Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women

Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet

In utero and children's exposure to per- and polyfluoroalkyl substances (PFAS) is a major concern in health risk assessment as early life exposures are suspected to induce adverse health effects. Our work aims to estimate children's exposure (from birth to 12 years old) to PFOA and PFOS, using a Physiologically-Based Pharmacokinetic (PBPK) modelling approach. A model for PFAS was updated to simulate the internal PFAS exposures during the in utero life and childhood, and including individual characteristics and exposure scenarios (e.g., duration of breastfeeding, weight at birth, etc.). Our approach was applied to the HELIX cohort, involving 1,239 mother-child pairs with measured PFOA and PFOS plasma concentrations at two sampling times: maternal and child plasma concentrations (6 to 12 y.o). Our model predicted an increase in plasma concentrations during fetal development and childhood until 2 y.o when the maximum concentrations were reached. Higher plasma concentrations of PFOA than PFOS were predicted until 2 y.o, and then PFOS concentrations gradually became higher than PFOA concentrations. From 2 to 8 y.o, mean concentrations decreased from 3.1 to 1.88 µg/L or ng/mL (PFOA) and from 4.77 to 3.56 µg/L (PFOS). The concentration-time profiles vary with the age and were mostly influenced by in utero exposure (on the first 4 months after birth), breastfeeding (from 5 months to 2 (PFOA) or 5 (PFOS) y.o of the children), and food intake (after 3 (PFOA) or 6 (PFOS) y.o of the children). Similar measured biomarker levels can correspond to large differences in the simulated internal exposures, highlighting the importance to investigate the children's exposure over the early life to improve exposure classification. Our approach demonstrates the possibility to simulate individual internal exposures using PBPK models when measured biomarkers are scarce, helping risk assessors in gaining insight into internal exposure during critical windows, such as early life.

Physiologically-Based Pharmacokinetic Modeling and Dosing Optimization of Cefotaxime in Preterm and Term Neonates

BACKGROUND

Cefotaxime is commonly used in treating bacterial infections in neonates. To characterize the pharmacokinetic process in neonates and evaluate different recommended dosing schedules of cefotaxime, a physiologically-based pharmacokinetic (PBPK) model of cefotaxime was established in adults and scaled to neonates.

METHODS

A whole-body PBPK model was built in PK-SIM® software. Three elimination pathways are composed of enzymatic metabolism in the liver, passive filtration through glomerulus, and active tubular secretion mediated by renal transporters. The ontogeny information was applied to account for age-related changes in cefotaxime pharmacokinetics. The established models were verified with realistic clinical data in adults and pediatric populations. Simulations in neonates were conducted and 100 % of the dosing interval where the unbound concentration in plasma was above the minimum inhibitory concentration (fT>MIC) was selected as the target index for dosing regimen evaluation.

RESULTS

The developed PBPK models successfully described the pharmacokinetic process of cefotaxime in adults and were scaled to the pediatric population. Good verification results were achieved in both adults' and neonates' PBPK models, indicating a good predictive performance. The optimal dosage regimen of cefotaxime was proposed according to the postnatal age (PNA) and gestational age (GA) of neonates. For preterm neonates (GA < 36 weeks), dosages of 25 mg/kg every 8 h in PNA 0-6 days and 25 mg/kg every 6 h in PNA 7-28 days were suggested. For term neonates (GA ≥ 36 weeks), dosages of 33 mg/kg every 8 h in PNA 0-6 days and 33 mg/kg every 6 h in PNA 7-28 days were recommended.

CONCLUSIONS

Our study may provide useful experience in practicing PBPK model-informed precision dosing in the pediatric population.

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

BACKGROUND AND OBJECTIVE

Physiologically based pharmacokinetic (PBPK) models for pregnant women have recently been successfully used to predict maternal and umbilical cord pharmacokinetics (PK). Because there is very limited opportunity for conducting clinical and PK investigations for fetal drug exposure, PBPK models may provide further insights. The objectives of this study were to extend a whole-body pregnancy PBPK model by multiple compartments representing fetal organs, and to predict the PK of cefuroxime in the maternal and fetal plasma, the amniotic fluid, and several fetal organs.

METHODS

To this end, a previously developed pregnancy PBPK model for cefuroxime was updated using the open-source software Open Systems Pharmacology (PK-Sim®/MoBi®). Multiple compartments were implemented to represent fetal organs including brain, heart, liver, lungs, kidneys, the gastrointestinal tract (GI), muscles, and fat tissue, as well as another compartment lumping organs and tissues not explicitly represented.

RESULTS

This novel PBPK model successfully predicted cefuroxime concentrations in maternal blood, umbilical cord, amniotic fluid, and several fetal organs including heart, liver, and lungs. Further model validation with additional clinical PK data is needed to build confidence in the model.

