Whole-body Physiologically Based Pharmacokinetic Model Research Articles

Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling.

The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure. A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules. The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures. The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.

Proof of concept of physiologically based pharmacokinetic modelling in paediatric acute lymphoblastic leukaemia.

Physiologically based pharmacokinetic (PBPK) modelling is an alternative modelling technique that is increasingly used in pharmacokinetics. Due to its nature, it can be complementarily employed to population pharmacokinetics, especially when it comes to small population size. Here, we report the proof of concept of its application to accurately describe the pharmacokinetics of a recombinant L-asparaginase in paediatric patients with acute lymphoblastic leukaemia. Data from two randomized, double-blind, phase II/III clinical studies (MC-ASP.4/ALL; MC-ASP.5/ALL) were included to setup and evaluate the final model, respectively. Final population values for basic pharmacokinetic parameters were calculated (clearance: 0.0569 L/h/19.5 kg, volume of distribution: 1.251 L, half-life: 18.5 h, trough concentration: 140.9 IU/L). Pharmacokinetic parameter prediction as well as predictive performance of the model proofed to be comparable to a separately developed population pharmacokinetic model with 13% deviation in predicted median L-asparaginase trough levels. To the best of our knowledge, this is the first whole-body PBPK model of a non-antibody therapeutic protein.

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

BackgroundCefotaxime 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. MethodsA 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. ResultsThe 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. ConclusionsOur study may provide useful experience in practicing PBPK model-informed precision dosing in the pediatric population.

A PBPK model for PRRT with [177Lu]Lu-DOTA-TATE: Comparison of model implementations in SAAM II and MATLAB/SimBiology

Physiologically based pharmacokinetic (PBPK) models offer the ability to simulate and predict the biodistribution of radiopharmaceuticals and have the potential to enable individualised treatment planning in molecular radiotherapy. The objective of this study was to develop and implement a whole-body compartmental PBPK model for peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-DOTA-TATE in SimBiology to allow for more complex analyses. The correctness of the model implementation was ensured by comparing its outputs, such as the time-integrated activity (TIA), with those of a PBPK model implemented in SAAM II software. MethodsA combined PBPK model for [68Ga]Ga-DOTA-TATE and [177Lu]Lu-DOTA-TATE was developed and implemented in both SAAM II and SimBiology. A retrospective analysis of 12 patients with metastatic neuroendocrine tumours (NETs) was conducted. First, time-activity curves (TACs) and TIAs from the two software were calculated and compared for identical parameter values. Second, pharmacokinetic parameters were fitted to activity concentrations, analysed and compared. ResultsThe PBPK model implemented in SimBiology produced TIA results comparable to those generated by the model implemented in SAAM II, with a relative deviation of less than 0.5% when using the same input parameters. The relative deviation of the fitted TIAs was less than 5% when model parameter values were fitted to the measured activity concentrations. ConclusionThe proposed PBPK model implemented in SimBiology can be used for dosimetry in radioligand therapy and TIA prediction. Its outputs are similar to those generated by the PBPK model implemented in SAAM II, confirming the correctness of the model implementation in SimBiology.

Personalized Chronomodulated 5-Fluorouracil Treatment: A Physiologically-Based Pharmacokinetic Precision Dosing Approach for Optimizing Cancer Therapy.

The discovery of circadian clock genes greatly amplified the study of diurnal variations impacting cancer therapy, transforming it into a rapidly growing field of research. Especially, use of chronomodulated treatment with 5-fluorouracil (5-FU) has gained significance. Studies indicate high interindividual variability (IIV) in diurnal variations in dihydropyrimidine dehydrogenase (DPD) activity - a key enzyme for 5-FU metabolism. However, the influence of individual DPD chronotypes on chronomodulated therapy remains unclear and warrants further investigation. To optimize precision dosing of chronomodulated 5-FU, this study aims to: (i) build physiologically-based pharmacokinetic (PBPK) models for 5-FU, uracil, and their metabolites, (ii) assess the impact of diurnal variation on DPD activity, (iii) estimate individual DPD chronotypes, and (iv) personalize chronomodulated 5-FU infusion rates based on a patient's DPD chronotype. Whole-body PBPK models were developed with PK-Sim(R) and MoBi(R). Sinusoidal functions were used to incorporate variations in enzyme activity and chronomodulated infusion rates as well as to estimate individual DPD chronotypes from DPYD mRNA expression or DPD enzymatic activity. Four whole-body PBPK models for 5-FU, uracil, and their metabolites were established utilizing data from 41 5-FU and 10 publicly available uracil studies. IIV in DPD chronotypes was assessed and personalized chronomodulated administrations were developed to achieve (i) comparable 5-FU peak plasma concentrations, (ii) comparable 5-FU exposure, and (iii) constant 5-FU plasma levels via "noise cancellation" chronomodulated infusion. The developed PBPK models capture the extent of diurnal variations in DPD activity and can help investigate individualized chronomodulated 5-FU therapy through testing alternative personalized dosing strategies.

