Physiologically-based pharmacokinetic modeling for absorption, transport, metabolism and excretion

Physiologically based pharmacokinetic model of brivaracetam to predict the exposure and dose exploration in hepatic impairment and elderly populations

Brivaracetam (BRV) is a new third-generation antiseizure medication for the treatment of focal epileptic seizures. Its use has been increasing among epileptic populations in recent years, but pharmacokinetic (PK) behavior may change in hepatic impairment and the elderly populations. Due to ethical constraints, clinical trials are difficult to conduct and data are limited. This study used PK-Sim® to develop a physiologically based pharmacokinetic (PBPK) model for adults and extrapolate it to hepatic impairment and the elderly populations. The model was evaluated with clinical PK data, and dosage explorations were conducted. For the adult population with mild hepatic impairment, the dose is recommended to be adjusted to 70 % of the recommended dose, and to 60 % for moderate and severe hepatic impairment. For the elderly population with mild hepatic impairment under 80 years old, it is recommended that the dose be adjusted to 60 % of the recommended dose and to 50 % for moderate and severe conditions. The elderly population with hepatic impairment over 80 years old is adjusted to 50 % of the recommended dose for all stages. Healthy elderly do not need to adjust. The BRV PBPK model was successfully developed, studying exposure in hepatic impairment and elderly populations and optimizing dosing regimens.

Physiologically based pharmacokinetic modeling to predict the pharmacokinetics of codeine in different CYP2D6 phenotypes

Objectives

Codeine, a prodrug used as an opioid agonist, is metabolized to the active product morphine by CYP2D6. This study aimed to establish physiologically based pharmacokinetic (PBPK) models of codeine and morphine and explore the influence of CYP2D6 genetic polymorphisms on the pharmacokinetics of codeine and morphine.

Methods

An initial PBPK modeling of codeine in healthy adults was established using PK-Sim® software and subsequently extrapolated to CYP2D6 phenotype-related PBPK modeling based on the turnover frequency (Kcat) of CYP2D6 for different phenotype populations (UM, EM, IM, and PM). The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between the predicted and observed values of the pharmacokinetic parameters to evaluate the accuracy of PBPK modeling. The validated models were then used to support dose safety for different CYP2D6 phenotypes.

Results

The developed and validated CYP2D6 phenotype-related PBPK model successfully predicted codeine and morphine dispositions in different CYP2D6 phenotypes. Compared with EMs, the predicted AUC0-∞ value of morphine was 98.6% lower in PMs, 60.84% lower in IMs, and 73.43% higher in UMs. Morphine plasma exposure in IMs administered 80 mg of codeine was roughly comparable to that in EMs administered 30 mg of codeine. CYP2D6 UMs may start dose titration to achieve an optimal individual regimen and avoid a single dose of over 20 mg. Codeine should not be used in PMs for pain relief, considering its insufficient efficacy.

Conclusion

PBPK modeling can be applied to explore the dosing safety of codeine and can be helpful in predicting the effect of CYP2D6 genetic polymorphisms on drug-drug interactions (DDIs) with codeine in the future.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

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

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

Predictive performance and verification of physiologically based pharmacokinetic model of propylthiouracil

Background: Propylthiouracil (PTU) treats hyperthyroidism and thyroid crisis in all age groups. A variety of serious adverse effects can occur during clinical use and require attention to its pharmacokinetic and pharmacodynamic characteristics in various populations.

Objective: To provide information for individualized dosing and clinical evaluation of PTU in the clinical setting by developing a physiologically based pharmacokinetic (PBPK) model, predicting ADME characteristics, and extrapolating to elderly and pediatric populations.

Methods: Relevant databases and literature were retrieved to collect PTU’s pharmacochemical properties and ADME parameters, etc. A PBPK model for adults was developed using PK-Sim® software to predict tissue distribution and extrapolated to elderly and pediatric populations. The mean fold error (MFE) method was used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. The model was validated using PTU pharmacokinetic data in healthy adult populations.

Result: The MFE ratios of predicted to observed values of AUC0-t, Cmax, and Tmax were mainly within 0.5 and 2. PTU concentrations in various tissues are lower than venous plasma concentrations. Compared to healthy adults, the pediatric population requires quantitative adjustment to the appropriate dose to achieve the same plasma exposure levels, while the elderly do not require dose adjustments.

