Physiologically-Based Pharmacokinetic Model of Morphine and Morphine-3-Glucuronide in Nonalcoholic Steatohepatitis

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.

Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD)

PURPOSE

This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics.

METHODS

A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, 11C-metformin, morphine and the glucuronide metabolite of morphine).

RESULTS

PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of 11C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72).

CONCLUSION

A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

Physiologically based pharmacokinetic modeling to characterize enterohepatic recirculation and predict food effect on the pharmacokinetics of hyzetimibe

BACKGROUND AND OBJECTIVE

Hyzetimibe is a cholesterol absorption inhibitor indicated for the treatment of hypercholesterolemia. This study aims to describe the multiple-peak pharmacokinetics (PK) of hyzetimibe and its active metabolite M1 through physiologically-based pharmacokinetic (PBPK) modeling, and to compare the model predictions of a virtual food effect study with the results of a clinical food effect study.

METHODS

The plasma concentration data used for PBPK modeling were obtained from a single-dose, two-period crossover bioequivalence study in the fasted state. Advanced Compartmental Absorption and Transit model was used for absorption. Enterohepatic recirculation process was modeled by changing the gut physiological state from fasted to fed at meal time. Based on the established PBPK models, a virtual food effect study was simulated. A clinical food effect study was used for model external validation.

RESULTS

PK profiles of hyzetimibe and M1 under fasting condition could be well described by the PBPK model, and the errors of Cmax, AUC0-∞, and AUC0-t were within the two-fold range. Simulated geometric mean ratios (GMRs, fed/fasted) showed that a high-fat breakfast slightly affected the PK of hyzetimibe, expressed as increased Cmax of hyzetimibe (130.6%). Simulated GMRs and 90% confidence intervals of AUC were within the preset bioequivalent range. The results of the simulated virtual food effect trial were consistent with those of the clinical food effect trial.

CONCLUSIONS

The established PBPK model could describe the concentration-time profiles of hyzetimibe and M1 well with good prediction performance. A fully mechanistic model of enterohepatic recirculation warrants further investigation.

Considerations for Physiologically Based Modeling in Liver Disease: From Nonalcoholic Fatty Liver (NAFL) to Nonalcoholic Steatohepatitis (NASH)

Nonalcoholic fatty liver disease (NAFLD), representing a clinical spectrum ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), is rapidly evolving into a global pandemic. Patients with NAFLD are burdened with high rates of metabolic syndrome-related comorbidities resulting in polypharmacy. Therefore, it is crucial to gain a better understanding of NAFLD-mediated changes in drug disposition and efficacy/toxicity. Despite extensive clinical pharmacokinetic data in cirrhosis, current knowledge concerning pharmacokinetic alterations in NAFLD, particularly at different stages of disease progression, is relatively limited. In vitro-to-in vivo extrapolation coupled with physiologically based pharmacokinetic and pharmacodynamic (IVIVE-PBPK/PD) modeling offers a promising approach for optimizing pharmacologic predictions while refining and reducing clinical studies in this population. Use of IVIVE-PBPK to predict intra-organ drug concentrations at pharmacologically relevant sites of action is particularly advantageous when it can be linked to pharmacodynamic effects. Quantitative systems pharmacology/toxicology (QSP/QST) modeling can be used to translate pharmacokinetic and pharmacodynamic data from PBPK/PD models into clinically relevant predictions of drug response and toxicity. In this review, a detailed summary of NAFLD-mediated alterations in human physiology relevant to drug absorption, distribution, metabolism, and excretion (ADME) is provided. The application of literature-derived physiologic parameters and ADME-associated protein abundance data to inform virtual NAFLD population development and facilitate PBPK/PD, QSP, and QST predictions is discussed along with current limitations of these methodologies and knowledge gaps. The proposed methodologic framework offers great potential for meaningful prediction of pharmacological outcomes in patients with NAFLD and can inform both drug development and clinical practice for this population.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles

To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K_p,mem), and PermQ. K_p,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V_ss and t_1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

Characterizing Pharmacokinetics in Children With Obesity-Physiological, Drug, Patient, and Methodological Considerations