Assessment of Quinidine-Induced Torsades de Pointes Risks Using a Whole-Body Physiologically Based Pharmacokinetic Model Linked to Cardiac Ionic Current Inhibition.

The lethality of torsades de pointes (TdP) by drugs is one of main reasons that some drugs were withdrawn from the market. In order to assess drug-induced TdP risks, a model of cardiac ionic current suppression in human ventricular myocytes (ToR-ORd model), combined with the maximum effective free therapeutic plasma concentration or the maximum effective free therapeutic myocyte concentration was often used, with the latter proved to be more relevant and more accurate. We aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model, incorporated with a human cardiomyocyte pharmacodynamic (PD) model, to provide a comprehensive assessment of drug-induced TdP risks in normal and specific scenarios. Quinidine served as an example to validate the PBPK-PD model via predicting plasma quinidine concentrations and quinidine-induced changes in QT interval (ΔQTc). The predicted plasma quinidine concentrations and ΔQTc values following oral administration or intravenous administration of quinidine were comparable to clinic observations. Visual predictive checks showed that most of the observed plasma concentrations and ΔQTc values fell within the 5th and 95th percentiles of simulations. The validated PBPK-PD model was further applied to assess the TdP risks using frequencies of early afterdepolarization and long-QT syndrome occurrence in 4 scenarios, such as therapeutic dose, supra-therapeutic dose, alkalosis, and hyperkalemia in 200 human subjects. In conclusion, the developed PBPK-PD model may be applied to predict the quinidine pharmacokinetics and quinidine-induced TdP risks in healthy subjects, but also simulate quinidine-induced TdP risks under disease conditions, such as hypokalemia and alkalosis.

Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin in relation to SLCO1B1 genetic polymorphism.

Pitavastatin, a potent 3-hydroxymethylglutaryl coenzyme A reductase inhibitor, is indicated for the treatment of hypercholesterolemia and mixed dyslipidemia. Hepatic uptake of pitavastatin is predominantly occupied by the organic anion transporting polypeptide 1B1 (OATP1B1) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, whichis a polymorphic gene that encodes OATP1B1. SLCO1B1 genetic polymorphism significantly alters the pharmacokinetics of pitavastatin. This study aimed to establish the physiologically based pharmacokinetic(PBPK) model to predict pitavastatin pharmacokinetics according to SLCO1B1 genetic polymorphism. PK-Sim® version 10.0 was used to establish the whole-body PBPK model of pitavastatin. Our pharmacogenomic data and a total of 27 clinical pharmacokinetic data with different dose administration and demographic properties were used to develop and validate the model, respectively. Physicochemical properties and disposition characteristics of pitavastatin were acquired from previously reported data or optimized to capture the plasma concentration-time profiles in different SLCO1B1 diplotypes. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and profiles to the observed data. Predicted plasma concentration-time profiles were visually similar to the observed profiles in the non-genotyped populations and different SLCO1B1 diplotypes. All fold error values for AUC and Cmax were included in the two fold range of observed values. Thus, the PBPK model of pitavastatin in different SLCO1B1 diplotypes was properly established. The present study can be useful to individualize the dose administration strategy of pitavastatin in individuals with various ages, races, and SLCO1B1 diplotypes.

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

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. 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. 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. 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 model to predict doxorubicin and paclitaxel exposure in infants through breast milk.