Conclusion: The PBPK model of PTU was successfully developed, externally validated, and applied to tissue distribution prediction and special population extrapolation, which provides a reference for clinical individualized drug administration and evaluation.

Metabolic Retroversion of Piperaquine (PQ) via Hepatic Cytochrome P450-Mediated N-Oxidation and Reduction: Not an Important Contributor to the Prolonged Elimination of PQ

As a partner antimalarial with an extremely long elimination half-life (∼30 days), piperaquine (PQ) is mainly metabolized into a pharmacologically active N-oxide metabolite [piperaquine N-oxide (PN1)] in humans. In the present work, the metabolic retroversion of PQ and PN1, potentially associated with decreased clearance of PQ, was studied. The results showed that interconversion existed for PQ and its metabolite PN1. The N-oxidation of PQ to PN1 was mainly mediated by CYP3A4, and PN1 can rapidly reduce back to PQ via cytochrome P450 (P450)/flavin-containing monooxygenase enzymes. In accordance with these findings, the P450 nonselective inhibitor (1-ABT) or CYP3A4 inhibitor (ketoconazole) inhibited the N-oxidation pathway in liver microsomes (>90%), and the reduction metabolism was inhibited by 1-ABT (>90%) or methimazole (∼50%). Based on in vitro physiologic and enzyme kinetic studies, quantitative prediction of hepatic clearance (CL_H) of PQ was performed, which indicated its negligible decreased elimination in humans in the presence of futile cycling, with the unbound CL_H decreasing by 2.5% (0.069 l/h per kilogram); however, a minor decrease in unbound CL_H (by 12.8%) was found in mice (0.024 l/h per kilogram). After an oral dose of PQ (or PN1) to mice, the parent form predominated in the blood circulation, and PN1 (or PQ) was detected as a major metabolite. Other factors probably associated with delayed elimination of PQ (intestinal metabolism and enterohepatic circulation) did not play a key role in PQ elimination. These data suggested that the metabolic interconversion of PQ and its N-oxide metabolite contributes to but may not significantly prolong its duration in humans. SIGNIFICANCE STATEMENT: This paper investigated the interconversion metabolism of piperaquine (PQ) and its N-oxide metabolite in vitro as well as in mice. The metabolic profiles of PQ were reestablished by this futile cycling, which contributes to but may not significantly prolong its elimination in humans. Enzyme phenotyping indicated a low possibility of interaction of PQ during artemisinin drug-based combination therapy treatment.

A hybrid model to evaluate the impact of active uptake transport on hepatic distribution of atorvastatin in rats

1. Mathematical modeling remains a useful tool to study the impact of transporters on overall and intracellular drug disposition. The impact of organic anion transporter protein mediated uptake on atorvastatin systemic and intracellular pharmacokinetics, specifically distribution volume, was studied in rats with mathematical modeling and conducting in vivo pharmacokinetic studies for atorvastatin in presence and absence of rifampicin. A previously developed 5-compartment explicit membrane model for the liver was combined with a compartmental model to develop a semi-physiological hybrid model for atorvastatin disposition. 2. Rifampicin treatment resulted in a decrease in systemic clearance and steady-state distribution volume, and an increase in half-life of atorvastatin. The hybrid model predicted higher unbound intracellular liver atorvastatin concentrations than unbound plasma concentrations in both rifampicin treated and untreated groups, indicating involvement of uptake transporters. The intracellular unbound concentrations during the distributive phase were unaffected by rifampicin. The dependence of clearance on blood flow as well as hepatic uptake for atorvastatin (a moderate-to-high extraction ratio drug) can explain this lack of change in intracellular concentrations if there is incomplete inhibition of transport at the tested rifampicin dose. 3. The hybrid model successfully allowed the evaluation of effect of active uptake on intracellular and plasma atorvastatin disposition.

Monitoring of erlotinib in pancreatic cancer patients during long-time administration and comparison to a physiologically based pharmacokinetic model

Purpose

In this study, a therapeutic drug monitoring (TDM) of erlotinib in pancreatic cancer patients was performed over 50 weeks to reveal possible alterations in erlotinib plasma concentrations. Additionally, a physiologically based pharmacokinetic (PBPK) model was created to assess such variations in silico.