CONCLUSIONS

Being developed with an open-source software, the presented generic model can be freely re-used and tailored to address specific questions at hand, e.g., to assist the design of clinical studies in the context of drug research or to predict fetal organ concentrations of chemicals in the context of fetal health risk assessment.

Physiologically-Based Pharmacokinetic Modeling of Anti-Tumor Necrosis Factor Agents for Inflammatory Bowel Disease Patients to Predict the Withdrawal Time in Pregnancy and Vaccine Time in Infants

Anti-tumor necrosis factor (anti-TNF) agents are widely applied for patients with inflammatory bowel disease (IBD); however, the timing of the last dosing for IBD pregnancy and time to elimination in anti-TNF agent-exposed infants is controversial. This study aimed to determine the optimal timing for the last dosing of anti-TNF agents (infliximab, adalimumab, and golimumab) in pregnant women with IBD, as well as to investigate the recommended vaccine schedules for infants exposed to these drugs. A physiologically-based pharmacokinetic (PBPK) model of anti-TNF agents was built for adults and extrapolated to pregnant patients, fetuses, and infants. The PBPK models successfully predicted and verified the pharmacokinetics (PKs) of infliximab, adalimumab, and golimumab in pregnancy, fetuses, and infants. The predicted PK data were within two-fold of the observed data. The simulated results were used as timing advice. According to the dose of administration, the suggested timing of the last dosing for infliximab, adalimumab, and golimumab is successfully provided based on PBPK predictions. PBPK models indicated that, for infants, the advocated timing of vaccination is 12, 8, and 5 months after birth for infliximab, adalimumab, and golimumab, respectively. Our study illustrated that PBPK models can provide a valuable tool to predict the PKs of large macromolecules in pregnant women, fetuses, and infants, ultimately informing drug-treatment decisions for pregnancy and vaccination regimens for infants.

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

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

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.

Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

Application of PBPK modeling in predicting maternal and fetal pharmacokinetics of levetiracetam during pregnancy

Levetiracetam is currently being used to treat epilepsy in pregnant women. The plasma concentration of levetiracetam drops sharply during pregnancy, and the inability of pregnant women to maintain therapeutic concentrations can lead to seizures. This study aimed to predict the changes in fetal and maternal plasma exposure to levetiracetam during pregnancy and provide advice on dose adjustment. The physiology-based pharmacokinetics (PBPK) model was developed using PK-Sim and Mobi software, and validated following comparison of the observed plasma concentration and pharmacokinetic parameters. The levetiracetam PBPK model for mother and the fetus at various stages of pregnancy was successfully established and verified. Predictions indicated that the area under the steady-state concentration-time curve for levetiracetam decreased to 83, 62, and 67% of baseline values in the first, second, and third trimesters, respectively. Based on PBPK predictions, the recommended dose of levetiracetam is 1.2, 1.6, and 1.5 times the baseline dose in the first, second, and third trimesters, respectively, not exceeding 4000 mg/day in the third trimester due to fetal safety. The levetiracetam PBPK model for pregnancy was successfully developed and validated, and could provide alternative levetiracetam dosing regimens across the stages of pregnancy.

Physiologically-based pharmacokinetic modeling of remdesivir and its metabolites in pregnant women with COVID-19

Pregnant individuals are at high risk for severe illness from COVID-19, and there is an urgent need to identify safe and effective therapeutics for this population. Remdesivir (RDV) is a SARS-CoV-2 nucleotide analog RNA polymerase inhibitor. Limited RDV pharmacokinetic (PK) and safety data are available for pregnant women receiving RDV. The aims of this study were to translate a previously published nonpregnant adult physiologically based PK (PBPK) model for RDV to pregnancy and evaluate model performance with emerging clinical PK data in pregnant women with COVID-19. The pregnancy model was built in the Open Systems Pharmacology software suite (Version 10) including PK-Sim® and MoBi® with pregnancy-related changes of relevant enzymes applied. PK were predicted in a virtual population of 1000 pregnant subjects, and prediction results were compared with in vivo PK data from the International Maternal, Pediatric, Adolescent AIDS Clinical Trials (IMPAACT) Network  2032 study. The developed PBPK model successfully captured RDV and its metabolites' plasma concentrations during pregnancy. The ratios of prediction versus observation for RDV area under the curve from time 0 to infinity (AUC0-∞ ) and maximum concentration (Cmax ) were 1.61 and 1.17, respectively. For GS-704277, the ratios of predicted versus observed were 0.94 for AUC0-∞ and 1.20 for Cmax . For GS-441524, the ratios of predicted versus observed were 1.03 for AUC0-24 , 1.05 for Cmax , and 1.07 for concentrations at 24 h. All predictions of AUC and Cmax for RDV and its metabolites were within a twofold error range, and about 60% of predictions were within a 10% error range. These findings demonstrate the feasibility of translating PBPK models to pregnant women to potentially guide trial design, clinical decision making, and drug development.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Maternal Pharmacokinetics and Fetal Exposure to Oxcarbazepine