Limited information is available concerning infant exposure and safety when breastfed by mothers receiving chemotherapy. Whereas defining distribution to breast milk is important to infer drug exposure, infant pharmacokinetics also determine to what extent the infant will be exposed to potential toxic effects. We aimed to assess the impact of chemotherapy containing breast milk on infants by predicting systemic and local (intestinal) exposure of paclitaxel and doxorubicin in infants through breast milk using a physiologically-based pharmacokinetic (PBPK) approach. Whole-body PBPK models of i.v. paclitaxel and doxorubicin were extended from the literature, with an oral absorption component to enable predictions in infants receiving paclitaxel or doxorubicin-containing breast milk. For safety considerations, worst-case scenarios were explored. Finally, paclitaxel and doxorubicin exposures in plasma and intestinal tissue of infants following feeding of breast milk from paclitaxel- or doxorubicin-treated mothers were simulated and breast milk discarding strategies were evaluated. The upper 95th percentile of the predicted peak concentrations in peripheral venous blood were 3.48 and 0.74 nM (0.4%-1.7% and 0.1%-1.8% of on-treatment) for paclitaxel and doxorubicin, respectively. Intestinal exposure reached peak concentrations of 1.0 and 140 μM for paclitaxel and doxorubicin, respectively. Discarding breast milk for the first 3 days after maternal chemotherapy administration reduced systemic and tissue exposures even further, to over 90% and 80% for paclitaxel and doxorubicin, respectively. PBPK simulations of chemotherapy exposure in infants after breastfeeding with chemotherapy containing breast milk suggest that particularly local gastrointestinal adverse events should be monitored, whereas systemic adverse events are not expected.

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.

Elucidation of Carboxylesterase Mediated Pharmacokinetic Interactions between Irinotecan and Oroxylin A in Rats via Physiologically Based Pharmacokinetic Modeling.

Our previous screening studies identified Oroxylin A (OXA) as a strong inhibitor on the carboxyolesterase mediated hydrolysis of irinotecan to SN-38. The current study employed a whole-body physiologically based pharmacokinetic (PBPK) modeling approach to investigate the underlying mechanisms of the carboxylesterase-mediated pharmacokinetics interactions between irinotecan and OXA in rats. Firstly, rats received irinotecan intravenous treatment at 35μmol/kg without or with oral OXA pretreatment (2800μmol/kg) daily for 5days. On day 5, blood and tissues were collected for analyses of irinotecan/SN-38 concentrations and carboxylesterase expression. In addition, effects of OXA on the enzyme kinetics of irinotecan hydrolysis and unbound fractions of irinotecan and SN-38 in rat plasma, liver and intestine were also determined. Finally, a PBPK model that integrated the physiological parameters, enzyme kinetics, and physicochemical properties of irinotecan and OXA was developed. Our PBPK model could accurately predict the pharmacokinetic profiles of irinotecan/SN-38, with AUC0-6h and Cmax values within ±27% of observed values. When OXA was included as a carboxylesterase inhibitor, the model could also predict the irinotecan/SN-38 plasma concentrations within twofold of those observed. In addition, the PBPK model indicated inhibition of carboxylesterase-mediated hydrolysis of irinotecan in the intestinal mucosa as the major underlying mechanism for the pharmacokinetics interactions between irinotecan and OXA. A whole-body PBPK model was successfully developed to not only predict the impact of oral OXA pretreatment on the pharmacokinetics profiles of irinotecan but also reveal its inhibition on the intestinal carboxylesterase as the major underlying mechanism.

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

ObjectiveThis study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models. MethodsThe PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin. ResultsThe developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range. ConclusionThe whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations.

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Physiologically-based pharmacokinetic modelling to predict intragastric rifabutin concentrations in the treatment of Helicobacter pylori infection.

