Developed Physiologically Based Pharmacokinetic Model Research Articles

In Vitro In Vivo Extrapolation and Bioequivalence Prediction for Immediate-Release Capsules of Cefadroxil Based on a Physiologically-Based Pharmacokinetic ACAT Model.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic concept, which helps to judge the effects of biopharmceutical properties of drug product such as in vitro dissolution on its pharmacokinetic and in vivo performance. With the application of virtual bioequivalence (VBE) study, the drug product development using model-based approach can help in evaluating the possibility of extending BCS-based biowaiver. Therefore, the current study was intended to develop PBPK model as well as in vitro in vivo extrapolation (IVIVE) for BCS class III drug i.e. cefadroxil.A PBPK model was created in GastroPlus™ 9.8.3 utilizing clinical data of immediate-release cefadroxil formulations. By the examination of simulated and observed plasma drug concentration profiles, the predictability of the proposed model was assessed for the prediction errors. Furthermore, mechanistic deconvolution was used to create IVIVE, and the plasma drug concentration profiles and pharmacokinetic parameters were predicted for different virtual formulations with variable cefadroxil in vitro release. Virtual bioequivalence study was also executed to assess the bioequivalence of the generic verses the reference drug product (Duricef®).The developed PBPK model satisfactorily predicted Cmax and AUC0-t after cefadroxil single and multiple oral dose administrations, with all individual prediction errors within the limits except in a few cases. Second order polynomial correlation function obtained accurately predict in vivo drug release and plasma concentration profile of cefadroxil test and reference (Duricef®) formulation. The VBE study also proved test formulation bioequivalent to reference formulation and the statistical analysis on pharmacokinetic parameters reported 90% confidence interval for Cmax and AUC0-t in the FDA acceptable limits.The analysis found that a validated and verified PBPK model with a mechanistic background is as a suitable approach to accelerate generic drug development.

Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins.

Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from invitro to invivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from invitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Development of physiologically-based pharmacokinetic models to predict food effects and co-administration of proton pump inhibitors on the pharmacokinetics of sonidegib and vismodegib

Development of physiologically-based pharmacokinetic models to predict food effects and co-administration of proton pump inhibitors on the pharmacokinetics of sonidegib and vismodegib

Assessing the relative contribution of CYP3A-and P-gp-mediated pathways to the overall disposition and drug-drug interaction of dabigatran etexilate using a comprehensive mechanistic physiological-based pharmacokinetic model.

Dabigatran etexilate (DABE) is a clinical probe substrate for studying drug-drug interaction (DDI) through an intestinal P-glycoprotein (P-gp). A recent in vitro study, however, has suggested a potentially significant involvement of CYP3A-mediated oxidative metabolism of DABE and its intermediate monoester BIBR0951 in DDI following microdose administration of DABE. In this study, the relative significance of CYP3A- and P-gp-mediated pathways to the overall disposition of DABE has been explored using mechanistic physiologically based pharmacokinetic (PBPK) modeling approach. The developed PBPK model linked DABE with its 2 intermediate (BIBR0951 and BIBR1087) and active (dabigatran, DAB) metabolites, and with all relevant drug-specific properties known to date included. The model was successfully qualified against several datasets of DABE single/multiple dose pharmacokinetics and DDIs with CYP3A/P-gp inhibitors. Simulations using the qualified model supported that the intestinal CYP3A-mediated oxidation of BIBR0951, and not the gut P-gp-mediated efflux of DABE, was a key contributing factor to an observed difference in the DDI magnitude following the micro-versus therapeutic doses of DABE with clarithromycin. Both the saturable CYP3A-mediated metabolism of BIBR0951 and the solubility-limited DABE absorption contributed to the relatively modest nonlinearity in DAB exposure observed with increasing doses of DABE. Furthermore, the results suggested a limited role of the gut P-gp, but an appreciable, albeit small, contribution of gut CYP3A in mediating the DDIs following the therapeutic dose of DABE with dual CYP3A/P-gp inhibitors. Thus, a possibility exists for a varying extent of CYP3A involvement when using DABE as a clinical probe in the DDI assessment, across DABE dose levels.

