the minimum therapeutic threshold of 1 ng/ml for 48 h (BET only; based on efficacy data in sheep). To our knowledge, this is the first study to dynamically predict fetal plasma concentrations of placental P-gp effluxed drugs. Our approach and our m-f PBPK model could be used in the future to predict maternal-fetal exposure to any drug and to design alternative dosing regimens of the drug.

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

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

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

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.

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.

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.

Regulatory Considerations for the Mother, Fetus and Neonate in Fetal Pharmacology Modeling

The regulatory framework for considering the fetal effects of new drugs is limited. This is partially due to the fact that pediatric regulations (21 CFR subpart D) do not apply to the fetus, and only US Health and Human Service (HHS) regulations apply to the fetus. The HHS regulation 45 CFR Part 46 Subpart B limits research approvable by an institutional review board to research where the risk to the fetus is minimal unless the research holds out the prospect of a direct benefit to the fetus or the pregnant woman (45 CFR 46.204). Research that does not meet these requirements, but presents an opportunity to understand, prevent, or alleviate a serious problem affecting the health of pregnant women, fetuses, or neonates, may be permitted by the Secretary of the HHS after expert panel consultation and opportunity for public review and comment (45 CFR 46.407). If the product is regulated by the US Food and Drug Administration (FDA), FDA may get involved in the review process. The FDA does however have a Reviewer Guidance on Evaluating the Risks of Drug Exposure in Human Pregnancies from 2005 and this guidance does discuss the intensity of drug exposure. Estimation of that exposure using physiologically based pharmacokinetic (PBPK) modeling has been suggested by some investigators. Given that drug exposure during pregnancy will impact the fetus, a number of new guidances in the last 2 years also address inclusion of pregnant women in clinical drug trials. Therefore, the drug-specific information on fetal pharmacology will increase dramatically in the next decade due to interest in drugs administered in pregnancy and with the assistance of model-informed drug development.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

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.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Current trends in drug metabolism and pharmacokinetics

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.

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

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

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.

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.

Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women

BACKGROUND

Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks.

OBJECTIVES

The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound (f _u) could be estimated in pregnant women using a proposed scaling approach.

METHODS

Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim^®/MoBi^®. This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f _u in pregnancy was evaluated by comparing the predicted f _u with experimentally observed f _u values of 32 drugs taken from the literature.

RESULTS

The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f _u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α₁-acidic glycoprotein.

CONCLUSION

Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population.

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations

BACKGROUND AND OBJECTIVES

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations.

METHODS

A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation.

RESULTS

The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time-concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments.

CONCLUSIONS

The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

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.

Development of a Physiologically Based Pharmacokinetic Modelling Approach to Predict the Pharmacokinetics of Vancomycin in Critically Ill Septic Patients

BACKGROUND AND OBJECTIVES

Sepsis is characterised by an excessive release of inflammatory mediators substantially affecting body composition and physiology, which can be further affected by intensive care management. Consequently, drug pharmacokinetics can be substantially altered. This study aimed to extend a whole-body physiologically based pharmacokinetic (PBPK) model for healthy adults based on disease-related physiological changes of critically ill septic patients and to evaluate the accuracy of this PBPK model using vancomycin as a clinically relevant drug.

METHODS

The literature was searched for relevant information on physiological changes in critically ill patients with sepsis, severe sepsis and septic shock. Consolidated information was incorporated into a validated PBPK vancomycin model for healthy adults. In addition, the model was further individualised based on patient data from a study including ten septic patients treated with intravenous vancomycin. Models were evaluated comparing predicted concentrations with observed patient concentration-time data.

RESULTS

The literature-based PBPK model correctly predicted pharmacokinetic changes and observed plasma concentrations especially for the distribution phase as a result of a consideration of interstitial water accumulation. Incorporation of disease-related changes improved the model prediction from 55 to 88% within a threshold of 30% variability of predicted vs. observed concentrations. In particular, the consideration of individualised creatinine clearance data, which were highly variable in this patient population, had an influence on model performance.

CONCLUSION

PBPK modelling incorporating literature data and individual patient data is able to correctly predict vancomycin pharmacokinetics in septic patients. This study therefore provides essential key parameters for further development of PBPK models and dose optimisation strategies in critically ill patients with sepsis.