Pregnancy is associated with physiological changes that may affect drug pharmacokinetics (PKs). The aim of this study was to establish a maternal-fetal physiologically based pharmacokinetic (PBPK) model of oxcarbazepine (OXC) and its active metabolite, 10,11-dihydro-10-hydroxy-carbazepine (MHD), to (1) assess differences in pregnancy, (2) predict changes in PK target parameters of these molecules following the current dosing regimen, (3) assess predicted concentrations of these molecules in the umbilical vein at delivery, and (4) compare different methods for estimating drug placental penetration. Predictions using the pregnancy PBPK model of OXC resulted in maternal concentrations within a 2-fold error, and extrapolation of the model to early-stage pregnancies indicated that changes in median PK parameters remained above target thresholds, requiring increased frequency of monitoring. The dosing simulation results suggested dose adjustment in the last two trimesters. We generally recommend that women administer ≥ 1.5× their baseline dose of OXC during their second and third trimesters. Test methods for predicting placental transfer showed varying performance, with the in vitro method showing the highest predictive accuracy. Exposure to MHD in maternal and fetal venous blood was similar. Overall, the above-mentioned models can enhance understanding of the maternal-fetal PK behavior of drugs, ultimately informing drug-treatment decisions for pregnant women and their fetuses.

Impact of pregnancy related hormones on drug metabolizing enzyme and transport protein concentrations in human hepatocytes

Pregnancy alters the disposition and exposure to multiple drugs indicated for pregnancy-related complications. Previous in vitro studies have shown that pregnancy-related hormones (PRHs) alter the expression and function of certain cytochrome P450s (CYPs) in human hepatocytes. However, the impact of PRHs on hepatic concentrations of non-CYP drug-metabolizing enzymes (DMEs) and transport proteins remain largely unknown. In this study, sandwich-cultured human hepatocytes (SCHH) from five female donors were exposed to vehicle or PRHs (estrone, estradiol, estriol, progesterone, cortisol, and placental growth hormone), administered individually or in combination, across a range of physiologically relevant PRH concentrations for 72 h. Absolute concentrations of 33 hepatic non-CYP DMEs and transport proteins were quantified in SCHH membrane fractions using a quantitative targeted absolute proteomics (QTAP) isotope dilution nanoLC-MS/MS method. The data revealed that PRHs altered the absolute protein concentration of various DMEs and transporters in a concentration-, isoform-, and hepatocyte donor-dependent manner. Overall, eight of 33 (24%) proteins exhibited a significant PRH-evoked net change in absolute protein concentration relative to vehicle control (ANOVA p &lt; 0.05) across hepatocyte donors: 1/11 UGTs (9%; UGT1A4), 4/6 other DMEs (67%; CES1, CES2, FMO5, POR), and 3/16 transport proteins (19%; OAT2, OCT3, P-GP). An additional 8 (24%) proteins (UGT1A1, UGT2B4, UGT2B10, FMO3, OCT1, MRP2, MRP3, ENT1) exhibited significant PRH alterations in absolute protein concentration within at least two individual hepatocyte donors. In contrast, 17 (52%) proteins exhibited no discernable impact by PRHs either within or across hepatocyte donors. Collectively, these results provide the first comprehensive quantitative proteomic evaluation of PRH effects on non-CYP DMEs and transport proteins in SCHH and offer mechanistic insight into the altered disposition of drug substrates cleared by these pathways during pregnancy.

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.

The Use of Pregnancy Physiologically Based Pharmacokinetic Modeling for Renally Cleared Drugs

Physiologically based pharmacokinetic modeling (PBPK) could be used to predict changes in exposure during pregnancy and possibly inform medicine use in pregnancy in situations where there are currently no available clinical data. The Medicines and Healthcare Product Regulatory Agency has been evaluating the available models for a number of medicines cleared by the kidney. Models were evaluated for ceftazidime, cefuroxime, metformin, oseltamivir, and amoxicillin. Because the passive renal process contributes significantly to the renal elimination of these drugs and changes of the process during pregnancy have been implemented in existing pregnancy physiology models, simulations using these models can reasonably describe the pharmacokinetics of ceftazidime changes during pregnancy and appears to generally capture the changes in the other medicines; however, there are insufficient data on drugs solely passively cleared to fully qualify the models. In addition, in many cases, active transport processes are involved in a drug's renal clearance. Knowledge of changes in renal transport functions during pregnancy is emerging, and incorporation of such changes in current physiologically based pharmacokinetic modeling software is a work in progress. Filling this gap is expected to further enhance predictive performance of the models and increase the confidence in predicting pharmacokinetic changes in pregnant women for other renally cleared drugs.

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.

Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine

Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.