Methods

Patients with advanced pancreatic cancer received a chemotherapeutic combination of 100 mg erlotinib q.d., 500–900 mg capecitabine b.d. and 5 mg/kg bevacizumab q.2wks. Samples were analyzed by HPLC and the results were compared to a PBPK model, built with the software GastroPlus™ and based on calculated and literature data.

Results

The erlotinib plasma concentrations did not show any accumulation, but displayed a high inter-patient variability over the whole investigated period. Trough plasma concentrations ranged from 0.04 to 1.22 µg/ml after day 1 and from 0.01 to 2.4 µg/ml in the long-term assessment. 7% of the patients showed concentrations below the necessary activity threshold of 0.5 µg/ml during the first week. The impact of some co-variates on the pharmacokinetic parameters C_max and AUC_0–24 were shown in a PBPK model, including food effects, changes in body weight, protein binding or liver function and the concomitant intake of gastric acid reducing agents (ARAs).

Conclusion

This study presents the approach of combining TDM and PBPK modeling for erlotinib, a drug with a high interaction potential. TDM is an important method to monitor drugs with increased inter-patient variability, additionally, the PBPK model contributed valuable insights to the interaction mechanisms involved, resulting in an effective combination from a PK perspective to ensure a safe treatment.

Physiologically based pharmacokinetic modeling revealed minimal codeine intestinal metabolism in first-pass removal in rats

The physiologically based model with segregated flow to the intestine (SFM-PBPK; partial, lower flow to enterocyte region vs. greater flow to serosal region) was found to describe the first-pass glucuronidation of morphine (M) to morphine-3β-glucuronide (MG) in rats after intraduodenal (i.d.) and intravenous (i.v.) administration better than the traditional model (TM), for which a single intestinal flow perfused the whole of the intestinal tissue. The segregated flow model (SFM) described a disproportionately greater extent of intestinal morphine glucuronidation for i.d. vs. i.v. administration. The present study applied the same PBPK modeling approaches to examine the contributions of the intestine and liver on the first-pass metabolism of the precursor, codeine (C, 3-methylmorphine) in the rat. Unexpectedly, the profiles of codeine, morphine and morphine-3β-glucuronide in whole blood, bile and urine, assayed by LCMS, were equally well described by both the TM-PBPK and SFM-PBPK. The fitted parameters for the models were similar, and the net formation intrinsic clearance of morphine (from codeine) for the liver was much higher, being 9- to 13-fold that of the intestine. Simulations, based on the absence of intestinal formation of morphine, correlated well with observations. The lack of discrimination of SFM and TM with the codeine data did not invalidate the SFM-PBPK model but rather suggests that the liver is the only major organ for codeine metabolism. Because of little or no contribution by the intestine to the metabolism of codeine, both the TM- and SFM-PBPK models are equally consistent with the data. Copyright © 2016 John Wiley & Sons, Ltd.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

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.

Physiologically based pharmacokinetic models of small molecules and therapeutic antibodies: a mini-review on fundamental concepts and applications

The mechanisms of absorption, distribution, metabolism and elimination of small and large molecule therapeutics differ significantly from one another and can be explored within the framework of a physiologically based pharmacokinetic (PBPK) model. This paper briefly reviews fundamental approaches to PBPK modeling, in which drug kinetics within tissues and organs are explicitly represented using physiologically meaningful parameters. The differences in PBPK models applied to small/large molecule drugs are highlighted, thus elucidating differences in absorption, distribution and elimination properties between these two classes of drugs in a systematic manner. The absorption of small and large molecules differs with respect to their common extravascular routes of delivery (oral versus subcutaneous). The role of the lymphatic system in drug distribution, and the involvement of tissues as sites of elimination (through catabolism and target mediated drug disposition) are unique features of antibody distribution and elimination that differ from small molecules, which are commonly distributed into the tissues but are eliminated primarily by liver metabolism. Fundamental differences exist in the ability to predict human pharmacokinetics based upon preclinical data due to differing mechanisms governing small and large molecule disposition. These differences have influence on the evolving utilization of PBPK modeling in the discovery and development of small and large molecule therapeutics.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Representative Sinusoids for Hepatic Four-Scale Pharmacokinetics Simulations

