Identification of intestinal loss of a drug through physiologically based pharmacokinetic simulation of plasma concentration-time profiles

A comprehensive review on modeling aspects of infusion-based drug delivery in the brain

Brain disorders represent an ever-increasing health challenge worldwide. While conventional drug therapies are less effective due to the presence of the blood-brain barrier, infusion-based methods of drug delivery to the brain represent a promising option. Since these methods are mechanically controlled and involve multiple physical phases ranging from the neural and molecular scales to the brain scale, highly efficient and precise delivery procedures can significantly benefit from a comprehensive understanding of drug-brain and device-brain interactions. Behind these interactions are principles of biophysics and biomechanics that can be described and captured using mathematical models. Although biomechanics and biophysics have received considerable attention, a comprehensive mechanistic model for modeling infusion-based drug delivery in the brain has yet to be developed. Therefore, this article reviews the state-of-the-art mechanistic studies that can support the development of next-generation models for infusion-based brain drug delivery from the perspective of fluid mechanics, solid mechanics, and mathematical modeling. The supporting techniques and database are also summarized to provide further insights. Finally, the challenges are highlighted and perspectives on future research directions are provided. STATEMENT OF SIGNIFICANCE: Despite the immense potential of infusion-based drug delivery methods for bypassing the blood-brain barrier and efficiently delivering drugs to the brain, achieving optimal drug distribution remains a significant challenge. This is primarily due to our limited understanding of the complex interactions between drugs and the brain that are governed by principles of biophysics and biomechanics, and can be described using mathematical models. This article provides a comprehensive review of state-of-the-art mechanistic studies that can help to unravel the mechanism of drug transport in the brain across the scales, which underpins the development of next-generation models for infusion-based brain drug delivery. More broadly, this review will serve as a starting point for developing more effective treatments for brain diseases and mechanistic models that can be used to study other soft tissue and biomaterials.

Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

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

INTRODUCTION

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.

AIM

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Mechanistic evaluation of the inhibitory effect of four SGLT-2 inhibitors on SGLT 1 and SGLT 2 using physiologically based pharmacokinetic (PBPK) modeling approaches

Sodium-glucose co-transporter type 2 (SGLT 2, gliflozins) inhibitors are potent orally active drugs approved for managing type 2 diabetes. SGLT 2 inhibitors exert a glucose-lowering effect by suppressing sodium-glucose co-transporters 1 and 2 in the intestinal and kidney proximal tubules. In this study, we developed a physiologically based pharmacokinetic (PBPK) model and simulated the concentrations of ertugliflozin, empagliflozin, henagliflozin, and sotagliflozin in target tissues. We used the perfusion-limited model to illustrate the disposition of SGLT 2 inhibitors in vivo. The modeling parameters were obtained from the references. Simulated steady-state plasma concentration-time curves of the ertugliflozin, empagliflozin, henagliflozin, and sotagliflozin are similar to the clinically observed curves. The 90% prediction interval of simulated excretion of drugs in urine captured the observed data well. Furthermore, all corresponding model-predicted pharmacokinetic parameters fell within a 2-fold prediction error. At the approved doses, we estimated the effective concentrations in intestinal and kidney proximal tubules and calculated the inhibition ratio of SGLT transporters to differentiate the relative inhibition capacities of SGLT1 and 2 in each gliflozin. According to simulation results, four SGLT 2 inhibitors can nearly completely inhibit SGLT 2 transporter at the approved dosages. Sotagliflozin exhibited the highest inhibition activity on SGLT1, followed by ertugliflozin, empagliflozin, and henagliflozin, which showed a lower SGLT 1 inhibitory effect. The PBPK model successfully simulates the specific target tissue concentration that cannot be measured directly and quantifies the relative contribution toward SGLT 1 and 2 for each gliflozin.