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

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Physiologically Based Pharmacokinetic Prediction of Linezolid and Emtricitabine in Neonates and Infants

INTRODUCTION

Modeling and simulation approaches are increasingly being utilized in pediatric drug development. Physiologically based pharmacokinetic (PBPK) modeling offers an enhanced ability to predict age-related changes in pharmacokinetics in the pediatric population.

METHODS

In the current study, adult PBPK models were developed for the renally excreted drugs linezolid and emtricitabine. PBPK models were then utilized to predict pharmacokinetics in pediatric patients for various age groups from the oldest to the youngest patients in a stepwise approach.

RESULTS

Pharmacokinetic predictions for these two drugs in the pediatric population, including infants and neonates, were within a twofold range of clinical observations. Based on this study, linezolid and emtricitabine pediatric PBPK models incorporating the ontogeny in renal maturation describe the pharmacokinetic differences between adult and pediatric populations, even though the contribution of renal clearance to the total clearance of two drugs was very different (30 % for linezolid vs. 86 % for emtricitabine).

CONCLUSION

These results suggest that PBPK modeling may provide one option to help predict the pharmacokinetics of renally excreted drugs in neonates and infants.

Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals

Background

Because of the vulnerability and frailty of elderly adults, clinical drug development has traditionally been biased towards young and middle-aged adults. Recent efforts have begun to incorporate data from paediatric investigations. Nevertheless, the elderly often remain underrepresented in clinical trials, even though persons aged 65 years and older receive the majority of drug prescriptions. Consequently, a knowledge gap exists with regard to pharmacokinetic (PK) and pharmacodynamic (PD) responses in elderly subjects, leaving the safety and efficacy of medicines for this population unclear.

Objectives

The goal of this study was to extend a physiologically based pharmacokinetic (PBPK) model for adults to encompass the full course of healthy aging through to the age of 100 years, to support dose selection and improve pharmacotherapy for the elderly age group.

Methods

For parameterization of the PBPK model for healthy aging individuals, the literature was scanned for anthropometric and physiological data, which were consolidated and incorporated into the PBPK software PK-Sim^®. Age-related changes that occur from 65 to 100 years of age were the main focus of this work. For a sound and continuous description of an aging human, data on anatomical and physiological changes ranging from early adulthood to old age were included. The capability of the PBPK approach to predict distribution and elimination of drugs was verified using the test compounds morphine and furosemide, administered intravenously. Both are cleared by a single elimination pathway. PK parameters for the two compounds in younger adults and elderly individuals were obtained from the literature. Matching virtual populations—with regard to age, sex, anthropometric measures and dosage—were generated. Profiles of plasma drug concentrations over time, volume of distribution at steady state (V_ss) values and elimination half-life (t_½) values from the literature were compared with those predicted by PBPK simulations for both younger adults and the elderly.

Results

For most organs, the age-dependent information gathered in the extensive literature analysis was dense. In contrast, with respect to blood flow, the literature study produced only sparse data for several tissues, and in these cases, linear regression was required to capture the entire elderly age range. On the basis of age-informed physiology, the predicted PK profiles described age-associated trends well. The root mean squared prediction error for the prediction of plasma concentrations of furosemide and morphine in the elderly were improved by 32 and 49 %, respectively, by use of age-informed physiology. The majority of the individual V_ss and t_½ values for the two model compounds, furosemide and morphine, were well predicted in the elderly population, except for long furosemide half-lifes.

Conclusion

The results of this study support the feasibility of using a knowledge-driven PBPK aging model that includes the elderly to predict PK alterations throughout the entire course of aging, and thus to optimize drug therapy in elderly individuals. These results indicate that pharmacotherapy and safety-related control of geriatric drug therapy regimens may be greatly facilitated by the information gained from PBPK predictions.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-016-0422-3) contains supplementary material, which is available to authorized users.

Maternal use of drug substrates of placental transporters and the effect of transporter-mediated drug interactions on the risk of congenital anomalies

BACKGROUND

A number of transporter proteins are expressed in the placenta, and they facilitate the placental transfer of drugs. The inhibition of P-glycoprotein (P-gp) was previously found to be associated with an increase in the risk of congenital anomalies caused by drug substrates of this transporter. We now explore the role of other placental transporter proteins.