The mammalian liver plays a key role for metabolism and detoxification of xenobiotics in the body. The corresponding biochemical processes are typically subject to spatial variations at different length scales. Zonal enzyme expression along sinusoids leads to zonated metabolization already in the healthy state. Pathological states of the liver may involve liver cells affected in a zonated manner or heterogeneously across the whole organ. This spatial heterogeneity, however, cannot be described by most computational models which usually consider the liver as a homogeneous, well-stirred organ.

The goal of this article is to present a methodology to extend whole-body pharmacokinetics models by a detailed liver model, combining different modeling approaches from the literature. This approach results in an integrated four-scale model, from single cells via sinusoids and the organ to the whole organism, capable of mechanistically representing metabolization inhomogeneity in livers at different spatial scales. Moreover, the model shows circulatory mixing effects due to a delayed recirculation through the surrounding organism.

To show that this approach is generally applicable for different physiological processes, we show three applications as proofs of concept, covering a range of species, compounds, and diseased states: clearance of midazolam in steatotic human livers, clearance of caffeine in mouse livers regenerating from necrosis, and a parameter study on the impact of different cell entities on insulin uptake in mouse livers.

The examples illustrate how variations only discernible at the local scale influence substance distribution in the plasma at the whole-body level. In particular, our results show that simultaneously considering variations at all relevant spatial scales may be necessary to understand their impact on observations at the organism scale.

Elucidating the in vivo fate of nanocrystals using a physiologically based pharmacokinetic model: a case study with the anticancer agent SNX-2112

Introduction

SNX-2112 is a promising anticancer agent but has poor solubility in both water and oil. In the study reported here, we aimed to develop a nanocrystal formulation for SNX-2112 and to determine the pharmacokinetic behaviors of the prepared nanocrystals.

Methods

Nanocrystals of SNX-2112 were prepared using the wet-media milling technique and characterized by particle size, differential scanning calorimetry, drug release, etc. Physiologically based pharmacokinetic (PBPK) modeling was undertaken to evaluate the drug’s disposition in rats following administration of drug cosolvent or nanocrystals.

Results

The optimized SNX-2112 nanocrystals (with poloxamer 188 as the stabilizer) were 203 nm in size with a zeta potential of −11.6 mV. In addition, the nanocrystals showed a comparable release profile to the control (drug cosolvent). Further, the rat PBPK model incorporating the parameters of particulate uptake (into the liver and spleen) and of in vivo drug release was well fitted to the experimental data following administration of the drug nanocrystals. The results reveal that the nanocrystals rapidly released drug molecules in vivo, accounting for their cosolvent-like pharmacokinetic behaviors. Due to particulate uptake, drug accumulation in the liver and spleen was significant at the initial time points (within 1 hour).

Conclusion

The nanocrystals should be a good choice for the systemic delivery of the poorly soluble drug SNX-2112. Also, our study contributes to an improved understanding of the in vivo fate of nanocrystals.

Mixed nanomicelles as potential carriers for systemic delivery of Z-GP-Dox, an FAPα-based doxorubicin prodrug: formulation and pharmacokinetic evaluation

Z-GP-Dox, the FAPα (fibroblast activation protein-α)-based doxorubicin prodrug, demonstrates excellent tumor targeting effects and a favorable toxicokinetic profile. However, the insoluble nature of Z-GP-Dox becomes a significant barrier to drug administration, particularly when it comes to the clinical stage. Here we developed a nanomicelle system to facilitate the systemic delivery of Z-GP-Dox, and evaluated its disposition in rats following administration of the micelles using a physiologically-based pharmacokinetic model. Z-GP-Dox-loaded mixed nanomicelles (ZGD-MNs) were prepared by dispersion of an ethanol solution of Z-GP-Dox, lecithin, and sodium oleate in water. The obtained ZGD-MNs were 86.6 nm in size with a drug loading of 14.03%. ZGD-MNs were fairly stable in phosphate-buffered saline and showed satisfactory physical and chemical stability over a 2-week observation period. Accumulative drug release was more than 56% within 24 hours. Further, the physiologically-based pharmacokinetic rat model consisting of various organs (ie, heart, liver, spleen, lung, kidney, and intestine) was fitted to the experimental data following administration of ZGD-loaded cosolvent (control) or micelles. Derived partition coefficient values revealed that the nanomicelles significantly altered the biodistribution of Z-GP-Dox. Of note, drug distribution to the lung, liver, and spleen was greatly enhanced and the fold change ranged from 2.4 to 33. In conclusion, this is the first report of a mixed micelle system being a viable carrier for delivery of Z-GP-Dox. Also, the pharmacokinetic behavior of Z-GP-Dox was satisfactorily described by the physiologically-based pharmacokinetic model.

Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification

Purpose

Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.

Methods

PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.

Results

Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.

Conclusions

The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-6) contains supplementary material, which is available to authorized users.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

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.

System approach to modeling of liver glucose metabolism with physiologically interpreted model parameters outgoing from [18F]FDG concentrations measured by PET

New mathematical models from physiologically interpreted parameters capable of evaluating glucose metabolism within the liver and/or the whole body were developed. The group of pigs in a fasting state and the group of pigs with euglycemic supraphysiological hyperinsulinemia were scanned by positron emission tomography after a single dose of [(18)F]FDG tracer. Simultaneously frequent sampling of the dynamic data of [(18)F]FDG plasma concentration in artery, portal vein and hepatic vein was obtained. A system approach to the liver and/or the whole-body system by the tools of linear dynamic sysztem theory was used. Three kinds of structural models, single input and single output or multiple outputs and multiple inputs and single output, were identified. Differences between the group of fasting pigs and the group of pigs in euglycemic supraphysiological hyperinsulinemia were identified by estimated parameters of the structural models. The suitability of the structural mathematical models for the estimation of physiologically interpreted parameters from PET was validated.

Application of physiologically based pharmacokinetic models in chemical risk assessment

Post-exposure risk assessment of chemical and environmental stressors is a public health challenge. Linking exposure to health outcomes is a 4-step process: exposure assessment, hazard identification, dose response assessment, and risk characterization. This process is increasingly adopting "in silico" tools such as physiologically based pharmacokinetic (PBPK) models to fine-tune exposure assessments and determine internal doses in target organs/tissues. Many excellent PBPK models have been developed. But most, because of their scientific sophistication, have found limited field application-health assessors rarely use them. Over the years, government agencies, stakeholders/partners, and the scientific community have attempted to use these models or their underlying principles in combination with other practical procedures. During the past two decades, through cooperative agreements and contracts at several research and higher education institutions, ATSDR funded translational research has encouraged the use of various types of models. Such collaborative efforts have led to the development and use of transparent and user-friendly models. The "human PBPK model toolkit" is one such project. While not necessarily state of the art, this toolkit is sufficiently accurate for screening purposes. Highlighted in this paper are some selected examples of environmental and occupational exposure assessments of chemicals and their mixtures.

The Øie-Tozer model of drug distribution and its suitability for drugs with different pharmacokinetic behavior

INTRODUCTION

Drug distribution is a major pharmacokinetic process that affects the time course of drug concentrations in tissues, biological fluids and the resulting pharmacological activities. Drug distribution may follow different pathways and patterns, and is governed by the drug's physicochemical properties and the body's physiology. The classical Øie-Tozer model is frequently used for predicting volume of drug distribution and for pharmacokinetic calculations.

AREAS COVERED

In this review, the suitability of the Øie-Tozer model for drugs that exhibit different distribution patterns is critically analyzed and illustrated. The method used is a pharmacokinetic modeling and simulation approach. It is demonstrated that the major limitation of the Øie-Tozer model stems from its focus on the total drug concentrations and not on the active (unbound) concentrations. Moreover, the Øie-Tozer model may be inappropriate for drugs with nonlinear or complex pharmacokinetic behavior, such as biopharmaceuticals, drug conjugates or for drugs incorporated into drug delivery systems. Distribution mechanisms and alternative distribution models for these drugs are discussed.