Physiologically Based Pharmacokinetic Modeling of Meropenem in Preterm and Term Infants

BACKGROUND

Meropenem is a broad-spectrum carbapenem antibiotic approved by the US Food and Drug Administration for use in pediatric patients, including treating complicated intra-abdominal infections in infants < 3 months of age. The impact of maturation in glomerular filtration rate and tubular secretion by renal transporters on meropenem pharmacokinetics, and the effect on meropenem dosing, remains unknown. We applied physiologically based pharmacokinetic (PBPK) modeling to characterize the disposition of meropenem in preterm and term infants.

METHODS

An adult meropenem PBPK model was developed in PK-Sim® (Version 8) and scaled to infants accounting for renal transporter ontogeny and glomerular filtration rate maturation. The PBPK model was evaluated using 645 plasma concentrations from 181 infants (gestational age 23-40 weeks; postnatal age 1-95 days). The PBPK model-based simulations were performed to evaluate meropenem dosing in the product label for infants < 3 months of age treated for complicated intra-abdominal infections.

RESULTS

Our model predicted plasma concentrations in infants in agreement with the observed data (average fold error of 0.90). The PBPK model-predicted clearance in a virtual infant population was successfully able to capture the post hoc estimated clearance of meropenem in this population, estimated by a previously published model. For 90% of virtual infants, a 4-mg/L target plasma concentration was achieved for > 50% of the dosing interval following product label-recommended dosing.

CONCLUSIONS

Our PBPK model supports the meropenem dosing regimens recommended in the product label for infants <3 months of age.

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.

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

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

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

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

Mechanistic Coupling of a Novel in silico Cotyledon Perfusion Model and a Physiologically Based Pharmacokinetic Model to Predict Fetal Acetaminophen Pharmacokinetics at Delivery

Little is known about placental drug transfer and fetal pharmacokinetics despite increasing drug use in pregnant women. While physiologically based pharmacokinetic (PBPK) models can help in some cases to shed light on this knowledge gap, adequate parameterization of placental drug transfer remains challenging. A novel in silico model with seven compartments representing the ex vivo cotyledon perfusion assay was developed and used to describe placental transfer and fetal pharmacokinetics of acetaminophen. Unknown parameters were optimized using observed data. Thereafter, values of relevant model parameters were copied to a maternal-fetal PBPK model and acetaminophen pharmacokinetics were predicted at delivery after oral administration of 1,000 mg. Predictions in the umbilical vein were evaluated with data from two clinical studies. Simulations from the in silico cotyledon perfusion model indicated that acetaminophen accumulates in the trophoblasts; simulated steady state concentrations in the trophoblasts were 4.31-fold higher than those in the perfusate. The whole-body PBPK model predicted umbilical vein concentrations with a mean prediction error of 24.7%. Of the 62 concentration values reported in the clinical studies, 50 values (81%) were predicted within a 2-fold error range. In conclusion, this study presents a novel in silico cotyledon perfusion model that is structurally congruent with the placenta implemented in our maternal-fetal PBPK model. This allows transferring parameters from the former model into our PBPK model for mechanistically exploring whole-body pharmacokinetics and concentration-effect relationships in the placental tissue. Further studies should investigate acetaminophen accumulation and metabolism in the placenta as the former might potentially affect placental prostaglandin synthesis and subsequent fetal exposure.

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.

Drug development research in pregnant and lactating women

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

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

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

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.

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.

Development of a physiologically based pharmacokinetic (PBPK) population model for Chinese elderly subjects

Aims

This study aims to develop and verify a physiologically based pharmacokinetic (PBPK) population model for the Chinese geriatric population in Simcyp.

Methods

Firstly, physiological information for the Chinese geriatric population was collected and later employed to develop the Chinese geriatric population model by recalibration of corresponding physiological parameters in the Chinese adult population model available in Simcyp (i.e., Chinese healthy volunteer model). Secondly, drug‐dependent parameters were collected for six drugs with different elimination pathways (i.e., metabolized by CYP1A2, CYP3A4 or renal excretion). The drug models were then developed and verified by clinical data from Chinese adults, Caucasian adults and Caucasian elderly subjects to ensure that drug‐dependent parameters are correctly inputted. Finally, the tested drug models in combination with the newly developed Chinese geriatric population model were applied to simulate drug concentration in Chinese elderly subjects. The predicted results were then compared with the observations to evaluate model prediction performance.

Results

Ninety‐eight per cent of predicted AUC, 95% of predicted C_max, and 100% of predicted CL values were within two‐fold of the observed values, indicating all drug models were properly developed. The drug models, combined with the newly developed population model, were then used to predict pharmacokinetics in Chinese elderly subjects aged 60–93. The predicted AUC, C_max, and CL values were all within two‐fold of the observed values.

Conclusion

The population model for the Chinese elderly subjects appears to adequately predict the concentration of the drug that was metabolized by CYP1A2, CYP3A4 or eliminated by renal clearance.