METHODS

A population-based case-referent study was performed using cases with congenital anomalies (N = 5,131) from EUROCAT Northern Netherlands, a registry of congenital anomalies. The referent population (N = 31,055) was selected from the pregnancy IADB.nl, a pharmacy prescription database.

RESULTS

Ten placental transporters known to have comparable expression levels in the placenta to that of P-gp, were selected in this study. In total, 147 drugs were identified to be substrates, inhibitors or inducers, of these transporters. Fifty-eight of these drugs were used by at least one mother in our cases or referent population, and 28 were used in both. The highest user rate was observed for the substrates of multidrug resistance-associated protein 1, mainly folic acid (6% of cases, 8% of referents), and breast cancer resistance protein, mainly nitrofurantoin (2.3% of cases, 2.9% of referents). In contrast to P-gp, drug interactions involving substrates of these transporters did not have a significant effect on the risk of congenital anomalies.

CONCLUSIONS

Some of the drugs which are substrates or inhibitors of placental transporters were commonly used during pregnancy. No significant effect of transporter inhibition was found on fetal drug exposure, possibly due to a limited number of exposures.

Physiologically based pharmacokinetic models of small molecules and therapeutic antibodies: a mini-review on fundamental concepts and applications

The mechanisms of absorption, distribution, metabolism and elimination of small and large molecule therapeutics differ significantly from one another and can be explored within the framework of a physiologically based pharmacokinetic (PBPK) model. This paper briefly reviews fundamental approaches to PBPK modeling, in which drug kinetics within tissues and organs are explicitly represented using physiologically meaningful parameters. The differences in PBPK models applied to small/large molecule drugs are highlighted, thus elucidating differences in absorption, distribution and elimination properties between these two classes of drugs in a systematic manner. The absorption of small and large molecules differs with respect to their common extravascular routes of delivery (oral versus subcutaneous). The role of the lymphatic system in drug distribution, and the involvement of tissues as sites of elimination (through catabolism and target mediated drug disposition) are unique features of antibody distribution and elimination that differ from small molecules, which are commonly distributed into the tissues but are eliminated primarily by liver metabolism. Fundamental differences exist in the ability to predict human pharmacokinetics based upon preclinical data due to differing mechanisms governing small and large molecule disposition. These differences have influence on the evolving utilization of PBPK modeling in the discovery and development of small and large molecule therapeutics.

The Promises of Quantitative Proteomics in Precision Medicine

Precision medicine approach has a potential to ensure optimum efficacy and safety of drugs at individual patient level. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models could play a significant role in precision medicine by predicting interindividual variability in drug disposition and response. In order to develop robust PBPK/PD models, it is imperative that the critical physiological parameters affecting drug disposition and response and their variability are precisely characterized. Currently used PBPK/PD modeling software, for example, Simcyp and Gastroplus, encompass information such as organ volumes, blood flows to organs, body fat composition, glomerular filtration rate, etc. However, the information on the interindividual variability of the majority of the proteins associated with PK and PD, for example, drug metabolizing enzymes, transporters, and receptors, are not fully incorporated into these PBPK modeling platforms. Such information is significant because the population factors such as age, genotype, disease, and gender can affect abundance or activity of these proteins. To fill this critical knowledge gap, mass spectrometry-based quantitative proteomics has emerged as an important technique to characterize interindividual variability in the protein abundance of drug metabolizing enzymes, transporters, and receptors. Integration of these quantitative proteomics data into in silico PBPK/PD modeling tools will be crucial toward precision medicine.

Population Pharmacokinetics of Rifampin in Pregnant Women with Tuberculosis and HIV Coinfection in Soweto, South Africa

