Usefulness of PBPK Modeling in Incorporation of Clinical Conditions in Personalized Medicine

Implications of Incorporating Plasma Lipoprotein Binding into a Physiologically-Based Pharmacokinetic Model: A Simulation Study with Amiodarone

Although various lipophilic drugs are bound to lipoproteins, lipoprotein binding in plasma is not usually considered in current physiologically-based pharmacokinetic (PBPK) models. Amiodarone is extensively bound to serum triglyceride-rich lipoproteins. Total plasma amiodarone concentration, which is the sum of both unbound and bound concentrations, increases with increasing serum triglyceride levels. We investigated the impact of lipoprotein binding on amiodarone pharmacokinetics using PBPK modeling and simulations. An amiodarone PBPK model that incorporates plasma lipoprotein binding (LPP model) was developed based on the correlation between serum triglyceride levels and lipoprotein-bound amiodarone. The predicted unbound fraction of amiodarone in plasma and systemic clearance in the LPP and base models (with albumin binding only) were similar, but the coefficients of variation for the LPP model were greater than those for the base model and were closer to the observed data. The total plasma amiodarone concentration predicted using the LPP model increased with higher levels of plasma lipoprotein binding and serum albumin. In contrast, changes in plasma lipoprotein binding and serum albumin levels did not influence the predicted unbound plasma amiodarone concentration at steady-state. This study demonstrates that incorporating plasma lipoprotein binding into a PBPK model improves the accuracy of predicting interindividual variabilities in amiodarone clearance by more reliably predicting the interindividual variability in the plasma unbound fraction of amiodarone. Plasma lipoprotein binding should be considered in PBPK modeling and simulations for lipoprotein-associated drugs if there is available information on the relationship between plasma lipoprotein binding and hyperlipidemia.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

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.

Revisiting the in-vitro and in-vivo considerations for in-silico modelling of complex injectable drug products

Complex injectable drug products (CIDPs) have often been developed to modulate the pharmacokinetics along with efficacy for therapeutic agents used for remediation of chronic disorders. The effective development of CIDPs has exhibited complex kinetics associated with multiphasic drug release from the prepared formulations. Consequently, predictability of pharmacokinetic modelling for such CIDPs has been difficult and there is need for advanced complex computational models for the establishment of accurate prediction models for in-vitro-in-vivo correlation (IVIVC). The computational modelling aims at supplementing the existing knowledge with mathematical equations to develop formulation strategies for generation of predictable and discriminatory IVIVC. Such an approach would help in reduction of the burden of effect of hidden factors on preclinical to clinical translations. Computational tools like physiologically based pharmacokinetics (PBPK) modelling have combined physicochemical and physiological properties along with IVIVC characteristics of clinically used formulations. Such techniques have helped in prediction and understanding of variability in pharmacodynamic parameters of potential generic products to clinically used formulations like Doxil®, Ambisome®, Abraxane® in healthy and diseased population using mathematical equations. The current review highlights the important formulation characteristics, in-vitro, preclinical in-vivo aspects which need to be considered while developing a stimulatory predictive PBPK model in establishment of an IVIVC and in-vitro-in-vivo relationship (IVIVR).

Effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on the pharmacokinetics of tolperisone

Tolperisone, a muscle relaxant used for post-stroke spasticity, is metabolized to its main metabolite by CYP2D6 and to a lesser extent by CYP2C19 and CYP1A2. We investigated the effects of CYP2D6 and CYP2C19 genetic polymorphisms and cigarette smoking on tolperisone pharmacokinetics. A 150 mg oral dose of tolperisone was given to 184 healthy Korean subjects and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A 3.14-fold significant increase in AUC0-∞ was observed in the CYP2D6*10/*10 group compared with the CYP2D6*wt/*wt group, whereas a 3.59-fold increase in AUC0-∞ was observed in CYP2C19PMs compared to CYP2C19EMs. Smokers had a 38.5% decrease in AUC0-∞ when compared to non-smokers. When these effects were combined, CYP2D6*10/*10-CYP2C19PM-Non-smokers had a 25.9-fold increase in AUC0-∞ compared to CYP2D6*wt/*wt-CYP2C19EM-Smokers. Genetic polymorphisms of CYP2D6 and CYP2C19 and cigarette smoking independently and significantly affected tolperisone pharmacokinetics and these effects combined resulted in a much greater impact on tolperisone pharmacokinetics.