Population Pharmacokinetics of Sertraline in Healthy Subjects: a Model-Based Meta-analysis

Sertraline pharmacokinetics is poorly understood and highly variable due to large between-subject variability with inconsistent reports for oral bioavailability. The study objective was to characterize sertraline pharmacokinetics by developing and validating a sertraline population pharmacokinetic (PK) model in healthy subjects using published clinical PK data. We carried a systematic literature search in PubMed in October 2015 and identified 27 pharmacokinetic studies of sertraline conducted in healthy adult subjects and reported in the English language. Sixty mean plasma concentration-time profiles made of 748 plasma concentrations following IV, single, and multiple oral doses ranging from 5 to 400 mg were extracted and analyzed for dose proportionality by a log-linear model and fitted to a 2-compartment pharmacokinetic model in NONMEM using a model-based meta-analysis (MBMA) approach. After a single oral dose, sertraline C_max and AUC_∞ increased with dose proportionally between 50 and 200 mg, and bioavailability increased nonlinearly with dose from 5 to 50 mg and plateaued afterwards while T_max and t_1/2 did not change with dose. Following multiple oral doses, C_max and AUC_∞ increased proportionally with dose across the entire dose range (5-200 mg) while bioavailability, T_max, and t_1/2 remained constant with dose. Sertraline absorption was time-dependent and best described by a sigmoidal E_max function of time after dose. Study findings indicate that sertraline PK is linear in healthy adult subjects at doses ≥ 50 mg, and exposures were nonlinear only after single oral doses < 50 mg likely due to reduced bioavailability.

Enzyme and Transporter Kinetics for CPT-11 (Irinotecan) and SN-38: An Insight on Tumor Tissue Compartment Pharmacokinetics Using PBPK

BACKGROUND

Computational tools are becoming more and more powerful and comprehensive as compared to past decades in facilitating pharmaceutical, pharmacological and clinical practice. Anticancer agents are used either as monotherapy or in combination therapy to treat malignant conditions of the body. A single antineoplastic agent may be used in different types of malignancies at different doses according to the stage of the disease.

OBJECTIVE

To study the behavior of CPT-11 (Irinotecan) and its metabolite SN-38 in tumor tissue compartment through the Whole Body-Physiologically Pharmacokinetics (WB-PBPK) and to determine the activity of metabolic enzymes and transporters participating in the disposition of CPT-11 and SN-38 working in their physiological environment inside the human body.

METHODS

Whole body PBPK approach is used to determine the activity of different metabolic enzymes and transporters involved in the disposition of CPT-11 and its active metabolite, SN-38. The concentrations and pharmacokinetic parameters of the parent compound and its metabolite administered at clinically applicable dose via the intravenous route in the tumor tissue are predicted using this approach.

RESULTS

The activity rate constants of metabolic enzymes and transporters of CPT-11 are derived at their natural anatomic locations. Concentration-time curves of CPT-11 and SN-38 with their 5th to 95th percentage range are achieved at the tumor tissue level. Mean tumor tissue pharmacokinetics of both compounds are determined in a population of 100 individuals.

CONCLUSION

Tumor tissue concentration-time curves of CPT-11 and SN-38 can be determined via PBPK modeling. Rate constants of enzymes and transporters can be shown for healthy and tumor bearing individuals. The results will throw light on the effective concentration of active compound at its target tissue at the clinically applied IV dose.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Evaluating the Role of Solubility in Oral Absorption of Poorly Water-Soluble Drugs Using Physiologically-Based Pharmacokinetic Modeling

Poor aqueous solubility and dissolution of drug candidates drive key decisions on lead series optimization during drug discovery, on formulation optimization, and clinical studies planning during drug development. The interpretation of the in vivo relevance of early pharmaceutical profiling is often confounded by the multiple factors affecting oral systemic exposure. There is growing evidence that in vitro drug solubility may underestimate the true in vivo solubility and lead to drug misclassification. Based on 10 poorly water‐soluble tyrosine kinase inhibitors, this paper demonstrates the use of physiologically‐based pharmacokinetic (PK) analysis in combination with early clinical PK data to identify drugs whose absorption is truly limited by solubility in vivo and, therefore, expected to exhibit food effect. Our study supports a totality of evidence approach using early clinical data to guide decisions on conducting drug interaction studies with food and acid‐reducing agents.

Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them

When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter-mediated secretion (F_g ) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F_g and, based on the outcome, to provide recommendations for the prediction of human F_g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models - the ADAM, Q_gut and Competing Rates - was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F_g was also explored. The ADAM, Q_gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE = 1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q_gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE = 0.44) as it seems systematically to under-predict human F_g . Hence, we would recommend the use of rat to identify the need for F_g assessment, followed by the use of HLM in simple models to predict human F_g . © 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.

Unequivocal evidence supporting the segregated flow intestinal model that discriminates intestine versus liver first-pass removal with PBPK modeling

Merits of the segregated flow model (SFM), highlighting the intestine as inert serosa and active enterocyte regions, with a smaller fractional (f_Q < 0.3) intestinal flow (Q_I ) perfusing the enterocyte region, are described. Less drug in the circulation reaches the enterocytes due to the lower flow (f_Q Q_I ) in comparison with drug administered into the gut lumen, fostering the idea of route-dependent intestinal removal. The SFM has been found superior to the traditional model (TM), which views the serosa and enterocytes totally as a well-mixed tissue perfused by 100% of the intestinal flow, Q_I . The SFM model is able to explain the lower extents of intestinal metabolism of enalapril, morphine and midazolam with i.v. vs. p.o. dosing. For morphine, the urine/bile ratio of the metabolite, morphine glucuronide MGurineMGbile for p.o. was 2.6× that of i.v. This was due to the higher proportion of intestinally formed morphine glucuronide, appearing more in urine than in bile due to its low permeability and greater extent of intestinal formation with p.o. administration. By contrast, the TM predicted the same MGurineMGbile for p.o. vs. i.v. The TM predicted that the contributions of the intestine:liver to first-pass removal were 46%:54% for both p.o. and i.v. The SFM predicted same 46%:54% (intestine:liver) for p.o., but 9%:91% for i.v. By contrast, the kinetics of codeine, the precursor of morphine, was described equally well by the SFM- and TM-PBPK models, a trend suggesting that intestinal metabolism of codeine is negligible. Fits to these PBPK models further provide insightful information towards metabolite formation: available fractions and the fractions of hepatic and total clearances that form the metabolite in question. The SFM-PBPK model is useful to identify not only the presence of intestinal metabolism but the contributions of the intestine and liver for metabolite formation. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting Drug Extraction in the Human Gut Wall: Assessing Contributions from Drug Metabolizing Enzymes and Transporter Proteins using Preclinical Models

Intestinal metabolism can limit oral bioavailability of drugs and increase the risk of drug interactions. It is therefore important to be able to predict and quantify it in drug discovery and early development. In recent years, a plethora of models-in vivo, in situ and in vitro-have been discussed in the literature. The primary objective of this review is to summarize the current knowledge in the quantitative prediction of gut-wall metabolism. As well as discussing the successes of current models for intestinal metabolism, the challenges in the establishment of good preclinical models are highlighted, including species differences in the isoforms; regional abundances and activities of drug metabolizing enzymes; the interplay of enzyme-transporter proteins; and lack of knowledge on enzyme abundances and availability of empirical scaling factors. Due to its broad specificity and high abundance in the intestine, CYP3A is the enzyme that is frequently implicated in human gut metabolism and is therefore the major focus of this review. A strategy to assess the impact of gut wall metabolism on oral bioavailability during drug discovery and early development phases is presented. Current gaps in the mechanistic understanding and the prediction of gut metabolism are highlighted, with suggestions on how they can be overcome in the future.

Metabolite Kinetics: The Segregated Flow Model for Intestinal and Whole Body Physiologically Based Pharmacokinetic Modeling to Describe Intestinal and Hepatic Glucuronidation of Morphine in Rats In Vivo