Effective treatment of tuberculosis during pregnancy is essential for preventing maternal and fetal mortality, but little is known about the effects of pregnancy on the disposition of antituberculosis drugs. We explored the effects of pregnancy on the pharmacokinetics of rifampin, the key sterilizing drug in tuberculosis treatment, in Tshepiso, a prospective cohort study involving pregnant HIV-infected women with or without tuberculosis in Soweto, South Africa. Participants receiving standard first-line tuberculosis treatment underwent sparse sampling for rifampin at 37 weeks' gestation or delivery and then postpartum. Cord blood was collected when possible. A population pharmacokinetic model was developed to investigate the effects of pregnancy on rifampin pharmacokinetics. Among the 48 participants, median age and weight were 28 years and 67 kg, respectively. A one-compartment model with first-order elimination, transit compartment absorption, and allometric scaling described the data well. Pregnancy reduced rifampin clearance by 14%. The median (interquartile range) model-estimated rifampin area under the concentration-time curve over 24 h (AUC0-24) during pregnancy or intrapartum was 40.8 h · mg/liter (27.1 to 54.2 h · mg/liter) compared to 37.4 h · mg/liter (26.8 to 50.3 h · mg/liter) postpartum. The maximum concentrations were similar during pregnancy and postpartum. Rifampin was detectable in 36% (8/22) of cord blood samples, and 88% (42/48) of the women had successful treatment outcomes. There was one case of perinatal tuberculosis. In conclusion, rifampin clearance is modestly reduced during the last trimester of pregnancy. Exposures are only slightly increased, so dose adjustment during pregnancy is not needed. Rifampin was detected in cord blood samples when delivery occurred soon after dosing. The consequences of exposure to this potent inducer of metabolizing enzymes among HIV-exposed infants are unclear.

Development of Physiologically Based Pharmacokinetic/Pharmacodynamic Model for Indomethacin Disposition in Pregnancy

Findings of a recent clinical study showed indomethacin has lower plasma levels and higher steady-state apparent clearance in pregnant subjects when compared to those in non-pregnant subjects reported in separate studies. Thus, in the current work we developed a pregnancy physiological based pharmacokinetic/pharmacodynamic (PBPK/PD) model for indomethacin to explain the differences in indomethacin pharmacokinetics between pregnancy and non-pregnancy. A whole-body PBPK model with key pregnancy-related physiological changes was developed to characterize indomethacin PK in pregnant women and compare these parameters to those in non-pregnant subjects. Data related to maternal physiological and biological changes were obtained from literature and incorporated into the structural PBPK model that describes non-pregnant PK data. Changes in indomethacin area under the curve (AUC), maximum concentration (Cmax) and average steady-state concentration (Cave) in pregnant women were predicted. Model-simulated PK profiles were in agreement with observed data. The predicted mean ratio (non-pregnant:second trimester (T2)) of indomethacin Cave was 1.6 compared to the observed value of 1.59. In addition, the predicted steady-state apparent clearance (CL/Fss) ratio was almost similar to the observed value (0.46 vs. 0.42). Sensitivity analysis suggested changes in CYP2C9 activity, and to a lesser extent UGT2B7, as the primary factor contributing to differences in indomethacin disposition between pregnancy and non-pregnancy. The developed PBPK model which integrates prior physiological knowledge, in vitro and in vivo data, allowed the successful prediction of indomethacin disposition during T2. Our PBPK/PD model suggested a higher indomethacin dosing requirement during pregnancy.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Physiologically-based pharmacokinetic modeling of renally excreted antiretroviral drugs in pregnant women

AIM

Physiological changes during pregnancy can affect drug disposition. Anticipating these changes will help to maximize drug efficacy and safety in pregnant women. Our objective was to determine if physiologically-based pharmacokinetics (PBPK) can accurately predict changes in the disposition of renally excreted antiretroviral drugs during pregnancy.

METHODS

Whole body PBPK models were developed for three renally excreted antiretroviral drugs, tenofovir (TFV), emtricitabine (FTC) and lamivudine (3TC). To assess the impact of pregnancy on PK, time-varying pregnancy-related physiological parameters available within the p-PBPK Simcyp software package were used. Renal clearance during pregnancy followed glomerular filtration changes with or without alterations in secretion. PK profiles were simulated and compared with observed data, i.e. area under the curves (AUC), peak plasma concentrations (Cmax ) and oral clearances (CL/F).

RESULTS

PBPK models successfully predicted TFV, FTC and 3TC disposition for non-pregnant and pregnant populations. Both renal secretion and filtration changed during pregnancy. Changes in renal clearance secretion were related to changes in renal plasma flow. The maximum clearance increases were approximately 30% (TFV 33%, FTC 31%, 3TC 29%).

CONCLUSIONS

Pregnancy PBPK models are useful tools to quantify a priori the drug exposure changes during pregnancy for renally excreted drugs. These models can be applied to evaluate alternative dosing regimens to optimize drug therapy during pregnancy.