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

Background and Objective

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

Methods

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

Results

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

Conclusion

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

Electronic supplementary material

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

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.

Physiologically Based Pharmacokinetic Modeling Framework to Predict Neonatal Pharmacokinetics of Transplacentally Acquired Emtricitabine, Dolutegravir, and Raltegravir

BACKGROUND AND OBJECTIVE

Little is understood about neonatal pharmacokinetics immediately after delivery and during the first days of life following intrauterine exposure to maternal medications. Our objective was to develop and evaluate a novel, physiologically based pharmacokinetic modeling workflow for predicting perinatal and postnatal disposition of commonly used antiretroviral drugs administered prenatally to pregnant women living with human immunodeficiency virus.

METHODS

Using previously published, maternal-fetal, physiologically based pharmacokinetic models for emtricitabine, dolutegravir, and raltegravir built with PK-Sim/MoBi^®, placental drug transfer was predicted in late pregnancy. The total drug amount in fetal compartments at term delivery was estimated and subsequently integrated as initial conditions in different tissues of a whole-body, neonatal, physiologically based pharmacokinetic model to predict drug concentrations in the neonatal elimination phase after birth. Neonatal elimination processes were parameterized according to published data. Model performance was assessed by clinical data.

RESULTS

Neonatal physiologically based pharmacokinetic models generally captured the initial plasma concentrations after delivery but underestimated concentrations in the terminal phase. The mean percentage error for predicted plasma concentrations was - 71.5%, - 33.8%, and 76.7% for emtricitabine, dolutegravir, and raltegravir, respectively. A sensitivity analysis suggested that the activity of organic cation transporter 2 and uridine diphosphate glucuronosyltransferase 1A1 during the first postnatal days in term newborns is ~11% and ~30% of that in adults, respectively.

CONCLUSIONS

These findings demonstrate the general feasibility of applying physiologically based pharmacokinetic models to predict washout concentrations of transplacentally acquired drugs in newborns. These models can increase the understanding of pharmacokinetics during the first postnatal days and allow the prediction of drug exposure in this vulnerable population.

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.

The Importance of Clinical Research in Pregnant Women to Inform Prescription Drug Labeling

Pregnant women have historically been an understudied population and have been excluded from clinical trials. Recent efforts by stakeholders have raised awareness of the importance of clinical research in pregnant women to inform prescribing decisions. The Food and Drug Administration continues working to improve the format and content of prescription drug labeling for pregnant and lactating women, as demonstrated with the Pregnancy and Lactation Labeling Rule (PLLR), effective in 2015. The pregnancy labeling subsection now includes a subheading dedicated to the inclusion of pharmacokinetic (PK) data that inform the need for dose adjustments during pregnancy and the postpartum period. In addition, the PLLR also requires prescription drug labeling to be updated when important pregnancy information becomes available. Although PLLR improved the presentation of pregnancy-related information in labeling, there is a need to increase the quality and quantity of human data on the use of prescription drugs during pregnancy. PK studies in pregnant women should be incorporated into drug development programs and prioritized to obtain important information about safe and appropriate doses of a drug when used during pregnancy. In addition, opportunistic PK studies, postapproval pregnancy safety studies, ex vivo studies, and in silico modeling can be leveraged to better inform the risks and benefits of using a drug during pregnancy to inform study design and to further understand various mechanisms impacting pharmacokinetic/pharmacodynamic of drugs during pregnancy. It is important to address the significant existing data gaps and better inform the safety and dosing of prescription drugs for pregnant women.

Assessing the impacts on fetal dosimetry of the modelling of the placental transfers of xenobiotics in a pregnancy physiologically based pharmacokinetic model

The developmental origin of health and diseases theory supports the critical role of the fetal exposure to children's health. We developed a physiologically based pharmacokinetic model for human pregnancy (pPBPK) to simulate the maternal and fetal dosimetry throughout pregnancy. Four models of the placental exchanges of chemicals were assessed on ten chemicals for which maternal and fetal data were available. These models were calibrated using non-animal methods: in vitro (InV) or ex vivo (ExV) data, a semi-empirical relationship (SE), or the limitation by the placental perfusion (PL). They did not impact the maternal pharmacokinetics but provided different profiles in the fetus. The PL and InV models performed well even if the PL model overpredicted the fetal exposure for some substances. The SE and ExV models showed the lowest global performance and the SE model a tendency to underprediction. The comparison of the profiles showed that the PL model predicted an increase in the fetal exposure with the pregnancy age, whereas the ExV model predicted a decrease. For the SE and InV models, a small decrease was predicted during the second trimester. All models but the ExV one, presented the highest fetal exposure at the end of the third trimester. Global sensitivity analyses highlighted the predominant influence of the placental transfers on the fetal exposure, as well as the metabolic clearance and the fraction unbound. Finally, the four transfer models could be considered depending on the framework of the use of the pPBPK model and the availability of data or resources to inform their parametrization.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Impact of Ethnicity-Specific Hepatic Microsomal Scaling Factor, Liver Weight, and Cytochrome P450 (CYP) 1A2 Content on Physiologically Based Prediction of CYP1A2-Mediated Pharmacokinetics in Young and Elderly Chinese Adults

BACKGROUND

The vast majority of physiological and biological data required for physiologically based predictions are primarily available in Caucasians rather than other ethnic populations, which leads to a lack of confidence in the application of physiologically based pharmacokinetic (PBPK) modeling for ethnicity-specific prediction of pharmacokinetics in the Chinese population.