Physiologically Based Pharmacokinetic Modelling to Predict Imatinib Exposures in Cancer Patients with Renal Dysfunction: A Case Study

Imatinib is mainly metabolised by CYP3A4 and CYP2C8 and is extensively bound to α-acid glycoprotein (AAG). A physiologically based pharmacokinetic (PBPK) model for imatinib describing the CYP3A4-mediated autoinhibition during multiple dosing in gastrointestinal stromal tumor patients with normal renal function was previously reported. After performing additional verification, the PBPK model was applied to predict the exposure of imatinib after multiple dosing in cancer patients with varying degrees of renal impairment. In agreement with the clinical data, there was a positive correlation between AAG levels and imatinib exposure. A notable finding was that for recovery of the observed data in cancer patients with moderate RI (CrCL 20 to 39 mL/min), reductions of hepatic CYP3A4 and CYP2C8 abundances, which reflect the effects of RI, had to be included in the simulations. This was not the case for mild RI (CrCL 40 to 50 mL/min). The results support the finding of the clinical study, which demonstrated that both AAG levels and the degree of renal impairment are key components that contribute to the interpatient variability associated with imatinib exposure. As indicated in the 2020 FDA draft RI guidance, PBPK modelling could be used to support an expanded inclusion of patients with RI in clinical studies.

Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers

Encorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

In-depth analysis of patterns in selection of different physiologically based pharmacokinetic modeling tools: PartI - Applications and rationale behind the use of open source-code software

PBPK applications published in the literature support a greater adoption of non-open source-code (NOSC) software as opposed to open source-code (OSC) alternatives. However, a significant number of PBPK modelers are still using OSC software, understanding the rationale for the use of this modality is important and may help those embarking on PBPK modeling. No previous analysis of PBPK modeling trends has included the rationale of the modeler. An in-depth analysis of PBPK applications of OSC software is warranted to determine the true impact of OSC software on the rise of PBPK. Publications focussing on PBPK modeling applications, which used OSC software, were identified by systematically searching the scientific literature for original articles. A total of 171 articles were extracted from the narrowed subset. The rise in the use of OSC software for PBPK applications was greater than the general discipline of pharmacokinetics (9 vs. 4), but less than the overall growth of the PBPK area (9 vs. 43). Our report demonstrates conclusively that the surge in PBPK usage is primarily attributable to the availability and implementations of NOSC software. Modelers preferred not to share the reasons for their selection of certain modeling software and no 'explicit' rationale was given to support the use of OSC analysed by this study. As the preference for NOSC versus OSC software tools in the PBPK area continues to be divided, initiatives to add the rationale in using one form over another to every future PBPK modeling report will be a welcomed and informative addition.

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data

INTRODUCTION

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

METHODS

The area under the curve ratio (AUC_ratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC_ratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

RESULTS

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

CONCLUSION

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

Physiologically based modelling of the antiplatelet effect of aspirin: A tool to characterize drug responsiveness and inform precision dosing

A computational approach involving mathematical modeling and in silico experiments was used to characterize the determinants of extent and duration of platelet cyclooxygenase (COX)-1 inhibition by aspirin and design precision dosing in patients with accelerated platelet turnover or reduced drug bioavailability. To this purpose, a recently developed physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) model of low-dose aspirin in regenerating platelets and megakaryocytes, was used to predict the main features and determinants of platelet COX-1 inhibition. The response to different aspirin regimens in healthy subjects and in pathological conditions associated with alterations in aspirin PK (i.e., severely obese subjects) or PD (i.e., essential thrombocytemya patients), were simulated. A model sensitivity analysis was performed to identify the main processes influencing COX-1 dynamics. In silico experiments and sensitivity analyses indicated a major role for megakaryocytes and platelet turnover in determining the extent and duration of COX-1 inhibition by once-daily, low-dose aspirin. They also showed the superiority of reducing the dosing interval vs increasing the once-daily dose in conditions of increased platelet turnover, while suggested specific dose adjustments in conditions of possible reduction in drug bioavailability. In conclusion, the consistency of our model-based findings with experimental data from studies in healthy subjects and patients with essential thrombocythemia supports the potential of our approach for describing the determinants of platelet inhibition by aspirin and informing precision dosing which may guide personalized antithrombotic therapy in different patient populations, especially in those under-represented in clinical trials or in those associated with poor feasibility.

Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs

Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.

PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment

Pharmacokinetics (PK) is a branch of pharmacology present and of vital importance for the research and development (R&D) of new drugs, post-market monitoring, and continued optimizations in clinical contexts. Ultimately, pharmacokinetics can contribute to improving patients’ clinical outcomes, helping enhance the efficacy of treatments, and reducing possible adverse side effects while also contributing to precision medicine. This article discusses the methods used to predict and study human pharmacokinetics and their evolution to the current physiologically based pharmacokinetic (PBPK) modeling and simulation methods. The importance of therapeutic drug monitoring (TDM) and PBPK as valuable tools for Model-Informed Precision Dosing (MIPD) are highlighted, with particular emphasis on antibiotic therapy since dosage adjustment of antibiotics can be vital to ensure successful clinical outcomes and to prevent the spread of resistant bacterial strains.

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

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

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

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio_(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

A physiologically based pharmacokinetic analysis to predict the pharmacokinetics of intravenous isavuconazole in patients with or without hepatic impairment

Isavuconazole (ISA) is an azole antifungal used in the treatment of invasive aspergillosis and mucormycosis. Patients with mild and moderate hepatic impairment have lower clearance (CL) as compared to the healthy population. Currently, there is no data on ISA in patients with severe hepatic impairment (Child-Pugh Class C). The purpose of this study was to build a physiologically based pharmacokinetic (PBPK) model to describe the pharmacokinetics (PK) of intravenous ISA, and to predict changes in ISA disposition in different patient populations and in patients with hepatic impairment to guide personalized dosing. By incorporating the systemic and drug specific parameters of ISA, the model was initially developed in healthy population and validated with 10 independent PK profiles obtained from healthy subjects and from patients with normal liver function. The results showed a satisfactory predictive capacity, with most of the relative predictive errors being between ±30% for area under the curve (AUC) and C_max The observed plasma concentration-time profiles of ISA were well described by the model predicted profiles. The model adequately predicted the reduced CL of ISA in patients with mild and moderate hepatic impairment. Furthermore, the model predicted a decrease in CL of about 60% in patients with severe hepatic impairment. Therefore, we recommend reducing the dose by 50% in patients with severe hepatic impairment. The model also predicted differences in the PK of ISA between Caucasian and Asian population, with the CL ratio of 0.67 in Chinese vs Caucasian population. The developed PBPK model of ISA provides a reasonable approach for optimizing the dosage regimen in different ethnic populations and in patients with severe hepatic impairment.

Obesity and Corticosteroid Dosing Guideline Adherence in Children Hospitalized With Asthma

OBJECTIVES

Drug dosing recommendations for children with obesity remain limited. This may lead to variability in medication dosing among children with obesity. Therefore, our objective was to determine differences in the prevalence of guideline-nonadherent systemic corticosteroid orders by weight category in children hospitalized for asthma.

METHODS

We performed a retrospective cross-sectional study of children aged 2 to 17 years hospitalized with asthma and prescribed systemic corticosteroids between January 1, 2010, and December 31, 2017, using the Cerner Health Facts deidentified database. Weight categories ranging from underweight to class III obesity were defined on the basis of BMI percentiles by using CDC guidelines. Corticosteroid orders were categorized as guideline adherent or nonadherent on the basis of total body weight-based dosing guidelines from the National Heart, Lung, and Blood Institute. χ² test and multivariable logistic regression models were used to determine differences in guideline adherence between weight categories.

RESULTS

We identified 21 488 children prescribed systemic corticosteroids during asthma hospitalizations. Most (54.2%) had a healthy weight, and 23.8% had obesity. Almost one-quarter received guideline-nonadherent orders (22.2%), with increasing prevalence among higher weight categories (19.4% of healthy weight children versus 36.0% of those with class III obesity; P < .001). After controlling for demographic and clinical covariates, weight category remained significantly associated with receiving a guideline-nonadherent order (P < .001).

CONCLUSIONS

The prevalence of guideline-nonadherent corticosteroid orders for children hospitalized with asthma increases linearly with weight category, disproportionately affecting children with severe obesity. Standardization of drug dosing guidelines for children with obesity may help reduce variability in drug doses prescribed that may increase risk of harm.