We used the intestinal segregated flow model (SFM) versus the traditional model (TM), nested within physiologically based pharmacokinetic (PBPK) models, to describe the biliary and urinary excretion of morphine 3β-glucuronide (MG) after intravenous and intraduodenal dosing of morphine in rats in vivo. The SFM model describes a partial (5%-30%) intestinal blood flow perfusing the transporter- and enzyme-rich enterocyte region, whereas the TM describes 100% flow perfusing the intestine as a whole. For the SFM, drugs entering from the circulation are expected to be metabolized to lesser extents by the intestine due to the segregated flow, reflecting the phenomenon of shunting and route-dependent intestinal metabolism. The poor permeability of MG crossing the liver or intestinal basolateral membranes mandates that most of MG that is excreted into bile is hepatically formed, whereas MG that is excreted into urine originates from both intestine and liver metabolism, since MG is effluxed back to blood. The ratio of MG amounts in urine/bile [Formula: see text] for intraduodenal/intravenous dosing is expected to exceed unity for the SFM but approximates unity for the TM. Compartmental analysis of morphine and MG data, without consideration of the permeability of MG and where MG is formed, suggests the ratio to be 1 and failed to describe the kinetics of MG. The observed intraduodenal/intravenous ratio of [Formula: see text] (2.55 at 4 hours) was better predicted by the SFM-PBPK (2.59 at 4 hours) and not the TM-PBPK (1.0), supporting the view that the SFM is superior for the description of intestinal-liver metabolism of morphine to MG. The SFM-PBPK model predicts an appreciable contribution of the intestine to first pass M metabolism.

Gut Wall Metabolism. Application of Pre-Clinical Models for the Prediction of Human Drug Absorption and First-Pass Elimination

Quantifying the multiple processes which control and modulate the extent of oral bioavailability for drug candidates is critical to accurate projection of human pharmacokinetics (PK). Understanding how gut wall metabolism and hepatic elimination factor into first-pass clearance of drugs has improved enormously. Typically, the cytochrome P450s, uridine 5'-diphosphate-glucuronosyltransferases and sulfotransferases, are the main enzyme classes responsible for drug metabolism. Knowledge of the isoforms functionally expressed within organs of first-pass clearance, their anatomical topology (e.g. zonal distribution), protein homology and relative abundances and how these differ across species is important for building models of human metabolic extraction. The focus of this manuscript is to explore the parameters influencing bioavailability and to consider how well these are predicted in human from animal models or from in vitro to in vivo extrapolation. A unique retrospective analysis of three AstraZeneca molecules progressed to first in human PK studies is used to highlight the impact that species differences in gut wall metabolism can have on predicted human PK. Compared to the liver, pharmaceutical research has further to go in terms of adopting a common approach for characterisation and quantitative prediction of intestinal metabolism. A broad strategy is needed to integrate assessment of intestinal metabolism in the context of typical DMPK activities ongoing within drug discovery programmes up until candidate drug nomination.

Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

Advances in Inhalation Dosimetry Models and Methods for Occupational Risk Assessment and Exposure Limit Derivation

The purpose of this article is to provide an overview and practical guide to occupational health professionals concerning the derivation and use of dose estimates in risk assessment for development of occupational exposure limits (OELs) for inhaled substances. Dosimetry is the study and practice of measuring or estimating the internal dose of a substance in individuals or a population. Dosimetry thus provides an essential link to understanding the relationship between an external exposure and a biological response. Use of dosimetry principles and tools can improve the accuracy of risk assessment, and reduce the uncertainty, by providing reliable estimates of the internal dose at the target tissue. This is accomplished through specific measurement data or predictive models, when available, or the use of basic dosimetry principles for broad classes of materials. Accurate dose estimation is essential not only for dose-response assessment, but also for interspecies extrapolation and for risk characterization at given exposures. Inhalation dosimetry is the focus of this paper since it is a major route of exposure in the workplace. Practical examples of dose estimation and OEL derivation are provided for inhaled gases and particulates.

Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling

AIM

To develop and evaluate a whole-body physiologically based pharmacokinetic (WB-PBPK) model of bisoprolol and to simulate its exposure and disposition in healthy adults and patients with renal function impairment.

METHODS

Bisoprolol dispositions in 14 tissue compartments were described by perfusion-limited compartments. Based the tissue composition equations and drug-specific properties such as log P, permeability, and plasma protein binding published in literatures, the absorption and whole-body distribution of bisoprolol was predicted using the 'Advanced Compartmental Absorption Transit' (ACAT) model and the whole-body disposition model, respectively. Renal and hepatic clearances were simulated using empirical scaling methods followed by incorporation into the WB-PBPK model. Model refinements were conducted after a comparison of the simulated concentration-time profiles and pharmacokinetic parameters with the observed data in healthy adults following intravenous and oral administration. Finally, the WB-PBPK model coupled with a Monte Carlo simulation was employed to predict the mean and variability of bisoprolol pharmacokinetics in virtual healthy subjects and patients.