Sustained intragastric antibiotic exposure is important for Helicobacter pylori eradication, yet little is known about gastric pharmacology of commonly used H. pylori regimens. For rifabutin, differing intragastric concentrations based on dosing regimen may account for differences in reported eradication rates. To compare intragastric rifabutin concentrations between low-dose rifabutin (50 mg three time daily; as in RHB-105) and generically dosed rifabutin 150 mg once daily, 150 mg twice daily, and 300 mg once daily using a validated Physiologically-based pharmacokinetic (PBPK) model. We obtained plasma pharmacokinetic data from the RHB-105 clinical development programs and used it to develop and validate a whole-body PBPK model using PK-SIM software. We modified the existing rifabutin model to include the impact of omeprazole on gastric pH and emptying time. Modelled intragastric rifabutin exposure was expressed as the time that each regimen maintained its concentration ≥MIC90 . Rifabutin 50 mg three times daily achieved significantly longer times with intragastric concentration above MIC90 (22.3 ± 1.1 h) than 150 mg once daily (8.3 ± 1.7 h), 150 mg twice daily (16.3 ± 2.3 h), or 300 mg once daily (8.5 ± 1.9 h) while providing the lowest mean maximal plasma concentration and mean area under the plasma concentration-time curve of all regimens studied. PBPK modelling showed rifabutin 50 mg three times daily had higher intragastric exposure times than 150 mg once daily or twice daily, or 300 mg once daily. This low-dose rifabutin regimen provides the highest potential for H. pylori eradication while minimising systemic rifabutin exposure.

Physicochemical characterization and phase II metabolic profiling of echinochrome A, a bioactive constituent from sea urchin, and its physiologically based pharmacokinetic modeling in rats and humans

Echinochrome A, a natural naphthoquinone pigment found in sea urchins, is increasingly being investigated for its nutritional and therapeutic value associated with antioxidant, anticancer, antiviral, antidiabetic, and cardioprotective activities. Although several studies have demonstrated the biological effects and therapeutic potential of echinochrome A, little is known regarding its biopharmaceutical behaviors. Here, we aimed to investigate the physicochemical properties and metabolic profiles of echinochrome A and establish a physiologically-based pharmacokinetic (PBPK) model as a useful tool to support its clinical applications. We found that the lipophilicity, color variability, ultraviolet/visible spectrometry, and stability of echinochrome A were markedly affected by pH conditions. Moreover, metabolic and pharmacokinetic profiling studies demonstrated that echinochrome A is eliminated primarily by hepatic metabolism and that four possible metabolites, i.e., two glucuronidated and two methylated conjugates, are formed in rat and human liver preparations. A whole-body PBPK model incorporating the newly identified hepatic phase II metabolic process was constructed and optimized with respect to chemical-specific parameters. Furthermore, model simulations suggested that echinochrome A could exhibit linear disposition profiles without systemic and local tissue accumulation in clinical settings. Our proposed PBPK model of echinochrome A could be a valuable tool for predicting drug interactions in previously unexplored scenarios and for optimizing dosage regimens and drug formulations.

Physiologically based pharmacokinetic modeling to predict OAT3-mediated drug-drug interactions of meropenem in varying stages of chronic kidney disease

ObjectiveThis study aimed to predict OAT3-mediated meropenem DDIs in varying stages of CKD using physiologically based pharmacokinetic (PBPK) models. MethodsMeropenem DDI model was performed with OAT3 perpetrators probenecid (strong inhibitor), piperacillin (moderate inhibitor) and acyclovir (weak inhibitor). Firstly, whole-body PBPK models were developed for meropenem and piperacillin in healthy adults with PK-Sim®. Then the models were translated to patients with varying stages of CKD by accounting for changes in eGFR, kidney volume, kidney blood flow, hematocrit, gastric emptying time, small intestinal transit time, OAT transport function and non-renal clearance. Subsequently, meropenem DDI model was verified by comparing the model simulation results of the interaction between meropenem and probenecid with the observational data from clinical studies. Finally, pharmacokinetics of meropenem under co-administration of piperacillin and acyclovir were predicted through simulations with the virtual population in varying stages of CKD. ResultsThe developed PBPK models of meropenem and piperacillin adequately described the pharmacokinetic characteristics in healthy adults and in adults with varying stages of CKD. The predicted AUC ratio of meropenem co-administered with probenecid to meropenem given alone was within 1.5-fold of the observed clinical data. The predicted steady-state AUC values of meropenem in healthy adults co-administered with probenecid, piperacillin or acyclovir were 1.593-fold (95% CI, 1.436-1.6244), 1.031-fold (95% CI, 1.017-1.045) and 1.005-fold (95% CI, 1.004-1.005), respectively, as compared to the AUC of meropenem given alone. And all predicted probenecid-meropenem, piperacillin-meropenem and acyclovir-meropenem DDI AUC ratios were decreased in renal impairment. Our presented meropenem DDI model demonstrated that patients with CKD may be less vulnerable to OAT3-mediated DDIs compared with healthy adults. However, further clinical DDI studies are required to verify our prediction results. ConclusionPBPK analysis provided a quantitative basis for guiding the clinical application of meropenem combined with OAT3 inhibitor in varying stages of CKD.