Application of physiologically based pharmacokinetic modeling to predict the pharmacokinetics of telavancin in obesity with renal impairment

Purpose

U.S. Food and Drug Administration (FDA) recommended telavancin dosing is based on total body weight (TBW) but lacks adjusted regimens for obese subjects with varying renal function. Our aim was to develop a physiologically based pharmacokinetic (PBPK) model of telavancin to design optimized dosing regimens for obese patients with hospital-acquired pneumonia (HAP) and varying renal function.

Methods

The PBPK model was verified using clinical pharmacokinetic (PK) data of telavancin in healthy populations with varying renal function and obese populations with normal renal function. Then, the PBPK model was applied to predict the PK in obese HAP patients with renal impairment (RI).

Results

The fold error values of PK parameters (AUC, C_max, T_max) were all within 1.5. The telavancin AUC_0-inf was predicted to increase 1.07-fold in mild RI, 1.23-fold in moderate RI, 1.41-fold in severe RI, and 1.57-fold in end-stage renal disease (ESRD), compared with that in obese HAP with normal renal function. The PBPK model combined with Monte Carlo simulations (MCS) suggested that dose adjustment based on a 750-mg-fixed dose could achieve effectiveness with reduced risk of toxicity, compared with current TBW-based dosing recommendations.

Conclusion

The PBPK simulation proposed that using TBW-based regimen in obesity with RI should be avoided. Dose recommendations in obesity from the PBPK model are 750 mg daily for normal renal function and mild RI, 610 mg daily for moderate RI, 530 mg daily for severe RI, and 480 mg daily for ESRD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-020-03072-y.

The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development

Computer-based (in silico) modeling & simulation tools have been embraced in different fields of pharmaceutics for a variety of applications. Among these, physiologically-based pharmacokinetic/biopharmaceutics modeling (PBPK/PBBM) emerged as a particularly useful tool in formulation development. PBPK/PBBM facilitated strategies have been increasingly evaluated over the past few years, as demonstrated by several reports from the pharmaceutical industry, and a number of research and review papers on this subject. Also, the leading regulatory authorities have recently issued guidance on the use of PBPK modeling in formulation design. In silico PBPK models can comprise different dosing routes (oral, intraoral, parenteral, inhalation, ocular, dermal etc.), although the majority of published examples refer to modeling of oral drugs performance. In order to facilitate the use of PBPK modeling tools, a couple of companies have launched commercially available software such as GastroPlus™, Simcyp™ PBPK Simulator and PK-Sim®. This paper highlights various application fields of PBPK/PBBM modeling, along with the basic principles, advantages and limitations of this approach, and provides relevant examples to demonstrate the practical utility of modeling & simulation tools in different stages of formulation development.

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

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

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

PBPK model reporting template for chemical risk assessment applications

Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.

Evaluating Lean Liver Volume as a Potential Scaler for In Vitro-In Vivo Extrapolation of Drug Clearance in Obesity Using the Model Drug Antipyrine

BACKGROUND

In vitro-in vivo extrapolation (IVIVE) of hepatic drug clearance (CL) involves the scaling of hepatic intrinsic clearance (CL_int,uH) by functional liver size, which is approximated by total liver volume (LV) as per the convention. However, in most overweight and obese patients, LV includes abnormal liver fat, which is not thought to contribute to drug elimination, thus overestimating drug CL. Therefore, lean liver volume (LLV) might be a more appropriate scaler of CL_int,uH.

OBJECTIVE

The objective of this work was to assess the application of LLV in CL extrapolation in overweight and obese patients (BMI >25 kg/m2) using a model drug antipyrine.

METHODS

Recently, a model to predict LLV from patient sex, weight, and height was developed and evaluated. In order to assess the LLV model's use in IVIVE, a correlation-based analysis was conducted using antipyrine as an example drug.

RESULTS

In the overweight group (BMI >25 kg/m2), LLV could describe 36% of the variation in antipyrine CL (R² = 0.36), which was >2-fold higher than that was explained by LV (R² = 0.17). In the normal-weight group (BMI ≤25 kg/m2), the coefficients of determination were 58% (R² = 0.58) and 43% (R²= 0.43) for LLV and LV, respectively.

CONCLUSION

The analysis indicates that LLV is potentially a more appropriate descriptor of functional liver size than LV, particularly in overweight individuals. Therefore, LLV has a potential application in IVIVE of CL in obesity.