Investigation of Antibody Pharmacokinetics in the Brain Following Intra-CNS Administration and Development of PBPK Model to Characterize the Data.

Despite the promising potential of direct central nervous system (CNS) antibody administration to enhance brain exposure, there remains a significant gap in understanding the disposition of antibodies following different intra-CNS injection routes. To bridge this knowledge gap, this study quantitatively investigated the brain pharmacokinetics (PK) of antibodies following intra-CNS administration. The microdialysis samples from the striatum (ST), cerebrospinal fluid (CSF) samples through cisterna magna (CM) puncture, plasma, and brain homogenate samples were collected to characterize the pharmacokinetics (PK) profiles of a non-targeting antibody, trastuzumab, following intracerebroventricular (ICV), intracisternal (ICM), and intrastriatal (IST) administration. For a comprehensive analysis, these intra-CNS injection datasets were juxtaposed against our previously acquired intravenous (IV) injection data obtained under analogous experimental conditions. Our findings highlighted that direct CSF injections, either through ICV or ICM, resulted in ~ 5-6-fold higher interstitial fluid (ISF) drug exposure than IV administration. Additionally, the low bioavailability observed following IST administration indicates the existence of a local degradation process for antibody elimination in the brain ISF along with the ISF bulk flow. The study further refined a physiologically based pharmacokinetic (PBPK) model based on new observations by adding the perivascular compartments, oscillated CSF flow, and the nonspecific uptake and degradation of antibodies by brain parenchymal cells. The updated model can well characterize the antibody PK following systemic and intra-CNS administration. Thus, our research offers quantitative insight into antibody brain disposition pathways and paves the way for determining optimal dosing and administration strategies for antibodies targeting CNS disorders.

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.

Physiologically Based Pharmacokinetic Modeling of Lacosamide in Patients With Hepatic and Renal Impairment and Pediatric Populations to Support Pediatric Dosing Optimization

PurposeLacosamide (LCM) is a new-generation anti-seizure medication that is efficacious in patients with focal seizures with or without secondary generalization. Until now, the efficacy, safety, and tolerability of LCM are still lacking in Chinese epilepsy patients, particularly for pediatric populations and patients with renal or hepatic impairment. MethodsThis study was conducted to develop a physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of LCM in Chinese populations and predict the pharmacokinetics of LCM in Chinese pediatric populations and patients with renal or hepatic impairment. Using data from clinical investigations, the developed PBPK model was validated by comparing predicted and observed blood concentration data. FindingsDoses should be reduced to approximately 82%, 75%, 63%, and 76% of the Chinese healthy adult dose in patients with mild, moderate, and severe renal impairment and end-stage renal disease; and approximately 89%, 72%, and 36% of the Chinese healthy adult dose in patients with Child Pugh-A, B, and C hepatic impairment. For pediatric populations, intravenous doses should be adjusted to 1.75 mg/kg for newborns, 2.5 mg/kg for toddlers, 2.2 mg/kg mg for preschool and school age, and 2 mg/kg mg for adolescents to achieve an equivalent plasma exposure of 2 mg/kg LCM in adults. The oral doses should be adjusted to 20 mg for toddlers, 32 mg for preschool, 45 mg for school age, and 95 mg for adolescents to achieve an approximately equivalent plasma exposure of 100 mg LCM in adults. ImplicationsThe PBPK model of LCM can be utilized to optimize dosage regimens for special populations.

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 the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling.

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggestcarbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review.

Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows. The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals. For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines. In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing. PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.

Evaluation of Age-Related Changes in Teneligliptin Pharmacokinetics in Japanese and European Descent Subjects Using a Physiologically Based Pharmacokinetic Model.