OBJECTIVES

In this study we recalibrate the system parameters of Chinese-specific PBPK modeling and explore for the first time the relative importance of ethnicity-specific microsomal protein per gram of liver (MPPGL), liver weight, and cytochrome P450 (CYP) 1A2 abundance to the projection of drug disposition mediated by CYP1A2 in young and elderly Chinese adults.

METHODS

Chinese MPPGL levels and associated variability were parameterized and incorporated for the first time into ethnicity-specific PBPK models for the Chinese adults. Parameterization of Chinese liver weights was also recalibrated on the basis of autopsy data from Chinese individuals (n = 4081) across the entire adult age range. Uncertainty surrounding the Chinese-specific CYP1A2 content has also been explored and clarified by conducting ethnicity-related PBPK simulations under different scenarios. Various ethnicity-related or 'what-if' scenarios for PBPK modeling were implemented to assess the predictive performance and explore the relative importance of ethnicity-specific MPPGL and liver weight to the projection of drug disposition mediated by CYP1A2 in terms of two typical CYP1A2 substrates, caffeine and theophylline, in young and elderly Chinese adults by comparing the predicted concentration-time data and associated pharmacokinetic parameter estimates with observations.

RESULTS

Compared with 0.85, the liver scalar of 0.9 generally produced more accurate liver weight levels in virtual Chinese peers. Additionally, simulated MPPGL levels on the basis of Caucasian data were not able to reflect the age-independent pattern observed in Chinese adults, dissimilar to that on the basis of Chinese-specific adult MPPGL data. The modeling Scenarios A and B provided similar predictions for theophylline pharmacokinetics in young Chinese adults across different age groups, while Scenario B provided the most accurate prediction for theophylline pharmacokinetics in elderly Chinese adults. However, the use of a stratified value of CYP1A2 content derived from a Han Chinese cohort with a small sample size instead of the pooled value of all Chinese cohorts involved regardless of Chinese sub-ethnicity resulted in inadequate prediction of CYP1A2-mediated pharmacokinetics in terms of caffeine and theophylline in either young or elderly Chinese subjects. Additionally, the impact of ethnic-specific MPPGL on predictive accuracy of theophylline pharmacokinetics in elderly Chinese subjects is more evident than that of liver weight.

CONCLUSION

We provided quantitative information pertaining to Chinese-specific levels of liver weight and MPPGL, and recalibrated these system parameters for PBPK modeling for young and elderly Chinese subjects. Uncertainty surrounding the Chinese-specific CYP1A2 content has also been clarified. PBPK modeling based on the recalibrated system parameters can accurately simulate CYP1A2-mediated pharmacokinetics in both young and elderly Chinese adults, particularly in elderly individuals.

Drug Dosing Recommendations for All Patients: A Roadmap for Change

Most drug labels do not contain dosing recommendations for a significant portion of real‐world patients for whom the drug is prescribed. Current label recommendations predominately reflect the population studied in pivotal trials that typically exclude patients who are very young or old, emaciated or morbidly obese, pregnant, or have multiple characteristics likely to influence dosing. As a result, physicians may need to guess the correct dose and regimen for these patients. It is now feasible to provide dose and regimen recommendations for these patients by integrating available scientific knowledge and by utilizing or modifying current regulatory agency‐industry practices. The purpose of this commentary is to explore several factors that should be considered in creating a process that will provide more effective, safe, and timely drug dosing recommendations for most, if not all, patients. These factors include the availability of real‐world data, development of predictive models, experience with the US Food and Drug Administration (FDA)’s pediatric exclusivity program, development of clinical decision software, funding mechanisms like the Prescription Drug Users Fee Act (PDUFA), and harmonization of global regulatory policies. From an examination of these factors, we recommend a relatively simple, efficient expansion of current practices designed to predict, confirm, and continuously improve drug dosing for more patients. We believe implementing these recommendations will benefit patients, payers, industry, and regulatory agencies.

Preliminary physiologically based pharmacokinetic modeling of renally cleared drugs in Chinese pregnant women

AIM

The aim of this study was to build and verify a preliminary physiologically based pharmacokinetic (PBPK) model of Chinese pregnant women. The model was used to predict maternal pharmacokinetics (PK) of 6 predominantly renally cleared drugs.

METHOD

Based on SimCYP Caucasian pregnancy population dataset, the preliminary Chinese pregnant population was built by updating several key parameters and equations according to physiological parameters of Chinese (or Japanese) pregnant women. Drug-specific parameters of 6 renally cleared drugs were validated through PBPK modeling of Caucasian non-pregnant, Caucasian pregnant and Chinese non-pregnant population. The preliminary PBPK model of Chinese pregnant population was then developed by integrating the preliminary Chinese pregnant population and the drug-specific parameters. This model was verified by comparing the predicted maternal PK of these 6 drugs with the observed in vivo data from the literature.

RESULTS

The preliminary Chinese pregnant population PBPK model successfully predicted the PK of 6 target drugs for different pregnancy stages. The predicted plasma concentrations time profiles fitted the observed data well, and most predicted PK parameters were within 2-fold of observed data.

CONCLUSIONS

The preliminary Chinese pregnant population PBPK model provided a useful tool to predict the maternal PK of 6 predominantly renally cleared drugs in Chinese pregnant women.

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.

Pregnancy-related pharmacokinetics and antimicrobial prophylaxis during fetal surgery, cefazolin and clindamycin as examples

Antimicrobial prophylaxis during surgery aims to prevent post-operative site infections. For fetal surgery, this includes the fetal and amniotic compartments. Both are deep compartments as drug equilibrium with maternal blood is achieved relatively late. Despite prophylaxis, chorio-amnionitis or endometritis following ex utero intrapartum treatment or fetoscopy occur in 4.13% and 1.45% respectively of the interventions. This review summarizes the observations on two commonly administered antimicrobials (cefazolin, clindamycin) for surgical prophylaxis during pregnancy, with emphasis on the deep compartments. For both compounds, antimicrobial exposure is on target when we consider the maternal and fetal plasma compartment. In contrast, amniotic fluid concentrations-time profiles display a delayed and much more blunted pattern, behaving as deep compartment. For cefazolin, there are data that document further dilution in the setting of polyhydramnios. Along this deep compartment concept, there is some accumulation during repeated administration, modeled for cefazolin and observed for clindamycin. The relative underexposure to antimicrobials in amniotic fluid may be reflected in the pattern of maternal-fetal complications after fetal surgery, and suggest that antimicrobial prophylaxis practices for fetal surgery should be reconsidered. Further studies should be designed by a multidisciplinary team (fetal surgeons, clinical pharmacologists and microbiologists) to facilitate efficient evaluation of antimicrobial prophylaxis.

Prediction of Maternal and Fetal Pharmacokinetics of Dolutegravir and Raltegravir Using Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Predicting drug pharmacokinetics in pregnant women including placental drug transfer remains challenging. This study aimed to develop and evaluate maternal-fetal physiologically based pharmacokinetic models for two antiretroviral drugs, dolutegravir and raltegravir.

Clinical Pharmacokinetic Studies in Pregnant Women and the Relevance of Pharmacometric Tools

BACKGROUND

In clinical pharmacokinetic (PK) studies, pregnant women are significantly underrepresented because of ethical and legal reasons which lead to a paucity of information on potential PK changes in this population. As a consequence, pharmacometric tools became instrumental to explore and quantify the impact of PK changes during pregnancy.

METHODS

We explore and discuss the typical characteristics of population PK and physiologically based pharmacokinetic (PBPK) models with a specific focus on pregnancy and postpartum.

RESULTS

Population PK models enable the analysis of dense, sparse or unbalanced data to explore covariates in order to (partly) explain inter-individual variability (including pregnancy) and to individualize dosing. For population PK models, we subsequently used an illustrative approach with ketorolac data to highlight the relevance of enantiomer specific modeling for racemic drugs during pregnancy, while data on antibiotic prophylaxis (cefazolin) during surgery illustrate the specific characteristics of the fetal compartments in the presence of timeconcentration profiles. For PBPK models, an overview on the current status of reports and papers during pregnancy is followed by a PBPK cefuroxime model to illustrate the added benefit of PBPK in evaluating dosing regimens in pregnant women.

CONCLUSIONS

Pharmacometric tools became very instrumental to improve perinatal pharmacology. However, to reach their full potential, multidisciplinary collaboration and structured efforts are needed to generate more information from already available datasets, to share data and models, and to stimulate cross talk between clinicians and pharmacometricians to generate specific observations (pathophysiology during pregnancy, breastfeeding) needed to further develop the field.

Integration of Placental Transfer in a Fetal-Maternal Physiologically Based Pharmacokinetic Model to Characterize Acetaminophen Exposure and Metabolic Clearance in the Fetus

BACKGROUND AND OBJECTIVE

Although acetaminophen is frequently used during pregnancy, little is known about fetal acetaminophen pharmacokinetics. Acetaminophen safety evaluation has typically focused on hepatotoxicity, while other events (fetal ductal closure/constriction) are also relevant. We aimed to develop a fetal-maternal physiologically based pharmacokinetic (PBPK) model (f-m PBPK) to quantitatively predict placental acetaminophen transfer, characterize fetal acetaminophen exposure, and quantify the contributions of specific clearance pathways in the term fetus.

METHODS

An acetaminophen pregnancy PBPK model was extended with a compartment representing the fetal liver, which included maturation of relevant enzymes. Different approaches to describe placental transfer were evaluated (ex vivo cotyledon perfusion experiments, placental transfer prediction based on Caco-2 cell permeability or physicochemical properties [MoBi^®]). Predicted maternal and fetal acetaminophen profiles were compared with in vivo observations.

RESULTS

Tested approaches to predict placental transfer showed comparable performance, although the ex vivo approach showed highest prediction accuracy. Acetaminophen exposure in maternal venous blood was similar to fetal venous umbilical cord blood. Prediction of fetal acetaminophen clearance indicated that the median molar dose fraction converted to acetaminophen-sulphate and N-acetyl-p-benzoquinone imine was 0.8% and 0.06%, respectively. The predicted mean acetaminophen concentration in the arterial umbilical cord blood was 3.6 mg/L.

CONCLUSION

The median dose fraction of acetaminophen converted to its metabolites in the term fetus was predicted. The various placental transfer approaches supported the development of a generic f-m PBPK model incorporating in vivo placental drug transfer. The predicted arterial umbilical cord acetaminophen concentration was far below the suggested postnatal threshold (24.47 mg/L) for ductal closure.

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.

Physiologically based pharmacokinetic evaluation of cefuroxime in perioperative antibiotic prophylaxis

Aims

Adequate plasma concentrations of antibiotics during surgery are essential for the prevention of surgical site infections. We examined the pharmacokinetics of 1.5 g cefuroxime administered during induction of anaesthesia with follow‐up doses every 2.5 hours until the end of surgery. We built a physiologically based pharmacokinetic model with the aim to ensure adequate antibiotic plasma concentrations in a heterogeneous population.

Methods

A physiologically based pharmacokinetic model (PK‐Sim^®/MoBi^®) was developed to investigate unbound plasma concentrations of cefuroxime. Blood samples from 25 thoracic surgical patients were analysed with high‐performance liquid chromatography. To evaluate optimized dosing regimens, physiologically based pharmacokinetic model simulations were conducted.

Results

Dosing simulations revealed that a standard dosing regimen of 1.5 g every 2.5 hours reached the pharmacokinetic/pharmacodynamic target for Staphylococcus aureus. However, for Escherichia coli, >50% of the study participants did not reach predefined targets. Effectiveness of cefuroxime against E. coli can be improved by administering a 1.5 g bolus immediately followed by a continuous infusion of 3 g cefuroxime over 3 hours.

Conclusion

The use of cefuroxime for perioperative antibiotic prophylaxis to prevent staphylococcal surgical site infections appears to be effective with standard dosing of 1.5 g preoperatively and follow‐up doses every 2.5 hours. In contrast, if E. coli is relevant in surgeries, this dosing regimen appears insufficient. With our derived dose recommendations, we provide a solution for this issue.

Population Pharmacokinetic Study of Cefazolin Dosage Adaptation in Bacteremia and Infective Endocarditis Based on a Nomogram

Optimal dosing of continuous-infusion cefazolin can be challenging in patients being treated for bacteremia or infective endocarditis. The aim of this work is to describe and analyze the pharmacokinetics of cefazolin in those patients using a population pharmacokinetics modeling approach and to establish a nomogram to determine the optimal daily dose. Population pharmacokinetics were modeled using the Pmetrics package for R. Plasma concentrations were collected retrospectively from patients treated with continuous-infusion cefazolin for bacteremia or infective endocarditis. The influence of multiple parameters, including renal function, total body weight, body mass index, body surface area (BSA), ideal weight, lean body weight, height, and age, was tested. The probabilities of target attainment for selected target concentrations (40, 60, and 80 mg/liter) were calculated. A dosing nomogram was then developed, using the absolute value of the glomerular filtration rate (aGFR), to determine the optimal daily dose required to achieve the target concentrations in at least 90% of patients. In total, 346 cefazolin plasma concentrations from 162 patients were collected. A one-compartment model best described the data set. The only covariate was aGFR, calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula and the patient's body surface area, for the rate of elimination. Using the nomogram, achieving a cefazolin concentration target of 40 mg/liter with a success rate of at least 90% and with an aGFR of 30, 60, 90, and 120 ml/min requires a daily dose of 2.6, 4.3, 6.1, and 8.0 g/day, respectively. These results confirm the interest of posology adaptation of cefazolin according to aGFR.

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.

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.