Toward precision medicine in pediatric population using cytochrome P450 phenotyping approaches and physiologically based pharmacokinetic modeling

The activity of drug-metabolizing enzymes (DME) shows high inter- and intra-individual variability. Genetic polymorphisms, exposure to drugs, and environmental toxins are known to significantly alter DME expression. In addition, the activity of these enzymes is highly age-dependent due to maturation processes that occur during development. Currently, there is a vast choice of phenotyping methods in adults using exogenous probes to characterize the activity of these enzymes. However, this can hardly be applied to children since it requires the intake of non-therapeutic xenobiotics. In addition, sampling may be challenging in the pediatric population for a variety of reasons: limited volume (e.g., blood), inappropriate sampling methods for age (e.g., urine), and metric requiring invasive or multiple blood samples. This review covers the main existing methods that can be used in the pediatric population to determine DME activity, with a particular focus on cytochrome P450 enzymes. Less invasive tools are described, including phenotyping using endogenous probes. Finally, the potential of pediatric model-informed precision dosing using physiologically based pharmacokinetic modeling is discussed.

Evaluation of the Effect of CYP2D6 Genotypes on Tramadol and O-Desmethyltramadol Pharmacokinetic Profiles in a Korean Population Using Physiologically-Based Pharmacokinetic Modeling

Tramadol is a μ-opioid receptor agonist and a monoamine reuptake inhibitor. O-desmethyltramadol (M1), the major active metabolite of tramadol, is produced by CYP2D6. A physiologically-based pharmacokinetic model was developed to predict changes in time-concentration profiles for tramadol and M1 according to dosage and CYP2D6 genotypes in the Korean population. Parallel artificial membrane permeation assay was performed to determine tramadol permeability, and the metabolic clearance of M1 was determined using human liver microsomes. Clinical study data were used to develop the model. Other physicochemical and pharmacokinetic parameters were obtained from the literature. Simulations for plasma concentrations of tramadol and M1 (after 100 mg tramadol was administered five times at 12-h intervals) were based on a total of 1000 virtual healthy Koreans using SimCYP^® simulator. Geometric mean ratios (90% confidence intervals) (predicted/observed) for maximum plasma concentration at steady-state (C_max,ss) and area under the curve at steady-state (AUC_last,ss) were 0.79 (0.69-0.91) and 1.04 (0.85-1.28) for tramadol, and 0.63 (0.51-0.79) and 0.67 (0.54-0.84) for M1, respectively. The predicted time-concentration profiles of tramadol fitted well to observed profiles and those of M1 showed under-prediction. The developed model could be applied to predict concentration-dependent toxicities according to CYP2D6 genotypes and also, CYP2D6-related drug interactions.

Development and Evaluation of Physiologically Based Pharmacokinetic Drug-Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations

The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp^® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio_(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio_(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.

Mind the Gaps: Ontogeny of Human Brain P-gp and Its Impact on Drug Toxicity

Available data on human brain P-glycoprotein ontogeny during infancy and childhood are limited. This review discusses the current body of data relating to maturation of human brain P-glycoprotein including transporter expression levels in post-mortem human brain samples, in vivo transporter activity using probe substrates, surrogate marker endpoints, and extrapolations from animal models. Overall, the data tend to confirm that human brain P-glycoprotein activity keeps developing after birth, although with a developmental time frame that remains unclear. This knowledge gap is a concern given the critical role of brain P-glycoprotein in drug safety and efficacy, and the vulnerable nature of the pediatric population. Future research could include the measurement of brain P-glycoprotein activity across age groups using positron emission tomography or central pharmacodynamic responses. For now, caution is advised when extrapolating adult data to children aged younger than 2 years for drugs with P-glycoprotein-dependent central nervous system activity.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

Simulation-Based Analysis of the Impact of Renal Impairment on the Pharmacokinetics of Highly Metabolized Compounds