Drugs often show differing pharmacokinetic (PK) profiles, such as higher plasma concentrations, in older people than in younger people owing to age-related decreasesin physiological functions. However, it is difficult to evaluate the PK in older populations. Therefore, we simulated the plasma age-related changes in the PK of teneligliptin, a dipeptidyl peptidase-4 inhibitor, using physiologically based PK (PBPK) models. The previously developed PBPK model was revalidated by comparison between simulated data and clinical study data that included older subjects (up to 75years old). We then simulated the plasma concentration-time profiles for teneligliptin at a dose of 20mg (single and multiple doses) in virtual Japanese (20-70years old) and European descent(20-98years old) subjects. PK parameters were calculated by race and age group. We confirmed the validity of the previous PBPK model by comparison between simulated data and clinical study data. In the evaluation of age-related changes in PK after single and multiple doses using the PBPK model, the area under the plasma concentration-time curve (AUC) of teneligliptin tended to increase slightly with age in both populations up to 70years old. However, no clear age-related change in the maximum plasma concentration (Cmax) of teneligliptin was observed. In the European descent subjects aged ≥ 70years, the AUC tended to increase but the ratio of the change in Cmax was smaller than that in AUC. In both populations, there were positive correlations between AUC and age, but not between Cmax and age. The simulation using a PBPK model showed a tendency for the AUC of teneligliptin to increase with age, whereas Cmax was less affected by age than AUC.

A Physiologically Based Pharmacokinetic Model to Predict Systemic Ondansetron Concentration in Liver Cirrhosis Patients.

Ondansetron is a drug that is routinely prescribed for the management of nausea and vomiting associated with cancer, radiation therapy, and surgical operations. It is mainly metabolized in the liver, and it might accumulate in patients with hepatic impairment and lead to unwanted adverse events. A physiologically based pharmacokinetic (PBPK) model was developed to predict the exposure of ondansetron in healthy and liver cirrhosis populations. The population-based PBPK simulator PK-Sim was utilized for simulating ondansetron exposure in healthy and liver cirrhosis populations. The developed model successfully described the pharmacokinetics of ondansetron in healthy and liver cirrhosis populations. The predicted area under the curve, maximum systemic concentration, and clearance were within the allowed twofold range. The exposure of ondansetron in the population of Child-Pugh class C has doubled in comparison to Child-Pugh class A. The dose has to be adjusted for liver cirrhosis patients to ensure comparable exposure to a healthy population. In this study, the developed PBPK model has described the pharmacokinetics of ondansetron successfully. The PBPK model has been successfully evaluated to be used as a tool for dose adjustments in liver cirrhosis patients.

Determination of blood:air, urine:air and plasma:air partition coefficients of selected microbial volatile organic compounds

Partition coefficients (PCs) are essential parameters for understanding the toxicokinetics of chemicals in the human body since they are used in the description of different processes of absorption, distribution, and excretion in physiologically based pharmacokinetic (PBPK) models used in chemical exposure and risk assessment. The goal of this study was to determine urine:air, blood:air and plasma:air partition coefficients (PCs) of microbial volatile organic compounds (mVOCs) previously selected as having high potential as biomarkers of indoor mold exposure. To achieve this goal, the vial-equilibration technique was used, and quantification was performed using headspace gas chromatography tandem mass spectrometry (HS-GC-MS/MS) analysis. Matrix:air PCs of 19 different mVOCs have been successfully determined and their values ranged between 14 and 3586 for urine:air, 78 and 4721 for blood:air and 64 and 5604 for plasma:air PCs. Water:air PCs were also determined, and their values varied between 16 and 2210, showing a good correlation with urine:air PCs for 17 compounds of the selected mVOCs (R2 = 0.97, slope close to unity) indicating that water:air PCs below 103 may be a good surrogate for urine:air PCs. All studied mVOCs have high blood:air PCs (greater than 78) indicating strong pulmonary uptake. Due to their high blood:urine PCs, some mVOCs may be more easily measured in blood than in urine. This work is an important preliminary step toward the use of mVOCs as potential biomarkers of indoor mold exposure. The data obtained in this study will help to determine the most appropriate matrix to use in this biomonitoring approach and will eventually facilitate the development of PBPK models for these chemicals.