RESULTS

The simulated and observed data after both intravenous and oral dosing showed good agreement for all of the dose levels in the reported normal adult population groups. The predicted pharmacokinetic parameters (AUC, C(max), and T(max)) were reasonably consistent (<1.3-fold error) with the observed values after single oral administration of doses ranging from of 5 to 20 mg using the refined WB-PBPK model. The simulated plasma profiles after multiple oral administration of bisoprolol in healthy adults and patient with renal impairment matched well with the observed profiles.

CONCLUSION

The WB-PBPK model successfully predicts the intravenous and oral pharmacokinetics of bisoprolol across multiple dose levels in diverse normal adult human populations and patients with renal insufficiency.

Mechanisms underlying food-drug interactions: inhibition of intestinal metabolism and transport

Food-drug interaction studies are critical to evaluate appropriate dosing, timing, and formulation of new drug candidates. These interactions often reflect prandial-associated changes in the extent and/or rate of systemic drug exposure. Physiologic and physicochemical mechanisms underlying food effects on drug disposition are well-characterized. However, biochemical mechanisms involving drug metabolizing enzymes and transport proteins remain underexplored. Several plant-derived beverages have been shown to modulate enzymes and transporters in the intestine, leading to altered pharmacokinetic (PK) and potentially negative pharmacodynamic (PD) outcomes. Commonly consumed fruit juices, teas, and alcoholic drinks contain phytochemicals that inhibit intestinal cytochrome P450 and phase II conjugation enzymes, as well as uptake and efflux transport proteins. Whereas myriad phytochemicals have been shown to inhibit these processes in vitro, translation to the clinic has been deemed insignificant or undetermined. An overlooked prerequisite for elucidating food effects on drug PK is thorough knowledge of causative bioactive ingredients. Substantial variability in bioactive ingredient composition and activity of a given dietary substance poses a challenge in conducting robust food-drug interaction studies. This confounding factor can be addressed by identifying and characterizing specific components, which could be used as marker compounds to improve clinical trial design and quantitatively predict food effects. Interpretation and integration of data from in vitro, in vivo, and in silico studies require collaborative expertise from multiple disciplines, from botany to clinical pharmacology (i.e., plant to patient). Development of more systematic methods and guidelines is needed to address the general lack of information on examining drug-dietary substance interactions prospectively.

Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates

The first objective of the present study was to predict the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human. The second objective was to predict pharmacokinetics of the selected drugs in presence of inhibitors of the intestinal and/or hepatic CYP3A activity. We developed a whole-body physiologically based pharmacokinetics (WB-PBPK) model accounting for presystemic elimination of midazolam (MDZ), alprazolam (APZ), triazolam (TRZ), and simvastatin (SMV). The model also accounted for concomitant administration of the above-mentioned drugs with CYP3A inhibitors, namely ketoconazole (KTZ), itraconazole (ITZ), diltiazem (DTZ), saquinavir (SQV), and a furanocoumarin contained in grape-fruit juice (GFJ), namely 6',7'-dihydroxybergamottin (DHB). Model predictions were compared to published clinical data. An uncertainty analysis was performed to account for the variability and uncertainty of model parameters when predicting the model outcomes. We also briefly report on the results of our efforts to develop a global sensitivity analysis and its application to the current WB-PBPK model. Considering the current criterion for a successful prediction, judged satisfied once the clinical data are captured within the 5th and 95th percentiles of the predicted concentration-time profiles, a successful prediction has been obtained for a single oral administration of MDZ and SMV. For APZ and TRZ, however, a slight deviation toward the 95th percentile was observed especially for C(max) but, overall, the in vivo profiles were well captured by the PBPK model. Moreover, the impact of DHB-mediated inhibition on the extent of intestinal pre-systemic elimination of MDZ and SMV has been accurately predicted by the proposed PBPK model. For concomitant administrations of MDZ and ITZ, APZ and KTZ, as well as SMV and DTZ, the in vivo concentration-time profiles were accurately captured by the model. A slight deviation was observed for SMV when coadministered with ITZ, whereas more important deviations have been obtained between the model predictions and in vivo concentration-time profiles of MDZ coadministered with SQV. The same observation was made for TRZ when administered with KTZ. Most of the pharmacokinetic parameters predicted by the PBPK model were successfully predicted within a two-fold error range either in the absence or presence of metabolism-based inhibition. Overall, the present study demonstrated the ability of the PBPK model to predict DDI of CYP3A substrates with promising accuracy.