Childhood obesity is an alarming public health problem. The pediatric obesity rate has quadrupled in the past 30 years, and currently nearly 20% of United States children and 9% of children worldwide are classified as obese. Drug distribution and elimination processes, which determine drug exposure (and thus dosing), can vary significantly between patients with and without obesity. Obesity-related physiological changes, such as increased tissue volume and perfusion, altered blood protein concentrations, and tissue composition can greatly affect a drug's volume of distribution, which might necessitate adjustment in loading doses. Obesity-related changes in the drug eliminating organs, such as altered enzyme activity in the liver and glomerular filtration rate, can affect the rate of drug elimination, which may warrant an adjustment in the maintenance dosing rate. Although weight-based dosing (i.e., in mg/kg) is commonly practiced in pediatrics, choice of the right body size metric (e.g., total body weight, lean body weight, body surface area, etc.) for dosing children with obesity still remains a question. To address this gap, the interplay between obesity-related physiological changes (e.g., altered organ size, composition, and function), and drug-specific properties (e.g., lipophilicity and elimination pathway) needs to be characterized in a quantitative framework. Additionally, methodological considerations, such as adequate sample size and optimal sampling scheme, should also be considered to ensure accurate and precise top-down covariate selection, particularly when designing opportunistic studies in pediatric drug development. Further factors affecting dosing, including existing dosing recommendations, target therapeutic ranges, dose capping, and formulations constraints, are also important to consider when undergoing dose selection for children with obesity. Opportunities to bridge the dosing knowledge gap in children with obesity include modeling and simulating techniques (i.e., population pharmacokinetic and physiologically-based pharmacokinetic [PBPK] modeling), opportunistic clinical data, and real world data. In this review, key considerations related to physiology, drug parameters, patient factors, and methodology that need to be accounted for while studying the influence of obesity on pharmacokinetics in children are highlighted and discussed. Future studies will need to leverage these modeling opportunities to better describe drug exposure in children with obesity as the childhood obesity epidemic continues.

Development and Evaluation of a Virtual Population of Children with Obesity for Physiologically Based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

While one in five children in the USA are now obese, and more than three-quarters receive at least one drug during childhood, there is limited dosing guidance for this vulnerable patient population. Physiologically based pharmacokinetic modeling can bridge the gap in the understanding of how pharmacokinetics, including drug distribution and clearance, changes with obesity by incorporating known obesity-related physiological changes in children. The objective of this study was to develop a virtual population of children with obesity to enable physiologically based pharmacokinetic modeling, then use the novel virtual population in conjunction with previously developed models of clindamycin and trimethoprim/sulfamethoxazole to better understand dosing of these drugs in children with obesity.

METHODS

To enable physiologically based pharmacokinetic modeling, a virtual population of children with obesity was developed using national survey, electronic health record, and clinical trial data, as well as data extracted from the literature. The virtual population accounts for key obesity-related changes in physiology relevant to pharmacokinetics, including increased body size, body composition, organ size and blood flow, plasma protein concentrations, and glomerular filtration rate. The virtual population was then used to predict the pharmacokinetics of clindamycin and trimethoprim/sulfamethoxazole in children with obesity using previously developed physiologically based pharmacokinetic models.

RESULTS

Model simulations predicted observed concentrations well, with an overall average fold error of 1.09, 1.24, and 1.53 for clindamycin, trimethoprim, and sulfamethoxazole, respectively. Relative to children without obesity, children with obesity experienced decreased clindamycin and trimethoprim/sulfamethoxazole weight-normalized clearance and volume of distribution, and higher absolute doses under recommended pediatric weight-based dosing regimens.

CONCLUSIONS

Model simulations support current recommended weight-based dosing in children with obesity for clindamycin and trimethoprim/sulfamethoxazole, as they met target exposure despite these changes in clearance and volume of distribution.

The Role of Uptake and Efflux Transporters in the Disposition of Glucuronide and Sulfate Conjugates

Glucuronidation and sulfation are the most typical phase II metabolic reactions of drugs. The resulting glucuronide and sulfate conjugates are generally considered inactive and safe. They may, however, be the most prominent drug-related material in the circulation and excreta of humans. The glucuronide and sulfate metabolites of drugs typically have limited cell membrane permeability and subsequently, their distribution and excretion from the human body requires transport proteins. Uptake transporters, such as organic anion transporters (OATs and OATPs), mediate the uptake of conjugates into the liver and kidney, while efflux transporters, such as multidrug resistance proteins (MRPs) and breast cancer resistance protein (BCRP), mediate expulsion of conjugates into bile, urine and the intestinal lumen. Understanding the active transport of conjugated drug metabolites is important for predicting the fate of a drug in the body and its safety and efficacy. The aim of this review is to compile the understanding of transporter-mediated disposition of phase II conjugates. We review the literature on hepatic, intestinal and renal uptake transporters participating in the transport of glucuronide and sulfate metabolites of drugs, other xenobiotics and endobiotics. In addition, we provide an update on the involvement of efflux transporters in the disposition of glucuronide and sulfate metabolites. Finally, we discuss the interplay between uptake and efflux transport in the intestine, liver and kidneys as well as the role of transporters in glucuronide and sulfate conjugate toxicity, drug interactions, pharmacogenetics and species differences.

Application of proteomic data in the translation of in vitro observations to associated clinical outcomes

Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.