Renal impairment (RI) is a highly prevalent disease which can alter the pharmacokinetics (PK) of xenobiotics, including those that are predominately metabolized. The expression and activity of drug metabolizing enzymes (DMEs) and protein binding of compounds has been demonstrated to be affected in RI. A simulation based approach allows for the characterization of the impact of changes in these factors on the PK of compounds which are highly metabolized and allows for improved prediction of PK in RI. Simulations with physiologically based pharmacokinetic (PBPK) modeling was utilized to define the impact of these factors in PK in RI for a model substrate, nifedipine. Changes in fraction unbound and DME expression/activity had profound effects on PK in RI. Increasing fraction unbound and DME expression resulted in a reduction in exposure of nifedipine, while the reduction of DME activity resulted in an increase in exposure. In vitro and preclinical data were utilized to inform simulations for nifedipine, sildenafil and zidovudine. Increasing fraction unbound and changes in the expression/activity of DMEs led to improved predictions of PK. Further characterization of the impact of RI on these factors is warranted in order to better inform a priori predictions of PK in RI.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection

In a recent multicenter population pharmacokinetic study of ciprofloxacin administered to children suffering from complicated urinary tract infection (cUTI), the apparent volume of distribution (V) and total plasma clearance (CL) were decreased by 83.6% and 41.5% respectively, compared to healthy children. To understand these differences, a physiologically-based pharmacokinetic model (PBPK) for ciprofloxacin was developed for cUTI children. First, a PBPK model in adults was developed, modified incorporating age-dependent functions and evaluated with paediatric data generated from a published model in healthy children. Then, the model was then adapted to a cUTI paediatric population according to the degree of renal impairment (KF) affecting renal clearance (CL_Renal,) and CYP1A2 clearance (CL_CYP1A2). Serum and urine samples obtained from 22 cUTI children were used for model evaluation. Lastly, a parameter sensitivity analysis identified the most influential parameters on V and CL. The PBPK model predicted the ciprofloxacin exposure in adults and children, capturing age-related pharmacokinetic changes. Plasma concentrations and fraction excreted unchanged in urine (f_e) predictions improved in paediatric cUTI patients once CL_renal and CL_CYP1A2 were corrected by KF. The presented PBPK model for ciprofloxacin demonstrates its adequacy to simulate different dosing scenarios to obtain PK predictions in a healthy population from 3 months old onwards. Model adaptation of CL_Renal and CL_CYP1A2 according to KF explained partially the differences seen in the plasma drug concentrations and f_e vs time profiles between healthy and cUTI children. Nevertheless, it is necessary to further investigate the disease-related changes in cUTI to improve model predictions.

Physiologically Based Pharmacokinetic Modeling of Nanoparticles

Nanoparticles are frequently designed to improve the pharmacokinetics profiles and tissue distribution of small molecules to prolong their systemic circulation, target specific tissue, or widen the therapeutic window. The multifunctionality of nanoparticles is frequently presented as an advantage but also results in distinct and complicated in vivo disposition properties compared with a conventional formulation of the same molecules. Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool in characterizing and predicting the systemic disposition, target exposure, and efficacy and toxicity of various types of drugs when coupled with pharmacodynamic modeling. Here we review the unique disposition characteristics of nanoparticles, assess how PBPK modeling takes into account the unique disposition properties of nanoparticles, and comment on the applications and challenges of PBPK modeling in characterizing and predicting the disposition and biological effects of nanoparticles.

Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals

The physiologically based pharmacokinetic (PBPK) models allow for predictive assessment of variability in population of interest. One of the future application of PBPK modeling is in the field of precision dosing and personalized medicine. The aim of the study was to develop PBPK model for amitriptyline given orally, predict the variability of cardiac concentrations of amitriptyline and its main metabolite-nortriptyline in populations as well as individuals, and simulate the influence of those xenobiotics in therapeutic and supratherapeutic concentrations on human electrophysiology. The cardiac effect with regard to QT and RR interval lengths was assessed. The Emax model to describe the relationship between amitriptyline concentration and heart rate (RR) length was proposed. The developed PBPK model was used to mimic 29 clinical trials and 19 cases of amitriptyline intoxication. Three clinical trials and 18 cases were simulated with the use of PBPK-QSTS approach, confirming lack of cardiotoxic effect of amitriptyline in therapeutic doses and the increase in heart rate along with potential for arrhythmia development in case of amitriptyline overdose. The results of our study support the validity and feasibility of the PBPK-QSTS modeling development for personalized medicine.

Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib

Alectinib is approved and recommended as the preferred first‐line treatment for patients with anaplastic lymphoma kinase (ALK)‐positive non–small cell lung cancer. The effect of hepatic impairment on the pharmacokinetics (PK) of alectinib was assessed with physiologically based PK modeling prospectively and in a clinical study. An open‐label study (NCT02621047) investigated a single 300‐mg dose of alectinib in moderate (n = 8) and severe (n = 8) hepatic impairment (Child‐Pugh B/C), and healthy subjects (n = 12) matched for age, sex, and body weight. Physiologically based PK modeling was conducted prospectively to inform the clinical study design and support the use of a lower dose and extended PK sampling in the study. PK parameters were calculated for alectinib, its major similarly active metabolite, M4, and the combined exposure of alectinib and M4. Unbound concentrations were assessed at 6 and 12 hours postdose. Administration of alectinib to subjects with hepatic impairment increased the area under the plasma concentration–time curve from time 0 to infinity of the combined exposure of alectinib and M4 to 136% (90% confidence interval [CI], 94.7‐196) and 176% (90%CI 98.4‐315), for moderate and severe hepatic impairment, respectively, relative to matched healthy subjects. Unbound concentrations for alectinib and M4 did not appear substantially different between hepatic‐impaired and healthy subjects. Moderate hepatic impairment had only a modest, not clinically significant effect on alectinib exposure, while the higher exposure observed in severe hepatic impairment supports a dose adjustment in this population.

Development of a Physiologically Based Pharmacokinetic Model of Ethionamide in the Pediatric Population by Integrating Flavin-Containing Monooxygenase 3 Maturational Changes Over Time

Currently, ethionamide is the most frequently prescribed second-line antituberculosis drug in children. After extensive metabolism by flavin-containing monooxygenase (FMO) isoform 3 in the liver, the drug may exert cytotoxic effects. The comparison of children in different age groups revealed a significant age-related increase in ethionamide elimination in vivo. However, to date, the exact mechanism underlying this dynamic increase in ethionamide elimination has not been elucidated. We hypothesized that the age-dependent changes in ethionamide elimination were predominantly a result of the progressive increases in the expression and metabolic capacity of FMO3 during childhood. To test this hypothesis, a full physiologically based pharmacokinetic (PBPK) model of ethionamide was established and validated in adults through incorporation of comprehensive metabolism and transporter profiles, then expanded to the pediatric population through integration of FMO3 maturational changes over time. Thus, a good prediction PBPK model was validated successfully both in adults and children and applied to demonstrate the critical contribution of FMO3 in the mechanistic elimination pathway of ethionamide. In addition, a significant correlation between clearance and age was observed in children by accounting for ethionamide maturation, which could offer a mechanistic understanding of the age-associated changes in ethionamide elimination. In conclusion, this study underlines the benefits of in vitro-in vivo extrapolation and a quantitative PBPK approach for the investigation of transporter-enzyme interplay in ethionamide disposition and the demonstration of FMO3 ontogeny in children.

Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide

AIMS

Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling.

METHODS

PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study.

RESULTS

Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12).

CONCLUSIONS

The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

Discontinuities and disruptions in drug dosage guidelines for the paediatric population

AIMS

This study investigates paediatric drug dosage guidelines with the aim of investigating their agreement with body surface area (BSA) scaling principles.

METHODS

A total of 454 drug dosage guidelines listed in the AMH-CDC 2015 were examined. Data extracted included the administration, frequency and dose per age bracket from 0 to 18 years. Drug treatments were categorized as follows: (1) The same dose recommendation in milligrams per kilogram (mg kg^-1 ) for all age/weights; (2) Change in the mg kg^-1 dosing according to age/weight; (3) Change in dose in mg according to age/weight; (4) Change from mg kg^-1 dosing to a dose in mg according to age/weight; (5) The same recommendation for all age/weight groups in mg; or (6) BSA dosing. Example drugs were selected to illustrate dose progression across ages.

RESULTS

Most drug treatments (63%) have the same mg kg^-1 dose for all age/weight groups, 14% are dosed in mg kg^-1 across all ages with dose changes according to age/weight, 13% were dosed in mg across all ages with dose changes, 10% switched from mg kg^-1 to a set dose in mg, 4.2% have the same dose in mg for all age and weight groups and 2.2% are dosed according to BSA.

CONCLUSIONS

Paediatric dosage guidelines are based on weight-based formulas, available dosing formulations and prior patterns of use. Substantial variation from doses predicted by BSA scaling are common, as are large shifts in recommended doses at age thresholds. Further research is required to determine if better outcomes could be achieved by adopting biologically based scaling of paediatric doses.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.