Physiological modeling to understand the impact of enzymes and transporters on drug and metabolite data and bioavailability estimates

PURPOSE

To obtain mathematical solutions that correlate drug and metabolite exposure and systemic bioavailability (F (sys)) with physiological determinants, transporters and enzymes.

METHODS

A series of physiologically-based pharmacokinetic (PBPK) models that included renal excretion and sequential metabolism within the intestine and/or liver as metabolite formation organs were developed. The area under the curve for drug (AUC) and formed metabolite (AUC{mi,P}) were solved by matrix inversion.

RESULTS

The PBPK models revealed that AUC{mi,P} was dependent on dispositional parameters (transport and elimination) for the drug and metabolite. The solution was unique for each metabolite formation organ and was dependent on the type of drug and metabolite elimination organs. The AUC ratio of the formed metabolite after oral and intravenous drug dosing was useful for determination of the fraction absorbed (F (abs)) and not the systemic bioavailability (F (sys)) when either intestine or liver was the only drug elimination organ.

CONCLUSIONS

The AUC ratio of the formed metabolite after oral and intravenous drug dosing differed from that for drug and would not provide F (sys). However, the AUC ratio of the formed metabolite for oral and intravenous drug dosing furnished the estimate of F (abs) when intestine or liver was the only drug metabolic organ.

To scale or not to scale: the principles of dose extrapolation

The principles of inter-species dose extrapolation are poorly understood and applied. We provide an overview of the principles underlying dose scaling for size and dose adjustment for size-independent differences. Scaling of a dose is required in three main situations: the anticipation of first-in-human doses for clinical trials, dose extrapolation in veterinary practice and dose extrapolation for experimental purposes. Each of these situations is discussed. Allometric scaling of drug doses is commonly used for practical reasons, but can be more accurate when one takes into account species differences in pharmacokinetic parameters (clearance, volume of distribution). Simple scaling of drug doses can be misleading for some drugs; correction for protein binding, physicochemical properties of the drug or species differences in physiological time can improve scaling. However, differences in drug transport and metabolism, and in the dose-response relationship, can override the effect of size alone. For this reason, a range of modelling approaches have been developed, which combine in silico simulations with data obtained in vitro and/or in vivo. Drugs that are unlikely to be amenable to simple allometric scaling of their clearance or dose include drugs that are highly protein-bound, drugs that undergo extensive metabolism and active transport, drugs that undergo significant biliary excretion (MW > 500, ampiphilic, conjugated), drugs whose targets are subject to inter-species differences in expression, affinity and distribution and drugs that undergo extensive renal secretion. In addition to inter-species dose extrapolation, we provide an overview of dose extrapolation within species, discussing drug dosing in paediatrics and in the elderly.

Whole body physiologically-based pharmacokinetic models: their use in clinical drug development

BACKGROUND

Whole-body physiologically-based pharmacokinetic (WB-PBPK) models mathematically describe an organism as a closed circulatory system consisting of compartments that represent the organs important for compound absorption, distribution, metabolism and elimination.

OBJECTIVES

To review the current state of WB-PBPK model use in the clinical phases of drug development.

METHODS

A qualitative description of the WB-PBPK model structure is included along with a review of the varying methods available for input parameterisation. Current and potential WB-PBPK model application in clinical development is discussed.

CONCLUSIONS

This modelling tool is at present used for small and large molecule drug development primarily as a means to scale pharmacokinetics from animals to humans based on physiology. The pharmaceutical industry is active in employing these models to clinical drug development although the applications in use now are narrow in comparison to the potential. Expanded integration of WB-PBPK models into the drug development process will only be achieved with staff training, managerial will, success stories and regulatory agency openness.