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling.

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Determination of the Number of Tissue Groups of Kinetically Distinct Transit Time in Whole-Body Physiologically Based Pharmacokinetic (PBPK) Models II: Practical Application of Tissue Lumping Theories for Pharmacokinetics of Various Compounds.

In our companion paper, we described the theoretical basis for tissue lumping in whole-body physiologically based pharmacokinetic (WB-PBPK) models and found that Kdet, a coefficient for determining the number of tissue groups of distinct transit time in WB-PBPK models, was related to the fractional change in the terminal slope (FCT) when tissues were progressively lumped from the longest transit time to shorter ones. This study was conducted to identify the practical threshold of Kdet by applying the lumping theory to plasma/blood concentration-time relationships of 113 model compounds collected from the literature. We found that drugs having Kdet < 0.3 were associated with FCT < 0.1 even when all peripheral tissues were lumped, resulting in comparable plasma concentration-time profiles between one-tissue minimal PBPK (mPBPK) and WB-PBPK models. For drugs with Kdet ≥ 1, WB-PBPK profiles appeared similar with two-tissue mPBPK models by applying the rule of FCT < 0.1 for lumping slowly equilibrating tissues. The two-tissue mPBPK model also appeared appropriate in terms of concentration-time profiles for drugs with 0.3 ≤ Kdet < 1, although, some compounds (15.9% of the total cases), but not all, in this range showed a slight (maximum of 18.9% of the total AUC) deviation from WB-PBPK models, indicating that the two-tissue model, with caution, could still be used for those cases. Comparison of kinetic parameters between traditional (model-fitting) and current (theoretical calculation) mPBPK analyses revealed their significant correlations. Collectively, these observations suggest that the number of tissue groups could be determined based on the Kdet/FCT criteria, and plasma concentration-time profiles from WB-PBPK could be calculated using equations significantly less complex.

Determination of the Number of Tissue Groups of Kinetically Distinct Transit Time in Whole-Body Physiologically Based Pharmacokinetic (PBPK) Models I: Theoretical Consideration of Bottom-Up Approach of Lumping Tissues in Whole-Body PBPK.

Minimal physiologically based pharmacokinetic (mPBPK) models, consisting of system-specific (e.g., tissue volume and blood flow) and drug-related (e.g., tissue-to-plasma partition coefficient) parameters, are practically useful for pharmacokinetic analyses. However, biopharmaceutical principles were not clear on how peripheral tissues, adopted in whole-body physiologically based pharmacokinetic (WB-PBPK) models, could be kinetically consolidated into one or two tissue groups in the mPBPK models. In this theoretical examination, we studied the relationship between the progressive tissue lumping in the direction from the longest mean transit time (MTTmax) to the shorter one(s) and the slopes of the terminal (λter)/distributional phases, assuming tissues with comparable MTTs are kinetically combined. The appropriateness of lumping was ascertained by evaluating the impact of difference in tissue MTTs during the lumping on the analytical solution of WB-PBPK models. We found that the ratio of MTTmax to the mean residence time in the body, viz., Kdet, is related to the change in λter by the progressive lumping and can serve as an index for the robustness of λter. Calculations with two extreme cases revealed that, for caffeine at Kdet < 0.03, the change in λter was minimal even when all peripheral tissues were collectively lumped, whereas for artesunic acid at Kdet > 50, the tissue of MTTmax could not be kinetically combined even with the tissue having the second-longest MTT without significantly affecting λter. Therefore, we proposed Kdet as an index for the robustness of λter during tissue lumping and for the number of tissue groups with distinct transit times in WB-PBPK.