EXPERT OPINION

The Øie-Tozer model can serve for predicting unbound volume of drug distribution for 'classical' small molecular mass drugs with linear pharmacokinetics. However, more detailed mechanism-based distribution models should be used in preclinical and clinical settings for drugs that exhibit more complex pharmacokinetic behavior.

Use of mathematical models to understand anticancer drug delivery and its effect on solid tumors

The transport of anticancer drugs and their effect on tumor cells involve a number of physical and biochemical processes. Mathematical modeling provides a tool to help us understand the interaction of these complex processes, thereby contributing to the improvement and optimization of drug delivery. This article starts with a discussion of the biological and physiological properties of tumors, which are often found as barriers to anticancer drug transport and effect. A broad spectrum of mathematical models is reviewed to give an overview of the current state of modeling approaches and different categories of models are outlined. These include pharmacokinetic and transport-based models for the prediction of temporal and temporal-spatial profiles of antidrug concentrations, as well as empirical or deterministic models to describe the effect of drug. We conclude that the systematic elucidation and integration of cellular signal transduction with the biophysical aspects of drug transport will lead to a better understanding of the entire drug-delivery process.

Use of physiologically based pharmacokinetic models to evaluate the impact of intestinal glucuronide hydrolysis on the pharmacokinetics of aglycone

Drug elimination via glucuronidation pathway is a complex process involving glucuronide excretion. Glucuronide excreted into the gut lumen either directly from the enterocytes or from the hepatobiliary route can be recovered back to the precursor (aglycone) through bacteria-mediated hydrolysis. As a result, the pharmacokinetics [e.g., plasma terminal half-life (T(1/2))] of aglycone might be altered. Here, impact of intestinal glucuronide hydrolysis on the pharmacokinetics of aglycone is evaluated using physiologically based pharmacokinetic (PBPK) models with liver and/or intestine as eliminating organs. It is found that compared with its absence, the presence of intestinal glucuronide hydrolysis leads to increases in the oral systemic bioavailability (F(sys)) of aglycone. The magnitude of fold increase is positively correlated with the level of metabolism, as metabolic clearance mainly contributes to recycled amount of glucuronide. Although F(sys) is independent of the glucuronide efflux in a traditional model and a segregated-flow model of the intestine, dependence of F(sys) on the glucuronide efflux can be observed in a segmental segregated-flow model of the intestine and whole-body PBPK models. Interestingly, when the ratio of apical versus basolateral efflux intrinsic clearances (of glucuronide) is fixed, their effects on the intestinal bioavailability and F(sys) cease to exist. In addition, glucuronide hydrolysis can lead to a significantly delayed elimination of the aglycone as evidenced by a prolonged (e.g., a 2.1-fold increase) T(1/2). Surprisingly, when a pharmacokinetic profile for aglycone is simulated with a flat terminal portion (a reflection of the experimental observations), changes in the aglycone bioavailabilities are limited (i.e., ≤ 1.3-fold). In conclusion, this study explores the possible role of intestinal glucuronide hydrolysis in the disposition of aglycone via simulations utilizing various PBPK models. The mechanistic observations should be helpful to better understand the complex glucuronidation in vivo.

Individualized dosing regimens in children based on population PKPD modelling: are we ready for it?

Despite profound differences in response between children and adults, and between children of different ages, drugs are still empirically dosed in mg/kg in children. Since maturation of expression and function is typically a non-linear dynamic process which differs between biotransformation routes and pharmacological targets, paediatric dosing regimens should be based on the changing pharmacokinetic-pharmacodynamic (PKPD) relationship in children. In this respect, the population approach is essential, allowing for sparse sampling in each individual child. An example is presented on morphine glucuronidation, for which two covariates were identified and subsequently used to derive a model-based dosing algorithm for a prospective clinical trial in children. Using this novel dosing algorithm, similar morphine concentrations are expected while, depending on age, lower and higher morphine dosages are administered compared to mg/kg/h dosing. As the covariate functions may reflect system-specific information on the maturation of a specific drug-disposition pathway, its use for other drugs that share the same pathway is explored. For this purpose, prospective clinical trials and cross-validation studies are urgently needed. In conclusion, PKPD modelling and simulation studies are important to develop evidence-based and individualized dosing schemes for children, with the ultimate goal to improve drug safety and efficacy in this population.