Development of 4-Pyridoxic Acid PBPK Model to Support Biomarker-Informed Evaluation of OAT1/3 Inhibition and Effect of Chronic Kidney Disease.

Monitoring endogenous biomarkers is increasingly used to evaluate transporter-mediated drug-drug interactions (DDIs) in early drug development and may be applied to elucidate changes in transporter activity in disease. 4-pyridoxic acid (PDA) has been identified as the most sensitive plasma endogenous biomarker of renal organic anion transporters (OAT1/3). Increase in PDA baseline concentrations was observed after administration of probenecid, a strong clinical inhibitor of OAT1/3 and also in patients with chronic kidney disease (CKD). The aim of this study was to develop and verify a physiologically-based pharmacokinetic (PBPK) model of PDA, to predict the magnitude of probenecid DDI and predict the CKD-related changes in PDA baseline. The PBPK model for PDA was first developed in healthy population, building on from previous population pharmacokinetic modeling, and incorporating a mechanistic kidney model to consider OAT1/3-mediated renal secretion. Probenecid PBPK model was adapted from the Simcyp database and re-verified to capture its dose-dependent pharmacokinetics (n = 9 studies). The PBPK model successfully predicted the PDA plasma concentrations, area under the curve, and renal clearance in healthy subjects at baseline and after single/multiple probenecid doses. Prospective simulations in severe CKD predicted successfully the increase in PDA plasma concentration relative to healthy (within 2-fold of observed data) after accounting for 60% increase in fraction unbound in plasma and additional 50% decline in OAT1/3 activity beyond the decrease in glomerular filtration rate. The verified PDA PBPK model supports future robust evaluation of OAT1/3 DDI in drug development and increases our confidence in predicting exposure and renal secretion in patients with CKD.

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

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

Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing.

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity. Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity. Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2g every 24h is lower than the accumulation of 1g every 12h dosing regimen. Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment.

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.

Unraveling Complexities in the Absorption and Disposition Kinetics of Abiraterone via Iterative PBPK Model Development and Refinement.

Abiraterone is a first-in-class inhibitor of cytochrome P450 17A1 (CYP17A1), and its pharmacokinetic (PK) profile is susceptible to intrinsic and extrinsic variabilities. Potential associations between abiraterone concentrations and pharmacodynamic consequences in prostate cancer may demand further dosage optimization to balance therapeutic outcomes. Consequently, we aim to develop a physiologically based pharmacokinetic (PBPK) model for abiraterone via a middle-out approach to prospectively interrogate the untested, albeit clinically relevant, scenarios. To characterize in vivo hydrolysis of prodrug abiraterone acetate (AA) and supersaturation of abiraterone, in vitro aqueous solubility data, biorelevant measurements, and supersaturation and precipitation parameters were utilized for mechanistic absorption simulation. CYP3A4-mediated N-oxidation and sulfotransferase 2A1-catalyzed sulfation of abiraterone were subsequently quantified in human liver subcellular systems. Iterative PBPK model refinement involved evaluation of potential organic anion transporting polypeptide (OATP)-mediated abiraterone uptake in transfected cells in the absence and presence of albumin. The developed PBPK model recapitulated the duodenal concentration-time profile of both AA and abiraterone after simulated AA administration. Our findings established abiraterone as a substrate of hepatic OATP1B3 to recapitulate its unbound metabolic intrinsic clearance. Further consideration of a transporter-induced protein-binding shift established accurate translational scaling factors and extrapolated the sinusoidal uptake process. Subsequent simulations effectively predicted the PK of abiraterone upon single and multiple dosing. Our systematic development of the abiraterone PBPK model has demonstrated its application for the prospective interrogation of the individual or combined influences of potential interindividual variabilities influencing the systemic exposure of abiraterone.

Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7-Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit-Drug Interactions.

Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24 hours after grapefruit juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK) grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs. The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4 substrates that were publicly available and already evaluated for CYP3A4-mediated drug-drug interactions. Overall, 43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin (DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4 victim drugs, simulating the effect of the CYP3A4 inactivation on the victims' pharmacokinetics as well as their main metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations.