Administration mode matters for 5-fluorouracil therapy: Physiologically based pharmacokinetic evidence for avoidance of myelotoxicity by continuous infusion but not intravenous bolus.

AIMS: Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens. METHODS: Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant. RESULTS: The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects). CONCLUSION: A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs.

Prediction of Maternal and Fetoplacental Concentrations of Cefazolin, Cefuroxime, and Amoxicillin during Pregnancy Using Bottom-Up Physiologically Based Pharmacokinetic Models.

Concerns over maternal and fetal drug exposures highlight the need for a better understanding of drug distribution into the fetus through the placental barrier. This study aimed to predict maternal and fetal drug disposition using physiologically based pharmacokinetic (PBPK) modeling. The detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 was used to predict the maternal and fetoplacental exposure of cefazolin, cefuroxime, and amoxicillin during pregnancy and at delivery. The mechanistic dynamic model includes physiologic changes of the maternal, fetal, and placental parameters over the course of pregnancy. Placental kinetics were parametrized using permeability parameters determined from the physicochemical properties of these compounds. Then, the PBPK predictions were compared with the observed data. Fully bottom-up fetoplacental PBPK models were developed for cefuroxime, cefazolin, and amoxicillin without any parameter fitting. Predictions in nonpregnant subjects and in pregnant subjects fall within 2-fold of the observed values. Predictions matched observed pharmacokinetic data reported in nine maternal (five fetoplacental) studies for cefuroxime, 10 maternal (five fetoplacental) studies for cefazolin, and six maternal (two fetoplacental) studies for amoxicillin. Integration of the fetal and maternal system parameters within PBPK models, together with compound-related parameters used to calculate placental permeability, facilitates and extends the applications of the maternal-placental-fetal PBPK model. The developed model can also be used for designing clinical trials and prospectively used for maternal-fetal risk assessment after maternally administered drugs or unintended exposure to environmental toxicants. SIGNIFICANCE STATEMENT: This study investigates the performance of an integrated maternal-placental-fetal PBPK model to predict maternal and fetal tissue exposure of renally eliminated antibiotics that cross the placenta through a passive diffusion mechanism. The transplacental permeability clearance was predicted from the drug physicochemical properties. Results demonstrate that the PBPK approach can facilitate the prediction of maternal and fetal drug exposure simultaneously at any gestational age to support its use in the maternal-fetal exposure assessments.

Prediction of basic drug exposure in milk using a lactation model algorithm integrated within a physiologically based pharmacokinetic model.

Medication use during breastfeeding can be a matter of concern due to unintended infant exposure to drugs through breast milk. The available information relating to the safety of most medications is limited and may vary. More precise information is needed regarding the safety to the newborn or infants of the medications taken by the mother during breastfeeding. Physiologically based Pharmacokinetic Model (PBPK) approaches can be utilized to predict the drug exposure in the milk of breastfeeding women and can act as a supporting tool in the risk assessment of feeding infants. This study aims to assess the predictive performance of an integrated 'log transformed phase-distribution' lactation model within a PBPK platform. The model utilizes the physicochemical properties of four basic drugs, namely tramadol, venlafaxine, fluoxetine, and paroxetine, and analyses the milk compositions to predict the milk-to-plasma (M/P) ratio. The M/P prediction model was incorporated within the Simcyp Simulator V20 to predict the milk exposure and to estimate the likely infant dose for these drugs. The PBPK models adequately predicted the maternal plasma exposure, M/P ratio, and the infant daily dose to within two-fold of the clinically observed values for all four compounds. Integration of the lactation model within PBPK models facilitates the prediction of drug exposure in breast milk. The developed model can inform the design of lactation studies and assist with the neonatal risk assessment after maternal exposure to such environmental chemicals or basic drugs which diffuse passively into the milk.

Use of a physiologically based pharmacokinetic-pharmacodynamic model for initial dose prediction and escalation during a paediatric clinical trial.

AIMS: To build and verify a physiologically based pharmacokinetic (PBPK) model for radiprodil in adults and link this to a pharmacodynamic (PD) receptor occupancy (RO) model derived from in vitro data. Adapt this model to the paediatric population and predict starting and escalating doses in infants based on RO. Use the model to guide individualized dosing in a clinical trial in 2- to 14-month-old children with infantile spasms. METHODS: A PBPK model for radiprodil was developed to investigate the systemic exposure of the drug after oral administration in fasted and fed adults; this was then linked to RO via a PD model. The model was then expanded to include developmental physiology and ontogeny to predict escalating doses in infants that would result in a specific RO of 20, 40 and 60% based on average unbound concentration following a twice daily (b.i.d.) dosing regimen. Dose progression in the clinical trial was based on observed concentration-time data against PBPK predictions. RESULTS: For paediatric predictions, the elimination of radiprodil, based on experimental evidence, had no ontogeny. Predicted b.i.d. doses ranged from 0.04 mg/kg for 20% RO, 0.1 mg/kg for 40% RO to 0.21 mg/kg for 60% RO. For all infants recruited in the study, observed concentration-time data following the 0.04 mg/kg and subsequent doses were within the PBPK model predicted 5th and 95th percentiles. CONCLUSION: To our knowledge, this is the first time a PBPK model linked to RO has been used to guide dose selection and escalation in the live phase of a paediatric clinical trial.

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.

Prediction of maternal pharmacokinetics using physiologically based pharmacokinetic models: assessing the impact of the longitudinal changes in the activity of CYP1A2, CYP2D6 and CYP3A4 enzymes during pregnancy.

Concerns over gestational effects on the disposition of drugs has highlighted the need for a better understanding of drug distribution and elimination during pregnancy. This study aimed at predicting maternal drug kinetics using a physiologically based pharmacokinetic (PBPK) modelling approach focusing on the observed gestational changes in three important Cytochrome P450 metabolizing enzymes, namely, CYP1A2, CYP2D6 and CYP3A4 at different gestational weeks (GWs). The Pregnancy PBPK model within the Simcyp Simulator V19 was used to predict the pharmacokinetics of sensitive probes to these enzymes; namely caffeine, theophylline, metoprolol, propranolol, paroxetine, midazolam, nifedipine and rilpivirine. PBPK model predictions were compared against clinical data collated from multiple studies for each compound to cover a wide spectrum of gestational ages. Pregnancy PBPK model predictions were within 2-fold error and indicated that CYP1A2 activity is approximately 0.70, 0.44 and 0.30 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. On the other hand, CYP2D6 activity increases by 1.36, 2.16 and 3.10 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. Likewise, CYP3A4 activity increases by 1.25, 1.75 and 2.32 fold of the non-pregnant level at the end of the first, second and third trimesters, respectively. The enzymes activity have been qualified throughout pregnancy. Quantified changes in drug dosing are most relevant during the third trimester, especially for drugs that are mainly eliminated by CYP1A2, CYP2D6 and CYP3A4 enzymes. The provided functions describing the continuous changes to the activity of these enzymes during pregnancy are important when modelling long term pharmacokinetic studies where longitudinal modelling or time-varying covariates are used.

Application of a physiologically-based pharmacokinetic model for the prediction of bumetanide plasma and brain concentrations in the neonate.

Bumetanide is a loop diuretic that is proposed to possess a beneficial effect on disorders of the central nervous system, including neonatal seizures. Therefore, prediction of unbound bumetanide concentrations in the brain is relevant from a pharmacological prospective. A physiologically-based pharmacokinetic (PBPK) model was developed for the prediction of bumetanide disposition in plasma and brain in adult and paediatric populations. A compound file was built for bumetanide integrating physicochemical data and in vitro data. Bumetanide concentration profiles were simulated in both plasma and brain using the Simcyp PBPK model. Simulations of plasma bumetanide concentrations were compared against plasma levels published in the literature. The model performance was verified with data from adult studies before predictions in the paediatric population were undertaken. The adult and paediatric intravenous models predicted pharmacokinetic factors, namely area under the concentration-time curve, maximum concentration in plasma and time to maximum plasma concentration, within two-fold of observed values. However, predictions of plasma concentrations within the neonatal intravenous model did not produce a good fit with the observed values. The PBPK approach used in this study produced reasonable predictions of plasma concentrations of bumetanide, except in the critically ill neonatal population. This PBPK model requires more information regarding metabolic intrinsic clearance and transport parameters prior to further validation of drug disposition predictions in the neonatal population. Given the lack of information surrounding certain parameters in this special population, the model is not appropriately robust to support the recommendation of a suitable dose of bumetanide for use as an adjunct antiepileptic in neonates.

Considering Age Variation When Coining Drugs as High versus Low Hepatic Extraction Ratio.

The hepatic extraction ratio (EH) is commonly considered an "inherent attribute" of drug. It determines the main physiological and biological elements of the system (patient attributes) that are most significant in interindividual variability of clearance. The EH consists of three age-dependent parameters: fraction of unbound drug in blood (fu.B), hepatic intrinsic clearance of unbound drug (CLu.int,H), and hepatic blood flow (QH). When the age-effects on these elements are not proportional, a given drug may shift from so-called high extraction status to low extraction. To demonstrate the impact of age-related changes on fu.B, CLu int,H, and QH, the EH of midazolam and two hypothetical drugs with 10-fold higher and 10-fold lower CLu.int,H than midazolam were investigated in pediatrics based on known ontogeny functions. The EH was simulated using Simcyp software, version 14. This was then complemented by a comprehensive literature survey to identify the commonly applied covariates in pediatric population pharmacokinetic (PopPK) studies. Midazolam EH decreased from 0.6 in adults to 0.02 at birth, making its clearance much more susceptible to changes in CLu.int,H and fu.B than in adults and reducing the impact of QH on clearance. The drug with 10-fold higher CLu.int,H was categorized as high extraction from 4 days old onward whereas the drug with 10-fold lower CLu.int,H remained low extraction from birth to adulthood. Approximately 50% of collected PopPK studies (n = 120) did not consider interaction between age and other covariates. Interaction between covariates and age should be considered as part of studies involving younger pediatric patients. The EH cannot be considered an inherent drug property without considering the effect of age.

Does age affect gastric emptying time? A model-based meta-analysis of data from premature neonates through to adults.

PURPOSE: Gastric emptying (GE) is often reported to be slower and more irregular in premature neonates than in older children and adults. The aim of this study was to investigate the impact of age and other covariates on the rate of GE. METHODS: The effect of age on the mean gastric residence times (MGRT) of liquid and solid food was assessed by analysing 49 published studies of 1457 individuals, aged from 28 weeks gestation to adults. The data were modelled using the nonlinear mixed-effects approach within NONMEM version 7.2 (ICON, Dublin, Ireland), with evaluation of postnatal age, gestational age and meal type as covariates. A double Weibull function was selected as a suitable model since it could account for the typical biphasic nature of GE. RESULTS: Age was not a significant covariate for GE but meal type was. Aqueous solutions were associated with the fastest emptying time (mean simulated gastric residence time of 45 min) and solid food was associated with the slowest (98 min). CONCLUSIONS: These findings challenge the assertion that GE is different in neonates, as compared with older children and adults due to age, and they reinforce the significance of food type in modulating GE.

Deciding on success criteria for predictability of pharmacokinetic parameters from in vitro studies: an analysis based on in vivo observations.

Prediction accuracy of pharmacokinetic parameters is often assessed using prediction fold error, i.e., being within 2-, 3-, or n-fold of observed values. However, published studies disagree on which fold error represents an accurate prediction. In addition, "observed data" from only one clinical study are often used as the gold standard for in vitro to in vivo extrapolation (IVIVE) studies, despite data being subject to significant interstudy variability and subjective selection from various available reports. The current study involved analysis of published systemic clearance (CL) and volume of distribution at steady state (Vss) values taken from over 200 clinical studies. These parameters were obtained for 17 different drugs after intravenous administration. Data were analyzed with emphasis on the appropriateness to use a parameter value from one particular clinical study to judge the performance of IVIVE and the ability of CL and Vss values obtained from one clinical study to "predict" the same values obtained in a different clinical study using the n-fold criteria for prediction accuracy. The twofold criteria method was of interest because it is widely used in IVIVE predictions. The analysis shows that in some cases the twofold criteria method is an unreasonable expectation when the observed data are obtained from studies with small sample size. A more reasonable approach would allow prediction criteria to include clinical study information such as sample size and the variance of the parameter of interest. A method is proposed that allows the "success" criteria to be linked to the measure of variation in the observed value.

An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population.

Physiological changes during pregnancy can alter maternal and fetal drug exposure. The objective of this work was to predict maternal and umbilical ceftazidime pharmacokinetics during pregnancy. Ceftazidime transplacental permeability was predicted from its physicochemical properties and incorporated into the model. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted ceftazidime concentrations in non-pregnant and pregnant subjects of different gestational weeks were within 2-fold of the observations, and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The calculated transplacental clearance (0.00137 L/h/mL of placenta volume) predicted an average umbilical cord-to-maternal plasma ratio of 0.7 after the first dose, increasing to about 1.0 at a steady state, which also agrees well with clinical observations. The developed maternal PBPK model adequately predicted the observed exposure and kinetics of ceftazidime in the pregnant population. Using a verified population-based PBPK model provides valuable insights into the disposition of drug concentrations in special individuals that are otherwise difficult to study and, in addition, offers the possibility of supplementing sparse samples obtained in vulnerable populations with additional knowledge, informing the dosing adjustment and study design, and improving the efficacy and safety of drugs in target populations.

Supplementing clinical lactation studies with PBPK modeling to inform drug therapy in lactating mothers: Prediction of primaquine exposure as a case example.

Evaluating the safety of primaquine (PQ) during breastfeeding requires an understanding of its pharmacokinetics (PKs) in breast milk and its exposure in the breastfed infant. Physiologically-based PK (PBPK) modeling is primed to assess the complex interplay of factors affecting the exposure of PQ in both the mother and the nursing infant. A published PBPK model for PQ describing the metabolism by monoamine oxidase A (MAO-A; 90% contribution) and cytochrome P450 2D6 (CYP2D6; 10%) in adults was applied to predict the exposure of PQ in mothers and their breastfeeding infants. Plasma exposures following oral daily dosing of 0.5 mg/kg in the nursing mothers in a clinical lactation study were accurately captured, including the observed ranges. Reported infant daily doses based on milk data from the clinical study were used to predict the exposure of PQ in breastfeeding infants greater than or equal to 28 days. On average, the predicted exposures were less than or equal to 0.13% of the mothers. Furthermore, in simulations involving neonates less than 28 days, PQ exposures remain less than 0.16% of the mothers. Assuming that MAO-A increases slowly with age, the predicted relative exposure of PQ remains low in neonates (<0.46%). Thus, the findings of our study support the recommendation made by the authors who reported the results of the clinical lactation study, that is, that when put into context of safety data currently available in children, PQ should not be withheld in lactating women as it is unlikely to cause adverse events in breastfeeding infants greater than or equal to 28 days old.

Mechanistic Framework to Predict Maternal-Placental-Fetal Pharmacokinetics of Nifedipine Employing Physiologically Based Pharmacokinetic Modeling Approach.

Nifedipine is used for treating mild to severe hypertension and preventing preterm labor in pregnant women. Nevertheless, concerns about nifedipine fetal exposure and safety are always raised. The aim of this study was to develop and validate a maternal-placental-fetal nifedipine physiologically based pharmacokinetic (PBPK) model and apply the model to predict maternal, placental, and fetal exposure to nifedipine at different pregnancy stages. A nifedipine PBPK model was verified with nonpregnant data and extended to the pregnant population after the inclusion of the fetoplacental multicompartment model that accounts for the placental tissue and different fetal organs within the Simcyp Simulator version 22. Model parametrization involved scaling nifedipine transplacental clearance based on Caco-2 permeability, and fetal hepatic clearance was obtained from in vitro to in vivo extrapolation encompassing cytochrome P450 3A7 and 3A4 activities. Predicted concentration profiles were compared with in vivo observations and the transplacental transfer results were evaluated using 2-fold criteria. The PBPK model predicted a mean cord-to-maternal plasma ratio of 0.98 (range, 0.86-1.06) at term, which agrees with experimental observations of 0.78 (range, 0.59-0.93). Predicted nifedipine exposure was 1.4-, 2.0-, and 3.0-fold lower at 15, 27, and 39 weeks of gestation when compared with nonpregnant exposure, respectively. This innovative PBPK model can be applied to support maternal and fetal safety assessment for nifedipine at various stages of pregnancy.

Forecasting Fetal Buprenorphine Exposure through Maternal-Fetal Physiologically Based Pharmacokinetic Modeling.

Buprenorphine readily crosses the placenta, and with greater prenatal exposure, neonatal opioid withdrawal syndrome (NOWS) likely grows more severe. Current dosing strategies can be further improved by tailoring doses to expected NOWS severity. To allow the conceptualization of fetal buprenorphine exposure, a maternal-fetal physiologically based pharmacokinetic (PBPK) model for sublingual buprenorphine was developed using Simcyp (v21.0). Buprenorphine transplacental passage was predicted from its physicochemical properties. The maternal-fetal PBPK model integrated reduced transmucosal absorption driven by lower salivary pH and induced metabolism observed during pregnancy. Maternal pharmacokinetics was adequately predicted in the second trimester, third trimester, and postpartum period, with the simulated area under the curve from 0 to 12 h, apparent clearance, and peak concentration falling within the 1.25-fold prediction error range. Following post hoc adjustment of the likely degree of individual maternal sublingual absorption, umbilical cord blood concentrations at delivery (n = 21) were adequately predicted, with a geometric mean ratio between predicted and observed fetal concentrations of 1.15 and with 95.2% falling within the 2-fold prediction error range. The maternal-fetal PBPK model developed in this study can be used to forecast fetal buprenorphine exposure and would be valuable to investigate its correlation to NOWS severity.

Development and Verification of a Japanese Pediatric Physiologically Based Pharmacokinetic Model with Emphasis on Drugs Eliminated by Cytochrome P450 or Renal Excretion.

Physiologically based pharmacokinetic (PBPK) models are useful in bridging drug exposure in different ethnic groups, and there is increasing regulatory application of this approach in adults. Reported pediatric PBPK models tend to focus on the North European population, with few examples in other ethnic groups. This study describes the development and verification of a Japanese pediatric PBPK population. The development of the model was based on the existing North European pediatric population. Japanese systems and clinical data were collated from public databases and the literature, and the underlying demographics and equations were optimized so that physiological outputs represented the Japanese pediatric population. The model was tested using 14 different small molecule drugs, eliminated by a variety of pathways, including cytochrome P450 3A4 (CYP3A4) metabolism and renal excretion. Given the limitations of the clinical data, the overall performance of the model was good, with 44/62 predictions for PK parameters (area under the plasma drug concentration-time curve, AUC; maximum serum concentration, Cmax ; clearance, CL) being within 0.8- to 1.25-fold, 56/62 within 0.67- to 1.5-fold, and 61/62 within 0.5- to 2.0-fold of the observed values. Specific results for the 5 CYP3A4 substrates showed 20/31 cases were predicted within 0.8- to 1.25-fold, 27/31 within 0.67- to 1.5-fold, and all were within 0.5- to 2.0-fold of the observed values. Given the increased regulatory use of pediatric PBPK in drug development, expanding these models to other ethnic groups are important. Considering qualifying these models based on the context of use, there is a need to expand on the current research to include a larger range of drugs with different elimination pathways. Collaboration among academic, industry, model providers, and regulators will facilitate further development.

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

AIMS: Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies. METHODS: A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations. RESULTS: The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women. CONCLUSIONS: In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study.

Prediction of Maternal and Fetal Acyclovir, Emtricitabine, Lamivudine, and Metformin Concentrations during Pregnancy Using a Physiologically Based Pharmacokinetic Modeling Approach.

BACKGROUND: Concerns over maternal and fetal drug exposure during pregnancy highlight the need for improved understanding of drug distribution to the fetus through the placental barrier. OBJECTIVE: Our objective was to predict maternal and fetal drug disposition using a physiologically based pharmacokinetic (PBPK) modeling approach. METHODS: We used the detailed maternal-placental-fetal PBPK model within the Simcyp Simulator V20 to predict the maternal and fetal drug exposure of acyclovir, emtricitabine, lamivudine, and metformin during pregnancy and at delivery. The dynamic model includes gestational changes to the maternal, fetal, and placental physiological parameters. Placental kinetics were parameterized using published ex vivo data for these four compounds. Amniotic data were included where available. PBPK predictions were compared with the observed data using twofold criteria. RESULTS: Maternal-fetal PBPK models were developed completely from the bottom up without any parameter adjustments. The PBPK model-predicted exposures matched the observed maternal and umbilical exposure for acyclovir (six maternal studies, all of which all reported umbilical exposure), emtricitabine (six maternal studies, of which four reported umbilical exposure), lamivudine, (five maternal studies, of which four reported umbilical exposure), and metformin (seven studies, of which six reported umbilical exposure). Predicted pharmacokinetic parameters were within twofold of the observed values. CONCLUSION: Integration of fetal and maternal system parameters within PBPK models, together with experimental data from ex vivo placental perfusion studies, facilitated and extended the application of the pregnancy PBPK model. Such models can also be used inform clinical trials and maternal/fetal risk assessment following maternally administered drugs or unintended exposure to environmental toxicants.

Quantification of Fetal Renal Function Using Fetal Urine Production Rate and Its Reflection on the Amniotic and Fetal Creatinine Levels During Pregnancy.

Adequate prediction of fetal exposure of drugs excreted by the kidney requires the incorporation of time-varying renal function parameters into a pharmacokinetic model. Published data on measurements of fetal urinary production rate (FUPR) and creatinine at various gestational ages were collected and integrated for prediction of the fetal glomerular filtration rate (GFR). The predicted GFR values were then compared to neonatal values recorded at birth. Collected data for FUPR across different gestational ages using both 3D (N = 517) and 2D (N = 845) ultrasound methods showed that 2D techniques yield significantly lower estimates of FUPR than 3D (p < 0.0001). A power law function was shown to best capture the change in FUPR with fetal age (FA) for both 2D ( F U P R 2 D ( m L min ) = 0 . 000169     FA 2 . 19 ); and 3D ( F U P R 3 D   ( m L min ) =   3 . 21 × 1 0 - 7   FA 4 . 21 ) data. The predicted FUPR based on the observed 3D data was shown to be strongly linearly related (R 2 = 0.95) to measured values of amniotic creatinine concentration (N = 664). The FUPR3D data together with creatinine levels in the fetal urine and serum resulted in median predicted fetal GFR values of 0.47, 1.2, 2.5, and 4.9 ml/min at 23, 28, 33, and 38 weeks of fetal age (50% CV), respectively. These values are in good agreement with neonatal values observed immediately at birth. The derived FUPR and creatinine functions can be utilized to assess fetal renal maturation and predict fetal renal clearance.

Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations.

Perinatal pharmacology is influenced by a myriad of physiological variables that are changing dynamically. The influence of these covariates has not been assessed systemically. The objective of this work was to use theophylline as a model drug and to predict its pharmacokinetics before, during (including prediction of the umbilical cord level), and after pregnancy as well as in milk (after single and multiple doses) and in neonates using a physiological-based pharmacokinetic (PBPK) model. Neonatal theophylline exposure from milk consumption was projected in both normal term and preterm subjects. Predicted infant daily doses were calculated using theophylline average and maximum concentration in the milk as well as an estimate of milk consumption. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted theophylline concentrations in non-pregnant and pregnant populations at different gestational weeks were within 2-fold of the observations and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The PBPK model predicted an average cord-to-maternal plasma ratio of 1.0, which also agrees well with experimental observations. Predicted postpartum theophylline concentration profiles in milk were also in good agreement with observations with a predicted milk-to-plasma ratio of 0.68. For an infant of 2 kg consuming 150 ml of milk per day, the lactation model predicted a relative infant dose (RID) of 12 and 17% using predicted average (Cavg,ss) and maximum (Cmax,ss) concentration in milk at steady state. The maximum RID of 17% corresponds to an absolute infant daily dose of 1.4 ± 0.5 mg/kg/day. This dose, when administered as 0.233 mg/kg every 4 h, to resemble breastfeeding frequency, resulted in plasma concentrations as high as 3.9 (1.9-6.8) mg/L and 2.8 (1.3-5.3) (5th-95th percentiles) on day 7 in preterm (32 GW) and full-term neonatal populations.

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers.

Physiologically-based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi-random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis-Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.